Mine available resource evaluation method and system

Abstract

The application relates to the technical field of mine evaluation, in particular to a mine available resource evaluation method and system.The method comprises the following steps: obtaining mine resource geological exploration data; performing data preprocessing on the mine resource geological exploration data to generate standard mine resource geological exploration data; performing three-dimensional geological modeling based on the standard mine resource geological exploration data to generate mine resource reserve distribution data; obtaining a surface deformation monitoring image through a synthetic aperture radar; performing time sequence slicing on the surface deformation monitoring image to generate surface deformation slice data; and performing stress change and microseismic phase imaging on the surface deformation slice data, so that time sequence stress data and microseismic phase data are obtained.The application improves the accuracy, dynamic nature and geological risk early warning capability of mine resource evaluation through multi-source dynamic data fusion and three-dimensional modeling, and improves the precision of mine available resource evaluation.

Description

Technical Field

[0001] This invention relates to the field of mine evaluation technology, and in particular to a method and system for evaluating the available resources of a mine. Background Technology

[0002] Early mine resource assessments relied primarily on the experience and judgment of mining and geological experts. These methods were simple and highly subjective, failing to accurately reflect the actual conditions of the deposits. With the development of geological exploration technology, geostatistical methods began to be applied to resource assessment, improving the scientific rigor and accuracy through mathematical models and statistical analysis. In the mid-to-late 20th century, the rise of computer technology enabled complex data processing and three-dimensional geological modeling, propelling mine resource assessment towards digitalization and three-dimensionality. In recent years, advancements in remote sensing, geophysical exploration, and big data analytics have expanded mine resource assessment beyond resource reserve estimation to include multi-dimensional factors such as resource quality, economic value, and environmental impact, forming a more comprehensive and systematic assessment framework. Simultaneously, artificial intelligence and machine learning technologies have been applied to mine data analysis, further enhancing assessment efficiency and accuracy. However, current traditional methods for monitoring surface deformation often rely on single-point-of-time data, failing to reflect dynamic changes. Furthermore, the identification of internal rock mass instability often depends on single parameters or experience-based judgments, lacking in-depth analysis of physical characteristics, thus resulting in low accuracy in mine resource assessments. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for evaluating the available resources of a mine to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for evaluating the available resources of a mine is provided, the method comprising the following steps: Step S1: Obtain geological exploration data of mineral resources; perform data preprocessing on the geological exploration data of mineral resources to generate standard geological exploration data of mineral resources; perform three-dimensional geological modeling based on the standard geological exploration data of mineral resources to generate mineral resource reserve distribution data; Step S2: Acquire surface deformation monitoring images using synthetic aperture radar; perform time-series slicing on the surface deformation monitoring images to generate surface deformation slice data; perform stress change and microseismic phase imaging on the surface deformation slice data to obtain time-series stress data and microseismic phase data. Step S3: Decode the relaxation time spectrum based on the microseismic phase data to obtain the acoustic memory characteristics data of the mine rock mass; use the acoustic memory characteristics data of the mine rock mass to analyze the acoustic memory effect of the mine resource reserve distribution data to generate the instability memory data of the internal structure of the rock mass. Step S4: Based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data, construct a dynamic assessment model for geological activity, and use the dynamic assessment model for geological activity to predict the geological stability of mine resource reserve distribution data, generating geological stability prediction data. Step S5: Use geological stability prediction data to map the mineral resource reserve distribution data into three-dimensional grid cells to generate mineral resource grid division data; summarize the recoverable values ​​of all grid cells in the mineral resource grid division data, output the dynamic recoverable resource quantity of the mine, and perform resource development risk warning based on the dynamic recoverable resource quantity.

[0005] This invention deeply integrates traditional geological exploration data, surface deformation information, microseismic facies, and rock mass acoustic memory characteristics, tightly coupling mine resource distribution data with the dynamic evolution characteristics of underground rock mass structures. This significantly enhances the ability to reflect the impact of geological activity on resource reserves. The constructed dynamic assessment model of geological activity integrates surface deformation, stress changes, and structural instability memory data, enabling spatiotemporal dynamic prediction of geological stability in mining areas. This helps to identify potential instability zones in advance and ensure production safety. Three-dimensional grid cell mapping spatially maps geological stability prediction results with resource distribution, obtaining grid-level dynamic recoverable resource quantities. This enables linked decision-making between resource development and geological risk early warning, providing data support for refined management and intelligent mining. By decoding the relaxation time spectrum of microseismic facies to obtain rock mass acoustic memory characteristics, the invention introduces the memory effect for the first time in mine rock mass stability analysis, effectively overcoming the limitations of traditional methods in identifying internal hidden structures and improving the accuracy of predicting underground structural instability. The overall process encompasses multiple stages, including data acquisition, preprocessing, modeling, decoding, fusion, prediction, mapping, and early warning. It is highly integrated and intelligent, suitable for dynamic monitoring and risk management of mineral resources in geologically complex and rapidly changing environments. Therefore, this invention improves the accuracy, dynamism, and geological risk early warning capabilities of mine resource evaluation through multi-source dynamic data fusion and 3D modeling, thereby enhancing the precision of mine resource availability assessment.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain geological exploration data for mineral resources; Step S12: Clean the geological exploration data of the mine resources to generate cleaned geological exploration data of the mine resources; fill in missing values ​​in the cleaned geological exploration data of the mine resources to generate filled geological exploration data of the mine resources; standardize the filled geological exploration data of the mine resources to generate standard geological exploration data of the mine resources. Step S13: Extract profile layers from standard mine resource geological exploration data to generate geological stratigraphic profile data; capture structural lines from the geological stratigraphic profile data to generate geological structural boundary data; generate interlayer closure envelopes from the geological structural boundary data to generate geological body boundary data. Step S14: Reconstruct the geological body boundary data into a three-dimensional entity to generate three-dimensional geological structure data; calibrate the ore body attributes of the three-dimensional geological structure data to generate ore body attribute mapping data; divide the ore body attribute mapping data into reserve unit grids to generate ore body grid unit data; calculate the resource quantity of the ore body grid unit data to generate mine resource reserve distribution data.

[0007] This invention, through the systematic design of step S1, forms a complete, high-precision, and intelligent 3D modeling and reserve analysis process for mineral resources. First, step S11 acquires geological exploration data for mineral resources. Then, in step S12, data cleaning, missing value imputation, and data standardization are performed sequentially to generate high-quality standard geological exploration data for mineral resources, fundamentally ensuring the accuracy and consistency of the modeling data. Subsequently, in step S13, geological profile layer extraction is performed based on the standard data to form geological stratigraphic profile data. Further structural line capture and inter-layer closure envelope generation are then performed to finally obtain geological body boundary volume data reflecting the true underground structural characteristics. Based on this, step S14 reconstructs the geological body boundary volume data into 3D entities, obtaining 3D geological structure data. Ore body attributes (such as grade and density) are mapped to the 3D entities to generate ore body attribute mapping data. By meshing the attribute mapping data, ore body grid cell data is formed, and resource reserves are calculated based on this, thereby outputting mineral resource reserve distribution data. This process not only enables standardized management of geological data, accurate modeling of complex structures, and spatial mapping of attributes, but also significantly improves the accuracy and efficiency of underground resource spatial representation and reserve calculation. It can effectively support subsequent geological stability analysis, resource recoverability assessment, and intelligent mining decision-making, and has good engineering applicability and scalability.

[0008] Preferably, step S2 includes the following steps: Step S21: Acquire surface deformation monitoring images using synthetic aperture radar; Step S22: Perform radiometric correction on the surface deformation monitoring image to generate a corrected deformation interferometric image; perform phase unfolding on the deformation interferometric image to generate surface deformation field data; analyze the time dimension of the surface deformation field data and perform time-series segmentation on the surface deformation field data to generate surface deformation slice data. Step S23: Calculate the spatial stress gradient of the surface deformation slice data; fit the principal stress direction based on the spatial stress gradient to generate time-series stress distribution data; detect the microseismic precursor mode of the surface deformation slice data and generate a microseismic triggering potential map. Step S24: Extract the phase characteristic waveforms of the microseismic triggering potential map, and perform phase inversion on the phase characteristic waveforms to generate microseismic phase structure map data; perform spatiotemporal alignment between the time-series stress distribution data and the microseismic phase structure map data to generate the final time-series stress data and microseismic phase data respectively.

[0009] This invention utilizes synthetic aperture radar (SAR) to acquire high-precision surface deformation monitoring images in step S21, providing fundamental remote sensing data support for subsequent deformation analysis. Subsequently, in step S22, radiometric correction and phase unfolding techniques are employed to eliminate sensing errors and non-topographic interference, extracting deformation field data and further processing it with time-series slices to form surface deformation slice data, comprehensively characterizing the spatiotemporal features of dynamic surface changes. In step S23, spatial stress gradients are calculated based on the deformation slice data, completing the fitting of the principal stress direction and inference of its evolution trend, generating time-series stress distribution data with temporal continuity. Simultaneously, by intelligently detecting microseismic precursor patterns in minor surface deformations, a microseismic triggering potential map is constructed, enhancing the early warning capability for potentially unstable local areas. After entering S24, the characteristic waveforms of the seismic phases in the potential map are further extracted. Microseismic phase structure data are obtained through seismic inversion and spatiotemporally aligned with the stress distribution results. Finally, high-precision, time-series stress evolution data and microseismic phase data are generated. This process not only realizes multi-level analysis from deformation images to seismic phase structures, but also greatly improves the dynamic perception of the response mechanism of underground rock mass through the fusion of time and space dimensions.

[0010] Preferably, the microseismic precursor modes for detecting surface deformation slice data in step S23 include: Local deformation abrupt changes are detected in surface deformation slice data to generate candidate mask data for abrupt change areas. Time window sliding clustering is performed on the candidate mask data of mutation regions to extract abnormal time-series response features and generate potential abnormal response region data; Statistical analysis of deformation gradient direction in potential abnormal response area data is used to extract regions with high gradients pointing to consistent areas and generate precursor trend response data. Perform pre-earthquake deformation time lag analysis on precursor trend response data to generate a high-confidence pre-earthquake deformation lag distribution map; Stress accumulation threshold fitting is performed on the high-confidence pre-earthquake deformation hysteresis distribution map to extract the critical stress point group with triggering tendency and generate microseismic excitation sensitive area data. Regional stability weight mapping is performed on the data of the microseismic excitation sensitive area to generate a microseismic triggering potential map.

