68 results about "Earthquake risk" patented technology

The invention provides a hot dry rock reservoir fracturing induced earthquake management and control system, which belongs to the field of hot dry rock development and supervision and comprises a fracturing construction monitoring subsystem, a microearthquake monitoring subsystem and a traffic light decision subsystem. The fracturing construction monitoring subsystem is used for monitoring fracturing construction data in the fracturing construction process and transmitting main construction parameters to the traffic light decision subsystem; the microseism monitoring subsystem is used for acquiring microseism waveform information, interpreting the microseism waveform information into microseism event data and transmitting the microseism event data to the traffic light decision subsystem; and the traffic light decision subsystem is used for comparing information parameters transmitted by the fracturing construction monitoring subsystem and the micro-earthquake monitoring subsystem with a plurality of set decision discrimination factors, judging and predicting the risk of inducing an earthquake event, carrying out graded early warning prompt on the earthquake risk through an indicator light, guiding the fracturing construction process in real time, and carrying out early warning on the earthquake risk. And the earthquake inducing risk is reduced.

The invention provides an early warning, prevention and control method and device for a hydrofracture-induced earthquake risk, and relates to the technical field of unconventional oil and gas development, and the method comprises the steps: collecting geological data of a target block, constructing a three-dimensional geomechanical model, classifying fracture surfaces in the block based on the three-dimensional geomechanical model, and recognizing fracture classification data. Fractured surface parameters are quantified; in the fracturing construction process, construction data and geological response data are collected; performing splitting and dimensionless processing on the crack classification data, the fracture surface parameters, the construction data and the geological response data to obtain sub-risk components, and inputting the sub-risk components into a multi-parameter weighting model based on an induced earthquake risk index system to obtain a comprehensive risk coefficient; determining early warning grading information corresponding to the comprehensive risk coefficient according to a preset early warning grading mechanism; and adopting a matched risk prevention and control strategy according to the early warning grading information in combination with well section features.

The invention provides a federated learning and privacy calculation-based cross-border earthquake risk assessment method, which solves the fundamental contradiction between data sovereignty and privacy security in cross-border data sharing by constructing a federated learning architecture fusing hierarchical desensitization and hybrid encryption. Through the multi-source data cooperative training and CNN-LSTM mixed disaster chain model, the problem that the assessment precision is insufficient due to the fact that a single country model cannot describe a complex cross-border disaster chain is solved; finally, a cross-border disaster scene is constructed, and the defect that a static evaluation tool is disjointed with an actual combat is overcome through a multi-agent dynamic policy simulation system; according to the method, the security collaboration under the condition that the data is not out of the scene is realized, the multi-country collaboration participation rate is improved by 60%, the risk assessment accuracy rate is improved to 92% or above, the policy assessment period is shortened from several months to several hours, a quantifiable scientific basis is provided for cross-border emergency decision making, and the joint prevention and joint control capability of regional earthquake disasters is effectively improved.

The invention discloses a broadband seismic oscillation simulation method and system based on probabilistic seismic risk analysis, and relates to the technical field of field seismic oscillation, and the method comprises the steps: carrying out the seismic risk decoupling through employing a Chinese probabilistic seismic risk analysis method, determining a target potential source area corresponding to the maximum contribution degree with probability significance and a potential source magnitude and an epicentral distance of the target potential source area; calculating a fault size parameter of the target potential source area based on the potential source magnitude and a preset scaling rate; calculating fault distribution parameters of the target potential source area based on the fault size parameters and the mixed seismic source model; combining the three-dimensional longitudinal wave velocity model and the shallow structure model to construct a three-dimensional calculation model; and performing deterministic low-frequency simulation and deterministic high-frequency simulation on the three-dimensional calculation model based on a spectral element method and a three-way stochastic finite fault method respectively, and then performing broadband time program sequence synthesis to obtain a broadband seismic oscillation simulation result. The technical problem of low simulation result accuracy in the prior art is relieved.

The invention relates to the technical field of emergency management, and discloses an earthquake emergency cooperative command and dispatch system, which comprises a data sensing module used for collecting and preprocessing an earthquake basic data set and a rescue resource data set, the earthquake basic data set comprising earthquake condition parameter data, geographical environment data and secondary disaster data, and the rescue resource data set comprising earthquake condition parameter data, geographical environment data and secondary disaster data; the rescue resource data set comprises team configuration data, material reserve data and traffic path data; and the cooperative processing module is used for processing the seismic basic data set and the rescue resource data set to obtain emergency response indexes, and grading the emergency response indexes to obtain a rescue priority result. According to the invention, real-time acquisition and integration of multi-source seismic data and rescue resource data are realized through the data sensing module, and the problems of data dispersion and islanding in traditional emergency command are solved; an emergency response index can be dynamically generated based on the earthquake risk and the resource guarantee capability through a rescue priority grading model constructed by the cooperative processing module, and a scientific basis is provided for decision making.

