166 results about "Earthquake prediction" patented technology

The invention discloses an earthquake prediction method and system based on multi-physics coupling and quantum optimization, and relates to the technical field of earthquake prediction, and the method comprises the following steps: obtaining earth surface deformation observation data, geomagnetic observation data, gravity gradient data, and infrasonic wave monitoring data, and carrying out the preprocessing; performing multi-physics field coupling calculation on the preprocessed earth surface deformation observation data, geomagnetic observation data, gravity gradient data and infrasonic wave monitoring data; optimizing the fault network by adopting a D-Wave quantum annealing technology to find a lowest energy state to obtain a quantum optimization result; and generating an earthquake probability distribution diagram, an earthquake magnitude-time window table and a fault stress field three-dimensional model, thereby greatly expanding the breadth and depth of data, and remarkably improving the comprehensiveness of earthquake prediction.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The subject matter of the invention is a system for earthquake predictions and forecasts. The system comprises a data collection module, where the data collection module contains at least one data channel. The system comprises further an integrated AI and forecast module, where the integrated AI and forecast module contains at least one autoencoder and a predictor. The second subject matter of the invention is a method for earthquake predictions and forecasts, which is conducted in the system according to the invention.

The invention relates to the technical field of earthquake prediction, in particular to a crustal stress azimuth earthquake prediction method based on a transverse wave speed change rate, which comprises the following steps: calculating a speed value of a transverse wave and a propagation distance, extracting a difference value to generate a change rate set, calculating an offset rate, screening a continuous overrun section, identifying a disturbance path section, and performing interpolation restoration to generate a purification sequence. Extracting forward and reverse propagation trends and counting direction identifiers, and generating a crustal stress main stress direction map; according to the method, a change rate set is generated through a sliding window segmentation speed sequence, data continuity is enhanced, local features are reserved, a sudden change area is screened and accurately positioned in combination with a continuous offset rate, multi-path lithology difference identification and disturbance repair are carried out, non-structural interference is eliminated, and forward and reverse path cross validation is carried out to screen a direction consistent section, so that the discrimination robustness is improved; the velocity direction is dynamically associated with the structure trend, synchronous analysis of the stress field and fault activity is realized, multi-stage processing is combined with dynamic threshold and direction verification, and the derivation precision and timeliness are improved.

The invention relates to the technical field of earthquake prediction, in particular to a three-dimensional earthquake motion field rapid prediction method based on deep learning and physical constraint, and the method comprises the following steps: S1, data collection: obtaining original data related to an earthquake event in real time; s2, data preprocessing and multi-modal data fusion: preprocessing the original data, and fusing the preprocessed original data into multi-modal data; s3, spatial interpolation: realizing seismic oscillation feature interpolation prediction of the epicentral region, and outputting spatial spectrum data; s4, time sequence prediction and physical information optimization: generating a seismic oscillation time domain prediction sequence, and correcting errors by using an SVM model; s5, real-time reasoning and emergency response: realizing real-time minute-level response under the GPU architecture, and outputting three-dimensional seismic motion field data; according to the method, high-precision minute-level prediction of the three-dimensional seismic motion field is realized, and the efficiency and reliability of post-earthquake emergency response are remarkably improved.

The invention provides a TLF-GV signal correlation earthquake magnitude prediction method and system, and a medium, belongs to the technical field of earthquake prediction, and aims at solving the problems that a traditional magnitude prediction model lacks physical constraints, TLF-GV features are not fully utilized, the interpretability is poor, and cooperation with a preorder technology is lacked. Comprising the following steps: extracting a multi-source feature vector of a TLF-GV signal containing a gravity component absolute amplitude, a vibration component relative amplitude and a signal phase; based on the G-R law, constructing the positive correlation physical characteristics log10 (GVCIpeak) and BF * Tanomal of the abnormal intensity and the magnitude of the TLF-GV signal; weighting the features according to a Bayesian factor BF value; fitting according to historical samples to obtain a physical empirical formula of positive correlation of GVCI peak logarithm and magnitude as a physical constraint, and constructing a loss function by taking the physical constraint as a regularization item and combining with a mean square error of a magnitude prediction task; and constructing an XGBoost model, and training according to the training set to obtain a prediction model for earthquake magnitude prediction. According to the method, quantitative prediction of earthquake magnitude is realized based on multi-source features of TLF-GV signals in combination with a physically constrained XGBoost regression model.

