479 results about "Geological formation" patented technology

The invention discloses a mining rock mass structure degradation monitoring method and system based on deep learning, particularly relates to the technical field of mine safety monitoring, and is used for solving the problems of monitoring lag and misjudgment caused by the fact that an existing static model cannot automatically adapt to rock mass damage dynamic evolution. The method comprises the following steps: constructing a dynamic damage incremental data set by collecting multi-source heterogeneous monitoring data under mining disturbance in real time, extracting newly-added damage features, and calculating a distribution offset degree between the newly-added damage features and historical features; when the deviation degree exceeds the limit, verifying and screening a damage feature subset according with a mechanical law based on a rock constitutive equation, and blocking non-physical noise interference; a geological structure evolution mode is matched through damage path dependence modeling, and a potential damage path thermodynamic diagram is generated in combination with a space gradient; performing cross-stage association on the thermodynamic diagram and a historical feature library by adopting a knowledge distillation mechanism to generate a dynamic weight matrix fused with a mining time sequence; and finally, updating the damage assessment model through a parameter reweighting mechanism to realize adaptive assessment of the rock mass degradation risk.

The embodiment of the invention provides a mineral target area intelligent delineation method and system based on multi-source heterogeneous geoscience data, and relates to the technical field of mineral resource exploration, and the method comprises the steps: dividing a multi-source heterogeneous geoscience data set comprising geological structure, geochemistry, geophysics and remote sensing image data into a plurality of data modules according to a geological action influence range; each data module records spatial distribution and attribute information, then a dynamic mapping relation between the data modules and a mineral product mineralization mechanism is constructed, a mineralization association chain between the data modules is generated based on the dynamic mapping relation, a cooperative change rule of nodes and connection paths is defined, then a target region feature set conforming to the mineralization association chain is screened, and a target region feature set is obtained; and finally, according to a collaborative change rule, judging the integrating degree of a target region feature set space unit and a mineralization condition, dynamically adjusting a space boundary range, and generating a mineral target region delineation result, thereby comprehensively utilizing multi-source data, deeply mining a relationship with a mineralization mechanism, and improving the accuracy and reliability of mineral target region delineation.

The invention discloses a geological structure three-dimensional reconstruction and analysis system based on multi-modal data fusion, and relates to the technical field of geological exploration information. The method comprises the following steps: acquiring and normalizing multi-source heterogeneous geological observation data through a preprocessing module, and generating basic data and a data coverage density map; the adaptive fusion module performs analysis to generate a dynamic adaptive weight field, and performs weighted fusion on the data to obtain a primary three-dimensional geological data volume; the construction and optimization module constructs a three-dimensional space residual field in a vector form based on the difference between the fusion body and the original data, and deduces the geometric morphology of the geological interface; the reconstruction module ensures the consistency between model layers through hierarchical bidirectional belief propagation processing; the quantification module calculates an uncertainty index; and the visualization module finally generates a three-dimensional geological scene. According to the method, the problems of conflict resolution and reliability evaluation in multi-source geological data fusion are effectively solved, and the precision of the three-dimensional geological model and the engineering decision support capability are improved.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention relates to the technical field of geological exploration, provides a gold ore metallogenic three-dimensional prediction method and system based on structure information fusion, and breaks through the limitation that a traditional three-dimensional modeling method stops in the aspect of geometric description through fusion processing of multi-source data such as geological structures, ore body evolution processes and geochemical anomalies. Ore control structure information is fused with a geochemical trend and a space structure for the first time, a structure-geochemistry-ore body form collaborative prediction mechanism is realized, and blind ore recognition and peripheral prospecting capabilities are effectively enhanced. Meanwhile, a target region division mechanism based on multi-source layer overlapping is constructed, the credibility and precision of a prediction result are remarkably improved, and the prospecting requirement of a high-work-degree gold mining area is met. By adopting a mode of combining engineering modeling and mid-segment graph modeling, space errors generated by traditional interpolation modeling in a data sparse or form complex area are effectively avoided, and the authenticity and continuity of an ore body structure are improved.

The invention belongs to the technical field of three-dimensional geological modeling, and discloses a real-time three-dimensional geological modeling system and method based on groundwater dynamics. The system comprises a geological data acquisition module, a complex geological structure modeling module, a geological body internal attribute simulation module, an underground water monitoring module, an underground water simulation module, a geological evolution simulation module, an underground water-geological body simulation module and a visual interaction module. By integrating multi-source data and applying advanced data cleaning and quality control technologies, the accuracy and consistency of the data are ensured, then by utilizing advanced geologic structure modeling and geologic body internal attribute simulation technologies, a high-precision three-dimensional geologic body model is constructed, and the accuracy and consistency of the geologic body model are improved. A reliable space frame is provided for dynamic simulation and analysis of underground water, and timely and accurate information support can be provided for water resource management and environmental protection. The method has a wide application prospect and an important practical value in the fields of dynamic monitoring, analysis and simulation of underground water.

