A method for constructing a mine area geological environment damage inducing factor layer

Abstract

The application discloses a kind of mine area geological environment destruction inducing factor layer construction methods, belong to mine area environment monitoring technical field, comprising: with microseismic event three-dimensional coordinates, occurrence time and release energy as input, introduce physical recovery time benchmark, space attenuation radius and time cause and effect constraint, obtain space-time microseismic energy density characteristic value;Coupling with surface deformation rate data, get deep and shallow coupling interactive damage index;From fault distance, rock mass cohesion and topographic slope, obtain static geological vulnerability background index;To deep and shallow coupling interactive damage index smooth filtering, combine its partial derivative with respect to time and background index, obtain dynamic induced precursor intensity value;Finally, convert into node weight value, construct administrative management and industrial monitoring induced factor layer matrix matrix.The application realizes deep microseismic and shallow deformation collaborative modeling, supports multistage early warning and automation decision, applicable to mine area geological environment destruction inducing factor dynamic identification and disaster scheduling.

Description

Technical Field

[0001] This invention belongs to the field of mining area environmental monitoring technology, and in particular relates to a method for constructing a layer of factors inducing geological environmental damage in mining areas. Background Technology

[0002] In the field of geological environment monitoring and disaster early warning in mining areas, traditional methods often rely on single surface deformation data or microseismic event statistics for risk assessment. For example, a method for generating gentle energy density cloud maps based on regional microseismic energy distribution published by China University of Mining and Technology (CN121634232A) and a method for identifying water-conducting channels in coal mine floor based on microseismic monitoring published by Dalian University of Technology (CN106437843A) both focus on the quantitative characterization of the spatial distribution of microseismic energy, but fail to incorporate the coupling effect between deep microseismic activity and changes in the shallow geological environment into a unified analytical framework. Similarly, a method and system for monitoring underground mining subsidence by integrating time series and spatiotemporal deformation from Guangdong University of Technology (CN118482685B) and a non-contact quantitative monitoring and early warning method and system for surface deformation from the Institute of Exploration Technology, Chinese Academy of Geological Sciences (CN117558106B) focus on high-precision monitoring of surface deformation and subsidence prediction, but lack effective integration with information on deep rock mass fracture.

[0003] At the data fusion level, a method for evaluating the degree of mine tremor-induced risk based on multi-source heterogeneous data (CN121142665A) applied for by Shandong Energy Group Co., Ltd. and others has achieved quantitative assessment of mine tremor risk by fusing multi-source data such as microseismic, geological, mining-induced, stress, and drill cuttings data. A method and system for monitoring and early warning of mine geological disasters based on multi-source data from air, space, and ground (CN122223879A) invented by Wang Kairui et al. has constructed a time-delay map including coupling strength and lag time indicators, quantifying the causal transmission relationship between underground microseismic activity and surface deformation into spatial distribution characteristics. However, the above methods still fail to solve the systematic fusion problem of multi-source heterogeneous data in terms of temporal causal constraints and spatial attenuation characteristics, resulting in lag, locality, and subjectivity in the identification of inducing factors and layer construction.

[0004] Furthermore, existing early warning decisions largely rely on manual experience and lack a dynamic response mechanism based on quantified node weights and automated thresholds, making it difficult to meet the integrated needs of rapid identification and administrative dispatch for early warning signs of geological environmental damage in mining areas. While the "Intelligent Monitoring and Early Warning System and Method for Geological Disasters in Mining Areas" applied for by Yankuang Energy et al. achieves the fusion of multi-source heterogeneous data, it does not involve a technical solution for converting dynamic early warning intensity values ​​into standardized node weights and constructing a layer matrix for administrative management and industrial monitoring and dispatch.

[0005] Therefore, there is an urgent need for a method to construct a geological environment damage inducing factor layer that can integrate multi-physics field information and has temporal causal constraints and spatial decay characteristics. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for constructing a layer of geological environment damage initiation factors in mining areas, thus solving the aforementioned problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a layer of geological environment damage initiation factors in mining areas, comprising:

[0008] S10. Using the three-dimensional spatial coordinates of the microseismic event, the occurrence time, and the energy released by the microseismic event as inputs, the inherent physical recovery time reference, the three-dimensional spatial attenuation reference radius, and the temporal causality constraint conditions are introduced to obtain the spatiotemporal microseismic energy density characteristic value.

[0009] S20. Couple the spatiotemporal microseismic energy density characteristic value with the surface deformation rate data to obtain the deep and shallow coupling interaction damage index;

[0010] S30. Obtain the static geological vulnerability background index from fault distance, rock mass cohesion and topographic slope; perform smoothing filtering on the deep-shallow coupling interaction damage index, and combine its partial derivative with time with the static geological vulnerability background index to obtain the dynamic induced precursor intensity value.

[0011] S40. Convert the dynamic induced precursor intensity value into a node weight value, and use it as the node weight value in the induced factor layer matrix for administrative management and industrial monitoring.

[0012] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0013] Further technical solution: In S10, the spatiotemporal microseismic energy density characteristic value is obtained by multiplying and summing the normalized energy release term, three-dimensional spatial attenuation characteristic term, and temporal causal recovery characteristic term corresponding to each microseismic event within the total number of effective microseismic events.

[0014] The normalized energy release term is obtained by comparing the microseismic release energy with a preset reference microseismic energy benchmark constant. The three-dimensional spatial attenuation feature term negatively correlates and convergently characterizes the deviation of the distance between the spatial node composed of the target point's abscissa, target point's ordinate, and the surface elevation benchmark plane coordinates and the three-dimensional spatial coordinate components of each microseismic event relative to the three-dimensional spatial attenuation reference radius. The time causal recovery feature term uses the time causality constraint condition to block the input of time node data greater than the current time node, and negatively correlates and convergently characterizes the time offset of the occurrence time of the microseismic event relative to the current time node as constrained by the attenuation magnitude of the inherent physical recovery time benchmark.

[0015] A further technical solution: In S20, the deep-shallow coupling interaction damage index is obtained by multiplying the spatiotemporal microseismic energy density characteristic value with the nonlinear saturation limiting term corresponding to the surface deformation rate data.

[0016] The nonlinear saturation limiting term is constructed based on the ratio scale feature between the absolute value of the surface deformation rate data under spatial coordinates and time nodes and the preset critical deformation rate benchmark value. The nonlinear saturation limiting term exhibits a positively correlated nonlinear growth as the absolute value of the surface deformation rate data increases, and its output numerical boundary strictly converges within the preset upper and lower limit intervals.

[0017] Further technical solution: In S30, the static geological vulnerability background index is obtained by linearly weighting and fusing the fault-rock mass composite vulnerability term and the topographic instability aggravation term under spatial coordinates according to the fault-rock mass interaction disaster-causing weight coefficient and the topographic slope disaster-causing weight coefficient.

[0018] Among them, the fault-rock mass composite vulnerability term is negatively correlated and nonlinearly convergent, characterizing the combined geological inhibition effect brought about by the ratio of the fault distance to the reference critical distance of the fault influence and the ratio of the rock mass cohesion to the reference value of the basic cohesion; the topographic instability aggravation term is positively correlated and nonlinearly saturated, characterizing the amplification effect of the angular deviation of the topographic slope relative to the reference critical angle of the topographic slope.

[0019] Further technical solution: In S30, the processing logic for smoothing the deep and shallow coupling interaction damage index includes: according to the discrete time step and the time step number traversal index, extracting the deep and shallow coupling interaction damage index under each historical time node within the preset total number of steps of the moving average time window, performing arithmetic averaging processing, and obtaining the smoothed deep and shallow coupling interaction damage index.