The invention provides a hydrofracture earthquake possibility prediction method, and relates to the technical field of hydrofracture, and the method comprises the steps: constructing a physical constraint depth operator network model combined with an importance sampling method; collecting and processing data in different scenes as training data; performing iterative training on the physical constraint depth operator network model to obtain a physical constraint depth operator network model weight of an optimal step; and initializing the physical constraint depth operator network model, and obtaining a hydrofracture earthquake possibility prediction result in the target scene according to the target hydrofracture earthquake prediction task data. According to the method, importance sampling is fused into the depth operator network, and sampling distribution is adaptively changed by calculating the importance of each function in a function set, so that model convergence is accelerated, model stability is improved, the problems of high training difficulty, low convergence speed and low solving precision in a non-label data scene are solved, and the method is suitable for large-scale popularization and application. And the possibility of the hydrofracture earthquake can be predicted more accurately.

The invention relates to the technical field of earthquake monitoring, and discloses a comprehensive earthquake prediction method, which comprises the following steps of: acquiring a continuous micro-motion data sequence to reconstruct an empirical green function sequence, and dividing the empirical green function sequence into a compression phase subset and a stretching phase subset by utilizing a synchronously acquired theoretical earth tide stress phase; respectively extracting a causal branch waveform corresponding to the positive time axis and a non-causal branch waveform corresponding to the negative time axis, and determining a weight index for inhibiting the non-structural interference by calculating a reciprocity residual error between a causal wave velocity offset and a non-causal wave velocity offset; correcting a wave velocity offset mean value by using the weight index to obtain a stress sensitivity coefficient reflecting the stress state of the earth crust medium; according to the method, a physical criterion is established by using an elastic wave propagation reciprocity principle, decoupling of medium attribute change and noise source spatial drift is realized, and visual wave velocity offset induced by environmental interference is eliminated.

The invention provides a coal mine earthquake prediction method and system based on Bayesian reasoning, and the method comprises the steps: obtaining observed micro-earthquake event data in a coal mine region; a microseism prediction model is constructed, an optimal solution of model parameters of the microseism prediction model is obtained based on the microseism event data in the historical time period, and the model parameters comprise event triggering parameters and event occurrence parameters; the optimal solution of the model parameters is used as prior information, and posteriori distribution is obtained based on Bayesian reasoning for observed micro-seismic event data; and extracting posterior probabilities in a set probability range from the posterior distribution, obtaining the maximum magnitude corresponding to the prediction time period based on the model parameters corresponding to each posterior probability, and calculating the occurrence probability of the maximum magnitude event based on the maximum magnitudes corresponding to all the posterior probabilities in the set probability range and a given magnitude threshold.

The invention discloses an earthquake forecasting and tsunami early warning method and system, and the method comprises the steps: monitoring the earth electromagnetic field data in real time, and recognizing and extracting a regional transient electromagnetic signal which accords with a preset abnormal feature as an earthquake precursor signal; taking the position of the precursor signal as a reference, and predicting the earthquake generation time, the magnitude and the potential influence area based on an earth crust stress wave propagation model; when the early warning condition is met, earthquake early warning information containing risk avoiding guidance is generated and issued, and if the prediction area relates to the coastal area and the earthquake magnitude reaches a threshold value, tsunami early warning information is synchronously generated and issued. The corresponding system comprises a geomagnetic monitoring module, a forecast analysis module and an early warning issuing module. According to the method, pre-earthquake prediction based on the physical precursor is realized, the prediction time is remarkably prolonged, the problems that earthquake and tsunami early warning information is separated and published lags are solved, and the disaster response capability is improved.