The invention relates to the technical field of geological exploration, and particularly discloses a geological structure automatic identification and interpretation method based on deep learning, and the method comprises the steps: obtaining seismic data, logging data, gravity data and / or magnetic data, and generating a fusion tensor through space-time alignment and dynamic weight distribution; performing space-frequency domain joint coding on the fusion tensor by using an improved Transform encoder, and generating multi-scale geologic feature representation; a generative adversarial network is constructed, a generator of the generative adversarial network outputs a geological structure pseudo label, and a discriminator and a geological expert rule base collaboratively optimize a prediction result; and quantizing a confidence interval of a prediction result based on Bayesian deep learning, and combining geological priori knowledge to generate a dynamic interpretation scheme. Through multi-source data fusion, an innovative algorithm and model collaboration, accurate recognition and scientific interpretation of the geological structure are achieved, the accuracy and efficiency are improved, the cost is reduced, and the method has remarkable advantages in complex geological exploration.

The invention provides an ice rock mass stability prediction method and system based on machine learning, and relates to the technical field of machine learning, and the method comprises the steps: firstly obtaining a geological data set containing ice rock mass distribution, geological structure and environment monitoring parameters in a target region, and carrying out the feature fusion processing of the geological data set; the method comprises the steps of obtaining an ice rock feature set covering an ice rock mass structure, geological stress and environmental dynamic features, constructing and training an ice rock stability prediction model by using a multi-layer neural network based on the ice rock feature set, and calling the trained ice rock stability prediction model to generate a real-time stability prediction result of a target area when monitoring data is input in real time. And finally, according to the real-time stability prediction result, outputting a stability alarm signal or optimizing and reinforcing suggestion parameters, so that various parameters can be integrated, accurate prediction of the stability of the ice rock mass is realized, and countermeasures are given.

The invention relates to the technical field of accurate calculation of mining quantity, in particular to an intelligent accurate calculation system of mining quantity based on a three-dimensional geologic model, which comprises a multi-source heterogeneous data acquisition module used for acquiring and preprocessing various data such as geologic structure and mineral distribution; the dynamic three-dimensional geologic modeling module constructs a digital geologic model; the precise reserve calculation module calculates exploitable reserves; the intelligent mining planning module is used for generating a mining path optimal solution set by adopting a hybrid taboo genetic optimization algorithm and evaluating the mining efficiency and the safety risk; the resource collaborative scheduling module is used for establishing a multi-objective optimization model of the equipment-personnel-transportation system by using a space-time constraint particle swarm optimization algorithm; and the visual decision support module supports real-time simulation and adjustment of the mining scheme through a WebGL three-dimensional visual engine and an AR interactive interface. According to the intelligent mining method, accurate calculation of the mining quantity and intelligent mining are achieved, the mining efficiency is improved, safety is guaranteed, resource allocation is optimized, and economic benefits are improved.

The invention belongs to the technical field of mineral resource exploration, and provides a multi-source remote sensing data and AI combined copper ore exploration method and system, and the method comprises the following steps: obtaining multi-source remote sensing data of a target region through a plurality of remote sensing devices; preprocessing the multi-source remote sensing data to obtain fusion data; the fusion data are analyzed through an AI algorithm, and mineralization alteration areas and geological structure information are obtained through recognition; performing overlay analysis on the mineralization alteration area and the known geochemical anomaly area, analyzing the spatial relationship between the mineralization area and the structure in combination with the geological structure information, and judging the favorable mineralization part of the copper ore to obtain an analysis result; performing field investigation on the potential copper ore area determined by the analysis result, collecting rock samples for laboratory analysis, and verifying the accuracy of the analysis result; according to the method, the multi-source remote sensing data and the AI technology are comprehensively utilized, the target area is rapidly analyzed, the potential copper ore area is positioned, the workload of blind exploration is reduced, and the exploration efficiency is improved.

The embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

Provided is a method for judging geology of a time-delayed extremely intense rockburst as to deep tunnel engineering. Information about geological structures is recorded according to methods such as geological mapping and advanced geological prediction of the tunnel face. Geometric characteristics of burst craters formed during the rockburst are recorded by utilizing advanced geological drilling and profile scanning technologies in a rockburst-damaged zone. 3D geostress in the rockburst-damaged zone is measured by using a stress relief method or a hydraulic fracturing method to determine the orientation of the maximum principal stress. A 3D numerical calculation model is established based on initial geostress testing results and physical and mechanical parameters of rock and structural discontinuities obtained through on-site geological investigations. By conducting excavation simulations under different combinations of structural discontinuities and performing numerical calculation, the risk of time-delayed extremely intense rockburst disasters at current locations after tunnel excavation is judged.

The invention discloses a coal seam frequency resonance seismic exploration system and method based on deep learning. The method comprises the following steps: S1, arranging passive source seismic equipment to collect background waveform data; s2, performing spatial autocorrelation spectrum analysis, and extracting a frequency dispersion curve; s3, speed inversion is carried out, and a speed profile map is output; s4, constructing a DeepLabV < 3 + > model to identify geological anomalies; s5, performing image preprocessing to generate standardized input; s6, performing model reasoning to obtain a segmentation result graph; s7, generating a structure map and a risk map in combination with the coordinates; and S8, exporting an interpretation result in a structured format. According to the method, a seismic data processing flow combining a spatial autocorrelation method and a DeepLabV3 + model is adopted, and automatic extraction of coal seam frequency resonance information and intelligent recognition and visual interpretation of a geological structure are achieved.

The invention discloses an intelligent prospecting prediction method based on a three-in-one metallogenic prediction theory, and relates to the technical field of prospecting prediction, and the method comprises the steps: determining various geological elements formed by a regional control ore deposit, obtaining data related to the elements formed by the regional control ore deposit, and storing the data in a database; comprising geological survey, engineering exploration, geophysics, geochemistry and remote sensing data; performing data preprocessing on the geological element data and dividing the geological element data into a plurality of categories; unsupervised learning is carried out on the geological data according to a deep learning algorithm, and the geological structure of the mining area is described in detail; in the areas with relatively high reconstruction errors, selecting the areas with relatively high geochemical anomaly and geophysical anomaly indication variables or reconstruction error values, comparing the selected areas with known typical ore deposits to determine the correlation of space structures, and extracting ore-forming favorable anomaly feature information from the selected areas; and the extracted abnormal information is matched with various geological elements to delineate the target region, so that the accuracy and efficiency of prospecting prediction are improved.

The invention relates to an analysis method and device for geological spatial data, equipment and a medium. The method comprises the steps that multi-source heterogeneous data are collected based on a preset spatial data standard, and the multi-source heterogeneous data comprise geological spatial data of multiple different geological data types; determining geological features of the geological spatial data of each geological data type, and establishing a feature matrix of the geological spatial data of the corresponding type based on the geological features; classifying the multi-source heterogeneous data on the dimension of the geological structure to obtain various geological structure data, each geological structure data corresponding to one geological structure type; and according to the feature matrix of the geological spatial data corresponding to the geological structure type, fusing the geological structure data of the corresponding type to obtain a fusion analysis result of the geological structure type. By adopting the method, fusion and analysis of geological data can be efficiently completed based on multi-source heterogeneous data.

The invention relates to the technical field of geomechanical data research, in particular to a geomechanical data visualization method and system based on a mixed data model. According to the technical scheme, the method comprises the steps of data collection and arrangement, mixed data model construction, visual algorithm design and application and visual result display and interaction. The data collection and arrangement comprises the step of collecting geomechanical data including geologic structure data, rock mechanics parameter data and ground stress data. According to the method, geologists can be assisted to deeply understand the complex relationship between geologic structure evolution and rock mechanical behaviors, and powerful data visualization support is provided for development of geologic theories; geological risks can be predicted in advance, an engineering design scheme is optimized, and engineering safety and economic benefits are improved; through accurate visualization and deep analysis of geomechanical data, the possibility and the potential influence range of disaster occurrence can be evaluated more accurately, and key technical support is provided for disaster early warning and prevention and control measure formulation.