[0020] Further technical solutions: The dynamic induced precursor intensity value is obtained by superimposing and associating the static background amplification feature and the dynamic trend surge feature; the static background amplification feature is obtained by measuring the product scale of the static geological vulnerability background index and the smoothed shallow-deep coupling interaction damage index; the dynamic trend surge feature is obtained by aligning the dimensions and measuring the trend amplification of the partial derivative of the smoothed shallow-deep coupling interaction damage index with respect to time using the precursor trend weight adjustment coefficient and the inherent physical recovery time benchmark.

[0021] Further technical solution: In S40, the node weight value is obtained by performing an S-shaped nonlinear constraint transformation on the excessive deviation of the dynamic induced precursor intensity value relative to the statutory threshold baseline for safety management under spatial coordinates and time nodes.

[0022] The S-shaped nonlinear constraint transformation uses the administrative response sensitivity coefficient to adjust the response steepness of the over-limit offset amplitude, and strictly normalizes and maps its value to a numerical system of zero to one in the open interval.

[0023] Further technical solution: The method also includes a multi-level early warning and automated personnel evacuation decision-making step based on the inducing factor layer matrix:

[0024] Traverse the node weight values ​​in the inducing factor layer matrix. When the node weight value of at least one node in the target area exceeds the preset first-level safety threshold, trigger a first-level early warning response. In the management backend, mark the target area as a key hidden danger area and generate a linkage dispatch instruction to prohibit new personnel from entering.

[0025] When the node weight value of at least one node in the target area exceeds the preset secondary safety threshold for a consecutive preset time period, a secondary evacuation response is triggered, automatically generating an automated personnel evacuation decision command, linking the personnel positioning system and emergency broadcasting equipment to play evacuation guidance information in the target area, and simultaneously controlling the physical access control equipment on the evacuation path to perform automatic unlocking actions.

[0026] This invention provides a method for constructing a layer of geological environment damage initiation factors in mining areas, which has the following advantages compared with the prior art:

[0027] 1. This invention introduces an inherent physical recovery time reference, a three-dimensional spatial attenuation reference radius, and temporal causality constraints to construct spatiotemporal microseismic energy density characteristic values, which can accurately characterize the non-uniform release law of microseismic events in the time and space dimensions, and improve the characterization ability of deep rock mass fracturing process.

[0028] 2. This invention nonlinearly couples the spatiotemporal microseismic energy density characteristic value with the surface deformation rate data to obtain the deep-shallow coupling interaction damage index, realizing the collaborative analysis of deep microseismic activity and shallow deformation process, and overcoming the technical defect of the separation of deep and shallow information in traditional methods.

[0029] 3. This invention integrates the static geological vulnerability background index and dynamic damage trend characteristics, and constructs a dynamic induced precursor intensity value through smoothing filtering and time partial derivative terms. It can effectively identify early precursor signals of geological environment damage and has good robustness and timeliness.

[0030] 4. This invention converts the dynamic precursor intensity value into an S-shaped normalized node weight value and constructs an induction factor layer matrix, supporting multi-level early warning and automated personnel evacuation decision-making, significantly improving the intelligence and response efficiency of risk management for geological environment damage in mining areas. Attached Figure Description

[0031] Figure 1 This is a diagram of the multi-source data coupling and precursor intensity extraction architecture.

[0032] Figure 2 This is the logic diagram for the automated multi-level early warning and linkage control of this invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0034] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0035] Please see Figure 1 The present invention provides a method for constructing a layer of geological environment damage inducing factors in mining areas, comprising:

[0036] S10. Using the three-dimensional spatial coordinates of the microseismic event, the occurrence time, and the energy released by the microseismic event as inputs, the inherent physical recovery time reference, the three-dimensional spatial attenuation reference radius, and the temporal causality constraint conditions are introduced to obtain the spatiotemporal microseismic energy density characteristic value.

[0037] S20. Couple the spatiotemporal microseismic energy density characteristic value with the surface deformation rate data to obtain the deep and shallow coupling interaction damage index;

[0038] S30. Obtain the static geological vulnerability background index from fault distance, rock mass cohesion and topographic slope; perform smoothing filtering on the deep-shallow coupling interaction damage index, and combine its partial derivative with time with the static geological vulnerability background index to obtain the dynamic induced precursor intensity value.

[0039] S40. Convert the dynamic induced precursor intensity value into a node weight value, and use it as the node weight value in the induced factor layer matrix for administrative management and industrial monitoring.

[0040] First, regarding the reflection of the deep-shallow coupling mechanism, this invention obtains a more accurate spatiotemporal microseismic energy density characteristic value by introducing an inherent physical recovery time reference, a three-dimensional spatial attenuation reference radius, and temporal causality constraints in step S10. Subsequently, in step S20, this characteristic value is coupled with surface deformation rate data to generate a deep-shallow coupling interaction damage index. Compared to existing methods that rely solely on single surface deformation data or microseismic event statistics, this invention can more comprehensively and accurately reflect the complex coupling mechanism between deep rock mass fracturing and shallow geological environment changes, thereby overcoming the limitations of traditional methods in comprehensive assessment.

[0041] Secondly, regarding the fusion of multi-source heterogeneous data and dynamic precursor identification, in step S30, this invention not only considers static geological vulnerability background factors such as fault distance, rock mass cohesion, and topographic slope, but more importantly, it performs smoothing filtering on the deep-shallow coupling interaction damage index and fuses it with its partial derivative with respect to time to finally obtain the intensity value of the dynamic induced precursor. This fusion method enables the method to capture the dynamic evolution trend of geological environment damage precursors, especially the signal of damage acceleration. Compared with the lack of systematic fusion methods in existing technologies, which leads to problems of lag, locality, and subjectivity in the identification of induced factors, this invention significantly improves the timeliness, comprehensiveness, and objectivity of induced factor identification by integrating multi-physics field information.

[0042] Finally, regarding early warning decision-making and administrative dispatch, in step S40 of this embodiment, the intensity value of the dynamic induced precursor is converted into a quantified node weight value, which is then used as the node weight value in the induced factor layer matrix for administrative management and industrial monitoring and allocation. This layer matrix provides managers with an intuitive and quantifiable basis for risk assessment. Compared to existing early warning decisions that rely heavily on manual experience and lack a dynamic response mechanism based on quantified node weights and automated thresholds, this embodiment achieves automated early warning and dispatch based on quantified indicators. This greatly improves the ability to quickly identify induced precursors of geological environmental damage in mining areas and the level of integrated administrative dispatch, meeting the needs of mining area safety production for efficient and accurate early warning.

[0043] Preferably, in S10, the spatiotemporal microseismic energy density characteristic value is obtained by multiplying and summing the normalized energy release term, three-dimensional spatial attenuation characteristic term, and temporal causal recovery characteristic term corresponding to each microseismic event within the total number of effective microseismic events:

[0044] The normalized energy release term is obtained by comparing the microseismic release energy with a preset reference microseismic energy benchmark constant. The three-dimensional spatial attenuation feature term negatively correlates and convergently characterizes the deviation of the distance between the spatial node formed by the target point's abscissa, target point's ordinate, and the surface elevation benchmark plane coordinates and the three-dimensional spatial coordinate components of each microseismic event relative to the three-dimensional spatial attenuation reference radius. The time causal recovery feature term uses the time causality constraint condition to block the input of time node data greater than the current time node, and negatively correlates and convergently characterizes the time offset of the occurrence time of the microseismic event relative to the current time node as constrained by the attenuation magnitude of the inherent physical recovery time benchmark.

