7573results about "Seismic signal processing" patented technology

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

The invention discloses a multi-factor prospecting prediction method suitable for tin polymetallic ore, and relates to the technical field of geological science. The prospecting prediction method comprises the following steps: collecting data; fusing multi-source data; extracting and analyzing mineralization information; constructing a comprehensive prediction model; and predicting mineralization. According to the method, a potential tin polymetallic ore deposit area is preliminarily selected by comprehensively considering multi-source information such as geology, geophysics, geochemistry and remote sensing, drilling data and profile data are collected in the area, the drilling data and the profile data are utilized to construct a comprehensive prediction model, and the model is combined with advanced algorithms such as geostatistics and machine learning, so that the prediction accuracy of the tin polymetallic ore deposit area is improved. The multi-source data is efficiently integrated, and a three-dimensional visualization result is provided, so that the position and the scale of a potential ore body can be more accurately predicted, and the success rate of prospecting is improved.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to the technical field of geotechnical engineering safety monitoring, in particular to a river valley area bedding slope stability monitoring method and related products, and the method comprises the steps: building a three-dimensional geology-mechanics numerical model of a slope; acquiring monitoring data of the slope state in real time; dynamically correcting mechanical parameters in the three-dimensional geology-mechanical numerical model through inversion calculation; evaluating the stability of the slope and outputting early warning information; according to the method, multi-source monitoring data are fused into the numerical model in real time, dynamic inversion and tracking of internal mechanical parameters of the slope are achieved, comprehensive indexes such as the real-time safety coefficient, the main sliding direction deviation angle and the damage evolution rate are calculated, graded early warning is triggered according to the indexes, and the perspectiveness and reliability of early warning are improved.

The invention discloses a coal and rock mass fracture seismic source and stress field joint inversion method and system, and the method comprises the following steps: arranging a multi-channel sonic sensor in a coal and rock mass, and collecting original acoustic emission / microseismic signal data; the head wave arrival time / amplitude of the filtered effective waveform data is automatically picked up through an improved AIC (Akaike Information Code), quantitative evaluation is carried out on the quality of a picked-up result, and high-precision positioning is carried out on waveform signals with a high signal-to-noise ratio and a low signal-to-noise ratio; constructing a tension fracture seismic source model based on a displacement discontinuous tensor, obtaining a moment tensor component of a fracture event, and calculating to obtain a spatial orientation, a tension-shear attribute, a deviation angle alpha, energy release and a moment magnitude of the fracture; establishing a stress field inversion model of the tension-shear fracture seismic source, and realizing the inversion of the stress direction and size of the tension-shear fracture seismic source; and a slip direction included angle minimization objective function is established for iteration until convergence conditions are met, and joint iteration inversion of the fracture and the stress field is realized.

The present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

The invention relates to the field of seismic state recognition, and provides a converter station vector seismic state recognition method and system based on distributed optical fibers, and the method comprises the steps: collecting multi-dimensional vibration signal data of a converter station, and obtaining seismic monitoring data through processing; performing phase demodulation processing, time difference analysis and space vector decomposition algorithm processing on the seismic monitoring data to obtain seismic wave propagation vector characteristic data, and performing characteristic integration and optimization to obtain a seismic wave comprehensive state characteristic data set; identifying and classifying the seismic wave comprehensive state feature data set to obtain a seismic wave form classification result; performing analysis based on the seismic wave form classification result to obtain a converter station seismic state evaluation result; and carrying out comparative analysis on the evaluation result and a threshold value, generating earthquake early warning information of the converter station, and providing a corresponding emergency response suggestion according to the earthquake early warning information. According to the invention, accurate identification and classification of seismic waves are realized, and the earthquake safety early warning capability of the converter station is improved.

The invention relates to the technical field of high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.

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 relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system is solved.