The invention belongs to the technical field of seismic exploration, and discloses a pre-stack gather optimization method based on multi-dimensional constraint dynamic time warping, which remarkably improves the space continuity and stability of a time shift field. According to the method, the time shift field which is smoother and more continuous in a three-dimensional space can be generated, and the common abnormal jump and unreasonable jitter of the time shift field in a conventional DTW method are effectively inhibited. This better conforms to the gradient characteristics of the geologic structure. Mismatching and processing illusion are effectively avoided, the adaptability of a complex structure area is improved, the global optimization capacity of the DTW algorithm is enhanced through multi-dimensional constraint, and the phenomenon that different horizons or effective waves are wrongly matched with interference waves due to local waveform complexity or noise interference is reduced. Therefore, common processing illusions such as event dislocation and local distortion in a conventional DTW method can be remarkably reduced.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention belongs to the technical field of coal mine working face simulation, and particularly relates to a dynamic management and control method and system for a geological structure transparency model of a coal mine working face, and the system comprises a geological transparent modeling platform and a geological model driving dynamic management and control platform. In combination with geological acquisition data obtained in real time, a working face fine geological model is generated after modeling processing; the geologic model driving dynamic management and control platform is used for preprocessing a working face fine geologic model, digitalizing the working face fine geologic model to generate a working face fine geologic model digital mirror image by using a CT slicing technology, and receiving equipment operation condition data of the sensing coal mining system in real time through a data interface technology. And dynamically updating the working face digital mining geologic model and performing linkage updating on the mining virtual scene to realize iterative updating of the geologic model. The problem that an existing coal mine working face geologic model lacks a real-time dynamic updating means can be solved.

The invention discloses a method and a system for constructing a multi-level geologic structure model based on electromagnetic detection, and the method comprises the steps: transmitting electromagnetic signals with different frequencies at all measurement points through an electromagnetic detector, carrying out the resistivity inversion through apparent resistance and impedance phase, obtaining a geologic electrical structure, dividing non-uniform and uniform electrical geology based on electromagnetic static displacement, and obtaining a multi-level geologic structure model. Analyzing an orthogonal component magnetic field to obtain an electromagnetic response parameter and a polarization ellipse parameter; determining an electric field diffusion curve; obtaining an electric field distribution model through finite difference method inversion; deducing underground geological spatial form and distribution law; and finally, calculating an electromagnetic response curved surface to obtain a two-dimensional profile curve, and performing three-dimensional reconstruction to obtain a multi-level geologic structure model. The method not only can improve the precision and reliability of the structure model, but also has good interpretability, and can be directly applied to a construction system of a multilevel geological structure model based on electromagnetic detection.

The invention provides a geological three-dimensional science popularization system based on a VR technology, and the system comprises an AI-driven geological data intelligent processing and three-dimensional scene dynamic construction module which is configured to intelligently extract key geological elements from one or more geological data sources through employing an artificial intelligence algorithm, and a high-precision and interactive three-dimensional geological virtual reality scene is constructed based on the geological elements, and the module can dynamically optimize the scene and load the scene as required. According to the system, the understandability and science popularization interestingness of geological phenomena are remarkably improved, learning interests are stimulated, complex geological structures, geomorphic forms and ancient environments can realistically reappear through an AI-enhanced high-precision three-dimensional scene construction technology, the cognitive threshold of geological knowledge is greatly reduced through an interactive geological process simulation function, and the system is suitable for popularization and application. Abstract is changed into concrete, and boring is changed into vivid, so that the user can be effectively stimulated.

The invention provides a three-dimensional geological structure modeling method, device and system based on drilling information. The method comprises the following steps: S1, collecting drilling data, seismic exploration data and remote sensing image data of a target area; s2, constructing a neural network model composed of a simple full-connection module, a complex convolution residual module and a hierarchical integration module which are connected in sequence; s3, training the initial three-dimensional geologic model by adopting a joint loss function; and S4, in the network training process, a plurality of geological scene models are generated, and each scene model corresponds to a group of different network parameter combinations. According to the three-dimensional geologic structure modeling method, device and system based on drilling information, a spatial receptive field for feature extraction is expanded through a three-dimensional cavity convolution residual block of a complex convolution residual module according to a symmetric expansion rate, input features and deep convolution features are added, processing is carried out through a hierarchical integration module, and the three-dimensional geologic structure modeling method, device and system based on drilling information are obtained. And geologic structure correlation characteristics across drill holes can be effectively captured.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention provides a mineral resource overburden risk assessment method and system, and the method comprises the steps: obtaining multi-source heterogeneous geological data, carrying out the deep fusion of the multi-source heterogeneous geological data through employing a game theory gradient control robust neural network training method, and obtaining a fusion data set; performing deep mining and reasoning on high-dimensional geological data by utilizing the pattern recognition and correlation analysis capability of a large model and combining a manifold perception regularization technology, and recognizing geological structure features; establishing a dynamic safety mining depth evaluation model by using a fractional peak differential equation neural network with efficient accompanying parameter training, and calculating safety mining depth intervals and risk probabilities under different confidence degrees by integrating geological structure risks, mining disturbance effects and engineering safety thresholds; and constructing an assessment report generation module including core risk point analysis, key technology demonstration and prospective risk early warning, and generating an intelligent overburden risk assessment report. According to the invention, the accuracy and reliability of the pressing and covering risk assessment can be improved.