[0011] This invention achieves the ability to mine microseismic precursor patterns from raw deformation slice data through multi-stage processing. First, it identifies areas of drastic deformation changes through local deformation abrupt change detection, generating candidate mask data for abrupt change zones, laying the foundation for anomaly region localization. Then, a sliding window clustering operation is performed in the time dimension to extract consistent and periodic anomaly time-series response features, further generating potential anomaly response region data, effectively eliminating sporadic noise interference and improving the spatiotemporal correlation of anomaly identification. Next, by statistically analyzing the deformation gradient direction of potential anomaly response regions, high-gradient response regions with consistent spatial orientation are extracted, generating precursor trend response data, thereby capturing stress concentration trends from deformation trends. Based on this, pre-seismic deformation time lag analysis is performed on these precursor trend regions to construct a high-reliability pre-seismic deformation lag distribution map, effectively revealing the evolutionary delay characteristics between stress and surface response. Furthermore, stress accumulation threshold fitting is performed based on the hysteresis distribution map to extract the key areas most likely to reach the critical stress state and have triggering potential, generating microseismic excitation sensitive area data. Then, regional stability weight mapping processing is performed on these areas to construct a microseismic triggering potential map with multi-factor comprehensive characterization.

[0012] Preferably, step S24 involves extracting the phase characteristic waveform of the microseismic triggering potential map and performing a seismic inversion on the phase characteristic waveform, including: Extract the phase characteristic waveforms of the microseismic triggering potential map, and use the phase characteristic waveforms to perform regional layered time window adaptive scanning of the microseismic triggering potential map to extract high-potential candidate segments of the phase and generate phase candidate sampling data; Asymmetric wavelet variational envelopes are extracted from candidate seismic phase sampling data to generate seismic phase microstructure waveform data. The spectral domain polarity reconstruction of the phase microstructure waveform data is performed to extract extreme strain triggering features and generate phase polarity field data; based on the phase polarity field data, source travel time back analysis is performed to generate the initial seismic back-evolution parameter set; The initial seismic inversion parameter set is mapped to the microseismic triggering potential map for chain-type topological constraint inversion to generate a seismic phase topological coupling map. The regional grid of the microseismic triggering potential map is analyzed and fitted with the geological fault section of the seismic phase topology coupling map to obtain the microseismic phase structure map data.

[0013] This invention employs adaptive scanning with regional layered time windows, dynamically adjusting scanning parameters based on seismic phase characteristic waveforms to adaptively capture weak seismic phase signals. This significantly enhances the accuracy of identifying high-latency seismic phase segments, solving the problem of difficult seismic phase identification under low signal-to-noise ratio conditions in traditional methods. By utilizing asymmetric wavelet variational envelope extraction of microstructural features from candidate seismic phase sampling data, it accurately characterizes local energy fluctuations and nonlinear behavior of seismic phases, obtaining high-resolution microstructural waveform data and effectively improving the spatiotemporal resolution of microseismic events. Through spectral domain polarity reconstruction of the microstructural waveform data, extreme strain triggering characteristics in the seismic phase can be extracted, establishing seismic phase polarity field data and quantitatively characterizing the directionality and triggering intensity of the microseismic source, providing a stable foundation for subsequent inversion. Based on travel-time backtracking analysis of the seismic phase polarity field data, the generated initial inversion parameters have strong physical interpretability. Combined with a chain-like topological constraint inversion mechanism, a seismic phase topological coupling map is further constructed in the microseismic potential map, structurally restricting the distribution of non-physical seismic phases and improving the geological consistency of the inversion results. By combining the regional grid structure of the microseismic triggering potential map with the seismic phase topology coupling map, spatial fitting with the geological fault section was completed, and finally the microseismic phase structure map data was constructed. This not only realized the structural manifestation of the microseismic triggering mechanism, but also provided high-precision data support for fault identification and earthquake risk assessment.

[0014] Preferably, step S3, which involves decoding the relaxation time spectrum based on the microseismic phase data, includes: Frequency bandwidth weighted separation is performed on microseismic phase data to extract microseismic energy attenuation segments and generate phase bandwidth response data; Non-stationary time-frequency expansion is performed on the phase bandwidth response data to construct a phase attenuation time domain distribution map and generate acoustic energy release time series data; Hysteresis response calibration is performed on acoustic energy release time-series data, instantaneous peak hysteresis factor is extracted, and micro-seismic relaxation response curves are generated; Dynamic spectral density fitting is performed on the microseismic relaxation response curve to solve the local relaxation time parameter distribution and generate a relaxation time parameter map. The waveform hysteresis coefficient is calculated from the relaxation time parameter diagram, and the stress-acoustic coupling mapping matrix is ​​constructed to generate acoustic memory response factor data. Spatiotemporal clustering and compression are performed on acoustic memory response factor data to generate acoustic memory characteristic data of mine rock mass.

[0015] This invention extracts microseismic energy attenuation segments through frequency bandwidth weighted separation, effectively distinguishing the energy evolution of different frequency bands within the seismic phase. The generated phase bandwidth response data is more directional and physically correlated, laying a data foundation for subsequent attenuation time-series analysis. A non-stationary time-frequency expansion is used to construct a phase attenuation time-domain distribution map, effectively characterizing the nonlinear temporal pattern of acoustic energy release in microseismic events. The generated acoustic energy release time-series data is highly time-sensitive, facilitating the dynamic inversion of microseismic triggering mechanisms. By calibrating the acoustic energy release time-series data with hysteresis response, the instantaneous peak hysteresis factor of the seismic phase can be extracted, further constructing microseismic relaxation response curves. This reveals the causality and hysteresis between stress release and acoustic energy response, providing reliable model support for stress coupling mechanism analysis. By fitting the dynamic spectral density of the microseismic relaxation response curves, a relaxation time distribution map of the local source region is obtained, reflecting the viscoelastic dissipation mechanism of the microseismic generation area and helping to reflect the acoustic energy dissipation behavior and stress release rhythm of the local rock mass. By constructing a stress-acoustic coupling mapping matrix through waveform hysteresis coefficient calculation and combining it with relaxation time parameter diagrams, high-resolution acoustic memory response factor data is generated. This is the first time that the response memory of rock mass to historical stress disturbances has been presented in a visualized and quantifiable form. Spatiotemporal clustering compression of the acoustic memory response factor data allows for the extraction of acoustic memory characteristics of mine rock masses at different spatial scales and temporal resolutions, providing a solid data foundation and judgment support for intelligent prediction of dynamic disasters such as rockbursts and water inrushes.

[0016] Preferably, step S3, which involves analyzing the acoustic memory effect of mine rock mass on the distribution data of mine resource reserves using data on the acoustic memory characteristics of the rock mass, includes: Structural regions are calibrated on the acoustic memory characteristics data of mine rock masses, high hysteresis coupling regions are extracted, and potential structurally sensitive region data are generated. Spatial registration analysis is performed on the potential structurally sensitive region data and the mine resource reserve distribution data to extract acoustic memory anomaly coverage areas and generate acoustic-reservoir coupling zone distribution data. Calculate the multi-point response gradient of the acoustic-storage coupling zone distribution data, extract the local acoustic stress mutation values, and generate structural anomaly change gradient data; Directional migration calculations are performed on the gradient data of structural anomalies to identify the sources of micro-displacement in the structure and generate data on the tendency of rock mass instability. Dynamic evolution sequence analysis was performed on the rock mass instability induction tendency data to extract high-risk factor combinations of evolution trends and generate structural instability sensitive sequence data; Memory mapping aggregation analysis is performed on the structural instability sensitive sequence data to generate memory data of internal structural instability of the rock mass.

[0017] This invention precisely extracts high-hysteresis coupling regions—regions with strong acoustic response but large hysteresis—by calibrating the structural regions of rock mass acoustic memory characteristic data. These regions are typically important locations for the accumulation of historical stress disturbances. The resulting data on potential structurally sensitive regions can provide early warning of local structural instability, offering a basis for early engineering risk screening. Spatial registration analysis between these potential structurally sensitive regions and mine resource reserve distribution data extracts acoustic memory anomaly coverage areas, generating acoustic-reservoir coupling zone distribution data. This reveals the coupling relationship between rock mass stress evolution and resource occurrence. This process can be used to identify high-risk overlapping areas of "resource enrichment-structural complexity," supporting precision mining. Multi-point response gradient calculation of the acoustic-reservoir coupling zone distribution data can extract local acoustic stress abrupt change points, generating structural anomaly change gradient data. This enables the identification and quantification of minute structural disturbances, creating conditions for early identification of potential rock mass disturbance zones. By performing directional migration calculations on anomalous gradient data, the sources of micro-displacement instability within the rock mass can be traced, generating data on the tendency of rock mass instability induction. This helps engineers identify the possible future movement directions of local rock strata, providing a scientific reference for tunnel support and work layout. Dynamic evolution sequence analysis of the instability induction tendency data can extract a series of high-risk factor combinations with indicative significance, forming structural instability sensitive sequence data, enabling phased tracking and prediction of structural stability status and trends. Finally, through memory mapping aggregation analysis of the structural instability sensitive sequence data, memory data on internal structural instability of the rock mass is generated. This data not only reflects the cumulative effect of the rock mass's response to historical disturbances but can also be used to construct a prediction model based on acoustic memory, achieving an intelligent management transformation from "passive response" to "active prediction."

[0018] Preferably, step S4 includes the following steps: Step S41: Extract regional strain gradient data from surface deformation monitoring data; fit the rate of change of time-series stress data, extract stress loading / unloading rhythm curves, and generate time-series stress evolution characteristic data; perform hysteresis impact scaling analysis on rock mass internal structure instability memory data, extract memory trigger threshold index, and generate rock mass memory trigger index data. Step S42: Integrate regional strain gradient data, time-series stress evolution characteristic data, and rock mass memory triggering index data into comprehensive feature data, and divide the comprehensive feature data into datasets to generate model training set and model test set; Step S43: Train the model on the training set using the Long Short-Term Memory Neural Network algorithm to generate a pre-model for dynamic assessment of geological activity; use the test set to optimize and iterate the pre-model for dynamic assessment of geological activity, thereby generating a dynamic assessment model for geological activity. Step S44: Use the dynamic assessment model of geological activity to predict the geological stability of the mineral resource reserve distribution data and generate geological stability prediction data.

