128 results about "Earthquake prediction" patented technology

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 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 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 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 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 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 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 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.

The present application relates to a kind of coal reservoir fracture prediction model construction methods of multi-source data body fusion, it includes: obtaining the coal sample of coal reservoir of certain well in certain study area, the non-overlapping part of fracture intensity obtained from artificial description core fracture intensity and imaging logging data is using the higher fracture intensity value in two, generate coal sample fracture intensity curve;Fractal dimension curve is established;The coal sample is taken and is carried out full diameter CT scanning, and single layer fracture rate quantitative calculation is carried out, and single layer fracture rate curve is obtained;The three kinds of curves obtained are fused, and comprehensive indication curve is established;Fault distance attribute body is constructed;Seismic data body is extracted, the correlation of simulated seismic data body and comprehensive indication curve is selected, and sensitive seismic body is selected;Fracture comprehensive discriminant function body is constructed;Coal reservoir fracture prediction model is constructed, and coal reservoir fracture prediction is carried out.The present application comprehensively takes core data, conventional logging, imaging logging, CT scanning and seismic fracture prediction data, improves the prediction precision of coal reservoir fracture.

A system and methods are disclosed. The method includes obtaining a seismic dataset including a plurality of recorded multiple events, generating a predicted multiple model using a multiple prediction method and the seismic dataset, and estimating a set of initial matching filters using a matching method, to match the plurality of estimated and recorded multiple events. The method further includes generating a tensor field based on the predicted multiple model, determining a set of structure-oriented matching filters based on the set of initial matching filters and the tensor field, generating a filtered multiple model based on the predicted multiple model and the set of structure-oriented matching filters, and generating a multiple-attenuated seismic dataset based on the filtered multiple model and the seismic dataset, forming a seismic image based, at least in part, on the multiple-attenuated seismic dataset, and determining a location of a hydrocarbon reservoir based on the seismic image.

The invention discloses an earthquake numerical value prediction method based on kernel density and machine learning, and belongs to the technical field of earthquake prediction. The method comprises the steps of performing magnitude standardization and energy association processing on original seismic directory data to form standard seismic directory data, performing completeness processing on the standard seismic directory data to determine a minimum complete magnitude, and performing screening to obtain a complete seismic directory; determining a first bandwidth and a second bandwidth for the complete seismic directory through a nuclear density bandwidth sensitivity test, calculating seismic frequency and energy nuclear density distribution of the target area in combination with a time and magnitude sliding window, and obtaining nuclear density anomaly distribution data by making a difference between the seismic frequency and energy nuclear density distribution and long-term average nuclear density distribution; and inputting the kernel density abnormal distribution data of the target magnitude range under multiple time scales into a pre-trained random forest model, and outputting an occurrence prediction result of a target magnitude earthquake in the target region. According to the method, the spatial accuracy and timeliness of earthquake prediction are improved, and technical support is provided for earthquake disaster risk prevention and control.

The invention belongs to the technical field of oil-gas exploration and development, and provides a deep carbonate reservoir azimuth pre-stack depth domain earthquake prediction method and device, and the method comprises the steps: employing OVT domain pre-stack pre-stack depth domain trace gather data, combining with the stress distribution characteristics of a target region, generating sector azimuth pre-stack depth domain trace gather data, retaining azimuth anisotropy information, and obtaining a deep carbonate reservoir azimuth pre-stack depth domain earthquake prediction result. And optimal orientation sensitive attribute pairs and multi-transformation coefficient solving are carried out, the porosity of the deep carbonate reservoir is predicted, and deep carbonate reservoir prediction is completed. The method mainly comprises the following steps: generating a sector azimuth pre-stack depth migration gather; performing sector azimuth pre-stack depth domain gather seismic attribute processing; preferably selecting sensitive seismic attribute pairs; solving multiple equation coefficients; and calculating a reservoir porosity three-dimensional data volume, and completing earthquake prediction of a favorable reservoir. The method not only solves the problem of resolution reduction caused by high-frequency information loss of a time domain, but also facilitates characterization of anisotropy information of the reservoir, can predict the anisotropy thin reservoir, improves thin layer identification precision, and effectively guides well location deployment.