[0045] Specifically, in S10, the formula for calculating the spatiotemporal microseismic energy density characteristic value is as follows:

[0046] ;

[0047] in, This represents the characteristic value of spatiotemporal microseismic energy density. This represents the total number of effective microseismic events. Indicates the first Index of microseismic events Let Heaviside step function be used for time causality constraints, when When the value is 1, The value is 0 at this time. Indicates the first The energy released by a microseismic event (in joules). This represents the preset reference microseismic energy baseline constant (in joules). The x-coordinate of the target point (in meters). The vertical coordinate of the target point (in meters). Indicates the current time point (in days). They represent the first The three-dimensional spatial coordinate components of a microseismic event (all in meters). Represents the plane coordinates of the ground surface elevation datum (in meters). This represents the three-dimensional spatial attenuation reference radius (in meters). Indicates the first The time of occurrence of each microseismic event (in days). Indicates the inherent physical recovery time reference (in days). Represents the normalized energy release term. Represents the three-dimensional spatial attenuation characteristic term. This represents the time-causal recovery feature.

[0048] Spatiotemporal microseismic energy density characteristic value It is a comprehensive quantity used to quantify the energy distribution of microseismic events at a specific spatiotemporal point and the intensity of their potential impact on the surrounding environment. It transforms discrete microseismic events into a continuous spatiotemporal field to facilitate subsequent analysis. The total number of valid microseismic events. This refers to the number of microseismic events included in the calculation within a specific analysis period and spatial range. It can be filtered based on preset energy thresholds, magnitude ranges, or spatial distances. For example, a minimum magnitude can be set, excluding microseismic events below that magnitude; or a maximum distance can be set, excluding microseismic events exceeding that distance. Index of microseismic events Used to identify and differentiate different microseismic events, ensuring that the parameters of each event are correctly referenced and calculated. Heaviside step function. It is a mathematical tool used to implement temporal causality constraints, ensuring that causality only applies to the current time. Later than or equal to the time of the microseismic event Only then does the energy of the microseismic event contribute to the current spatiotemporal microseismic energy density characteristic value, which conforms to the chronological order of events in the physical world. Energy released by microseismic events This refers to the seismic wave energy released by a single microseismic event, which can usually be obtained from waveform data recorded by a seismograph (current technology). A preset reference microseismic energy baseline constant is used. This is a constant used to normalize the energy released by microseismic events, allowing microseismic events of different energy levels to be compared and processed on a uniform scale. For example, it can be set to the energy of the largest microseismic event in the history of the mining area, or an industry-standard energy value. The x-coordinate of the target point. y-axis Current time node These are the target spatial and temporal points for calculating the characteristic values ​​of spatiotemporal microseismic energy density. Three-dimensional spatial coordinate components of a microseismic event This describes the precise location of microseismic events in three-dimensional space. Surface elevation datum plane coordinates. This is used to unify the vertical distance in three-dimensional spatial attenuation calculations to a surface reference plane. This helps project the effects of subsurface microseismic events onto the surface and correlate them with surface deformation data. For example, it can be set as the average surface elevation of the mining area or a uniform reference altitude. Three-dimensional spatial attenuation reference radius. This parameter characterizes the rate attenuation of microseismic energy in space. It defines the scale of the influence range of a microseismic event; the energy significantly attenuates beyond this radius. This parameter can be determined based on empirical data or simulation results, such as the physical properties and geological structure of the rock mass in the mining area. Specifically, it can be calibrated through on-site blasting vibration tests or rock mass acoustic emission tests. The specific calibration method is as follows: a controlled micro-blasting source is arranged at known three-dimensional spatial coordinates in the mining area, or a standard microseismic event at a known location is used. A network of microseismic detectors is arranged at nodes at different distances in three-dimensional space, and the actual microseismic energy received at each node is recorded. The corresponding spatial straight-line distance Subsequently, the actual test data were substituted into the theoretical spatial decay function model for fitting and calibration. The fitting formula is as follows: In the formula, Indicates distance from the epicenter The actual microseismic energy measured at the location, This represents the total energy of the microseismic events initially released at the earthquake source. Represents the three-dimensional straight-line distance between the monitoring node and the seismic source. This represents the three-dimensional spatial attenuation reference radius to be determined. In practice, it is expressed as the square of the distance. As the independent variable, the energy decay ratio Using the natural logarithm of the equation as the dependent variable, a nonlinear regression fitting is performed, and the spatial distance constant obtained by inverse solution is the three-dimensional spatial attenuation reference radius under specific geological conditions of the mining area. Inherent physical recovery time reference This parameter characterizes the rate attenuation of the impact of microseismic events over time. It reflects the time scale required for the physical state of a geological system to gradually recover to a stable state after being disturbed by microseismic events. This parameter can be determined based on empirical data or experimental results such as the creep characteristics and stress relaxation time of rock masses in the mining area. Specifically, the physical mechanism can be calibrated through rock mechanics stress relaxation tests. The specific calibration method is as follows: prepare standard rock specimens that conform to the original lithology of the mining area, use a rock mechanics servo testing machine to simulate the actual in-situ stress environment in the deep part of the mining area, rapidly apply a step stress increment to the specimen to simulate microseismic disturbance, then keep the strain of the specimen absolutely constant, and continuously monitor and record the process data of the gradual decrease (i.e., relaxation) of the residual stress inside the specimen over time. Substitute the measured time-stress sequence data into the stress relaxation function for regression calculation. The calculation formula is: In the formula, Indicates the specimen is in The instantaneous stress remaining inside at that moment, This represents the initial peak disturbance stress generated during the step loading stage. This represents the absolute time elapsed since the disturbance occurred. This represents the intrinsic physical recovery time reference to be determined; by extracting the relaxation time constant from the best-fit curve, the intrinsic physical recovery time reference characterizing the stress release and recovery to a stable state of the geological system in this region can be accurately obtained. Normalized energy release term By comparing the actual released energy of a microseismic event with a reference baseline, a dimensionless relative energy value is obtained, facilitating quantitative comparisons between different events. Three-dimensional spatial attenuation characteristic term. This describes the attenuation law of microseismic energy from the source to the target point in three-dimensional space, typically using an exponential decay model. Time-causal recovery characteristic term. By combining temporal causality constraints and time decay characteristics, it is ensured that the energy of a microseismic event contributes to the current spatiotemporal microseismic energy density characteristic value only after the event occurs and within the physical recovery time reference, and gradually weakens over time.

[0049] In the method for constructing a layer of factors inducing geological environment damage in mining areas, this application introduces a specific mathematical model to calculate the spatiotemporal microseismic energy density characteristic values ​​in order to accurately quantify the impact of microseismic activity on the geological environment. This model treats each microseismic event as a source of energy release at a specific spatiotemporal point, whose impact decays with spatial distance and time. Specifically, for each target spatial point... and current time The system will iterate through all valid microseismic events. For each event, it is first processed through the Heaviside step function. Ensure that only during the time the event occurs Earlier than or equal to the current time Only then is the event included in the calculation to satisfy the temporal causal relationship. Next, the micro-vibrations of the event release energy. It will be consistent with the preset reference microseismic energy reference constant. Normalization is performed to obtain the normalized energy release term. Simultaneously, an exponential decay function is used to calculate the microseismic energy from the source. To the target point The degree of attenuation in three-dimensional space forms a three-dimensional attenuation characteristic term. Furthermore, a time decay mechanism is introduced to recover the time reference through inherent physical mechanisms. This describes the attenuation process of microseismic effects over time, forming a time-causal recovery characteristic term. Finally, these contributions from all valid microseismic events are summed to obtain the spatiotemporal microseismic energy density characteristic value of the target point at the current time. This calculation method not only considers the energy magnitude, spatial location, and occurrence time of microseismic events, but also introduces physical attenuation and recovery mechanisms. This allows the obtained feature values ​​to more accurately and precisely reflect the dynamic impact of microseismic activity on the geological environment of the mining area, providing a solid foundation for subsequent calculation of the deep-shallow coupling interaction damage index, thereby improving the accuracy and reliability of the entire inducing factor layer construction method.