The invention relates to the technical field of seismic signal detection, in particular to a seismic first arrival pick-up deep learning method based on an attention mechanism and a hybrid expert strategy, which comprises three steps of model building, a training stage and first arrival prediction. The core components of the model comprise a front-end feature extraction module, a hybrid expert module and a time domain decoding module. According to the invention, a more efficient expert routing mechanism is adopted in the network to reduce calculation redundancy, a more optimized load balancing strategy is used to improve training stability, and quantification and sparse technologies are combined to further reduce calculation and storage cost. According to the method, high-precision seismic first arrival pickup can still be kept under the conditions of complex seismic phase superposition and regional noise interference, high adaptability and generalization ability to various scenes are achieved, and the computing resource occupancy rate and cost can be greatly reduced under a large number of model parameters.

The invention belongs to the technical field of geophysical monitoring, and relates to an electromagnetic interference detection method for earthquake monitoring, which comprises the following steps: deploying a multi-dimensional electromagnetic field sensing unit and an earthquake signal acquisition unit and realizing high-precision synchronization; synchronously acquiring multi-dimensional electromagnetic field and seismic physical signal data; performing multi-feature and noise feature extraction on the two; inputting the features into a pre-trained machine learning / deep learning model for identification, and outputting information such as interference type, intensity and azimuth angle; and carrying out influence evaluation and quality marking on the seismic data according to the output, and selectively inhibiting or compensating. Through the above scheme, multi-dimensional perception and characterization, high-precision synchronization and correlation analysis, intelligent identification and spatial positioning are realized, and the reliability and accuracy of earthquake monitoring data are significantly improved.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention relates to the technical field of geophysical exploration, in particular to an exploration surveying and mapping method based on remote sensing and a surveying and mapping instrument thereof. The exploration surveying and mapping method based on remote sensing comprises the following steps: step 1, multi-platform collaborative data dynamic acquisition; step 2, multi-modal data fusion and feature enhancement: vegetation noise filtering is performed on the laser radar point cloud data, and a surface terrain model under vegetation coverage is reconstructed; seismic wave speed data and ground penetrating radar dielectric constant data are combined to construct an underground medium physical property combined distribution model; associating the reflectivity characteristics of the multispectral image with the underground physical property parameters, and identifying a geological abnormal region; and step 3, hidden geologic body dynamic modeling and risk assessment. By means of the method, real-time monitoring and risk assessment of the complex geologic body can be achieved, and the geological disaster prediction and prevention and control capacity is greatly improved.

The invention provides an ionic type rare earth ore bed rock crack detection method and system, and the method comprises the steps: laying an active seismic source at a preset position of a target detection region, and enabling the active seismic source to emit an elastic wave with a preset frequency and a preset amplitude; a micro-seismic sensor network arranged in a target detection area collects sound wave signals which are emitted by the active seismic source and are scattered and polarized by the crack; extracting a plurality of crack characteristic parameters related to the bedrock crack from the bedrock crack microseismic sound wave signal, and performing preliminary analysis to obtain a possible crack region and preliminary key parameter information of the crack; based on the multiple fracture characteristic parameters, a multi-parameter joint inversion method is adopted to construct a fracture model containing fracture geometric parameters and physical parameters, and regularization optimization is carried out on the fracture model; and finally, based on the optimized crack model, carrying out inversion to obtain key parameters of high-precision spatial positions and distribution of the cracks. On the basis that the ore body is not damaged, the bedrock crack detection precision can be improved.

The invention provides a seismic activity parameter calculation method and system. The method comprises the following steps: fitting the magnitude and the cumulative annual average occurrence rate of all seismic events in a seismic statistical area to obtain a first fitting result, and calculating activity parameters in the fitting process to obtain a group of activity parameter optimization values; evaluating the first fitting result by utilizing multiple indexes, and adjusting the values of the activity parameters by adopting multiple adjustment modes according to the activity parameter optimization value to obtain multiple groups of adjusted values of the activity parameters; and according to a plurality of second fitting effect evaluation results, an optimal value after activity parameter adjustment is selected from the adjusted values, and each second fitting result is a fitting result corresponding to a group of values after activity parameter adjustment. According to the technical scheme provided by the invention, the activity parameters are optimized through multi-index evaluation and multi-mode parameter adjustment, and the accuracy and reliability of relationship fitting of the magnitude and the cumulative annual average occurrence rate are improved.