[0019] This invention generates stress evolution characteristic data by extracting regional strain gradient data from surface deformation monitoring, fitting loading and unloading rhythm curves of time-series stress, and extracting memory trigger threshold indicators from hysteresis impact scaling analysis of rock mass internal structural instability memory data to construct rock mass memory trigger index data. This achieves accurate capture of multi-source coupled indicators, comprehensively reflecting the rock mass response mechanism. Subsequently, the above data are integrated to form a comprehensive feature dataset, which is scientifically divided into model training and testing sets to ensure the training effect and generalization ability of the model. Based on the Long Short-Term Memory (LSTM) neural network algorithm, the time dependence and nonlinear variation law of geological data are fully explored to construct a dynamic assessment model of geological activity. By dynamically predicting the stability of mine resource reserve distribution data, high spatiotemporal resolution geological stability prediction data is generated. This method not only improves the comprehensiveness and timeliness of geological state characterization and strengthens time-series modeling capabilities, but also realizes intelligent prediction of geological stability in mining resource areas. It greatly enhances the scientific decision-making capabilities for mine disaster early warning, engineering layout optimization, and resource mining strategy adjustment, providing key technical support for the construction of intelligent mine geological systems and safe mining.

[0020] Preferably, step S5 includes the following steps: Step S51: Reconstruct spatial coordinates and perform three-dimensional regional layering on the geological stability prediction data to generate stability layer index data; map the stability layer index data to the three-dimensional structural domain of the mineral resource reserve distribution data, perform mesh cell nesting mapping processing, and generate mineral resource mesh partitioning data; Step S52: Perform resource grade analysis and stability coefficient screening on each grid cell in the mine resource grid division data, extract the grid set that meets the mining conditions, and generate mineable grid cell data; Step S53: Perform weighted integral calculation on all grid cells in the mine resource grid division data to obtain the dynamic recoverable resources of the mine; compare the preset historical resource development curve and the dynamic recoverable resources for trend warning to execute resource development risk warning operation.

[0021] This invention reconstructs spatial coordinates and performs three-dimensional regional layering on geological stability prediction data to generate stability layering index data. This index data is then mapped to the three-dimensional structural domain of mine resource reserves, completing nested mapping of grid cells to form mine resource grid division data, achieving efficient integration of geological stability and resource spatial distribution. Subsequently, resource grade analysis and stability coefficient screening are performed on each grid cell to accurately extract data from mineable grid cells that meet mining conditions, ensuring the safety and economy of mining operations. Furthermore, by calculating the weighted integral of all grid cells, the total amount of mine recoverable resources is dynamically quantified, enabling real-time monitoring of dynamic changes in mine resources. Simultaneously, the dynamic recoverable resource quantity is compared with the preset historical resource development curve to conduct resource development risk warnings, strengthening the scientific and forward-looking nature of mine resource management. Overall, step S5 achieves three-dimensional coupled management of mine resource reserves and geological stability, improving resource utilization efficiency and mining safety assurance capabilities, and providing a solid data foundation and technical support for dynamic monitoring, risk assessment, and intelligent decision-making in mine resource development.

[0022] This specification provides a mine availability resource assessment system for performing the aforementioned mine availability resource assessment method. The mine availability resource assessment system includes: The geological modeling module is used to acquire geological exploration data of mineral resources; preprocess the geological exploration data of mineral resources to generate standard geological exploration data of mineral resources; and perform three-dimensional geological modeling based on the standard geological exploration data of mineral resources to generate mineral resource reserve distribution data. The seismic phase imaging module is used to acquire surface deformation monitoring images through synthetic aperture radar; to perform time-series slicing on the surface deformation monitoring images to generate surface deformation slice data; and to perform stress change and microseismic phase imaging on the surface deformation slice data to obtain time-series stress data and microseismic phase data. The rock mass memory module is used to decode the relaxation time spectrum based on microseismic phase data to obtain acoustic memory characteristic data of mine rock mass; and to use the acoustic memory characteristic data of mine rock mass to analyze the acoustic memory effect of rock mass on the distribution data of mine resource reserves, thereby generating memory data of instability of internal rock mass structure. The activity assessment module is used to construct a dynamic assessment model of geological activity based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data. It also uses the dynamic assessment model of geological activity to predict the geological stability of mineral resource reserve distribution data and generate geological stability prediction data. The resource mining early warning module is used to map the distribution data of mine resource reserves into three-dimensional grid cells using geological stability prediction data, generate mine resource grid division data, summarize the recoverable values ​​of all grid cells in the mine resource grid division data, output the dynamic recoverable resource quantity of the mine, and perform resource development risk early warning based on the dynamic recoverable resource quantity.

[0023] The beneficial effects of this invention are as follows: The geological modeling module efficiently acquires and standardizes the processing of geological exploration data for mineral resources, completing accurate three-dimensional geological modeling based on standardized data, and accurately generating mineral resource reserve distribution data, providing a solid foundation for subsequent analysis; the seismic imaging module acquires high spatiotemporal resolution surface deformation monitoring images through synthetic aperture radar, and extracts temporal stress data and microseismic phase data using time-series slicing and microseismic phase imaging techniques, comprehensively reflecting the dynamic changes in mine geology; the rock mass memory module performs relaxation time spectrum decoding based on microseismic phase data, revealing the acoustic memory characteristics of the mine rock mass, and combines resource reserve data to deeply analyze the instability memory of the internal structure of the rock mass, effectively capturing potential unstable factors; the activity assessment module integrates surface deformation, temporal stress, and rock mass memory data to construct a dynamic assessment model of geological activity, realizing intelligent prediction of the geological stability of mineral resource reserve distribution, significantly improving the accuracy and timeliness of prediction; the resource mining early warning module completes three-dimensional grid division based on geological stability prediction data, dynamically calculates recoverable resources, and conducts risk early warning based on historical development trends, assisting in the scientific management and safety assurance of mineral resource development. Overall, this system achieves deep integration and intelligent analysis of mineral resource reserves and geological dynamic information, greatly enhancing the safety, economy, and management efficiency of mineral resource development, and providing strong technical support for smart mine construction and sustainable development. Therefore, this invention, through multi-source dynamic data fusion and 3D modeling, improves the accuracy, dynamism, and geological risk early warning capabilities of mineral resource evaluation, thereby increasing the precision of the evaluation of usable mineral resources. Attached Figure Description

[0024] Figure 1 A schematic diagram illustrating the steps of a method for evaluating the available resources of a mine. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S5. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above objectives, please refer to Figures 1 to 3 A method for evaluating the available resources of a mine, the method comprising the following steps: Step S1: Obtain geological exploration data of mineral resources; perform data preprocessing on the geological exploration data of mineral resources to generate standard geological exploration data of mineral resources; perform three-dimensional geological modeling based on the standard geological exploration data of mineral resources to generate mineral resource reserve distribution data; Step S2: Acquire surface deformation monitoring images using synthetic aperture radar; perform time-series slicing on the surface deformation monitoring images to generate surface deformation slice data; perform stress change and microseismic phase imaging on the surface deformation slice data to obtain time-series stress data and microseismic phase data. Step S3: Decode the relaxation time spectrum based on the microseismic phase data to obtain the acoustic memory characteristics data of the mine rock mass; use the acoustic memory characteristics data of the mine rock mass to analyze the acoustic memory effect of the mine resource reserve distribution data to generate the instability memory data of the internal structure of the rock mass. Step S4: Based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data, construct a dynamic assessment model for geological activity, and use the dynamic assessment model for geological activity to predict the geological stability of mine resource reserve distribution data, generating geological stability prediction data. Step S5: Use geological stability prediction data to map the mineral resource reserve distribution data into three-dimensional grid cells to generate mineral resource grid division data; summarize the recoverable values ​​of all grid cells in the mineral resource grid division data, output the dynamic recoverable resource quantity of the mine, and perform resource development risk warning based on the dynamic recoverable resource quantity.

[0029] This invention deeply integrates traditional geological exploration data, surface deformation information, microseismic facies, and rock mass acoustic memory characteristics, tightly coupling mine resource distribution data with the dynamic evolution characteristics of underground rock mass structures. This significantly enhances the ability to reflect the impact of geological activity on resource reserves. The constructed dynamic assessment model of geological activity integrates surface deformation, stress changes, and structural instability memory data, enabling spatiotemporal dynamic prediction of geological stability in mining areas. This helps to identify potential instability zones in advance and ensure production safety. Three-dimensional grid cell mapping spatially maps geological stability prediction results with resource distribution, obtaining grid-level dynamic recoverable resource quantities. This enables linked decision-making between resource development and geological risk early warning, providing data support for refined management and intelligent mining. By decoding the relaxation time spectrum of microseismic facies to obtain rock mass acoustic memory characteristics, the invention introduces the memory effect for the first time in mine rock mass stability analysis, effectively overcoming the limitations of traditional methods in identifying internal hidden structures and improving the accuracy of predicting underground structural instability. The overall process encompasses multiple stages, including data acquisition, preprocessing, modeling, decoding, fusion, prediction, mapping, and early warning. It is highly integrated and intelligent, suitable for dynamic monitoring and risk management of mineral resources in geologically complex and rapidly changing environments. Therefore, this invention improves the accuracy, dynamism, and geological risk early warning capabilities of mine resource evaluation through multi-source dynamic data fusion and 3D modeling, thereby enhancing the precision of mine resource availability assessment.

[0030] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of a method for evaluating the usable resources of a mine according to the present invention. In this example, the method for evaluating the usable resources of a mine includes the following steps: Step S1: Obtain geological exploration data of mineral resources; perform data preprocessing on the geological exploration data of mineral resources to generate standard geological exploration data of mineral resources; perform three-dimensional geological modeling based on the standard geological exploration data of mineral resources to generate mineral resource reserve distribution data; Step S2: Acquire surface deformation monitoring images using synthetic aperture radar; perform time-series slicing on the surface deformation monitoring images to generate surface deformation slice data; perform stress change and microseismic phase imaging on the surface deformation slice data to obtain time-series stress data and microseismic phase data. Step S3: Decode the relaxation time spectrum based on the microseismic phase data to obtain the acoustic memory characteristics data of the mine rock mass; use the acoustic memory characteristics data of the mine rock mass to analyze the acoustic memory effect of the mine resource reserve distribution data to generate the instability memory data of the internal structure of the rock mass. Step S4: Based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data, construct a dynamic assessment model for geological activity, and use the dynamic assessment model for geological activity to predict the geological stability of mine resource reserve distribution data, generating geological stability prediction data. Step S5: Use geological stability prediction data to map the mineral resource reserve distribution data into three-dimensional grid cells to generate mineral resource grid division data; summarize the recoverable values ​​of all grid cells in the mineral resource grid division data, output the dynamic recoverable resource quantity of the mine, and perform resource development risk warning based on the dynamic recoverable resource quantity.