[0050] As a specific implementation method, in the method for constructing a layer of geological environment damage initiating factors in mining areas, the following steps can be adopted to obtain the spatiotemporal microseismic energy density characteristic values. First, the three-dimensional spatial coordinates of microseismic events are collected and recorded in real time through a microseismic monitoring network deployed in the mining area. Time of occurrence and energy release from micro-vibrations These data are preprocessed to filter out valid microseismic events; for example, events with a magnitude greater than a certain threshold can be defined as valid events. Then, a series of key parameters are set, such as a preset reference microseismic energy constant. The energy peak value of the largest historical microseismic event in the mining area can be used, or an industry-standard value can be adopted; surface elevation datum plane coordinates It can be set to the average altitude of the mining area; the three-dimensional spatial attenuation reference radius. and inherent physical recovery time benchmark Calibration can be performed through experiments. During the calculation process, a gridded target area can be set, and each grid point can be calibrated accordingly. At each time step The calculation is performed. For each grid point and time step, all valid microseismic events that have occurred are traversed, and the normalized energy, spatial decay, and temporal decay effects of each event are superimposed. For example, when calculating the energy density of a specific grid point at a certain moment, the system checks all historical microseismic events. If the event occurred earlier than or equal to the current moment, its contribution value is calculated using the above formula based on its distance from the grid point, the event energy, and the time difference between the event's occurrence time and the current moment. All contribution values ​​are then summed to obtain the spatiotemporal microseismic energy density characteristic value of that grid point at the current moment.

[0051] Through the above technical solution, this application provides a method for accurately quantifying the spatiotemporal energy density characteristic values ​​of microseismic events. This method introduces a clear mathematical model, comprehensively considering the energy, spatial location, occurrence time, and energy attenuation and recovery mechanisms in space and time of microseismic events. This allows the obtained characteristic values ​​to more accurately and precisely reflect the dynamic impact of microseismic activity on the geological environment of the mining area, avoiding the ambiguity and subjectivity of traditional methods in assessing microseismic impacts. Compared with methods that simply count the number or magnitude of microseismic events, this solution provides a microseismic impact index with clearer physical meaning and a higher degree of quantification. This provides more reliable and refined input data for the subsequent calculation of the deep-shallow coupling interaction damage index, significantly improving the accuracy and predictive ability of the method for constructing the geological environment damage inducing factor layer of the entire mining area.

[0052] Preferably, in S20, the deep-shallow coupling interaction damage index is obtained by multiplying the spatiotemporal microseismic energy density characteristic value with the nonlinear saturation limiting term corresponding to the surface deformation rate data:

[0053] The nonlinear saturation limiting term is constructed based on the ratio scale feature between the absolute value of the surface deformation rate data under spatial coordinates and time nodes and the preset critical deformation rate benchmark value. The nonlinear saturation limiting term exhibits a positively correlated nonlinear growth as the absolute value of the surface deformation rate data increases, and its output numerical boundary strictly converges within the preset upper and lower limit intervals.

[0054] Specifically, in S20, the formula for calculating the deep-shallow coupling interaction damage index is as follows:

[0055] ;

[0056] in, Indicates the interaction damage index between deep and shallow coupling. This represents the characteristic value of spatiotemporal microseismic energy density. The data representing the rate of surface deformation are denoted as . (Unit: mm / day) This represents the preset critical deformation rate reference value (in millimeters per day). Represents spatial coordinates (in meters). Indicates a time point (in days). This represents the nonlinear saturation limiting term.

[0057] The deep and shallow coupling interaction damage index The aim is to quantify the combined effects of deep microseismic activity and surface deformation within mining areas, providing a unified indicator reflecting the degree of geological damage. By combining underground energy release and surface response, it makes the assessment of geological hazard risk more comprehensive and intuitive. Among these indicators is the spatiotemporal microseismic energy density characteristic value. Calculated based on the three-dimensional spatial coordinates, occurrence time, and energy released by microseismic events, this data characterizes the energy release intensity of underground rock masses within a specific spatiotemporal range and directly reflects the level of activity in deep geological formations. Surface deformation rate data. Surface deformation rate reflects the rate of displacement change of the Earth's surface at a specific point in time and space. It is a direct observational indicator of geological instability or deformation. Surface deformation rate data can be obtained in various ways. For example, large-scale, high-precision surface deformation monitoring can be performed using Synthetic Aperture Radar Interferometry (InSAR) technology, or deformation information at specific points can be obtained through continuous observation stations of the Global Positioning System (GPS) and precise leveling measurements. A preset critical deformation rate benchmark value is established. This is a pre-set threshold used to define at what level the surface deformation rate becomes significant or requires attention in terms of its contribution to geological damage. This value can be set based on factors such as the geological conditions of the mining area, historical disaster data, and safety regulations. For example, it can be set to 0.5 mm / day or 1 mm / day to distinguish between normal surface subsidence and potential abnormal deformation. It can also be calibrated by combining physical model tests of similar materials with inversion of historical disaster data from the mining area. Specifically, the calibration method involves constructing a scaled-down physical model of the mining area in the laboratory, using high-precision digital image correlation (DIC) technology to monitor the surface deformation rate in real time during the simulated excavation process, and extracting the instantaneous deformation rate corresponding to the critical inflection point where the surface abruptly transitions from the isochronous subsidence stage to the accelerated deformation and failure stage. Simultaneously, the average peak deformation rate is calculated by extracting InSAR monitoring time-series data from the historical subsidence areas of the mining area 3 to 7 days before the disaster. The accelerated rate obtained from the physical tests is then weighted and averaged with historical disaster statistics, and a threshold is set for the actual surface deformation rate. When this average value is reached, the nonlinear saturation limiting term reaches first-order mathematical saturation (i.e., Thus, the feature calibration equation is established: (In the formula, This represents the absolute value of the deformation rate at the inflection point of accelerated destruction, obtained by weighting together physical experiments and historical data. It represents the negative first power of the natural constant (approximately 0.368). (This represents the critical deformation rate reference value to be determined). By solving the above calibration equation, the critical deformation rate reference value that triggers a significant geological damage response can be derived in reverse. This is used as the physical baseline for triggering the nonlinear saturation mechanism. Nonlinear saturation limiting term. The nonlinear effect of surface deformation rate on the deep-shallow coupling interaction damage index is introduced. Its role is that when the surface deformation rate... When the value is low, this term is close to 0, indicating that surface deformation contributes little to the damage index; as the deformation rate increases, the value of this term gradually increases, but when the deformation rate far exceeds the critical deformation rate benchmark value... When the value of this item approaches 1 and reaches saturation, it avoids the damage index from increasing infinitely due to extremely high surface deformation rates, thus more realistically reflecting the actual impact of surface deformation on overall geological damage and preventing the excessive amplification of a single factor.