The application provides a substation steel structure earthquake risk early warning method and device, equipment and medium, and the method comprises the steps of: constructing a foundation soil-substation steel structure model and a half-space site model; based on an indirect boundary element method, simulating the seismic response according to the half-space site model and the foundation soil-substation steel structure model to determine a risk grade early warning table; acquiring a target displacement response of the substation steel structure in real time; and analyzing the target displacement response and the risk grade early warning table to determine the seismic risk grade of the current substation steel structure. The application simulates and calculates the displacement response of the substation steel structure under different seismic risk grades based on the indirect boundary element method, the half-space site model and the foundation soil-substation steel structure model, can automatically meet the infinite boundary condition, reduces the problem dimension, improves the solving accuracy, thereby obtains more accurate seismic response of the substation steel structure, and can accurately monitor the risk grade of the substation steel structure.

The invention discloses a small sample mine earthquake risk identification method based on a physical information convolution-attention neural network, and belongs to the field of mine dynamic disaster prevention and control. According to the method, firstly, a correlation model of deformation localization and mine earthquakes is established, and mine earthquake occurrence conditions are defined from the angle of mechanical instability; secondly, micro-seismic signals are processed through a bilinear time-frequency feature extraction module, and cross-domain feature representation is optimized in combination with an interactive iterative learning strategy; secondly, learning spatial distribution of key physical parameters based on a physical information neural network so as to regularize a stress-strain response mechanism; and fusing the time-frequency features and the physical parameters by using a convolution-attention neural network, realizing mine earthquake risk identification, designing a physical-data mixed loss function, and integrating a feature extraction module and a physical parameter learning module through a collaborative optimization strategy to jointly optimize the network performance. According to the method, the mine earthquake risk can be identified and key physical parameters can be revealed under the small sample condition, and an effective technical path is provided for prediction and early warning of mine disasters.

The invention discloses a hot dry rock reservoir transformation mutual feedback multi-stage dynamic earthquake control method based on comprehensive prediction, and relates to the field of enhanced geothermal system development, and the method comprises the following steps: S1, dividing a limited earthquake magnitude into four stages according to a site earthquake control demand; s2, establishing an induced earthquake real-time monitoring system, and providing a real-time induced earthquake catalog; s3, comprehensively predicting and evaluating an induced earthquake risk; s4, according to the different limited earthquake magnitudes determined in the step S1 and the induced earthquake risk comprehensive analysis provided in the step S3, dynamically adjusting hierarchical management and control early warning conditions; s5, performing dynamic limitation on injection parameters by utilizing the earthquake risk analysis in the step S3; and S6, taking corresponding countermeasures according to different dynamic early warning conditions in the step S4 to realize prospective active earthquake control. And S7, through induced earthquake real-time monitoring in S2, mutual feedback type analysis in S3 and S5, dynamic injection limiting parameters are provided, dynamic early warning conditions are provided for S4 in combination with S3, prospective active earthquake control adjustment measures in S6 are realized, a real-time earthquake risk analysis and dynamic parameter adjustment mutual feedback type multi-stage earthquake control system is formed, and finally the purpose of safe earthquake control is achieved.

The invention discloses a complex terrain seismic oscillation amplification coefficient prediction and correction method and system based on a convolutional neural network model, and belongs to the application of a deep learning method in the field of seismic engineering. The method comprises the following steps: firstly, respectively establishing seismic oscillation databases of isolated terrains and regional complex terrains; training by using a BP neural network to obtain an isolated terrain seismic oscillation amplification coefficient prediction model, and training by using a convolutional neural network to obtain a regional terrain influence coefficient prediction model; and multiplying the isolated terrain prediction coefficient of the to-be-measured site by the regional terrain influence prediction coefficient to obtain an accurately corrected seismic oscillation amplification coefficient. The method overcomes the defect that an existing method neglects the regional terrain coupling effect, effectively improves the prediction precision of the seismic oscillation amplification coefficient under the complex terrain condition, and provides more reliable technical support for engineering aseismic design and seismic risk assessment.

This disclosure presents a method and apparatus for simulating landslide area generation in post-earthquake secondary disasters. One specific implementation of the method includes: acquiring panchromatic and multispectral image sets, and obtaining original images and elevation datasets; fusing the panchromatic image with each multispectral image in the multispectral image set to generate a fused image; identifying landslide areas on the fused image to generate an identification result; in response to the identification result indicating that the post-earthquake risk area includes landslide areas, performing landslide area edge detection based on the original image, elevation dataset, and fused image to generate a first edge coordinate sequence and a second edge coordinate sequence; and performing landslide area simulation processing based on the original image, fused image, first edge coordinate sequence, and second edge coordinate sequence to generate the landslide surface area and landslide coverage area. This implementation can improve the accuracy of the generated landslide surface area and landslide coverage area.