The invention provides a TLF-GV signal correlation earthquake three-element joint probability prediction method and system, belongs to the technical field of earthquake prediction, and aims to solve the problems that traditional three-element prediction lacks joint fusion, evidence conflicts are difficult to process, and weights are fixed. Comprising the following steps: extracting physical features of TLF-GV signals, carrying out preorder three-element prediction results and model credibility parameters, splicing the physical features into feature vectors, inputting the feature vectors into a dynamic weight prediction model constructed by a shallow MLP, and outputting time, magnitude and position weights; dividing the three elements into 3 * 3 * 3 = 27'time-magnitude-position 'propositions, generating basic probability assignment (BPA) of each proposition based on weights, and quantifying evidence contradictions through a conflict coefficient K; and adopting a Dempster combination rule to obtain a joint BPA, calculating a mean value of a reliability function and a likelihood function of each proposition as a comprehensive probability, and extracting a maximum probability proposition as a coupling result. According to the method, the scientificity and practicability of strong earthquake short-term and temporary prediction are remarkably improved.

The invention discloses a fault gas online monitoring system and method for seismic observation, and relates to the field of seismic monitoring, and the method comprises the steps: deploying a multi-parameter gas monitoring device near a fault zone, collecting gas concentration data at preset time intervals, building a historical gas concentration time sequence, and building a training feature set through preprocessing and feature extraction; an earthquake prediction model is built based on LSTM, model training is carried out with the training feature set as input and the earthquake occurrence probability as output until a loss function converges, and an optimized earthquake prediction model is obtained; acquiring gas concentration data in a certain time period before the current time node, and predicting an earthquake occurrence probability after the current time node through the optimized earthquake prediction model; when the earthquake occurrence probability exceeds a preset threshold value, early warning information is automatically generated and published to the public through multiple channels, and earthquake risks are prompted. According to the invention, early recognition and early warning of earthquake activities can be realized, and powerful support is provided for earthquake monitoring.

The invention provides an earthquake quantitative prediction method for the compaction effect intensity of carbonate rocks, and the method comprises the following steps: (1) carrying out the statistics and analysis of logging data and core analysis data of a compacted stratum in a to-be-analyzed region, and building a fitting relation between the apparent compaction rate and the longitudinal wave speed; (2) performing phase-control frequency-division inversion in combination with the seismic response characteristics, and then calculating and analyzing the actual longitudinal wave speed of the to-be-analyzed region; and (3) determining the distribution of the apparent compaction rate according to the fitting relation between the apparent compaction rate and the longitudinal wave speed obtained in the step (1) and the actual longitudinal wave speed obtained in the step (2). According to the carbonate rock diagenesis strength earthquake prediction method based on the apparent compaction rate, the problem that quantitative prediction of the carbonate rock compaction strength is difficult is solved, the distribution characteristics of different compaction strength in space are defined, and the exploration and development success rate can be improved.

The invention provides a gas field reservoir lithology earthquake prediction method based on CCIDI cleaning and TransMSAE, and the method comprises the steps: constructing a CCIDI cleaning module, and employing a well-divided lithology-divided strategy: employing IQR to remove statistical anomalies, employing DBSCAN to remove boundary noise, employing Isolation Forest to remove high-dimensional residual noise, and forming a high-quality logging data set; the method comprises the following steps: constructing a TransMSAE model: pre-standardizing a Transform encoder to capture long-range dependence, and providing robust time sequence representation; multi-scale convolution is introduced into the MSCCAM to extract a local-global mode, and cross attention is fused to improve complex deposition dynamic perception; aMFEM gating residual self-adaptively enhances minority class features, and minority class recognition is remarkably improved; designing NAFL-ProtoLoss mixed loss: NAFL focus inhibition easy-to-classify samples, ecological niche punishment over-confidence prevention, ProtoLoss zoom-in class inward pushing-out between classes, and optimization of unbalanced classification; finally, training and reasoning are carried out based on the model, and high-precision lithology prediction is achieved. The method provided by the invention has high generalization and accuracy, and has excellent reservoir prediction performance in a complex geological environment.