[0058] The scheme in this application uses the spatiotemporal microseismic energy density characteristic value With surface deformation rate data Coupling was performed, and a deep-shallow coupling interaction damage index was constructed. Specifically, the index is calculated by taking the characteristic value of spatiotemporal microseismic energy density. As a base, multiply by a value derived from surface deformation rate data. The determined nonlinear saturation limiting term. Spatiotemporal microseismic energy density eigenvalues. It provides information on the intensity of energy release from underground rock masses, reflecting the level of activity in deep geological formations. The nonlinear saturation limiting term modulates the impact of deep microseismic activity based on the magnitude of surface deformation rate. When the surface deformation rate is low or close to zero, the value of this limiting term is small, resulting in a lower value for the deep-shallow coupling interaction damage index. Mainly composed of spatiotemporal microseismic energy density characteristic values The determination reflects a damage state dominated by deep-seated activity. As the surface deformation rate increases, the value of this limiting term gradually increases, thereby enhancing the effect of surface deformation on the deep-shallow coupled interactive damage index. The contribution of this factor allows the index to more sensitively reflect the interaction between surface and deep geological activities. Simultaneously, the nonlinear saturation characteristic of this limiting term ensures that once the surface deformation rate reaches a certain level, its effect on the increase in the damage index tends to level off, avoiding excessive dominance of extreme deformation data in the overall assessment. This, in turn, makes the deep-shallow coupling interaction damage index more effective. This mechanism can more stably and comprehensively characterize the degree of damage to the geological environment of mining areas. It effectively combines the release of deep underground energy with the deformation response of shallow surface, making up for the limitations of assessment based on a single data source. This makes the assessment of damage to the geological environment of mining areas more realistic and provides a more reliable basis for subsequent risk warning and management.

[0059] The following is a concrete example to illustrate this. Suppose we are at a specific point in a mining area. and time nodes The spatiotemporal microseismic energy density characteristic value obtained through step S10 The concentration is 0.8 joules per cubic meter. Meanwhile, the surface deformation monitoring system obtained data at this point... Surface deformation rate data at time The rate is 1.5 mm / day. This is the preset critical deformation rate reference value. The value is 1.0 mm / day. At this point, the nonlinear saturation limiting term can be calculated as follows: Furthermore, the deep and shallow coupling interaction damage index It can be calculated as This value comprehensively reflects the location of this point. The degree of geological damage caused by the combined effects of deep microseismic activity and surface deformation. If the surface deformation rate is small, for example, 0.2 mm / day, then the nonlinear saturation limiting term is... ,at this time Will be This indicates that although the intensity of deep microseismic activity is the same, the overall damage is low due to the insignificant surface deformation.

[0060] Through the aforementioned technical solution, this application organically combines deep-seated inducing factors (microseismic activity) and shallow-seismic response factors (surface deformation) of geological environment damage in mining areas, overcoming the limitations of assessment based on a single data source. The introduction of the deep-shallow coupled interactive damage index makes the assessment of geological hazard risk more comprehensive and accurate, more realistically reflecting the complex interaction between energy release within the geological body and surface deformation. In particular, the design of the nonlinear saturation limiting term effectively avoids the excessive influence of extreme surface deformation data on the overall damage assessment, enabling the index to provide stable and physically meaningful assessment results at different deformation rates. This provides a more scientific and reliable basis for the monitoring, early warning, and management of the geological environment in mining areas.

[0061] Preferably, in S30, the static geological vulnerability background index is obtained by linearly weighting and fusing the fault-rock mass composite vulnerability term and the topographic instability aggravation term under spatial coordinates according to the fault-rock mass interaction disaster-causing weight coefficient and the topographic slope disaster-causing weight coefficient.

[0062] Among them, the fault-rock mass composite vulnerability term is negatively correlated and nonlinearly convergent, characterizing the combined geological inhibition effect brought about by the ratio of the fault distance to the reference critical distance of the fault influence and the ratio of the rock mass cohesion to the reference value of the basic cohesion; the topographic instability aggravation term is positively correlated and nonlinearly saturated, characterizing the amplification effect of the angle deviation of the topographic slope relative to the reference critical angle of the topographic slope.

[0063] Specifically, in S30, the formula for calculating the static geological vulnerability background index is as follows:

[0064] ;

[0065] in, This represents the static geological vulnerability background index. This represents the disaster-causing weighting coefficient of the interaction between faults and rock masses. This indicates the fault distance (in meters). The reference critical distance (in meters) indicates the impact of the fault. This represents the cohesion of the rock mass (in megapascals). This indicates the reference value for basic cohesion (in megapascals). This represents the disaster-causing weighting coefficient based on terrain slope. Indicates the slope of the terrain (in degrees). Indicates the critical angle for terrain slope (in degrees). Represents spatial coordinates (in meters). Indicates the composite vulnerability of the fault rock mass. This indicates an increase in terrain instability.

[0066] The static geological vulnerability background index Used to quantify a spatial point in a mining area Without considering dynamic disturbances, its inherent geological vulnerability comprehensively reflects the influence of static factors such as regional geological structure, soil and rock properties, and topography on the potential for geological disasters. The disaster-causing weight coefficient of the interaction between faults and rock masses... This parameter is used to adjust the contribution of the combined vulnerability of faults and rock masses in the static geological vulnerability background index. Its value is typically determined through a comprehensive assessment of factors such as the geological conditions of the mining area, fault activity, engineering geological properties of the rock mass, and historical disaster experience. For example, it can be determined using expert scoring, the analytic hierarchy process (AHP), or by training historical data with a machine learning model. Fault distance. Represents the target space point The vertical distance to the nearest fault is crucial. A fault is a structural surface in the Earth's crust where rock masses shift and fracture. The area surrounding a fault is typically a geologically weak zone, easily disturbed and damaged. Fault distances can be extracted from detailed geological exploration reports, geophysical data (such as seismic and electrical resistivity tomography), or high-precision geological maps. The reference critical distance for fault influence is also important. A reference boundary is defined for the fault's influence range. When the target point is less than this critical distance from the fault, the fault's impact on geological vulnerability is significant; as the distance exceeds this value, the impact gradually weakens. This reference distance is typically set based on regional geological structural characteristics, fault scale, rock mass properties, and engineering experience. For example, it can be empirically set based on the fault's activity level or regional stress field distribution, or determined through numerical simulation analysis of the fault stress field propagation range. Rock mass cohesion. Represents the target space point The ability of a rock mass to resist shear failure is an important indicator of its strength. Higher cohesion indicates a stronger resistance to deformation and failure, and lower geological vulnerability. Rock mass cohesion can be obtained through laboratory rock mechanics tests (such as direct shear tests and triaxial compression tests) or in-situ tests (such as shear wave velocity tests and pressuremeter tests), and spatial interpolation can be performed in conjunction with the geological survey report. Basic cohesion reference values. This is a benchmark value used to normalize the cohesion of rock masses, representing the general or ideal cohesion level of rock masses within a mining area. By comparing it with this reference value, the relative strength of local rock mass cohesion can be assessed. This reference value can be set according to the main rock type of the mining area, the regional geological background, or industry standards. For example, the average cohesion of healthy and intact rock masses within the mining area can be selected as a reference. Topographic slope disaster-causing weight coefficient. The contribution of terrain slope as a hazard factor to the static geological vulnerability background index is adjusted. Its value is typically determined through a comprehensive assessment of the mining area's topographic features, soil and rock stability, and the frequency and scale of historical landslides, debris flows, and other disasters. For example, it can be determined through expert experience, statistical analysis, or multi-criteria decision-making methods based on Geographic Information Systems (GIS). Terrain slope Represents the target space point The slope of the land surface is a key indicator; the steeper the slope, the higher the likelihood of instability of the soil and rock mass under gravity, and the greater the geological vulnerability. Topographic slope can be calculated using digital elevation model (DEM) data. For example, GIS software can be used to analyze the slope of DEM data and obtain the slope value for each grid point. A critical reference angle for topographic slope is also provided. It is a reference critical value used to assess the impact of terrain slope on geological vulnerability. When the actual slope approaches or exceeds this critical angle, the risk of terrain instability increases significantly. This reference critical angle is usually set based on the physical and mechanical properties of the regional soil and rock mass, hydrogeological conditions, and historical disaster experience. For example, the angle of repose or critical slip angle of the local soil and rock mass can be referenced.

