213 results about "Earthquake prediction" patented technology

The invention relates to a multi-dimensional seismic risk assessment method based on seismic oscillation space-time distribution. The method comprises the following steps: S1, selecting IM indexes; s2, key structure parameters are extracted; s3, recording an engineering demand parameter EDP; s4, selecting an optimal IM index; s5, establishing an agent model of structural seismic response; s6, establishing an earthquake prediction equation; s7, establishing an aftershock risk analysis model; s8, obtaining aftershock intensity samples of the positions of the target structures; s9, obtaining an aftershock vulnerability curve; and S10, determining repair time and repair cost according to the repair of the structure and the repair of the structure function so as to finally determine the total repair cost of the structure of the regional line and the anti-seismic capacity after repair. Compared with the prior art, the method has the advantages of improving the accuracy of earthquake risk estimation and the like.

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 a multi-source data volume fused coal reservoir fracture prediction model construction method, which comprises the following steps of: acquiring a coal reservoir coal sample of a well in a certain research area, and adopting a higher fracture strength value in core fracture strength and imaging logging information for a fracture surface strength non-overlapping part obtained by artificially describing the core fracture strength and the imaging logging information; generating a coal sample crack strength curve; establishing a fractal dimension curve; carrying out full-diameter CT (Computed Tomography) scanning on the coal sample, and carrying out quantitative calculation on the single-layer fracture rate to obtain a single-layer fracture rate curve; fusing the obtained three types of curves, and establishing a comprehensive indication curve; constructing a fault distance attribute body; extracting a seismic data body, simulating the correlation between the seismic data body and the comprehensive indication curve, and selecting a sensitive seismic body; constructing a crack comprehensive discrimination function body; and a coal reservoir fracture prediction model is constructed, and coal reservoir fracture prediction is carried out. According to the method, coring data, conventional logging, imaging logging, CT scanning and earthquake crack prediction data are integrated, and the prediction precision of the coal reservoir crack is improved.

The invention relates to an ionosphere total electron content (TEC) pre-earthquake abnormal information extraction method based on an artificial intelligence technology. The method comprises the steps of S1, data preprocessing and sequence construction, S2, time sequence convolutional network (TCN) model construction with an auxiliary attention mechanism (AuxAttention), S3, model optimization and S4, earthquake monitoring application efficiency evaluation. According to the method, the TCN and the auxiliary attention mechanism are combined, so that the ionized layer TEC anomaly detection precision is greatly improved, the pre-earthquake anomaly signals can be effectively captured, and more accurate and timely early warning information is provided for earthquake prediction. The method shows higher prediction accuracy under different geographic positions and conditions, and is suitable for large-range earthquake monitoring application.

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 invention discloses a seismic anomaly intelligent identification method based on Zhang Heng No.1 satellite multiple parameters. The method comprises the following steps: step 1, obtaining and preprocessing multi-parameter satellite data; step 2, extracting pre-earthquake ionosphere disturbance characteristics based on a deep learning model and analyzing the pre-earthquake ionosphere disturbance characteristics; 3, earthquake abnormal information is extracted and intelligently recognized; and step 4, optimizing the model and evaluating earthquake prediction performance. According to the method, ionosphere and other parameter data provided by Zhang Zheng No.1 satellites are acquired, a convolutional neural network and a bidirectional long-short-term memory network (Bi-LSTM) are combined, and pre-earthquake ionosphere disturbance anomaly is identified by using a deep learning technology. Compared with the prior art, the method has the advantages that by integrating the multi-parameter remote sensing data and the deep learning model, the recognition precision of the earthquake abnormal signal before the earthquake is effectively improved; and in combination with a transfer learning technology, the recognition effect under the condition of less training data is further improved.

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 discloses a pre-earthquake abnormal information extraction method for an earthquake prediction multi-source remote sensing data set. The method specifically comprises the following steps: S1, obtaining and preprocessing multi-source remote sensing data; S2, establishing an earthquake abnormal feature extraction model based on an IBPT framework; S3, extracting and analyzing earthquake abnormal information; according to the invention, by introducing the multi-source remote sensing data and combining the IBPT algorithm, the recognition precision of the seismic abnormal signal is improved, and the problem of prediction instability caused by single parameter analysis in the traditional method is solved; and by utilizing model optimization and evaluation, the accuracy and generalization ability of earthquake prediction are enhanced, and the pre-earthquake abnormal information can be extracted more efficiently and 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 embodiment of the invention provides a mudstone interlayer prediction method and device, electronic equipment and a storage medium. The method comprises the steps of performing high-resolution frequency extraction processing on pre-stack sub-angle gather data; pre-stack seismic elastic inversion of well logging constraint is carried out by using high longitudinal and transverse resolutions of well logging and earthquakes; determining independent and sensitive reservoir elastic parameters of the reservoir predicted by the mudstone interlayer; the selected reservoir elastic parameters serve as independent variables, a mudstone factor recognition function of the mudstone interlayer is constructed, and a calculation model of the mudstone factor recognition function of the mudstone interlayer is established; and according to the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, a division standard is established, and mudstone interlayer prediction is completed. According to the method, the earthquake identification capability of the mudstone interlayer can be improved, the accuracy of turbidite sandstone earthquake prediction can be improved, and the method has great application values for analyzing the connectivity of oil reservoir sand bodies, optimizing an oil reservoir development scheme and improving the oil reservoir recovery efficiency.