The invention provides a geological disaster identification method based on seismic wave dynamics and kinematics characteristics, the geological disaster is mountain torrent or debris flow, and the method comprises the following steps: collecting seismic signals in a geological disaster potential risk area; the seismic signals are preprocessed; extracting early warning parameters from the pre-processed seismic signal, wherein the early warning parameters comprise a long and short time window energy ratio, a time-frequency spectrum parameter, a power spectrum density and a seismic signal duration; establishing an earthquake prediction model of the single-measuring-point geological disaster according to the extracted early warning parameters; fusing the earthquake prediction models of the geological disasters at multiple measuring points, and judging whether the earthquake signals are earthquake signals triggered by the geological disasters or not; and when it is judged that the seismic signal is triggered by the geological disaster flow, real-time early warning information issuing is carried out on the downstream risk area. The method can be applied to monitoring and early warning of geological disasters caused by artificial or natural activities such as mountain torrents, dam break, landslides, collapse and the like, and has wide engineering technology application and scientific research prospects.

The invention discloses a fault zone-based earthquake intelligent identification method, which comprises the following steps: acquiring earthquake catalog data, drawing an earthquake activity image, and extracting deep feature parameters and spatial distribution features in the image by using a convolutional neural network; predicting the maximum magnitude and time of the earthquake by using a long-short term memory neural network model; using a random forest algorithm to predict the position of the earthquake; and evaluating the earthquake prediction accuracy through the earthquake position prediction evaluation index. According to the invention, multiple indexes are adopted to evaluate the reliability of earthquake prediction, and the technical effect of improving the reliability of the earthquake prediction result is achieved. Compared with a traditional prediction method based on a single model, the long and short term memory network model and the random forest show excellent regression and classification capabilities under the condition of multiple features and multiple samples, and prediction results with low credibility can be effectively filtered. By introducing a dual evaluation mechanism, the problem of inaccurate judgment of an earthquake prediction result in the prior art is solved, and the reliability and the stability of prediction are improved.

The invention discloses a fluid injection induced earthquake experiment method under a true triaxial stress condition, which comprises the following steps of: manufacturing a cube rock model containing a fault, beveling to form a fault surface, arranging water injection and water pressure detection holes, injecting water to infiltrate the fault surface until the fault surface is saturated, applying three-way unequal stress by using a true triaxial device, and keeping the stress stable. Then, fault spontaneous instability is driven through displacement, the inter-earthquake instability period is calculated, finally, a circulating or constant-rate water injection scheme is set, industrial fluid activity is simulated, and a water injection induced earthquake mechanism is explored. The fluid injection induced earthquake experiment method under the true triaxial stress condition can accurately simulate the true stratum stress and the water injection condition, provides a reliable means for researching the water injection induced earthquake, and is of great significance for earthquake prediction, geological disaster prevention and control and related industrial safety.

The invention relates to the technical field of geophysical exploration, in particular to a method for performing well leakage early warning by utilizing an earthquake prediction result, which comprises the steps of basic data acquisition, regional geological condition analysis, earthquake data preprocessing, small fracture and fracture-cavity earthquake fine description and shadow relief display of a description result. According to the method, the quality of basic data is improved by introducing anisotropic diffusion and a three-dimensional multi-stage median joint filtering technology, multi-attribute fusion based on adjustable parameters is carried out on pre-stack multi-azimuth frequency attenuation gradient crack prediction and post-stack structural coherent tensor, and finally, a multi-attribute fusion result is spatially displayed by utilizing a shadow relief technology. The precision of small fracture and fracture-cavity body depicting is improved, the spatial forms of fractures and fracture-cavity bodies are displayed more visually, and well position design, well drilling design and well drilling trajectory optimization can be effectively guided.

