18 results about "Earthquake forecasting" patented technology

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 belongs to the technical field of advanced detection, and provides a shield tunnel optical fiber earthquake forecasting method and system, and the method comprises the steps: obtaining the carrying position and number of seismic wave excitation seismic source equipment based on a shield tunneling machine structure type, installing a pneumatic hammering seismic source, calculating the dynamic correlation between the air pressure and the hammering force, and determining the shield air pressure range. Determining the relative spatial position of the seismic source and the optical fiber sensor, and establishing an observation mode; according to the relative spatial position and the observation mode, arranging an optical fiber sensor in a spatial range behind the shield tail of the shield tunnel, controlling a pneumatic hammering seismic source to hammer surrounding rock to generate seismic waves to obtain geological forecast data, preprocessing the obtained geological forecast data to obtain geological wave field data, and calculating the geological wave field data according to the geological wave field data. And calculating rock mass mechanical parameters, a reflection coefficient and a stress gradient of the shield excavation surface, obtaining a three-dimensional image, carrying out feature analysis, obtaining a disaster-causing structure position, a disaster-causing structure scale and an underground water occurrence condition, and completing prediction of the shield tunnel optical fiber earthquake.

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 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 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 provides a method, system, and medium for predicting earthquake source locations using TLF-GV signals, belonging to the field of earthquake prediction technology. The method includes: constructing a U-Net++ model, where multiple encoding blocks are used to sequentially generate feature maps of different spatial scales based on the gradient tensors extracted from TLF-GV; multiple decoding blocks are used to decode and output earthquake source probability heatmaps based on the feature maps at different spatial scales; obtaining active fault distribution maps, and constructing a geological prior penalty term based on the distance from each grid cell in the earthquake source probability heatmap to the nearest active fault, and constructing a loss function by combining the cross-entropy loss between the probability heatmap and the actual source mask; constructing a training set, using the concatenated features of the gradient tensors extracted from the gravity gradient grid and the fault binary mask as input to train and obtain the prediction model. This method utilizes the spatial grid features of the TLF-GV gravity gradient, combined with the small-scale feature capture capability of U-Net++, to effectively improve the accuracy of earthquake source location prediction.

The application discloses a kind of earthquake precursor information extraction method, device, equipment and storage medium, it is related to earthquake monitoring technical field, including the following steps: the dynamic gravity signal of the region to be measured is continuously collected;The amplitude of dynamic gravity signal and abnormal early warning threshold are different to obtain the first difference, the amplitude of dynamic gravity signal and abnormal noise threshold are different to obtain the second difference;When the first difference and the second difference are in abnormal interval, start abnormal detection time window to detect the amplitude of dynamic gravity signal, when the duration reaches the set value, the number of times that the amplitude of dynamic gravity signal is in abnormal interval is counted, and the total number of times counted is used as early warning number;If early warning number is greater than set number, calculate the dispersion coefficient of the amplitude of dynamic gravity signal in abnormal interval, and output the amplitude data with dispersion coefficient less than 1 as earthquake precursor information.The application first collects the dynamic gravity signal related to earthquake precursor, then sets multiple control variables, judges the disturbance signal in dynamic gravity signal through multiple control variables, and obtains the judgment result, i.e., earthquake precursor information.The method of the application can accurately obtain earthquake precursor information, and provide research data for earthquake prediction work.

The application discloses a short-term strong earthquake occurrence time prediction method and device, medium and equipment, and relates to the technical field of earthquake prediction. The method performs standardization preprocessing on atmospheric tide gravity signals in a preset time window, inputs a plurality of target days into a prediction model constructed in advance after detecting an atmospheric tide gravity anomaly event, and obtains the probability of strong earthquakes occurring in each target day after the atmospheric tide gravity anomaly event. The prediction model is trained according to the statistical correlation characteristics between the atmospheric tide gravity anomaly event occurrence time and the strong earthquake occurrence time. The strong earthquake refers to an earthquake with a magnitude greater than a preset threshold. According to the probability of strong earthquakes occurring in each target day after the anomaly, the strong earthquake occurrence time prediction result is determined. The method realizes quantitative probability prediction of the strong earthquake occurrence time, and has the advantages of being interpretable, calibratable and online updatable.