The invention discloses a hydraulic fracture monitoring system and method based on an underground micro-seismic sensor network, and belongs to the field of coal mining, and the system comprises a plurality of micro-seismic sensors which are used for collecting micro-seismic signals generated by rock fracture; the plurality of data acquisition substations are used for receiving and processing the micro-seismic signals; the data transmission substation is used for converting the micro-seismic signal processed by the data acquisition substation into an optical signal; the micro-seismic server performs micro-seismic event analysis according to the optical signal; the mining network switch is connected with the data transmission substation and the micro-seismic server and is used for network data exchange; the in-mine ring main unit is connected with the mining network switch and the micro-seismic server to form a communication network of the system; in order to solve the problem that hydraulic fracture detection precision is low due to the fact that micro-seismic signals generated by hydraulic fracture and coal mining activities are difficult to accurately distinguish in a complex underground environment, the hydraulic fracture detection method and device distinguish the micro-seismic signals generated by hydraulic fracture and coal mining activities by dividing a fracture area and a mining area, and the real-time monitoring precision of hydraulic fracture expansion is improved.

The invention relates to the technical field of geological analysis, in particular to a weathering crust reservoir fracture distribution intelligent identification system which comprises a fracture trend path construction module, a shielding extension fracture interpolation module, a cross-layer fracture directional connection module, a structural fracture space collection module and a fracture trend joint density analysis module. According to the method, directional connection of cross-layer cracks is achieved through joint judgment of three-dimensional space coordinates of the crack end point, the direction vector included angle and the ground plane normal included angle, the precision and reliability of a crack through structure are effectively improved, space distribution clustering of the cracks is achieved through statistics of the number of times of intersection of the structural unit cracks, concentration degree calculation and comparison of the concentration reference value, and the reliability of the crack through structure is improved. And combined with fracture path length, azimuth angle data and fracture quantity density statistics, combined density value calculation and integration of fracture trend and length weighting are constructed, so that reservoir fracture structure prediction precision and spatial positioning definiteness are greatly improved.

According to the micro-seismic source intelligent positioning method and the model training method and system, physical laws such as a seismic wave equation are integrated, physical loss and data loss are comprehensively considered, the adaptability of the model to a complex geological environment is improved, micro-seismic source positioning challenges in the complex geological environment can be effectively dealt with, and the positioning accuracy of the micro-seismic source in the complex geological environment is improved. Noise immunity and positioning precision are improved, and more reliable technical support is provided for safety monitoring of underground engineering.

The invention discloses a post-stack seismic data denoising method based on multi-dip-angle structure guided filtering, and belongs to the field of seismic data denoising, and the method comprises the following steps: carrying out the processing of input seismic data through guided filtering, and obtaining a fault likelihood attribute; dividing the seismic data into a fault region and a continuous stratum region based on fault likelihood attributes; in the fault region, performing filtering processing on the seismic data by using an edge-preserving smoothing algorithm to obtain a filtering result of the fault region; in the continuous stratum area, calculating a local inclination angle of the seismic data, and performing filtering processing on the seismic data by adopting a structure guiding filtering algorithm which performs smoothing along the direction of the local inclination angle to obtain a filtering result of the continuous stratum area; and fusing the filtering result of the fault region and the filtering result of the continuous stratum region to obtain final de-noised seismic data. According to the method, efficient noise suppression can be performed on post-stack seismic data, and meanwhile, the protection capability on a complex geological structure is remarkably improved.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention provides a coal seam surrounding rock stability monitoring method based on micro-seismic monitoring, which relates to the technical field of coal seam surrounding rock stability monitoring and comprises four steps of micro-seismic sensor network arrangement, real-time data acquisition and processing, surrounding rock stability evaluation model construction and real-time early warning and feedback of coal seam surrounding rock stability. According to the invention, a roof-side wall-support body cooperative monitoring architecture is adopted to cooperate with the micro-seismic sensor, and three-dimensional sensing network deployment is established, so that the micro-fracture activity in the surrounding rock of the whole stope can be continuously monitored in real time in a three-dimensional space full-coverage manner, the limitation of traditional point-type monitoring is overcome, and the dynamic evolution law of the micro-seismic activity is analyzed, so that the micro-fracture activity in the surrounding rock of the whole stope can be monitored in a real-time manner. According to the method, before macroscopic physical damage occurs, an area with abnormal stress concentration and accelerated damage accumulation can be identified, advanced early warning is realized, and precious time is gained for active prevention and control.