A computer-implemented method for drilling a wellbore in the Earth's subsurface. The computer-implemented method comprises obtaining one or more geological descriptions of one or more geological formations within the Earth's subsurface while drilling the wellbore. The computer-implemented method comprises generating, via a learning machine, respective images of the one or more geological formations based on one or more geological descriptions.

The invention relates to the technical field of seepage detection, and provides a water curtain hole fractured rock mass seepage detection method and device and a storage medium, when the inner wall of an oil storage chamber has a water seepage area meeting the water seepage condition, potential associated water curtain holes having a water guide association relationship with the water seepage area above the water seepage area are determined; when the potential associated water curtain hole is a target abnormal water curtain hole, preliminarily determining a preliminary water guide structure between the water seepage area and the target abnormal water curtain hole, acquiring in-hole geological structure development information of the target abnormal water curtain hole, and determining an in-hole abnormal interval according to the in-hole geological structure development information; and determining a target water guide structure between the water seepage area and the in-hole abnormal interval, and taking the target water guide structure as a target area for seepage control treatment. According to the method, the specific position, the permeability characteristic and the grouting characteristic of the water curtain hole water guide structure can be accurately and rapidly detected, so that local grouting treatment of water guide cracks is performed through the water curtain holes in a targeted manner, and the water seepage amount of the oil storage cavern is effectively controlled.

The invention relates to the technical field of tunnel construction geological disaster early warning, and discloses a tunnel construction geological disaster early warning method and system. According to the method, multi-source geological monitoring data and environment dynamic parameters of a tunnel construction area are collected; and then through data fusion and feature engineering processing, data contradiction and redundancy are eliminated, and a standardized training data set is formed. And then training and verifying a disaster risk prediction model by using the data set and adopting a machine learning algorithm so as to obtain an optimized model to accurately predict the disaster risk. And performing risk value prediction on different geological structure units by applying the optimization model, generating a risk distribution map, and determining an optimal geological structure unit through a search algorithm. Based on this, numerical simulation of geological disaster triggering and propagation is executed, a disaster evolution rule is analyzed, and a path simulation data set is output. And finally, performing multi-objective optimization on the data set according to a safety early warning objective, generating a geological disaster early warning scheme, and performing iterative calibration.

The invention relates to the technical field of mining methods, in particular to a mining method for a coal gangue filling goaf, which comprises the following steps of: SI, early-stage geological exploration; s2, selecting equipment and constructing a monitoring system; s3, gangue pretreatment and filling material preparation; s4, mining and filling operation; s5, quality detection and evaluation; s6, later maintenance; according to the mining method, the geological structure, the rock mechanics parameters and the hydrogeological conditions of the goaf are analyzed in detail by comprehensively applying the geological radar, the drilling technology, the rock mechanics experiment and the Darcy law. The water guide channel can be found in advance, water inrush accidents are prevented, and a reliable basis is provided for follow-up mining and filling operation; an automatic monitoring system is built, sensors are installed at key positions of a goaf and a filling system, pressure, displacement and flow data are transmitted to a monitoring center in real time through the Internet of Things technology, filling parameters can be adjusted in time according to the monitoring data, and the stability and safety of the filling process are guaranteed.

The invention discloses a geological exploration data intelligent cloud management and analysis system. The system comprises a data access module which is responsible for supporting automatic or manual importing of geological exploration data from multiple sources; the data management and integration module is used for receiving the data transmitted from the data acquisition and uploading module, performing version control and integrating the data; the data processing module receives the data provided by the data management and integration module, is responsible for data cleaning, format conversion and normalization processing, introduces an adversarial training technology and generates an adversarial sample; the intelligent analysis module is used for constructing a prediction model, the prediction model is used for modeling time series geological data by using LSTM, capturing a long-term dependency relationship in the data and identifying a geological change trend, then the output of the LSTM is used as a feature to be input into an SVM classifier, classification or regression prediction is carried out on a geological structure, and the geological process and a disaster occurrence mechanism are simulated to obtain a geological model; and generating more synthetic data similar to real data and used for training a prediction model.

Method and systems are provided that characterize a geological formation, which involve collecting or obtaining electrical signal data that results from interaction of electrical signals with the formation. The electrical signal data is processed to determine data characterizing relative permittivity and conductivity of the formation. The data characterizing relative permittivity and conductivity of the formation are used as inputs to models to determine data characterizing water saturation of the formation, particularly useful in formation evaluation and reservoir saturation dynamic monitoring in fresh water and mixed salinity environments.