[0031] In this embodiment of the invention, geological exploration data of mineral resources is acquired, including multi-source geological information such as borehole columnar sections, geological profiles, lithological records, and geophysical well logging data. The acquired data undergoes standardization processing, including outlier removal, missing value imputation, data resampling, and unit unification, to generate standard geological exploration data for mineral resources. Subsequently, based on this standardized data, three-dimensional geological modeling is performed using modeling algorithms (such as hierarchical geological modeling or voxel modeling techniques) to identify the spatial extension and physical property distribution of different ore layers, thereby generating mineral resource reserve distribution data. The output is a three-dimensional spatial raster model or isosurface model, describing the location, reserves, and grade variations of each ore body. Synthetic Aperture Radar (SAR) technology is used to acquire surface deformation monitoring images of the mining area, and differential interferometry (D-InSAR) is used to acquire surface deformation information at multiple time phases. The monitoring images are sliced ​​according to the time axis to generate surface deformation slice data, with each slice representing a surface displacement map at different times. Further processing of the surface deformation slice data involves time-series stress inversion and microseismic phase imaging. Using spatial deformation inversion models and phase imaging algorithms (such as Kirchhoff imaging or reverse time migration), local stress variation trends and microseismic activity signals are extracted from the ore body, yielding time-series stress data and microseismic phase data reflecting the evolution of the underground structure. Relaxation time spectrum decoding is performed on the microseismic phase data to extract the response characteristics of the rock mass to historical stress disturbances. This step utilizes the frequency response curve of the attenuation segment in the seismic waveform, and through short-time Fourier transform or generalized cross-correlation function analysis, inverts the stress-acoustic response history of the rock mass, extracting spectral signals with memory characteristics, thereby obtaining acoustic memory characteristic data of the mine rock mass. Subsequently, combined with the distribution data of mine resource reserves, and by comparing parameters such as stress response hysteresis and energy absorption ratio reflected in the acoustic memory characteristics, the acoustic response patterns of each reserve unit in the historical stress field are evaluated, and the acoustic memory effect of the rock mass is analyzed. This generates internal structural instability memory data of the rock mass reflecting the characteristics of structural stability deterioration. A dynamic assessment model for geological activity is constructed by integrating surface deformation monitoring data, time-series stress data, and rock mass internal structural instability memory data. This model employs a multi-dimensional input fusion strategy, combining machine learning models (such as random forests and deep neural networks) with geomechanical analytical algorithms to model and analyze dynamic geological processes within the mining area, outputting a geological activity level index for each unit area. Based on the assessment results, geological stability predictions are performed on each reserve unit in the mine resource reserve distribution data, determining the structural integrity and stress-induced instability risk of reserve units at different spatial locations, thereby generating geological stability prediction data to guide development priority allocation and safety control.Geological stability prediction data is used to map the distribution data of mineral resource reserves into three-dimensional grid cells, dividing the entire reserve distribution space into multiple three-dimensional mineralization unit grids (e.g., cubic cells with sides of 10m × 10m × 10m). Within each cell, a mineability score is calculated by integrating reserve indicators, stability prediction results, and orebody attributes, forming the mineral resource grid division data. Subsequently, the mineability values ​​of all grid cells are weighted and summarized to output a dynamic recoverable resource quantity, reflecting the true recoverable resource range under current geological conditions and structural stability. Based on the dynamic recoverable resource quantity, a resource development risk assessment model is further established to compare the recoverability levels, geological uncertainties, and mining continuity of resources in different regions, generating early warning information to support intelligent mining scheduling and risk control decision-making.

[0032] Preferably, step S1 includes the following steps: Step S11: Obtain geological exploration data for mineral resources; Step S12: Clean the geological exploration data of the mine resources to generate cleaned geological exploration data of the mine resources; fill in missing values ​​in the cleaned geological exploration data of the mine resources to generate filled geological exploration data of the mine resources; standardize the filled geological exploration data of the mine resources to generate standard geological exploration data of the mine resources. Step S13: Extract profile layers from standard mine resource geological exploration data to generate geological stratigraphic profile data; capture structural lines from the geological stratigraphic profile data to generate geological structural boundary data; generate interlayer closure envelopes from the geological structural boundary data to generate geological body boundary data. Step S14: Reconstruct the geological body boundary data into a three-dimensional entity to generate three-dimensional geological structure data; calibrate the ore body attributes of the three-dimensional geological structure data to generate ore body attribute mapping data; divide the ore body attribute mapping data into reserve unit grids to generate ore body grid unit data; calculate the resource quantity of the ore body grid unit data to generate mine resource reserve distribution data.

[0033] In this embodiment of the invention, geological exploration data of mineral resources is acquired through drilling platforms and geological surveying equipment (including resistivity meters, magnetometers, gamma spectrometers, etc.) deployed at the mining exploration site. The collected data includes borehole number, three-dimensional coordinates of the borehole location (represented in the WGS84 coordinate system), borehole depth (in meters), lithology, color, structure, mineral composition, porosity, water content, and ore grade (expressed as a mass fraction, e.g., Fe 32.5%) at each depth. Core samples from each borehole are photographed, numbered, and their geological characteristics are recorded. Resistivity, natural gamma, and induced gamma data measured by electrical logging instruments are recorded in layers according to drilling depth. All data must be collected into a CSV or dedicated database format file, with a unified coding rule such as: borehole number_depth_attribute. For example, K102_28.5_FeGrade represents the iron ore grade at a depth of 28.5 meters in borehole K102. The data cleaning process employed a Pandas data processing script written in Python. The `.dropna(axis=0)` command was used to remove records with missing key fields (such as coordinates, ore type, and ore grade), and the `duplicated()` function was used to detect duplicates and remove redundant data. Data missing value imputation was divided into three categories: continuous physical data (such as porosity and resistivity) were imputed using third-order spline interpolation; categorical fields (such as lithology) were imputed using the maximum frequency class among adjacent borehole depths. The imputation threshold was set to a maximum interpolation depth of 1.5 meters; segments exceeding this distance were designated as "unusable segments." Standardization was based on Z-Score normalization. The standardized results were then reorganized into a standard field format: borehole number, depth, coordinates, standardized physical parameters, and standardized grade, and stored uniformly in a structured database (such as a PostgreSQL+PostGIS environment). Profile stratification extraction employed an automatic geological profile segmentation method based on borehole data. Samples were taken every 1 meter at depth, and boundary identification was performed by comparing lithological changes, grade abrupt changes, and resistivity abrupt changes within continuous segments. The threshold for judging lithological changes is the change in lithological designation; the threshold for grade abrupt change is set to ∆ grade ≥ 15%; and the threshold for resistivity change is ∆R ≥ 25 Ohm·m. A breakpoint interpolation algorithm is used to set layer nodes at each fault or boundary, generating profile data and outputting it in shapefile format. Structural line capture is based on the "Automatic Fault Extraction Tool" in a GIS platform (such as ArcGIS or QGIS), combined with remote sensing imagery and field geological records to identify planar structures and fault traces. Fault boundaries are determined to meet the following conditions: vertical displacement exceeding 2 meters, strong lithological contrast on both sides (lithological designation difference ≥ 3 levels), and accompanying fracture zone records. The extracted linear data undergoes node topology checking, with a line matching tolerance set to 0.2 meters. After completion, geological structural boundary data (vector format) is output. Interlayer closure envelopes are constructed using structural lines in conjunction with geological stratigraphic data, employing TIN (Triangular Irregular Network) or polygon raster methods to seal the edges of geological units.Boundary closure must satisfy seamless connectivity. A "closed-loop check" module is used to detect the existence of unclosed points. If any are found, boundary completion segments are forcibly generated. All closed polygons must record a unique ID, the layer number they enclose, and the mineral type identifier.

[0034] Preferably, step S2 includes the following steps: Step S21: Acquire surface deformation monitoring images using synthetic aperture radar; Step S22: Perform radiometric correction on the surface deformation monitoring image to generate a corrected deformation interferometric image; perform phase unfolding on the deformation interferometric image to generate surface deformation field data; analyze the time dimension of the surface deformation field data and perform time-series segmentation on the surface deformation field data to generate surface deformation slice data. Step S23: Calculate the spatial stress gradient of the surface deformation slice data; fit the principal stress direction based on the spatial stress gradient to generate time-series stress distribution data; detect the microseismic precursor mode of the surface deformation slice data and generate a microseismic triggering potential map. Step S24: Extract the phase characteristic waveforms of the microseismic triggering potential map, and perform phase inversion on the phase characteristic waveforms to generate microseismic phase structure map data; perform spatiotemporal alignment between the time-series stress distribution data and the microseismic phase structure map data to generate the final time-series stress data and microseismic phase data respectively.