[0067] The proposed solution introduces a static geological vulnerability background index. This index aims to comprehensively assess the inherent vulnerability of the geological environment in mining areas. Its calculation considers three key static geological factors: faults, rock mass cohesion, and topographic slope. Specifically, the composite vulnerability item of faults and rock masses... The aim is to quantify the geological vulnerability resulting from the combined effects of fault distribution and rock mass strength. Among these factors is the fault distance. The smaller the value, the closer the target point is to a fault, and the less stable the geological structure; the stronger the rock mass cohesion. The lower the value, the weaker the rock mass's ability to resist deformation and failure. These two factors are coupled through an exponential function and determined by the disaster-causing weighting coefficient of the fault and rock mass interaction. Adjustments were made to reflect its contribution to overall vulnerability. Meanwhile, the topographic instability exacerbation factor... This is used to assess the impact of terrain slope on geological stability; terrain slope The larger the value, the higher the risk of instability of the soil and rock mass under gravity, and the larger the value of this item is. This item is determined by the disaster-causing weighting coefficient of terrain slope. Weighting is applied. By superimposing these two composite terms, this application can comprehensively and quantitatively characterize the static geological vulnerability of the mining area at a specific spatial point. This approach, which comprehensively considers static geological factors, compensates for the limitations that may exist in relying solely on dynamic microseismic and surface deformation data, making the assessment of the inducing factors of geological environmental damage in the mining area more comprehensive and accurate, and providing a solid background support for the subsequent calculation of dynamic inducing precursor intensity values.

[0068] Through the aforementioned technical solution, this application incorporates inherent static factors such as the geological structure, rock mass properties, and topography of the mining area into the assessment system of geological environment damage inducing factors. This overcomes the potential bias in assessments that may result from relying solely on dynamic microseismic and surface deformation data, leading to a more comprehensive and in-depth understanding of the vulnerability of the mining area's geological environment. The introduction of the static geological vulnerability baseline index provides stable baseline support for the subsequent calculation of dynamic induced precursor intensity values, ensuring that the final inducing factor layer matrix can more accurately reflect the true risks of the mining area's geological environment, thereby improving the reliability of early warning and decision-making.

[0069] Preferably, in S30, the processing logic for smoothing the deep and shallow coupling interaction damage index includes: extracting the deep and shallow coupling interaction damage index under each historical time node within a preset total number of steps in the moving average time window according to the discrete time step and the time step number traversal index, performing arithmetic averaging processing, and obtaining the smoothed deep and shallow coupling interaction damage index.

[0070] Specifically, in S30, the smoothing filter adopts a discrete-time moving average model, and the calculation formula for the deep-shallow coupling interaction damage index after smoothing filtering is as follows:

[0071] ;

[0072] in, This represents the deep-shallow coupling interaction impairment index after smoothing filtering. This represents the total number of steps in the moving average time window. This indicates the index for traversing time steps. Indicates the discrete time step (in days). Indicates at a time node The deep and shallow coupling interaction damage index at the location;

[0073] The dynamic induced precursor intensity value is obtained by superimposing and associating static background amplification features with dynamic trend surge features; the static background amplification features are obtained by measuring the product scale of the static geological vulnerability background index and the smoothed shallow-deep coupling interaction damage index; the dynamic trend surge features are obtained by aligning the dimensions and measuring the trend amplification using the partial derivative of the smoothed shallow-deep coupling interaction damage index with respect to time, and the precursor trend weight adjustment coefficient and the inherent physical recovery time benchmark. The formula for calculating the dynamic induced precursor intensity value is as follows:

[0074] ;

[0075] in, This indicates the intensity value of the precursor to dynamic induction. This represents the static geological vulnerability background index. This represents the leading trend weighting adjustment coefficient. This represents the partial derivative of the deep-shallow coupling interaction damage index with respect to time after smoothing filtering (in reciprocal of days). Indicates the inherent physical recovery time reference (in days). Represents spatial coordinates (in meters). Indicates a time point (in days). This indicates the static background amplification characteristic. It indicates a surge in dynamic trends.

[0076] Specifically, the smoothing filter employs a discrete-time moving average model, a common signal processing technique that eliminates short-term fluctuations and noise by averaging data over a period of time, thereby revealing long-term trends in the data. This model can use a simple arithmetic mean, i.e., the average of all data points within the sliding window, or a weighted moving average, assigning different weights to data points at different times within the window; for example, data closer to the current time has a greater weight. The smoothing filter results in a deep / shallow coupling interaction impairment index. The calculation formula, through a moving average model, applies the original deep and shallow coupling interaction damage index. The processing yields smoother and more stable time-series data, which helps reduce the impact of transient noise on subsequent calculations and makes the trend characteristics of geological environmental damage more apparent. The formula explicitly defines the calculation method, namely, within the time window. Inside, regarding the past each discrete time step of The values ​​are averaged. The total number of steps in the moving average time window. The range of smoothing filters can be determined based on the sampling frequency of actual monitoring data and the time scale of geological hazard evolution, through experience or historical data analysis, or by cross-validation or optimization algorithms to select the range that best performs the model. Value. Discrete time step. This frequency is usually consistent with the data collection frequency, for example, data is collected once a day. The timeframe is 1 day, but the original data can also be resampled as needed to obtain different results. Dynamically induced precursor intensity value The calculation formula comprehensively considers the static vulnerability background of the geological environment and the smoothing trend and rate of change of dynamic damage, in order to more comprehensively and accurately assess the intensity of the precursors to geological environmental damage. The formula consists of two parts: static background amplification characteristics... and dynamic trend surge characteristics Among them, the precursor trend weight adjustment coefficient The relative importance of dynamic trend surge characteristics in the total precursor intensity value can be set through expert experience or through machine learning methods, using historical disaster data for training and optimization to automatically learn the optimal value. The value can also be determined using a data-driven calibration method based on historical monitoring datasets of the mining area, employing algorithm optimization and backtracking testing. Specifically, the calibration method involves extracting long-term historical monitoring data from the mining area and constructing a time-series sample library containing "safe periods" and "disaster occurrence periods." The objective function is to maximize the early warning hit rate and minimize the false alarm rate. The gradient descent algorithm is then used to adjust the parameters. During training, the system iteratively optimizes and substitutes historical sample data into the dynamically induced precursor intensity values. The core computational model was used for the exercise, and the calculation formula is as follows: Through continuous adjustment The value of is such that the calculated value within a specific time window before the disaster occurs... It can stably exceed the preset high-risk trigger threshold while remaining stable during the safe period. When the area under the ROC curve (AUC value) on the validation set reaches maximum convergence, the locked parameter value is the optimal precursor trend weight adjustment coefficient. .