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 tuff identification method, device and equipment and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: based on logging curve characteristics of various lithologies in volcanic rocks, performing classification processing on logging data of a target area; generating an elastic parameter curve corresponding to each lithology based on the elastic parameters of each lithology in the classified logging data; selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct lithology identification factors; and identifying the tuff from the seismic data of the target area by using the lithology identification factor, and determining the distribution range of the tuff. Different lithologic characteristics and distribution rules of volcanic rocks are determined through well logging and earthquake rock physical characteristic analysis, the influence of tuff on reservoir prediction is reduced, sensitive factors for identification of the tuff are constructed on the basis of rock physical analysis, the multiplicity of solutions of volcanic rock prediction is reduced, and the lithologic characteristics of the volcanic rocks and the earthquake prediction precision of the reservoir can be improved.

The invention relates to the technical field of earthquake prediction, and particularly provides a lightweight in-situ seismic oscillation parameter prediction model construction method and an earthquake early warning system. The lightweight in-situ seismic oscillation parameter prediction model construction method comprises the steps of training an initial prediction model according to acceleration waveform data when a seismic P wave arrives, and obtaining a pre-trained prediction model; taking the pre-trained prediction model as a teacher model, and migrating knowledge of the teacher model to a student model through distillation; and constructing a lightweight in-situ seismic oscillation parameter prediction model based on the student model. According to the method, knowledge in the pre-training prediction model is migrated to the student model through distillation, so that the complexity and the calculation amount of the student model can be reduced; and the lightweight in-situ seismic oscillation parameter prediction model is constructed based on the student model, so that the in-situ seismic oscillation parameter prediction efficiency can be ensured, the demand of the model for hardware resources can be reduced, and the deployable scene of the model can be expanded.

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.

To predict earthquakes.SOLUTION: An earthquake prediction system comprises means for utilizing AI, means for collecting minor information from various regions, and means for providing earthquake predictions.SELECTED DRAWING: Figure 1

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 utility model discloses hot spring gas collecting and processing equipment for earthquake prediction, which comprises a box body, a gas inlet pipe connected with an external gas collecting hood and a gas outlet pipe connected with external detection equipment, the gas outlet pipe and the gas inlet pipe are respectively connected with the upper end and the lower end of a gas supply branch pipe, and the upper end and the lower end of the gas supply branch pipe are U-shaped. Hot spring gas fed by the gas inlet pipe is divided into two paths to be conveyed in the gas supply branch pipe and is combined into one path at the gas outlet pipe to be conveyed into external detection equipment, and other parts, except for the two ends, of the gas supply branch pipe are all arranged in the box body. The spiral cooling liquid circulating pipe is arranged on the outer side of the air supply branch pipe to conduct low-temperature freezing on the hot spring gas, the freezing effect can be greatly improved, water vapor in the hot spring gas is rapidly desublimated into ice, and the purpose of rapid dehumidification is achieved. The part, in the box body, of the air supply branch pipe is bent in an S shape, so that the freezing time of hot spring air can be prolonged, the removal rate is improved, and water vapor is prevented from entering the detection equipment to influence the accuracy of a detection result.

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 coal mine high-energy mine earthquake prediction and main control factor judgment method based on machine learning. The method comprises the following steps: acquiring micro-earthquake data, geological data and mining data during mining of a working face, and determining indexes corresponding to various types of data; constructing a plurality of machine learning models and carrying out model training; performing comprehensive evaluation on each model through a set prediction index, and obtaining an optimal model; and explaining a machine learning model prediction result by adopting an SHAP method, and sorting various prediction indexes by taking the size of an SHAP value as a main control factor judgment standard to obtain a main control factor. The method can comprehensively analyze the factors causing the mine earthquake, improves the accuracy of mine earthquake prediction, and guarantees the safe mining of a coal mine and the precise prevention and control of the mine earthquake.

The invention provides a prediction model training method, an earthquake prediction method and device and electronic equipment, and relates to the technical field of earthquake monitoring and earthquake prediction. The training method comprises the steps that multiple pieces of aftershock data in historical seismic waveform data of a target area are synthesized with preset noise data, and an earthquake precursor data set is obtained; intercepting a plurality of continuous waveform data containing small seismic group events from the historical seismic waveform data to obtain a random event data set; determining a noise data set by generating a white noise sequence or selecting waveform data of a plurality of noise segments of which the magnitude is below a preset seismic magnitude from historical seismic waveform data; and training the neural network by using the earthquake precursor data set, the random event data set and the noise data set to obtain an earthquake prediction model. According to the invention, the recognition precision of earthquake precursor anomalies is 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 discloses a reinforced concrete column earthquake damage mode prediction method and device, a medium and a product, and relates to the technical field of earthquake prediction, and the method comprises the steps: constructing a training set; expanding the characteristic parameter value set in the training set bending shear failure mode and the training set shear failure mode to obtain an expanded data set; removing outliers in the expanded data set to obtain a high-quality expanded data set; constructing a plurality of models with different kernels; the model comprises all feature parameters in a feature parameter set; determining a final model in all models according to all subsets of the feature parameter set, the training set, the expansion data set and the high-quality expansion data set; the final model is the model corresponding to the subset with the highest prediction precision of the bending-shear failure mode. The method and the device can solve the problem of low prediction accuracy of the bending-shear failure mode.