The invention discloses a tectonic geochemical earthquake precursor anomaly discrimination index extraction method and device, and the method comprises the steps: obtaining monitoring data of a monitored target fault zone, carrying out the analysis of the monitoring data based on a tectonic geochemical method, determining the cross-fault soil gas spatio-temporal evolution characteristic data of the monitored target fault zone, and carrying out the analysis of the cross-fault soil gas spatio-temporal evolution characteristic data of the monitored target fault zone. And according to the monitored cross-fault soil gas spatio-temporal evolution characteristic data of the target fault zone and the response change relationship of the cross-fault soil gas concentration intensity of the fault zone to the earthquake, extracting an earthquake precursor anomaly discrimination index. Monitoring data of a target fault zone are analyzed based on a tectonic geochemical method, and then the fault zone is analyzed according to the analyzed cross-fault soil gas spatio-temporal evolution data and the response change relation of the cross-fault soil gas concentration intensity of the fault zone to an earthquake, which is obtained through a solid body earthquake-inducing mode. According to the invention, data quantification of the earthquake precursor anomaly discrimination index can be realized, the problem that only the earthquake precursor anomaly index can be qualitatively researched at present is solved, and the accuracy of earthquake prediction is improved.

Provided is an earthquake prediction method for predicting that a specific period is a period with a high likelihood of earthquake occurrence on the basis of information on a seismic intensity or magnitude of interest for prediction in a particular area on the earth.　An earthquake prediction method according to the present invention comprises: an astronomical information collection step S1 for collecting astronomical information for a predetermined period in the past and a predetermined period in the future; a parameter acquisition step S2 for acquiring a parameter based on the collected astronomical information; an earthquake occurrence range identification step S3 for identifying an earthquake occurrence range, which is a range of the parameter in an earthquake occurrence period in which an earthquake actually occurred with the seismic intensity or magnitude of interest for prediction in the predetermined period in the past; and an earthquake prediction step S4 for extracting a period in which the parameter is within the earthquake occurrence range from the predetermined period in the future and predicting that the extracted period is a period with a likelihood of earthquake occurrence.

The application relates to the technical field of earthquake prediction, and particularly provides a lightweight on-site ground motion parameter prediction model construction method and an earthquake early warning system. The lightweight on-site ground motion parameter prediction model construction method comprises the following steps: training an initial prediction model according to acceleration waveform data when a P wave of an earthquake arrives, to obtain a pre-training prediction model; taking the pre-training prediction model as a teacher model, and transferring the knowledge of the teacher model to a student model through distillation; and constructing a lightweight on-site ground motion parameter prediction model based on the student model. The method can reduce the complexity and calculation amount of the student model by transferring the knowledge in the pre-training prediction model to the student model through distillation; and the lightweight on-site ground motion parameter prediction model can be constructed based on the student model, so that the demand of the model for hardware resources can be reduced while the on-site ground motion parameter prediction efficiency is ensured, and the deployable scene of the model is expanded.

The present application relates to the field of geophysical exploration technology, and particularly relates to a method for well leakage early warning by using seismic prediction results, comprising: basic data acquisition, regional geological condition analysis, seismic data preprocessing, small fracture and fracture-vug body seismic fine delineation, and shadow relief display of the delineation result. The present application improves the basic data quality by introducing anisotropic diffusion and three-dimensional multi-level median joint filtering technology, and carries out multi-attribute fusion based on adjustable parameters by combining pre-stack multi-azimuth frequency attenuation gradient fracture prediction and post-stack structural coherent tensor, finally realizes spatial display of the multi-attribute fusion result by using shadow relief technology, improves the accuracy of small fracture and fracture-vug body delineation, and more directly shows the spatial form of the fracture and fracture-vug body, which can effectively guide well location design, drilling design and drilling trajectory optimization.

The invention discloses a maximum aftershock prediction method based on online enhancement, and belongs to the technical field of earthquake prediction. The core of the method is that a personalized prediction model is dynamically generated for a current event through online retrieval of similar historical sequences and data enhancement aiming at a newly occurring main earthquake. Firstly, a historical seismic database is constructed from a global seismic directory, and an initial prediction model is trained. When a new main earthquake occurs, an online process is triggered immediately: based on the characteristics of the new main earthquake, K most similar sequences are quickly retrieved from historical data; then, the similar sequence data are enhanced (such as noise adding and interpolation), so that a diversified enhanced training set is constructed; and finally, performing rapid fine adjustment on the initial model by using the enhancement set to obtain an optimization model for the current main earthquake, and predicting the maximum aftershock magnitude. According to the method, the limitation of one-time cutting of the existing static model is overcome, the problem of inaccurate prediction in an earthquake small sample scene is effectively solved, and the accuracy, robustness and timeliness of prediction are remarkably improved.