[0077] The solution in this application firstly uses the interaction damage index of deep and shallow coupling. A discrete-time moving average model is applied for smoothing filtering to generate a smoothed deep-shallow coupling interaction damage index. This process effectively reduces potential transient noise and short-term fluctuations in the original data, enabling the obtained damage index to more stably and reliably reflect the potential trend of geological environmental damage. Subsequently, this smoothed damage index... Compared with the static geological vulnerability background index Multiplication results in a static background amplification feature. This feature quantifies the fundamental contribution of smoothed interactive damage to precursor intensity under the background of inherent geological vulnerability. Simultaneously, this scheme also calculates the partial derivative of the smoothed shallow-deep coupling interactive damage index with respect to time. This partial derivative can capture the rate of change of the damage index over time, i.e., the dynamic trend of geological environmental degradation. This is achieved through a precursor trend weighting coefficient. and inherent physical recovery time benchmark Adjusting this partial derivative resulted in a dynamic trend surge characteristic. Finally, the static background amplification feature is added to the dynamic trend surge feature to obtain the dynamic induced precursor intensity value. This calculation method, which comprehensively considers static vulnerability, smooth damage level, and their dynamic rate of change, ensures that the precursor intensity value can resist data noise and sensitively reflect the persistence and acceleration of geological environmental damage, thus providing a more comprehensive and accurate early warning indicator.

[0078] The following is a concrete example to illustrate this. Assume that the geological environment monitoring system in the mining area collects the deep-shallow coupling interaction damage index once a day. Data. To obtain stable precursor intensity values, the total number of steps in the moving average time window can be set. The discrete time step is 7. The duration is 1 day. At this point, the deep-shallow coupling interaction damage index after smoothing filtering... By using the current time And the past 6 days (i.e. )of The values ​​are calculated by arithmetic averaging. This smoothing process can be implemented in a data processing server or a dedicated monitoring software module that continuously receives raw data. Data and updates in real time In calculating the intensity value of dynamically induced precursors. At that time, the system will first obtain the corresponding spatial coordinates. static geological vulnerability background index Next, using the latest calculations... The value, combined with the previous moment's value. The partial derivative of the deep-shallow coupling interaction damage index with respect to time after smoothing filtering is approximately calculated using the finite difference method. For example, it can be adopted Finally, adjust the preset precursor trend weight coefficient. and inherent physical recovery time benchmark Substituting into the formula, we can calculate... The entire calculation process can be integrated into the intelligent early warning platform for geological disasters in mining areas, enabling automated data processing and precursor intensity assessment.

[0079] By smoothing and filtering the deep-shallow coupling interaction damage index, the interference of instantaneous noise and short-term fluctuations in the original data on the calculation of the induced precursor intensity value is effectively reduced, making the obtained dynamic induced precursor intensity value more stable and reliable. Simultaneously, by introducing the partial derivative of the damage index with respect to time, this scheme can sensitively capture the dynamic evolution trend and acceleration signs of geological environmental damage. Combined with the static geological vulnerability background index, it achieves a comprehensive and accurate assessment of the induced precursors of geological environmental damage. This significantly improves the timeliness and accuracy of early warning of geological environmental damage in mining areas, providing a more solid data foundation for subsequent administrative management and industrial monitoring and allocation.

[0080] Preferably, in S40, the node weight value is obtained by performing an S-shaped nonlinear constraint transformation on the excess offset of the dynamic induced precursor intensity value under the spatial coordinates and time node relative to the statutory threshold baseline for safety management;

[0081] The S-shaped nonlinear constraint transformation uses the administrative response sensitivity coefficient to adjust the response steepness of the over-limit offset amplitude, and strictly normalizes its value to map it into a numerical system of zero to one in the open interval.

[0082] Specifically, in S40, the formula for calculating the node weight value is as follows:

[0083] ;

[0084] in, Represents the node weight value. This represents the sensitivity coefficient of administrative response. This indicates the intensity value of the precursor to dynamic induction. This represents the legally mandated threshold baseline for safety management. Represents spatial coordinates, Indicates a time point (in days). Indicates the extent of the offset exceeding the limit. This represents the S-shaped nonlinear constraint transformation.

[0085] Node weight value It is an indicator used to quantify the risk or importance of geological environment damage initiating factors in mining areas at a specific time and space point. It measures the continuously changing dynamic precursor intensity value. This is mapped to a finite numerical range, typically between 0 and 1, to facilitate unified risk assessment, prioritization, and decision-making by administrative management and industrial monitoring systems. Administrative Response Sensitivity Coefficient It is an adjustment parameter used to control the node weight value. For the intensity value of the precursor of dynamic induction The sensitivity to changes in response, this coefficient determines the steepness of the S-shaped nonlinear constraint transition curve, i.e., the slope of the precursor intensity value induced dynamically. Approaching the statutory threshold baseline for safety management At that time, the node weight value The rate of increase or decrease can be empirically set based on the actual geological environment of the mining area, the risk preferences of the management department, or the results of historical data analysis (e.g., taking the average of historical data), or it can be adaptively adjusted through optimization algorithms. The statutory threshold baseline for safety management. It is a preset critical value, representing the highest intensity level considered safe or acceptable in terms of the precursor intensity of geological environment damage in the mining area. When the dynamic precursor intensity value... Exceeding this baseline typically means that corresponding early warning or intervention measures need to be initiated. This baseline can be determined through expert experience or historical data analysis (e.g., taking the average value). S-shaped nonlinear constraint transformation. The sigmoid function is a commonly used nonlinear function characterized by mapping input values ​​to a finite output range (typically 0 to 1), and the output value changes rapidly as the input value approaches a certain point, forming an S-shaped curve. This transformation method can simulate the perception and response to risk in actual management; that is, the response is gradual when the risk is low, rapidly increases when the risk approaches or exceeds a threshold, and tends to saturate when the risk is extremely high. Besides the sigmoid function, the tanh function or other nonlinear functions with similar S-shaped characteristics can also be used for transformation, or piecewise functions can be used to simulate this nonlinear response.

[0086] The solution in this application introduces an S-shaped nonlinear constraint transformation to convert the dynamic induced precursor intensity value obtained by the aforementioned method into a signal strength value. Combined with the statutory threshold baseline for safety management and administrative response sensitivity coefficient Converted into node weight values Specifically, when the intensity value of the precursor is dynamically induced... Far below the legal threshold baseline At that time, the excess offset amplitude For large negative values, after S-shaped nonlinear constraint transformation, the node weight values ​​are... A value approaching 0 indicates a low risk. This varies depending on the dynamic intensity value of the precursor. Gradually approaching and exceeding the legal threshold baseline Excessive offset The value will change from negative to positive. At this point, the output of the S-shaped nonlinear constraint transformation will rapidly rise from a low value, causing the node weight values ​​to change. Rapidly increasing. Administrative response sensitivity coefficient. This process modifies the steepness of the ascent, making it more pronounced. The value means in around When the surrounding area changes, the node weight value The changes are more drastic, making the management system more sensitive and rapid in its response to risks; conversely, smaller changes... The value results in a smoother response. This conversion mechanism effectively transforms the continuous and potentially unbounded dynamic precursor intensity value. This is mapped to a bounded node weight value with a definite physical meaning. This value can be directly used in the inducing factor layer matrix for administrative management and industrial monitoring and allocation, serving as a basis for risk assessment and decision-making. In this way, this application solves the problem of transforming continuous intensity values ​​into actionable management indicators, providing a quantitative foundation for subsequent early warning and decision-making.