[0035] In this embodiment of the invention, radar images covering the mining area are acquired using the Synthetic Aperture Radar (SAR) of the Sentinel-1 satellite. The frequency band selected is C-band, the polarization mode is VV polarization, the imaging mode is set to IW (Interferometric Wide Swath) mode, the spatial resolution is 10 meters, and the time baseline does not exceed 12 days to ensure usability for surface deformation monitoring. The image acquisition area is clearly defined using latitude and longitude coordinates, the image format is GeoTIFF, and the acquisition timestamp and orbital number are recorded. The downloaded SAR images are read using SNAP (Sentinel Application Platform) software, and image pairs are imported, with the primary image and secondary image paired to form interferometric pairs, ready for interferometric processing. Radiometric correction processing is performed using the Radiometric Calibration module in SNAP, with the correction type set to [missing information]. Backscattering coefficients and beam patterns were set to the default IW1-W3 to remove systematic and slant range errors. The corrected images underwent multi-look processing, with multi-look factors set to 5 in both azimuth and range directions to reduce coherence noise. The interferogram was generated by calculating the phase difference between the primary and secondary images using the InSAR module. During phase unwrapping, the Minimum Cost Flow (MCF) algorithm was employed. Before processing, a Goldstein filter was used to denoise the interferogram, with a filter window set to 7×7 pixels. Phase unwrapping restored the original [-π, π] fuzzy phase data to continuously varying values. The unwrapped data was converted into a surface deformation field, calibrated in millimeters, with coordinates consistent with geographic projection, and exported in NetCDF or GeoTIFF formats. For temporal analysis, the deformation field data was sorted using image timestamps, with the time interval uniformly adjusted to a 12-day cycle (consistent with Sentinel-1). Subsequently, time-series segmentation was performed, dividing the deformation field into multiple slices according to the acquisition time, named in the format: Deformation_YYYYMMDD.tif. Each slice records the full-area deformation displacement information corresponding to that date. The spatial stress gradient calculation of the surface deformation slice data was performed using a two-dimensional gradient operator, employing the Sobel gradient operator to calculate the displacement change rate in both the x (east-west) and y (north-south) directions. The stress gradient matrix is ​​defined as follows: ;in This is the displacement value, in millimeters. and The stress distribution was calculated using the central difference method, implemented using the `numpy.gradient` function in Python, and the output was a gradient intensity image. Stress principal direction fitting employed a two-dimensional tensor analysis method to calculate the principal axis of the stress direction for each pixel. Specifically, this was done by calculating the principal direction of the stress tensor for each pixel (the direction corresponding to the largest eigenvalue), with the direction angle represented in the range [0°, 180°) in degrees, and the output was a directional map. Time-series stress distribution data was generated by overlaying time slices, in the format of a sequence of multiple directional images with timestamp indices. Microseismic precursor pattern detection was achieved through spatial and temporal anomaly detection of surface deformation slices. Short-time Fourier transform (STFT) was used to identify abrupt deformation regions, with the criterion being: a mutation rate exceeding three times the mean of the previous period; these abrupt regions were marked as "potential microseismic trigger zones." After extracting these areas, a microseismic triggering potential map was generated through raster reclassification, labeled with potential levels as low (0–0.3), medium (0.3–0.7), and high (0.7–1.0), with a raster resolution of 10 meters. Seismic wave acquisition nodes were deployed in areas with potential values ​​greater than 0.7 in the microseismic triggering potential map. Short-period seismographs with 24-bit precision and a sampling frequency of at least 100Hz were used for acquisition. After acquiring phase data, the first arrival times of P-waves and S-waves were matched, and their waveform morphology was extracted to calculate phase waveform characteristic parameters, including dominant frequency (Hz), duration (seconds), and amplitude (μm / s). These characteristic waveforms were decomposed using wavelet transform (Daubechies-4), and principal components were extracted for subsequent inversion. Seismic inversion employed the near-field source inversion method, combining the seismograph deployment location and phase waveforms to solve for the focal mechanism. The processing utilizes the SEISAN or MATLAB seismic inversion module. Inputting the epicenter location and wave velocity model (defined according to the mining area's stratigraphic levels, with each layer's velocity set to a constant, e.g., 2.5 km / s for the surface and 5.6 km / s for bedrock), the microseismic focal moment tensor is calculated, thereby generating a microseismic facies structure map. The output is a vector-based layer containing the focal location, focal type (normal fault, reverse fault, strike-slip fault), and magnitude. The final spatiotemporal alignment is achieved using a Geographic Information System (such as ArcGIS) in conjunction with a Python script. Each time-series stress map (keyed by date) is aligned with the facies structure map within the same time window. Time alignment uses a 12-hour tolerance, and spatial alignment uses a 50-meter tolerance. An index is established for each data set, generating unified formats for "Time-Series Stress Data (TimeSeriesStress.tif)" and "Microseismic Facies Data (SeismicPhase.geojson)".

[0036] Preferably, the microseismic precursor modes for detecting surface deformation slice data in step S23 include: Local deformation abrupt changes are detected in surface deformation slice data to generate candidate mask data for abrupt change areas. Time window sliding clustering is performed on the candidate mask data of mutation regions to extract abnormal time-series response features and generate potential abnormal response region data; Statistical analysis of deformation gradient direction in potential abnormal response area data is used to extract regions with high gradients pointing to consistent areas and generate precursor trend response data. Perform pre-earthquake deformation time lag analysis on precursor trend response data to generate a high-confidence pre-earthquake deformation lag distribution map; Stress accumulation threshold fitting is performed on the high-confidence pre-earthquake deformation hysteresis distribution map to extract the critical stress point group with triggering tendency and generate microseismic excitation sensitive area data. Regional stability weight mapping is performed on the data of the microseismic excitation sensitive area to generate a microseismic triggering potential map.

[0037] In this embodiment of the invention, deformation difference images are acquired within each time interval. The time interval is based on the synthetic aperture radar acquisition frequency, such as one frame every 12 days. Deformation difference images between all adjacent frames are calculated, and the overall mean and standard deviation of deformation changes for all pixels are statistically analyzed. A mutation identification threshold is set based on the statistical results, typically the mean plus 2.5 times the standard deviation. Pixels in each frame's difference image are compared one by one; any pixel whose change value exceeds the threshold is marked as a mutation pixel. Spatial connectivity clustering is performed on adjacent mutation pixels, using an 8-neighborhood approach to construct connected regions. The aggregated mutation regions are candidate mutation regions, output as raster mask data format, where a value of 1 indicates a mutation region and a value of 0 indicates background. A sliding time window approach is used to process the data sequence of mutation regions, with a fixed time window length of 90 days and a sliding step size of 12 days. Within each time window, deformation change sequences of all mutation regions are extracted. These sequences are input into a time series similarity analysis algorithm, such as a distance metric based on dynamic time warping, to cluster the sequences of all mutation regions. The clustering method employs a hierarchical clustering algorithm, with the clustering threshold calculated based on time series similarity, aggregating abrupt change regions with similar response patterns. If a region repeatedly appears in similar anomaly classes across multiple sliding time windows, it is considered to have significant anomalous response characteristics. These regions are extracted and marked as potential anomalous response regions, output as a vector layer, with anomaly frequency information recorded for each region. For each potential anomalous response region, the spatial gradient direction of the deformation is calculated using a difference method based on deformation slice data within its spatial range. Specifically, the change value of each pixel is calculated along both the east-west and north-south directions, and the gradient direction of each pixel is determined by these two values. After summing the gradient directions of all pixels in the entire region, a statistical analysis of directional consistency is performed. If the gradient directions of the vast majority of pixels in the region are similar, it indicates a strong deformation pointing trend. Regions with concentrated and consistent gradient directions are identified as regions with precursory trend responses. These regions are extracted into a new layer, accompanied by the main pointing direction data for each region. By retrieving the historical microseismic event catalog, the precursory trend response times of these events and their corresponding regions are compared. For each historical microseismic event, surface deformation data from 30 days prior is used to extract the time series of regional deformation and pinpoint the moment of maximum change. The time of maximum change is compared with the actual microseismic event time to calculate the time interval between the deformation response and the microseismic event for each region. If a region exhibits a stable lag time interval across multiple historical events, for example, concentrated between 3 and 10 days, this region is marked as a high-confidence pre-seismic deformation lag zone. The final generated lag distribution map records the average lag time per pixel, with the layer format being a raster map and the unit being days. The deformation accumulation process in the lag zone is analyzed, selecting areas with accumulated deformation exceeding 20 mm and extracting their historical change sequences.Curve fitting was used to fit the cumulative trend of deformation in these regions, assessing the rate of stress accumulation. A threshold for the rate of change was set to identify regions with significantly higher deformation accumulation rates than the average. The center locations of representative pixels within these regions were extracted as critical stress point groups. All critical points were output in spatial coordinates and, combined with information such as stress change rate, lag time, and directional consistency, were labeled as sensitive points with a tendency for microseismic excitation. This was ultimately summarized into a sensitive point group layer, formatted as spatial point data with multiple attribute fields. A lithology coding layer, an engineering disturbance layer, and a historical seismic activity layer were extracted from existing geological data. Stability evaluation factor values ​​were assigned to each data type. For example, in the lithology factor, hard rock regions were assigned low instability values, while soft rock regions were assigned high instability values; regions with high engineering disturbance were assigned high values, while natural regions were assigned low values; and regions with frequent historical earthquakes were assigned high source weight values. After normalizing the three factor data, a factor weight superposition analysis was performed on each critical stress point to obtain a comprehensive regional stability score. The score values ​​are mapped back to the spatial region where the sensitive points are located, and a kernel density estimation method is used for smooth expansion to generate a continuous microseismic triggering potential map. The layer unit is standardized potential value, the output resolution is 10 meters, and the image format is GeoTIFF.

[0038] Preferably, step S24 involves extracting the phase characteristic waveform of the microseismic triggering potential map and performing a seismic inversion on the phase characteristic waveform, including: Extract the phase characteristic waveforms of the microseismic triggering potential map, and use the phase characteristic waveforms to perform regional layered time window adaptive scanning of the microseismic triggering potential map to extract high-potential candidate segments of the phase and generate phase candidate sampling data; Asymmetric wavelet variational envelopes are extracted from candidate seismic phase sampling data to generate seismic phase microstructure waveform data. The spectral domain polarity reconstruction of the phase microstructure waveform data is performed to extract extreme strain triggering features and generate phase polarity field data; based on the phase polarity field data, source travel time back analysis is performed to generate the initial seismic back-evolution parameter set; The initial seismic inversion parameter set is mapped to the microseismic triggering potential map for chain-type topological constraint inversion to generate a seismic phase topological coupling map. The regional grid of the microseismic triggering potential map is analyzed and fitted with the geological fault section of the seismic phase topology coupling map to obtain the microseismic phase structure map data.