The invention discloses a natural earthquake risk analysis method based on an artificial neural network, and the method comprises the steps: S1, obtaining a target analysis region, and dividing the target analysis region into a plurality of sub-regions; s2, acquiring a historical seismic data set corresponding to the target analysis area and preprocessing the historical seismic data set; s3, performing sub-region marking on the preprocessed data set to obtain a plurality of seismic source sub-regions and non-seismic source sub-regions; s4, acquiring data subsets corresponding to the seismic source sub-region and the non-seismic source sub-region, and inputting the data subsets into an artificial neural network for feature extraction; s5, calculating the similarity between the non-seismic source sub-region and the plurality of seismic source sub-regions; s6, training the convolutional neural network by using the historical seismic data set to obtain a seismic prediction model; and S7, inputting the data subsets of the different regions into the earthquake prediction model for prediction to obtain an earthquake prediction result, and outputting an earthquake risk analysis report according to the earthquake prediction result. According to the method, earthquake risk analysis of multiple areas and potential earthquake areas is realized.

The invention belongs to the technical field of exploration and development of fault block oil and gas reservoirs, and particularly relates to a seismic prediction method and system for igneous rock intrusive fault zone sealing performance, and the method comprises the steps: determining the seismic attribute and seismic frequency attribute of a highlight body of a target interval according to petroleum seismic exploration data; determining magma invasion characteristic parameters according to the seismic attribute and the seismic frequency attribute of the highlight body, and determining fracture characteristic parameters of the fracture zone according to an eigenvalue characteristic algorithm operation result in the third-generation coherent attribute of the seismic data; determining an internal structure index of the intrusive fault zone according to the magma invasion characteristic parameters and the fracture characteristic parameters of the fault zone; and comparing the internal structure index of the intrusive fault zone with a closed lower limit threshold of the internal structure index of the known oil reservoir fault zone, and predicting the fault zone closure and the closure capability thereof according to a comparison result, thereby realizing quantitative determination of the oil and gas migration and dredging capability of the fault with magma invasion in the clastic rock stratum.

The application discloses a coalbed methane enrichment area prediction method based on multiple geological parameter constraints, determines a research area, analyzes geological features of the research area to determine lithology, physical property features and elastic parameters of rock strata, predicts geological elements of a coalbed methane enrichment area of the research area, and establishes a nonlinear mapping relationship between main geological parameters of the coalbed methane and enrichment characteristics of the coalbed methane, namely, a geological attribute discrimination mode of the coalbed methane enrichment area, by means of artificial neural network method and constraint of the geological parameters. The method for predicting the coalbed methane enrichment area under the constraint of multiple geological parameters simultaneously controls the convergence direction of the solution of the enrichment area by multiple mutually irrelevant geological parameters, reduces the prediction scale of the enrichment unit to obtain an optimal solution, avoids the uncertainty of single parameter prediction, improves the accuracy of the prediction result, and solves the technical problem that the method for predicting the coalbed gas content by single seismic attribute or parameter in the prior art has strong multiple solutions, thereby leading to inaccurate prediction results.

The invention relates to the field of bridge seismic performance evaluation, and specifically discloses a method for evaluating the seismic performance of a rural highway bridge. The invention obtains geometric characteristics, material properties, structural details and boundary conditions of the rural highway bridge to be built, and uses computer-aided design software and finite element analysis software to establish a digital model of the rural highway bridge to be built; according to an earthquake prediction model and earthquake historical records of the area where the rural highway bridge to be built is located, the earthquake risk of the area where the rural highway bridge to be built is evaluated, and a suitable seismic wave is selected as input, so that the seismic performance of the designed bridge is compatible with the actual seismic demand of the bridge; the response information of each component in the digital model of the rural highway bridge to be built under the action of earthquake load is obtained, and the seismic performance of the rural highway bridge to be built is evaluated, so that the seismic performance evaluation result of the bridge is more reliable and accurate, and the overall stability and safety of the bridge are guaranteed.