[0087] As a specific implementation method, a legally mandated threshold baseline for security management can be set. The administrative response sensitivity coefficient is 0.5. The value is 10. This refers to the intensity value of dynamic induced precursors in a certain region. When the value is 0.3, its over-limit offset is At this time, the node weight value This indicates that the area has a low risk and is in a safe state. When the dynamic induced precursor intensity value... When it rises to 0.5, the over-limit offset is At this time, the node weight value This indicates that the risk in the area has reached a threshold level. When the dynamic induced precursor intensity value... When it further increases to 0.7, the over-limit offset is At this time, the node weight value This indicates a significant increase in risk in the region, potentially necessitating a higher level of early warning response. This S-shaped nonlinear transformation clearly maps precursor values ​​of varying intensities to weighted values ​​with explicit management significance.

[0088] The above technical solution will enable the continuously changing dynamic precursor intensity value to be generated. Through S-shaped nonlinear constraint transformation, the values ​​are mapped to bounded and standardized node weights. This allows continuous intensity values, which were originally difficult to use directly for decision-making, to be effectively transformed into easily understood and operational quantitative indicators for administrative management and industrial monitoring and allocation. This node weight value... It can clearly reflect the risk level of geological environment damage in mining areas, especially the intensity value of dynamic induced precursors. Approaching or exceeding the statutory threshold baseline for safety management At that time, weight value The changes are more pronounced, enabling timely and accurate triggering of corresponding early warning and response mechanisms. This greatly improves the accuracy of geological environmental risk assessment and the efficiency of management decision-making in mining areas, providing a solid data foundation for achieving refined and intelligent mine safety management.

[0089] Preferably, the method further includes a multi-level early warning and automated personnel evacuation decision-making step based on the inducing factor layer matrix:

[0090] Traverse the node weight values ​​in the inducing factor layer matrix. When the node weight value of at least one node in the target area exceeds the preset first-level safety threshold, trigger a first-level early warning response. In the management backend, mark the target area as a key hidden danger area and generate a linkage dispatch instruction to prohibit new personnel from entering.

[0091] When the node weight value of at least one node in the target area exceeds the preset secondary safety threshold for a consecutive preset time period, a secondary evacuation response is triggered, automatically generating an automated personnel evacuation decision command, linking the personnel positioning system and emergency broadcasting equipment to play evacuation guidance information in the target area, and simultaneously controlling the physical access control equipment on the evacuation path to perform automatic unlocking actions.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a layer of geological environment damage inducing factors in mining areas, characterized in that, include: S10. Using the three-dimensional spatial coordinates of the microseismic event, the occurrence time, and the energy released by the microseismic event as inputs, the inherent physical recovery time reference, the three-dimensional spatial attenuation reference radius, and the temporal causality constraint conditions are introduced to obtain the spatiotemporal microseismic energy density characteristic value. S20. Couple the spatiotemporal microseismic energy density characteristic value with the surface deformation rate data to obtain the deep and shallow coupling interaction damage index; S30. Obtain the static geological vulnerability background index from fault distance, rock mass cohesion and topographic slope; perform smoothing filtering on the deep-shallow coupling interaction damage index, and combine its partial derivative with time with the static geological vulnerability background index to obtain the dynamic induced precursor intensity value. S40. Convert the dynamic induced precursor intensity value into a node weight value, and use it as the node weight value in the induced factor layer matrix for administrative management and industrial monitoring.

2. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 1, characterized in that, In S10, the spatiotemporal microseismic energy density characteristic value is obtained by multiplying and summing the normalized energy release term, three-dimensional spatial attenuation characteristic term, and temporal causal recovery characteristic term corresponding to each microseismic event within the total number of effective microseismic events: The normalized energy release term is obtained by comparing the microseismic release energy with a preset reference microseismic energy benchmark constant; The three-dimensional spatial attenuation feature term negatively correlates and convergently characterizes the deviation of the distance between the spatial node composed of the target point's abscissa, target point's ordinate, and the ground surface elevation reference plane coordinates and the three-dimensional spatial coordinate components of each microseismic event relative to the three-dimensional spatial attenuation reference radius. The time-causal recovery feature uses the time-causal constraint to block the input of time node data that is greater than the current time node, and convergingly represents the time offset of the occurrence time of the microseismic event relative to the current time node as being constrained by the attenuation magnitude of the inherent physical recovery time reference.

3. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 2, characterized in that, In S20, the deep-shallow coupling interaction damage index is obtained by multiplying the spatiotemporal microseismic energy density characteristic value with the nonlinear saturation limiting term corresponding to the surface deformation rate data: The nonlinear saturation limiting term is constructed based on the ratio scale feature between the absolute value of the surface deformation rate data under spatial coordinates and time nodes and the preset critical deformation rate benchmark value. The nonlinear saturation limiting term exhibits a positively correlated nonlinear growth as the absolute value of the surface deformation rate data increases, and its output numerical boundary strictly converges within the preset upper and lower limit intervals.

4. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 1, characterized in that, In S30, the static geological vulnerability background index is obtained by linearly weighting and fusing the fault-rock mass composite vulnerability term and the topographic instability aggravation term under spatial coordinates according to the fault-rock mass interaction disaster-causing weight coefficient and the topographic slope disaster-causing weight coefficient. The composite vulnerability term of the fault rock mass is negatively correlated and nonlinearly convergent, characterizing the combined geological inhibition effect brought about by the ratio of the fault distance to the reference critical distance of the fault influence and the ratio of the rock mass cohesion to the reference value of the basic cohesion. The term describing the exacerbation of terrain instability exhibits a positively correlated, nonlinearly saturated characterization of the amplification effect of the angular deviation of the terrain slope relative to the reference critical angle of terrain slope.

5. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 3 or 4, characterized in that, In S30, the processing logic for smoothing the deep and shallow coupling interaction damage index includes: extracting the deep and shallow coupling interaction damage index at each historical time node within a preset total number of steps in the moving average time window based on the discrete time step and the time step traversal index, performing arithmetic averaging processing, and obtaining the smoothed deep and shallow coupling interaction damage index.

6. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 5, characterized in that, In S30, the dynamic induced precursor intensity value is obtained by superimposing and associating the static background amplification feature with the dynamic trend surge feature; The static background amplification feature is obtained by measuring the product scale of the static geological vulnerability background index and the smoothed and filtered deep-shallow coupling interaction damage index. The dynamic trend surge feature is obtained by aligning the dimensions and measuring the trend amplification using the partial derivative of the smoothed and filtered deep and shallow coupling interaction damage index with respect to time, the precursor trend weight adjustment coefficient, and the inherent physical recovery time benchmark.

7. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 6, characterized in that, In S40, the node weight value is obtained by performing an S-shaped nonlinear constraint transformation on the excess deviation of the dynamic induced precursor intensity value relative to the statutory threshold baseline for safety management in terms of spatial coordinates and time nodes. The S-shaped nonlinear constraint transformation uses the administrative response sensitivity coefficient to adjust the response steepness of the over-limit offset amplitude, and strictly normalizes its value to map it into a numerical system of zero to one in the open interval.

8. The method for constructing a layer of geological environment damage inducing factors in mining areas according to claim 7, characterized in that, The method also includes a multi-level early warning and automated personnel evacuation decision-making step based on the inducing factor layer matrix: Traverse the node weight values ​​in the inducing factor layer matrix. When the node weight value of at least one node in the target area exceeds the preset first-level safety threshold, trigger a first-level early warning response. In the management backend, mark the target area as a key hidden danger area and generate a linkage dispatch instruction to prohibit new personnel from entering. When the node weight value of at least one node in the target area exceeds the preset secondary safety threshold for a consecutive preset time period, a secondary evacuation response is triggered, automatically generating an automated personnel evacuation decision command, linking the personnel positioning system and emergency broadcasting equipment to play evacuation guidance information in the target area, and simultaneously controlling the physical access control equipment on the evacuation path to perform automatic unlocking actions.

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