[0039] In this embodiment of the invention, the sensitive area for microseismic excitation is extracted from the microseismic triggering potential map to limit the scanning area. Seismic waveform data is loaded within this area in a continuous waveform stream file format (such as miniSEED format), with a uniform sampling frequency of 500Hz. Channels containing obvious seismic phase characteristics are selected from the multi-channel waveform data. The initial seismic phase triggering time is identified using the short-time energy ratio method, and the minimum triggering duration is set to 0.4 seconds. A dynamic time window is set around each triggering point, dynamically expanding the scanning window length based on the forward energy accumulation growth rate and the backward energy decay rate. The maximum window length is set to 5 seconds, and the minimum length to 1.5 seconds. If the waveform energy continuously fluctuates to a set threshold (based on 3 times the original average energy), the window is retained. All windows meeting the conditions are archived and saved as candidate seismic phase sampling data in a standardized seismic waveform array format, with accompanying location information and sampling time labels. An asymmetric wavelet packet decomposition method is used on the candidate seismic phase sampling data, selecting a third-order Daubechies wavelet function as the mother wavelet, with a decomposition depth set to 6 layers. Each sampled waveform segment is decomposed, and the mode envelope of the low-frequency subband signal is extracted. The asymmetric distribution of wavelet packet energy in the time domain is calculated using the variational mode constraint method. A microstructure variational envelope curve is constructed for each sampled waveform to reflect the detailed evolution characteristics of the microseismic waveform at different scales. The main envelope energy peak, fluctuation period, and asymmetry degree of each envelope curve are used as principal feature parameters to construct phase microstructure waveform data. Each data structure includes elements such as the original waveform segment, the asymmetric wavelet envelope curve, and the location of the main energy extrema. The phase microstructure waveform data is converted to the frequency domain, and a fast Fourier transform is used to obtain the spectral energy distribution map. Symmetry analysis is performed on the spectrum, and the principal polarity direction is extracted using Hilbert envelope analysis. The polarity change trend within the frequency range of 10–40 Hz is extracted, and the location and directionality of frequency jump points are identified. Polarity feature encoding is performed on each waveform using indicators such as the number of polarity reversals and the amplitude of principal polarity direction jumps. Each encoded result is mapped back to its spatial location, and phase polarity field data is constructed based on polarity structure changes, forming a three-dimensional data layer with spatial location as the index and polarity characteristics as the value. The raster accuracy is 10 meters, and the unit is polarity jump number. The region with the highest jump density in the phase polarity field data is selected as the candidate location of the source initial point. All phase waveform data in this region and its surrounding area are collected, and a propagation path dataset from the source to each observation point is established based on the waveform trigger time. Using a regional geological velocity model, the P-wave velocity and S-wave velocity of different strata are set (e.g., in granite regions, the P-wave velocity is set to 5.5 km / s, and the S-wave velocity to 3.2 km / s), and the theoretical propagation time is calculated for each path. The measured trigger time and theoretical travel time are compared and analyzed to obtain the spatial distribution of the source backpropagation error.Multi-parameter fitting of focal depth, location, and time is performed on the region with the minimum error to extract the optimal source backtracking parameters. These parameters are compiled into an initial parameter set for seismic inversion, including the coordinates of the initiation point, the initial triggering time, and the propagation velocity path structure. The initiation point and propagation path in the initial source inversion parameter set are spatially mapped to the grid area in the microseismic triggering potential map. Starting from the source point, path growth modeling is performed according to the wavefront propagation direction. A depth-first search algorithm based on graph theory is used to constrain path growth within the region with a potential value higher than 0.6. A spatial connectivity matrix between paths is constructed, and the number of path intersections and common influence areas are marked. All paths and their topological relationships are combined to construct a seismic phase chain topology network, which is transformed into a node-edge graph structure. Nodes represent spatial locations, and edge weights reflect the degree of propagation coupling, generating a seismic phase topology coupling graph. This graph is used to represent the potential associated propagation paths between different sources. The data structure is a JSON format topology network description file, containing spatial coordinates and a propagation weight matrix. The existing geological structure layer, especially fault strike and dip data, is loaded and spatially overlaid with a seismic phase topology coupling map. Spatial statistical fitting is performed on the overlapping areas, and the minimum deviation method is used to fit the spatial projection relationship between the topological path and the geological fault section, generating a three-dimensional fault section model. Based on the fitting results, the spatial location of the microseismic phase propagation path is adjusted, and data for potential hidden fault areas are supplemented to construct a complete microseismic phase structure map. This layer is a three-dimensional vector structure model, including each source point, propagation path, fault section location, dip, strike, and other elements, in a three-dimensional vector model file format (such as *.3ds or *.gltf), and can be used for three-dimensional visualization or further modeling and analysis.

[0040] Preferably, step S3, which involves decoding the relaxation time spectrum based on the microseismic phase data, includes: Frequency bandwidth weighted separation is performed on microseismic phase data to extract microseismic energy attenuation segments and generate phase bandwidth response data; Non-stationary time-frequency expansion is performed on the phase bandwidth response data to construct a phase attenuation time domain distribution map and generate acoustic energy release time series data; Hysteresis response calibration is performed on acoustic energy release time-series data, instantaneous peak hysteresis factor is extracted, and micro-seismic relaxation response curves are generated; Dynamic spectral density fitting is performed on the microseismic relaxation response curve to solve the local relaxation time parameter distribution and generate a relaxation time parameter map. The waveform hysteresis coefficient is calculated from the relaxation time parameter diagram, and the stress-acoustic coupling mapping matrix is ​​constructed to generate acoustic memory response factor data. Spatiotemporal clustering and compression are performed on acoustic memory response factor data to generate acoustic memory characteristic data of mine rock mass.

[0041] In this embodiment of the invention, the acquired microseismic phase data is loaded into a multi-channel waveform processing module, and a sliding bandpass filter is used to weighted separate vibration signals of different frequency bands. The separation frequency band range is set to 1Hz to 100Hz, and a segmented weighting method is adopted. The filter gain is dynamically adjusted according to the center frequency of the frequency band and its corresponding signal-to-noise ratio to improve the response sensitivity of the mid-to-high frequency band. After completing the frequency band weighted separation, the vibration energy change rate of each frequency band is extracted. Based on the energy attenuation rate being greater than a set threshold (e.g., 10% / s) as the judgment condition, waveform segments with significant phase energy attenuation are identified and marked as energy attenuation segments. The timestamps, center frequencies, and energy intensity values ​​corresponding to these segments are used to construct structured data, which is output as phase bandwidth response data for subsequent time-frequency analysis. The short-time Fourier transform (STFT) is used to perform non-stationary time-frequency expansion on the bandwidth response data, with the window size set to 0.1 seconds and the window sliding step size set to 0.02 seconds. For each segment of the seismic phase bandwidth response signal, a two-dimensional time-frequency spectrum is generated, with time on the horizontal axis and frequency on the vertical axis. The pixel values ​​of the image represent the instantaneous energy density. Energy attenuation trajectory analysis along the time axis is performed on the spectrum to extract the complete release process of acoustic energy from the peak to the lowest energy level. Each segment of acoustic energy release trajectory is resampled to a unified time reference, and acoustic energy release time-series data is constructed. The data format is a list of time-energy key-value pairs, where each item includes key indicators such as the release start time, release duration, and cumulative energy release. The acoustic energy release time-series data is grouped according to the release duration. By statistically analyzing the occurrence time of the energy peak during the acoustic energy release process, the lag interval of this peak relative to the release start time is calculated and defined as the instantaneous peak lag factor. Using time density analysis, the lag factor in each group of data is recorded, and a correspondence between it and the release rates before and after it is established. The lag factor is smoothly interpolated on the time axis to generate a microseismic relaxation response curve. This curve reflects the average lag behavior of the energy peak during the microseismic process and is used to characterize the nonlinear relaxation features of the phase response. Each curve's structure includes elements such as start time, hysteresis factor sequence, and peak density variation curve. Each microseismic relaxation response curve is imported into the spectral density modeling module, and a sliding band integration method is used for dynamic spectral density reconstruction to extract the main energy fluctuation structure in the local frequency band. Through adaptive energy threshold analysis, the spectral density curve within each small segment is piecewise fitted to obtain the local energy release rate variation behavior. Based on this, the local relaxation time parameter for each small segment is deduced, representing the complete release duration of microseismic energy within that frequency band. All local parameters are mapped back to the original spatial location of the seismic phase waveform, and a relaxation time parameter map is constructed through spatial interpolation. This map uses spatial coordinates as an index and includes fields such as average relaxation time, fluctuation amplitude, and energy release density at each location, with a raster resolution of 5 meters. Continuous waveform paths are extracted from the relaxation time parameter map, and combined with the original seismic phase waveform data, the waveform hysteresis coefficient is calculated for each path.This coefficient measures the stress release hysteresis characteristics during energy release. The value is derived by comparing the peak hysteresis time with the overall release time, and a classification threshold is set to divide it into three categories: high hysteresis, medium hysteresis, and low hysteresis. A stress-acoustic coupling mapping matrix is ​​constructed using the spatial regions corresponding to the hysteresis coefficients. Each cell in the matrix corresponds to a spatial grid region, and the matrix value is an index of the synchronization between acoustic energy release and stress field changes within that region. This matrix is ​​standardized and time-encoded to obtain acoustic memory response factor data, in the format of a time-series multidimensional array, used to characterize the post-earthquake regional memory response trend. Density-based spatiotemporal clustering analysis (e.g., using the DBSCAN algorithm) is performed on the acoustic memory response factor data, using time windows and spatial adjacency as clustering constraints to extract highly consistent, high-density acoustic response regions. Each cluster unit represents a rock mass region with similar acoustic stress response characteristics. The clustering results are compressed and encoded, removing redundant data points and low-weight responses, while retaining key spatiotemporal segments. The final result is acoustic memory characteristic data of the mine rock mass, including core fields such as rock mass zoning number, main response type, typical hysteresis mode, and acoustic energy release hysteresis type. This data structure can be used for subsequent mine pressure early warning modeling and rock mass stability analysis.

[0042] Preferably, step S3, which involves analyzing the acoustic memory effect of mine rock mass on the distribution data of mine resource reserves using data on the acoustic memory characteristics of the rock mass, includes: Structural regions are calibrated on the acoustic memory characteristics data of mine rock masses, high hysteresis coupling regions are extracted, and potential structurally sensitive region data are generated. Spatial registration analysis is performed on the potential structurally sensitive region data and the mine resource reserve distribution data to extract acoustic memory anomaly coverage areas and generate acoustic-reservoir coupling zone distribution data. Calculate the multi-point response gradient of the acoustic-storage coupling zone distribution data, extract the local acoustic stress mutation values, and generate structural anomaly change gradient data; Directional migration calculations are performed on the gradient data of structural anomalies to identify the sources of micro-displacement in the structure and generate data on the tendency of rock mass instability. Dynamic evolution sequence analysis was performed on the rock mass instability induction tendency data to extract high-risk factor combinations of evolution trends and generate structural instability sensitive sequence data; Memory mapping aggregation analysis is performed on the structural instability sensitive sequence data to generate memory data of internal structural instability of the rock mass.