The invention is suitable for the technical field of volcanic rock reservoir gas saturation and permeability prediction, and provides a volcanic rock reservoir gas saturation and permeability seismic inversion method and system, and the method comprises the following steps: collecting data, and building a rock physical model of a volcanic rock reservoir; establishing a rock physical parameter in-well inversion method, and calculating a permeability indication parameter # imgabs0 # and a micro-fracture porosity parameter # imgabs1 #; based on the parameters # imgabs2 # and # imgabs3 #, constructing a gas-bearing seismic indication factor # imgabs4 # and a permeability seismic indication factor # imgabs5 # in combination with logging data; the quantitative relation between the # imgabs6 # and the gas saturation # imgabs7 # and the quantitative relation between the # imgabs8 # and the permeability # imgabs9 # are established through measured data in the well; in combination with rock physics and seismic reflection theories, a PP wave reflection coefficient formula about # imgabs10 # and # imgabs11 # is constructed; the pre-stack seismic inversion method suitable for the volcanic rock reservoir is developed, seismic indication factors # imgabs12 # and # imgabs13 # are calculated, and the seismic indication factors # imgabs14 # and the seismic indication factors # imgabs13 # are quantified into gas saturation # imgabs14 # and permeability # imgabs15 #. According to the method, the quantitative prediction of the spatial distribution of the volcanic rock reservoir gas saturation and permeability is realized.

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 relates to a gold mine air-ground-in-well rapid collaborative exploration method, belongs to the field of geological exploration, and aims to solve the technical problems of data splitting, low target area positioning precision, insufficient deep prediction capability and the like in traditional gold mine exploration. By integrating air remote sensing, surface geological survey and underground detection data, a multi-source data standardization processing flow is established, and a progressive exploration system from wide-area screening to accurate positioning is realized by combining geological, geophysical and geochemical comprehensive analysis methods. The system adopts a dynamic feedback mechanism to optimize an exploration process, forms closed-loop analysis and prediction, improves exploration efficiency and accuracy, and reduces resource waste and exploration cost. The method is suitable for gold ore resource exploration under complex geological conditions, has remarkable economic benefits and application value, and can be widely applied to the field of mineral resource development.

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 relates to the technical field of mine safety and geological environment evaluation, in particular to a mine goaf geological environment dynamic evaluation method based on digital twinning. The method comprises the following steps: constructing a digital twinborn model of a mine goaf, designing a data structure and a virtual-real mapping rule, collecting multi-source heterogeneous data, and performing spatio-temporal data indexing processing to form a spatio-temporal associated data stream; microseismic travel time data are extracted from the space-time associated data stream; performing parallel ray tracing and rapid inversion on the microseismic travel time data to obtain a wave velocity distribution initial value; carrying out wave velocity field self-adaptive grid refinement on the micro-seismic travel time data by utilizing the wave velocity distribution initial value to obtain a multi-resolution wave velocity field; and carrying out wave velocity change rate analysis on the multi-resolution wave velocity field to obtain a wave velocity variability tensor. According to the method, the abstract energy transfer process is concrete into a data field capable of being quantitatively analyzed, so that the geological environment of the goaf is accurately and dynamically evaluated.