This invention discloses an earthquake prediction method, system, device, and storage medium, relating to the field of earthquake prediction technology. The method includes the following steps: determining a corresponding dedicated kernel function based on the proportion of different heterogeneous feature types in the current multidimensional heterogeneous features; mapping the current multidimensional heterogeneous features to a high dimension using the dedicated kernel function to obtain a mapping vector; assigning different weights to different earthquake precursor data based on the correlation between different multidimensional heterogeneous features and the earthquake prediction target; linearly fusing multiple mapping vectors with multiple weights to obtain a preliminary fusion vector; reducing the dimensionality of the preliminary fusion vector to obtain a fusion feature vector; and inputting the fusion feature vector into sub-models corresponding to different earthquake prediction targets to obtain the probability values ​​of the corresponding prediction results. This invention can dynamically adapt to the differences in importance of multi-source features, weighting the mapping vector with corresponding weights to achieve linear fusion of heterogeneous features and improve the accuracy of earthquake prediction.

This invention provides a method and system for joint probabilistic prediction of three earthquake elements associated with TLF-GV signals, belonging to the field of earthquake prediction technology. It aims to solve the problems of traditional three-element prediction lacking joint fusion, difficulty in handling evidence conflicts, and fixed weights. The method includes: extracting the physical characteristics of the TLF-GV signal, taking the prediction results of the previous three elements and model confidence parameters, concatenating them into a feature vector, inputting it into a dynamic weighted prediction model constructed by a shallow MLP, and outputting time, magnitude, and location weights; dividing the three elements into 3×3×3=27 "time-magnitude-location" propositions, generating a basic probability assignment (BPA) for each proposition based on the weights, and quantifying evidence contradictions through a conflict coefficient K; obtaining the joint BPA using the Dempster combination rule, calculating the mean of the confidence function and likelihood function of each proposition as the comprehensive probability, and extracting the proposition with the highest probability as the coupling result. This method significantly improves the scientific rigor and practicality of short-term strong earthquake prediction.

The invention provides a TLF-GV signal correlation seismic source position prediction method and system and a medium, and belongs to the technical field of seismic forecasting, and the method comprises the steps: constructing a U-Net + + model, and enabling a plurality of coding blocks to be used for generating feature maps of different spatial scales according to gradient tensors extracted by TLF-GV in sequence; the plurality of decoding blocks are used for decoding and outputting a seismic source probability thermodynamic diagram according to the feature maps of different spatial scales; obtaining an active fault distribution diagram, constructing a geological priori penalty term according to the distance from each grid unit in the seismic source probability thermodynamic diagram to the nearest active fault, and constructing a loss function in combination with cross entropy loss between the probability thermodynamic diagram and a real source mask; and constructing a training set, taking gradient tensor extracted by the gravity gradient grid and fault binary mask splicing features as input, and training to obtain a prediction model. According to the method, the spatial grid features of the TLF-GV gravity gradient are utilized, and the small-scale feature capture capability of U-Net + + is combined, so that the accuracy of seismic source position prediction is effectively improved.

The invention provides an underground fluid dissolved gas sample collecting device for earthquake forecasting, and belongs to the technical field of earthquake forecasting. Comprising a pre-releasing structure, a diving structure, a sealing structure and a collecting structure, the pre-releasing structure comprises a pre-releasing pipe and a pneumatic booster pump, a mounting ring is welded to the top end of the pre-releasing pipe, and an air pipe is mounted at the communicating end of the pneumatic booster pump. By arranging a pre-placing structure, a diving structure, a sealing structure and a collecting structure, after a collector is placed in a pre-placing pipe, a sealing plate is fixed to the upper portion of the pre-placing pipe, a pneumatic booster pump is started to inflate the pre-placing pipe, an electric telescopic rod is started to enable the telescopic end of the electric telescopic rod to drive a sealing block to move, and therefore the sealing block is stored in a mounting shell; and the collector is pressed downwards into the diving pipe by resisting water pressure through air pressure, and finally, the sliding ring is blocked by the baffle ring to move the collector to an appointed position for operation, so that the diving difficulty of the collector is solved.