The invention relates to a dual-pore structure factor permeability earthquake prediction method and system, a medium and equipment. The method comprises the following steps: acquiring logging data, core experiment data and earthquake data of a target area; performing inversion based on the rock physical model to obtain pore structure parameters and a dynamic soft / hard pore aspect ratio; performing model construction according to the porosity, the pore structure parameters and the dynamic soft / hard pore aspect ratio to obtain a multivariate nonlinear regression model; training and verifying the multivariate nonlinear regression model and the neural network by using a training set in the logging data to obtain a permeability regression model; and carrying out inversion based on the seismic data to obtain inversion data, and carrying out prediction on the inversion data and the permeability regression model to obtain a permeability body. According to the method, three-dimensional quantitative prediction of the permeability of the low porosity and permeability reservoir is realized on the basis of the double-pore structure factors in combination with the pre-stack seismic inversion and neural network technologies, the prediction precision can be remarkably improved, and reliable basis and technical support are provided for efficient development of oil and gas reservoirs.

The invention discloses a short critical strong earthquake occurrence time prediction method and device, a medium and equipment, and relates to the technical field of earthquake prediction. The method comprises the following steps: performing standardized preprocessing on an atmospheric tidal gravity signal in a preset time window, and after an atmospheric tidal gravity abnormal event is detected, inputting a plurality of target days into a pre-constructed prediction model respectively to obtain the probability of strong shock in each target day after the atmospheric tidal gravity abnormal event occurs; the prediction model is obtained by training according to statistical correlation characteristics between the occurrence time of the atmospheric tidal gravity abnormal event and the occurrence time of the strong earthquake; the strong earthquakes represent earthquakes of which the magnitude is greater than a preset threshold value; and determining a strong earthquake occurrence time prediction result according to the probability of occurrence of the strong earthquake in each target day after the abnormality occurs. According to the method, quantitative probability prediction of the strong earthquake occurrence time is realized, and the method has the advantages of interpretability, calibration and online updating.

This invention relates to the field of earthquake prediction, specifically to a method, system, device, and medium for earthquake parameter prediction based on spatial Bayesian Copula. The method includes obtaining the coupling relationship between the earthquake parameters to be predicted and multivariate environmental test data based on pre-constructed Copula models of known sites in a target area; solving for the prior distribution of parameters of the Copula model of the site to be predicted using a general Kriging model based on the spatial correlation and coupling relationship between the multivariate environmental test data of each site; updating the prior distribution of the solved parameters using a Bayesian update method to obtain a Bayesian posterior Copula model; and predicting the earthquake parameters of the site to be predicted using the Bayesian posterior Copula model with local observed earthquake data of the site as variables. This invention can dynamically predict earthquake parameters, significantly improving the spatial consistency and prediction accuracy of earthquake risk assessment.

The invention provides a satellite-based earthquake early warning method and device, electronic equipment and a storage medium, and relates to the field of satellite monitoring. The satellite-based earthquake early warning method provided by the invention comprises the following steps: determining initial crustal deformation data of a target monitoring area corresponding to an initial satellite; under the condition that the initial crustal deformation data is greater than preset crustal deformation data, determining a plurality of target satellites corresponding to the initial crustal deformation data and acquiring first radar image information corresponding to the plurality of target satellites; determining a plurality of earth crust deformation differences based on the plurality of pieces of first radar image information and second radar image information corresponding to the initial satellite; and based on the plurality of earth crust deformation differences, determining a target terminal and generating earthquake early warning information, and sending the earthquake early warning information to the target terminal. According to the invention, the earthquake prediction efficiency and accuracy can be improved.

The application discloses a kind of oil-water interface seismic prediction method, equipment and storage medium, belong to oil and gas exploration technical field.The method includes the following steps: S1.Based on well logging data, the intersection interpretation template of longitudinal wave impedance and shear wave impedance curve of reservoir actual state and water-saturated state is established;S2.Based on the prestack bayesian inversion of bayesian sparse inversion theory and model soft constraint, the inversion result of longitudinal wave impedance and shear wave impedance in study area is obtained;S3.According to the intersection interpretation template, the inversion result of longitudinal wave impedance and shear wave impedance is predicted for the distribution of water-bearing layer in study area;S4.The time-depth relationship that meets the depth requirement is established, and the time-depth conversion is carried out for the distribution of water-bearing layer in study area, and the shallowest depth position of water-bearing layer is the position of oil-water interface.The application can qualitatively-semi-quantitatively predict oil-water interface, and can estimate oil-water interface according to a small amount of well data in early exploration, to provide technical support for next step exploration of oil field.