The invention discloses an earthquake monitoring and early warning method and system based on an earthquake precursor, and relates to the technical field of earthquake forecasting, and the method comprises the steps: collecting the core data and auxiliary state data of the earthquake precursor, marking a sample label, and forming a training data set; establishing a core parameter screening model, and training the core parameter screening model by using the training data set to obtain core parameters; constructing a typical working condition library, calculating an Euclidean distance between a real-time data point and a historical working condition sample point, selecting first K neighbor points, and returning a working condition category with the highest frequency as a current operation working condition; corresponding anomaly prediction models are established according to the different operation conditions, and anomaly types under the different current operation conditions are predicted according to the anomaly prediction models; an EWI index is calculated through the core parameters, and when the EWI index is larger than an index threshold value, earthquake early warning is triggered; otherwise, continuously monitoring. According to the invention, real-time early warning of the earthquake precursor is realized under the condition that the equipment is abnormal.

The invention discloses a TLF-GV-based earthquake prediction signal anomaly detection method and system, and relates to the technical field of earthquake monitoring and prediction, and the method comprises the steps: collecting TLF-GV original time sequence signals; decomposing the TLF-GV original time sequence signal to obtain an IMF component; a gravity-related IMF component and a vibration-related IMF component of a frequency band corresponding to the TLF-GV original time sequence signal are screened out from the IMF components, and noise reduction and reconstruction are carried out respectively; performing physical enhancement processing on the reconstructed dynamic gravity signal and vibration signal; calculating a gravity-vibration contribution index GVCI and a Bayesian factor BF; effective abnormal points are screened out through three-level abnormal detection; according to the method, aliasing-free separation of dynamic gravity and vibration is realized, the signal separation precision is improved, a mode number does not need to be preset, and the method adapts to the aperiodic characteristics of TLF-GV signals.

The invention relates to the technical field of earthquake prediction, in particular to an earthquake continuous monitoring and predicting system which comprises the following steps: S1, a data center receives a real-time signal transmitted by an earthquake monitoring beacon and acquires multi-dimensional physical information of stratum change; s2, the data processing center carries out noise reduction preprocessing on the information; and S3, gridding the multi-beacon signal, and carrying out cross calculation to determine an underground vibration signal source point and a corresponding geological fluid mode. According to the invention, through building the inversion model and carrying out inversion verification by using the collected seismic wave signals, finer geological structure information can be obtained. The earthquake prediction system performs earthquake rehearsal according to the earthquake inversion model, and early warns the occurrence of an earthquake by monitoring characteristic signals extracted from the earthquake rehearsal, so as to provide more accurate warning information for people and provide support for rescue work after the occurrence of the earthquake.

The invention discloses an earthquake forecasting method and system based on optical fiber micro-vibration monitoring, and the method comprises the steps: S1, collecting a micro-vibration signal through employing a dynamic adaptive collection frequency adjustment mechanism, carrying out the data collection at a frequency of 1kHz in a distributed optical fiber sensing network, obtaining a data fluctuation condition, setting a fluctuation threshold value, and carrying out the calculation of the fluctuation threshold value; when the fluctuation condition of the micro-vibration signals in m continuous time windows continuously exceeds a fluctuation threshold value, increasing the acquisition frequency to 10kHz for data acquisition, and counting all micro-vibration signal data; s2, constructing a cross correlation coefficient matrix of the micro-vibration signal data among different acquisition frequencies through all the micro-vibration signal data, obtaining direction information and propagation speed information among different monitoring points based on corresponding values in the cross correlation coefficient matrix to form spatial characteristic parameters, and comprehensively capturing spatial propagation characteristics of the signals, and the one-sidedness problem of a traditional method is effectively solved.