[0043] In this embodiment of the invention, regions with high hysteresis coefficients are selected from the acoustic memory characteristic data of mine rock masses, and aggregated based on their acoustic stress response patterns (such as long hysteresis peak duration and asymmetric waveform). Spatial partitioning is performed according to the mine rock mass structure layer, mapping high-hysteresis acoustic regions to corresponding rock mass structure units to form high-hysteresis coupling region annotation results. Structural region correction is performed on the annotation results by incorporating key geological boundary information such as mine fault zones and goaf boundaries. Finally, potential structurally sensitive region data is generated, with data structures including fields such as spatial location, associated structural unit, hysteresis level, and historical acoustic response type. Mine resource reserve distribution data is imported and subjected to coordinate system unification and spatial projection transformation with the potential structurally sensitive region data to ensure consistent geographic reference. GIS spatial overlap analysis methods are used to spatially register the potential structurally sensitive regions with the resource reserve distribution layer. Raster units or vector blocks where resource-rich areas and acoustic anomaly areas overlap are extracted as acoustic memory anomaly coverage areas, generating acoustic-reservoir coupling region distribution data. This data describes areas within the ore body that are both rich in reserves and exhibit structural acoustic anomalies, serving as a core data source for subsequent structural risk assessment. Spatial grid sampling is performed on the acoustic response characteristics (such as acoustic energy release rate and hysteresis factor variation amplitude) of each spatial point in the acoustic-reservoir coupling zone, and the acoustic stress gradient between adjacent points is calculated with a fixed step size. The rate of change of acoustic response intensity in space is obtained through multi-point response gradient calculation. Points with abrupt changes exceeding a specified threshold (e.g., more than twice the average value of adjacent grid points) are extracted and identified as acoustic stress abrupt change points. All abrupt change points are organized according to spatial location, abrupt change level, and waveform response type to construct structural anomaly change gradient data, providing a precise basis for subsequent directional structural analysis. Based on the structural anomaly change gradient data, vector directionality analysis is performed on the abrupt change points. The consistency of gradient direction between adjacent abrupt change points is analyzed, and combined with local waveform phase migration characteristics, the potential micro-displacement direction is determined. Based on the directional shift results, structural stress concentration boundaries or strain concentration paths are calibrated, identifying the sources of structural micro-displacement. These areas are typically located at fracture edges, mining boundaries, or historically low-stability zones. Data on the tendency of rock mass instability to be induced is generated, and the data structure includes information such as potential induction points, direction of action, type of induction mechanism (e.g., tensional, shear), and nearby acoustic memory level. Time series information of the rock mass instability-induced tendency data is collected and its changing trends are analyzed by arranging it along a time axis. The evolution path of regional stress disturbance is extracted using a time sliding window method. High-intensity acoustic stress direction shift phenomena, continuous hysteresis peak enhancement phenomena, and anomalous energy focusing phenomena that appear in multiple time windows are screened from the dynamic sequences and identified as high-risk factor combinations. These factor combinations are recombined according to their temporal and spatial trajectories to generate structural instability sensitive sequence data. Each sequence represents a multidimensional evolution path of a potential instability process and is labeled with a risk level.By performing spatiotemporal mapping and matching between structural instability sensitive sequence data and previous acoustic memory characteristic data, and aggregating and analyzing regions with significant response overlap, overlapping memory response areas in the instability path are identified. A spatiotemporal matching template clustering method is used to extract and merge paths with common evolution patterns from multiple evolution sequences, aggregating them to form internal structural instability memory units of the rock mass. The final output is internal structural instability memory data of the rock mass, including instability memory region numbers, principal control stress paths, acoustic stress hysteresis structural patterns, risk levels, historical evolution trends, etc., providing basic data support for mine stability prediction and resource exploitability optimization.

[0044] As an example of the present invention, reference is made to Figure 2 As shown, step S4 in this example includes: Step S41: Extract regional strain gradient data from surface deformation monitoring data; fit the rate of change of time-series stress data, extract stress loading / unloading rhythm curves, and generate time-series stress evolution characteristic data; perform hysteresis impact scaling analysis on rock mass internal structure instability memory data, extract memory trigger threshold index, and generate rock mass memory trigger index data. Step S42: Integrate regional strain gradient data, time-series stress evolution characteristic data, and rock mass memory triggering index data into comprehensive feature data, and divide the comprehensive feature data into datasets to generate model training set and model test set; Step S43: Train the model on the training set using the Long Short-Term Memory Neural Network algorithm to generate a pre-model for dynamic assessment of geological activity; use the test set to optimize and iterate the pre-model for dynamic assessment of geological activity, thereby generating a dynamic assessment model for geological activity. Step S44: Use the dynamic assessment model of geological activity to predict the geological stability of the mineral resource reserve distribution data and generate geological stability prediction data.

[0045] In this embodiment of the invention, based on continuously acquired surface deformation monitoring data, regions are divided according to spatial grids or fault structures. First-order derivative processing is performed on the deformation data to extract the intensity and direction of local strain changes, generating regional strain gradient data. This data reflects the non-uniformity and concentration trend of surface deformation on a spatial scale. Geostress monitoring data from different monitoring periods are loaded, and stress-time curves are calculated. Each stress curve is fitted to identify a clear loading-peak-unloading-hysteresis process, extracting rhythmic characteristic indicators such as loading rate, peak duration, and unloading asymmetry to generate time-series stress evolution characteristic data. Pre-generated rock mass internal structure instability memory data is called, and hysteresis response matching analysis is performed between its historical triggering paths and current stress disturbances. Typical hysteresis impact feedback regions are identified, memory activation thresholds (such as acoustic stress critical amplitude, upper limit of the hysteresis time window, etc.) are extracted, and fused into rock mass memory triggering index data. The regional strain gradient data, time-series stress evolution characteristic data, and rock mass memory triggering index data are standardized and time-aligned to form a unified spatial-temporal feature vector. Each comprehensive feature record corresponds to a regional unit, a time segment, and its feature response state. Based on a time or regional partitioning strategy, the comprehensive feature data is divided into a training set and a test set. The training set mainly contains feature data of historically known geological response events, used for model learning; the test set contains feature data before potential unknown events, used for model validation. The training and test sets maintain consistency in data structure, typically including: spatial location encoding, strain gradient, stress rhythm features, memory trigger indicators, and annotation results (such as whether structural instability has occurred). A Long Short-Term Memory (LSTM) neural network with temporal feature learning capabilities is constructed. The model training set is input into the LSTM, sequentially learning the dynamic relationship between strain changes, stress rhythms, and memory responses, extracting the temporal causal chain within the geological system, and generating a preliminary pre-model for dynamic assessment of geological activity. During model training, the weights are automatically adjusted by optimizing the loss function to improve predictive ability and robustness. The pre-model is validated using the model test set. Its prediction accuracy, recall, and error distribution are analyzed, and the model structure or hyperparameters are automatically optimized iteratively (e.g., adjusting the number of hidden layer neurons, learning rate, or time window length). After multiple rounds of optimization and iteration, a stable dynamic assessment model for geological activity was generated. This model has the ability to predict and analyze geological response trends in different regions and at different time scales. The trained dynamic assessment model for geological activity was applied to mineral resource reserve distribution data to predict the structural stability of each orebody block within a given time window. The model input is the comprehensive feature vector corresponding to each orebody, and the output is the geological stability prediction data.The prediction results are presented in the form of a grid or structural unit, and the output fields include: stability level (e.g., high stability, medium stability, low stability); risk warning level (e.g., acceptable, requires monitoring, work suspended); and contribution weight of driving factors (e.g., stress-dominated, structural memory-dominated, etc.).

[0046] As an example of the present invention, reference is made to Figure 3 As shown, step S5 in this example includes: Step S51: Reconstruct spatial coordinates and perform three-dimensional regional layering on the geological stability prediction data to generate stability layer index data; map the stability layer index data to the three-dimensional structural domain of the mineral resource reserve distribution data, perform mesh cell nesting mapping processing, and generate mineral resource mesh partitioning data; Step S52: Perform resource grade analysis and stability coefficient screening on each grid cell in the mine resource grid division data, extract the grid set that meets the mining conditions, and generate mineable grid cell data; Step S53: Perform weighted integral calculation on all grid cells in the mine resource grid division data to obtain the dynamic recoverable resources of the mine; compare the preset historical resource development curve and the dynamic recoverable resources for trend warning to execute resource development risk warning operation.

[0047] In this embodiment of the invention, a three-dimensional spatial coordinate system is reconstructed by utilizing geographical information (such as latitude and longitude, depth, etc.) from geological stability prediction data, combined with the actual topography and drilling data of the mine. A projection transformation method is used to uniformly map the prediction data to the mining area's engineering coordinate system, ensuring all data are within the same spatial reference frame. Based on the stratigraphic thickness and stratigraphic division standards obtained from geological exploration, the stability prediction data is layered according to depth coordinates. The thickness of each layer is determined based on the actual geological sequence of the mine, typically ranging from 5 to 20 meters, ensuring clear distinction between layers. Voxelization technology is used to divide the three-dimensional space into layered units, generating stability layer index data. This stability layer index data is mapped to the existing three-dimensional structural domain of mine resource reserves. Based on the ore body boundary and reserve grid unit size, nested mapping of grid units is performed, and a three-dimensional spatial query algorithm is used to determine the stability layer index corresponding to each reserve unit, realizing the spatial correspondence between the two. The mapping results are integrated to generate mine resource grid division data, the data structure of which includes: spatial coordinates, stability layer index, ore body attributes, and reserve values. The data is stored using a three-dimensional array or spatial database, supporting subsequent queries and analysis. Ore grade indicators (such as metal content and ore quality parameters) are extracted from each grid cell in the mine resource grid division data. Grade thresholds are set according to national and industry mining standards (e.g., metal grade not less than 1.5%, ore recovery rate not less than 85%), and cells meeting the grade requirements are selected. Combining the three-dimensional stability stratified index data, the stability coefficient of each grid cell is calculated. The stability coefficient calculation is based on mine geomechanical parameters, such as rock strength, fault activity indicators, and the stability level output by the prediction model. A stability threshold is set (e.g., stability coefficient ≥ 0.75), and grid cells with safe mining conditions are selected. Grid cells meeting both grade and stability selection criteria are aggregated to form a set of mineable grid cell data. Data records include grid spatial location, ore body attributes, stability coefficient, and resource quantity, for subsequent dynamic resource assessment and scheduling. Based on the mineable grid cell data, a weighted integral calculation is performed, combining the resource reserves and stability coefficient of each cell. The weighting factors include a stability coefficient (reflecting mining safety) and resource grade, comprehensively reflecting the dynamic mining potential of the mine resources. The integration process employs a numerical integration method, traversing all eligible grid cells and summing them to obtain the dynamic recoverable resources at the current time point. Combined with historical resource development curves (derived from historical mining records and production plans), the current dynamic recoverable resources are compared with historical trends over time. A sliding window method is used to analyze the rate of change in dynamic resources, setting early warning thresholds (e.g., a resource decline rate exceeding 5% of the monthly average) to monitor resource development risks in real time. When the dynamic resource trend deviates from the historical trajectory and reaches the early warning threshold, a risk warning operation is triggered.The early warning results are pushed out in real time through the mine dispatch system, including the risk level, the warning time, and the affected area, to help the mine management department adjust production plans and safety measures.