An earthquake precursor information early warning device based on well water level change disclosed by the present invention comprises a fixed seat and a water well, the fixed seat is fixedly installed above the water well, the bottom of the fixed seat is fixedly connected with a support rod, one end of the support rod far away from the fixed seat is fixedly connected with a ball, and the ball is slidably installed in a circular groove block. The bottom of the circular groove block is fixedly connected with an inclined plate, the side wall of the water well is rotatably connected with an extrusion liquid block, an extrusion block used for extruding the hydraulic rod is arranged above the extrusion liquid block, early warning equipment is designed based on the change of the water level of well water, and the early warning equipment is independent of signal transmission and external power supply. A traditional early warning device is difficult to play a role, however, the device can trigger early warning by monitoring the change of the water level of well water and transmit precursor information to professional analysts at the first time, the situation that the professional analysts miss the opportunity of capturing the earthquake precursor information due to information transmission blocking is avoided, and the earthquake forecasting capacity is remarkably improved.

The invention discloses an earthquake forecasting method based on remote sensing interpretation earthquake data, and relates to the field of earthquake monitoring and evaluation. The method comprises the following steps: determining a to-be-monitored target area on a longitude and latitude map; configuring a space-time sampler for the target area; determining G abnormal intensities of pregnancy and earthquake based on a space-time sampler; constructing G space-time anomaly triples according to the G abnormal intensities of pregnancy and earthquake and the time window and the regular grid of the space-time sampler; performing density clustering on the G space-time anomaly triples based on the space-time positions represented by the space-time anchor points to obtain K high-risk anomalous bodies; constructing an anomalous body disaster grade map based on the K high-risk anomalous bodies; according to the method, the time continuity and the space influence range of the remote sensing interpretation parameter anomaly can be reflected at the same time, the high-priority anomaly position with the large coverage range and the long duration time can be identified, and an objective and quantifiable disaster assessment grading basis is provided for earthquake precursor forecasting.

The invention belongs to the technical field of seismic geophysical monitoring, and provides a seismic geophysical monitoring image recognition and data anomaly detection method based on deep learning, which comprises the following steps: S1, acquiring seismic geophysical monitoring image data; s2, performing feature extraction on the image data by using a deep learning model; s3, identifying the image data according to the extracted features so as to detect data abnormity or other interference phenomena; s4, performing data analysis on an identification result; according to the method, the deep learning model is introduced, so that automatic feature extraction and efficient recognition of seismic geophysical monitoring image data are realized, and the accuracy and efficiency of image recognition are greatly improved; meanwhile, by using a preset anomaly detection algorithm, the method can accurately detect an abnormal value in the data and output an abnormal alarm in time, thereby providing powerful support for earthquake forecasting work and enhancing reliability and practicability of earthquake monitoring data.

The present invention relates to the technical field of satellite image processing, and discloses a seismic prediction method, device, equipment and medium based on remote sensing data. The method includes: acquiring original remote sensing data of a target area within a first time range, and performing seismic anomaly detection on the original remote sensing data to generate initial anomaly data; filtering the initial anomaly data based on a preset seismic anomaly recognition condition to generate target anomaly data; wherein the preset seismic anomaly recognition condition includes a spatial anomaly recognition condition and a temporal anomaly recognition condition, and the target anomaly data is used to represent anomalies caused by seismic activities; determining at least one local area in the target area based on the target anomaly data, and performing seismic prediction on the local area within a second time range. Through the above technical solution, the recognition accuracy of data anomalies of geophysical parameters caused by earthquakes is improved, thereby improving the accuracy of seismic prediction.

The invention discloses an earthquake forecasting method based on deep well water level data. The method comprises the following steps: S1, acquiring deep well water level data; s2, comparing the water level data of the deep well with historical water level data; and S3, outputting earthquake forecast data according to a comparison result. The anti-interference capability is strong, the earthquake can be forecasted in advance, the social safety is effectively guaranteed, the harm is reduced, and the loss is reduced.