The invention discloses a density attribute earthquake prediction method. The method comprises the following steps: forward modeling a longitudinal wave velocity, a transverse wave velocity and a density curve of saturated fluid rock; continuing forward modeling to generate a large number of longitudinal wave velocity, transverse wave velocity and density curves of different pseudo wells by using the bulk modulus and density parameters of the rock skeleton in the research area, and generating and synthesizing a pre-stack seismic gather under the guidance of a Zoeppritz equation; convolutional neural network training is carried out by using the longitudinal wave velocities, the transverse wave velocities and the density curves of the different pseudo wells and the synthetic pre-stack seismic gathers, and a trained convolutional neural network model is obtained; and applying the convolutional neural network model to an actual well and trace gather, correcting the trained convolutional neural network model, and performing density attribute prediction by using the corrected convolutional neural network model and applying the actual trace gather. Density attribute prediction is carried out through a deep learning method based on rock physical model sample expansion, and the problem that in the prior art, conventional elastic parameters cannot identify effective reservoirs under the medium-deep layer condition is solved.

The present invention discloses a method for fluid injection-induced earthquake experiments under true triaxial stress conditions. The method first creates a cubic rock model containing a fault, obliquely cuts it to form a fault plane, lays out water injection and water pressure detection holes, and injects water to saturate the fault plane. A true triaxial device is then used to apply three-dimensional unequal stresses and maintain stability. Displacement is then used to drive the fault to spontaneous instability, and the inter-seismic instability period is calculated. Finally, a cyclic or constant-rate water injection scheme is set to simulate industrial fluid activity and explore the mechanism of water injection-induced earthquakes. This method for fluid injection-induced earthquake experiments under true triaxial stress conditions can accurately simulate actual stratum stress and water injection conditions, providing a reliable means for studying water injection-induced earthquakes and having significant implications for earthquake prediction, geological disaster prevention and control, and related industrial safety.

The invention provides a coal seam high-precision identification and positioning method based on dynamic modeling and real-time updating to solve the problems that coal seam conditions are complex and changeable, data processing of a coal mining guiding system is insufficient in data precision, data visualization is not comprehensive enough, and a real-time dynamic monitoring system is not perfect enough. Through collection, arrangement and analysis of related data of a working face, geostatistics inversion prediction of a coal seam is carried out, and then geological guarantee and coal mining guidance system platform construction are carried out. On the basis of collected related geological data and provided three-dimensional earthquake prediction coal seam interface data, fine modeling of an initial three-dimensional geological model is carried out, the sampling precision of the model is improved, and then high-precision geological model data is imported into a constructed geological guarantee and coal mining guidance system platform. By tracking the mining condition of a coal mining machine, collecting and returning coal seam top and bottom interface data in real time, geological tracking and updating of a coal seam interface are realized by utilizing geological guarantee and an internal algorithm of a coal mining guidance system, and the difference and regularity between the actually measured coal seam interface and predicted coal seam model data are compared and analyzed, so that the coal seam interface is accurately and accurately measured. And correcting and updating the subsequent coal mining route of the coal mining machine in real time. According to the method, the coal seam can be accurately positioned, the coal mining scheme can be quickly updated and optimized, the path planning precision of the coal mining machine is remarkably improved, geological risks are reduced, and intelligent mining is promoted.

The present application provides a kind of reservoir brittleness index seismic prediction method, device, computer readable storage medium and electronic equipment, the method comprises: according to the relationship of the elastic parameter of target area rock with the change of pressure Determine the relationship of the reservoir brittleness index of target area with the change of effective confining pressure;By inversion, determine the reservoir brittleness index data volume of target area without considering the influence of effective confining pressure;Based on the well logging data and seismic data of target area, calculate the formation pressure of target area, and determine the effective confining pressure data volume of target area according to the formation pressure;Based on the reservoir brittleness index data volume of target area without considering the influence of effective confining pressure, the effective confining pressure data volume of target area, according to the relationship of the reservoir brittleness index of target area with the change of effective confining pressure, determine the reservoir brittleness index data volume of target area considering the influence of effective confining pressure.The technical scheme of the present application improves the accuracy and rationality of reservoir brittleness index seismic prediction, especially provides technical support for guiding deep shale gas well site deployment and well trajectory design.