[0048] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0049] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for evaluating the usable resources of a mine, characterized in that, Includes the following steps: Step S1: Obtain geological exploration data for mineral resources; perform data preprocessing on the geological exploration data for mineral resources to generate standard geological exploration data for mineral resources; Three-dimensional geological modeling is performed based on standard mine resource geological exploration data to generate mine resource reserve distribution data; Step S2: Acquire surface deformation monitoring images using synthetic aperture radar; Time-series slicing of surface deformation monitoring images generates surface deformation slice data; stress change and microseismic phase imaging are performed on the surface deformation slice data to obtain time-series stress data and microseismic phase data. Step S3: Decode the relaxation time spectrum based on the microseismic phase data to obtain the acoustic memory characteristics data of the mine rock mass; use the acoustic memory characteristics data of the mine rock mass to analyze the acoustic memory effect of the mine resource reserve distribution data to generate the instability memory data of the internal structure of the rock mass. Step S4: Based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data, construct a dynamic assessment model for geological activity, and use the dynamic assessment model for geological activity to predict the geological stability of mine resource reserve distribution data, generating geological stability prediction data. Step S5: Use geological stability prediction data to perform three-dimensional grid cell mapping on the mineral resource reserve distribution data to generate mineral resource grid division data; It summarizes the recoverable values ​​of all grid cells in the mine resource grid division data, outputs the dynamic recoverable resource quantity of the mine, and performs resource development risk warning based on the dynamic recoverable resource quantity.

2. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain geological exploration data for mineral resources; Step S12: Clean the geological exploration data of the mine resources to generate cleaned geological exploration data of the mine resources; fill in missing values ​​in the cleaned geological exploration data of the mine resources to generate filled geological exploration data of the mine resources; standardize the filled geological exploration data of the mine resources to generate standard geological exploration data of the mine resources. Step S13: Extract profile layers from standard mine resource geological exploration data to generate geological stratigraphic profile data; capture structural lines from the geological stratigraphic profile data to generate geological structural boundary data; generate interlayer closure envelopes from the geological structural boundary data to generate geological body boundary data. Step S14: Reconstruct the geological body boundary data into a three-dimensional entity to generate three-dimensional geological structure data; calibrate the ore body attributes of the three-dimensional geological structure data to generate ore body attribute mapping data; divide the ore body attribute mapping data into reserve unit grids to generate ore body grid unit data; calculate the resource quantity of the ore body grid unit data to generate mine resource reserve distribution data.

3. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Acquire surface deformation monitoring images using synthetic aperture radar; Step S22: Perform radiometric correction on the surface deformation monitoring image to generate a corrected deformation interferometric image; perform phase unfolding on the deformation interferometric image to generate surface deformation field data; analyze the time dimension of the surface deformation field data and perform time-series segmentation on the surface deformation field data to generate surface deformation slice data. Step S23: Calculate the spatial stress gradient of the surface deformation slice data; fit the principal stress direction based on the spatial stress gradient to generate time-series stress distribution data; detect the microseismic precursor mode of the surface deformation slice data and generate a microseismic triggering potential map. Step S24: Extract the phase characteristic waveforms of the microseismic triggering potential map, and perform phase inversion on the phase characteristic waveforms to generate microseismic phase structure map data; perform spatiotemporal alignment between the time-series stress distribution data and the microseismic phase structure map data to generate the final time-series stress data and microseismic phase data respectively.

4. The method for evaluating the usable resources of a mine according to claim 3, characterized in that, The microseismic precursor modes for detecting surface deformation slice data in step S23 include: Local deformation abrupt changes are detected in surface deformation slice data to generate candidate mask data for abrupt change areas. Time window sliding clustering is performed on the candidate mask data of mutation regions to extract abnormal time-series response features and generate potential abnormal response region data; Statistical analysis of deformation gradient direction in potential abnormal response area data is used to extract regions with high gradients pointing to consistent areas and generate precursor trend response data. Perform pre-earthquake deformation time lag analysis on precursor trend response data to generate a high-confidence pre-earthquake deformation lag distribution map; Stress accumulation threshold fitting is performed on high-confidence pre-earthquake deformation hysteresis distribution maps to extract critical stress point groups with triggering tendency and generate microseismic excitation sensitive area data. Regional stability weight mapping is performed on the data of the microseismic excitation sensitive area to generate a microseismic triggering potential map.

5. The method for evaluating the usable resources of a mine according to claim 3, characterized in that, Step S24 involves extracting the phase characteristic waveforms from the microseismic triggering potential map and performing seismic inversion on the phase characteristic waveforms, including: Extract the phase characteristic waveforms of the microseismic triggering potential map, and use the phase characteristic waveforms to perform regional layered time window adaptive scanning of the microseismic triggering potential map to extract high-potential candidate segments of the phase and generate phase candidate sampling data; Asymmetric wavelet variational envelopes are extracted from candidate seismic phase sampling data to generate seismic phase microstructure waveform data. The spectral domain polarity reconstruction of the phase microstructure waveform data is performed to extract extreme strain triggering features and generate phase polarity field data; based on the phase polarity field data, source travel time back analysis is performed to generate the initial seismic back-evolution parameter set; The initial seismic inversion parameter set is mapped to the microseismic triggering potential map for chain-type topological constraint inversion to generate a seismic phase topological coupling map. The regional grid of the microseismic triggering potential map is analyzed and fitted with the geological fault section of the seismic phase topology coupling map to obtain the microseismic phase structure map data.

6. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S3, which involves decoding the relaxation time spectrum based on the microseismic phase data, includes: Frequency bandwidth weighted separation is performed on microseismic phase data to extract microseismic energy attenuation segments and generate phase bandwidth response data; Non-stationary time-frequency expansion is performed on the phase bandwidth response data to construct a phase attenuation time domain distribution map and generate acoustic energy release time series data; Hysteresis response calibration is performed on acoustic energy release time-series data, instantaneous peak hysteresis factor is extracted, and micro-seismic relaxation response curves are generated; Dynamic spectral density fitting is performed on the microseismic relaxation response curve to solve the local relaxation time parameter distribution and generate a relaxation time parameter map. The waveform hysteresis coefficient is calculated from the relaxation time parameter diagram, and the stress-acoustic coupling mapping matrix is ​​constructed to generate acoustic memory response factor data. Spatiotemporal clustering compression is performed on acoustic memory response factor data to generate acoustic memory characteristic data of mine rock mass.

7. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S3 involves analyzing the acoustic memory effect of mine rock mass on the distribution data of mine resource reserves using data on the acoustic memory characteristics of the rock mass. Structural regions are calibrated on the acoustic memory characteristics data of mine rock masses, high hysteresis coupling regions are extracted, and potential structurally sensitive region data are generated. Spatial registration analysis is performed on the potential structurally sensitive region data and the mine resource reserve distribution data to extract acoustic memory anomaly coverage areas and generate acoustic-reservoir coupling zone distribution data. Calculate the multi-point response gradient of the acoustic-storage coupling zone distribution data, extract the local acoustic stress mutation values, and generate structural anomaly change gradient data; Directional migration calculations are performed on the gradient data of structural anomalies to identify the sources of micro-displacement in the structure and generate data on the tendency of rock mass instability. Dynamic evolution sequence analysis was performed on the rock mass instability induction tendency data to extract high-risk factor combinations of evolution trends and generate structural instability sensitive sequence data; Memory mapping aggregation analysis is performed on the structural instability sensitive sequence data to generate memory data of internal structural instability of the rock mass.

8. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Extract regional strain gradient data from surface deformation monitoring data; fit the rate of change of time-series stress data, extract stress loading / unloading rhythm curves, and generate time-series stress evolution characteristic data; perform hysteresis impact scaling analysis on rock mass internal structure instability memory data, extract memory trigger threshold index, and generate rock mass memory trigger index data. Step S42: Integrate regional strain gradient data, time-series stress evolution characteristic data, and rock mass memory triggering index data into comprehensive feature data, and divide the comprehensive feature data into datasets to generate model training set and model test set; Step S43: Train the model on the training set using the Long Short-Term Memory Neural Network algorithm to generate a pre-model for dynamic assessment of geological activity; use the test set to optimize and iterate the pre-model for dynamic assessment of geological activity, thereby generating a dynamic assessment model for geological activity. Step S44: Use the dynamic assessment model of geological activity to predict the geological stability of the mineral resource reserve distribution data and generate geological stability prediction data.

9. The method for evaluating the usable resources of a mine according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Reconstruct spatial coordinates and perform three-dimensional regional layering on the geological stability prediction data to generate stability layer index data; map the stability layer index data to the three-dimensional structural domain of the mineral resource reserve distribution data, perform mesh cell nesting mapping processing, and generate mineral resource mesh partitioning data; Step S52: Perform resource grade analysis and stability coefficient screening on each grid cell in the mine resource grid division data, extract the grid set that meets the mining conditions, and generate mineable grid cell data; Step S53: Perform weighted integral calculation on all grid cells in the mine resource grid division data to obtain the dynamic recoverable resources of the mine; compare and warn the trend of the preset historical resource development curve and the dynamic recoverable resources to perform resource development risk warning operation.

10. A system for evaluating the usable resources of a mine, characterized in that, For performing the mine resource assessment method as described in claim 1, the mine resource assessment system comprises: The geological modeling module is used to acquire geological exploration data of mineral resources; preprocess the geological exploration data of mineral resources to generate standard geological exploration data of mineral resources; and perform three-dimensional geological modeling based on the standard geological exploration data of mineral resources to generate mineral resource reserve distribution data. The seismic phase imaging module is used to acquire surface deformation monitoring images through synthetic aperture radar; to perform time-series slicing on the surface deformation monitoring images to generate surface deformation slice data; and to perform stress change and microseismic phase imaging on the surface deformation slice data to obtain time-series stress data and microseismic phase data. The rock mass memory module is used to decode the relaxation time spectrum based on microseismic phase data to obtain acoustic memory characteristic data of mine rock mass; and to use the acoustic memory characteristic data of mine rock mass to analyze the acoustic memory effect of rock mass on the distribution data of mine resource reserves, thereby generating memory data of instability of internal rock mass structure. The activity assessment module is used to construct a dynamic assessment model of geological activity based on surface deformation monitoring data, time-series stress data, and rock mass internal structure instability memory data. It also uses the dynamic assessment model of geological activity to predict the geological stability of mineral resource reserve distribution data and generate geological stability prediction data. The resource mining early warning module is used to map the distribution data of mine resource reserves into three-dimensional grid cells using geological stability prediction data, generate mine resource grid division data, summarize the recoverable values ​​of all grid cells in the mine resource grid division data, output the dynamic recoverable resource quantity of the mine, and perform resource development risk early warning based on the dynamic recoverable resource quantity.

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