The invention relates to the technical field of anti-seismic safety of power systems and structures, and provides a multi-source data intelligent-driven power transmission line earthquake risk prediction method and system in order to solve the problems that in the prior art, a model is single in applicable object, cannot be dynamically updated and lacks risk closed-loop management. The multi-source data intelligent driving power transmission line earthquake risk prediction method comprises the steps of processing an actual earthquake wave time history by using a continuously updated mixed deep learning model, and predicting a top displacement time history of a power transmission line to serve as a power transmission line dynamic response index; comparing the predicted top displacement time history of the power transmission line with a known structural performance limit value of the power transmission line, judging a structural performance state of the power transmission line and a corresponding risk level of the structural performance state, and converting the predicted top displacement time history into occurrence probabilities of different structural performance states by using a vulnerability curve method; the method has the characteristics of high prediction precision and strong adaptability, and can realize real-time risk early warning.

The invention relates to the technical field of coal mine safety, and provides a deep well roadway surrounding rock blasting measure optimization design method and system. According to the determined residual mine earthquake energy of the migration of the key layer of each layer covering the target working face to the roadway supporting system and the theoretical impact resistance of the roadway supporting system of the target working face, determining a threat factor of the migration of the key layer of each layer to the roadway supporting system so as to identify a top plate blasting layer of the target working face; and then, a construction path and blasting parameters of target working face roof blasting are generated based on the recognized roof blasting position, and threat factors of migration of all key layers covering the target working face to a roadway supporting system are cyclically updated. Therefore, the impact resistance of a roadway support system is constrained, the'disaster-inducing-disaster-bearing 'matching of the blasting horizon is realized, the quantitative screening of the target blasting horizon is realized, the identification efficiency and accuracy are improved, and the rock burst and mine earthquake risks caused by misjudgment or missed judgment are reduced.

The invention discloses a landslide seismic signal precursor identification and early warning method based on machine learning. The method comprises the following steps: acquiring original seismic signal waveform data to generate seismic signal waveform data; judging a potential seismic phase arrival time point based on the seismic signal waveform, and generating a preliminary seismic phase arrival time sequence; acquiring position coordinate estimation of the landslide event based on the initial seismic phase arrival time sequence; original seismic oscillation signal waveform data are screened, landslide seismic scale classification is carried out, and seismic risk levels are output; if the earthquake risk level is a high level, original earthquake signal waveform data, geological displacement and rainfall data are extracted, the earthquake risk level and position coordinate estimation of the landslide event are integrated, and a landslide early warning signal is obtained and output. According to the technical scheme, signal purity can be improved, noise interference can be reduced, manual subjective errors can be reduced, accuracy and real-time performance of landslide position estimation can be improved, rapid risk assessment can be realized, reliability and response speed of landslide early warning can be improved, and effective support can be provided for disaster prevention and control.

The invention belongs to the technical field of potential earthquake risk prediction analysis, and discloses an earthquake risk analysis method considering historical earthquake energy release, which comprises the following steps: constructing a strain energy three-dimensional grid of a research area; calculating historical earthquake energy release and a release range; calculating accumulated strain energy of each grid; calculating residual strain energy and identifying a potential earthquake generation area; and earthquake risk analysis. According to the method, an original analysis method of a numerical simulation result is broken through, energy release of historical earthquakes and energy accumulation between earthquakes are considered from a new perspective and method, and a new thought for carrying out earthquake risk analysis based on the numerical simulation result is provided.

To provide an earthquake risk evaluation device, an earthquake risk evaluation method, and a program which can easily and accurately evaluate an earthquake risk of the ground.SOLUTION: An earthquake risk evaluation device 1 includes a ground risk calculation section 114 for calculating a Fourier spectrum amplitude ratio between horizontal and vertical motions of the ground as an evaluation object from microtremor information on the ground, so as to calculate a ground amplification factor on the basis of the Fourier spectrum amplitude ratio. The ground risk calculation section 114 calculates a ground risk index indicating the susceptibility of the ground to disaster from a peak frequency and a peak value of the ground amplification factor.SELECTED DRAWING: Figure 1

The application discloses a kind of based on the fusion of multiple remote sensing and LiDAR point cloud linear structure intelligent identification and segmentation system and method of fracture zone.It is extracted to construct tensor by the deformation anomaly guided LiDAR acquisition, input three-dimensional attention deformable convolution network to output fracture probability body after the system obtains InSAR and LiDAR data, realizes kinematic segmentation after tracking fracture main line and time series clustering and mutation point detection.The method breaks through the limitation of single data source, improves the identification accuracy and segmentation quantification level under complex terrain, and adapts to the needs of seismic risk assessment.

The invention discloses a structure earthquake risk assessment method based on WiBS-PLS-RSM. Firstly, design parameters of structural earthquake risk assessment and damage indexes of a to-be-assessed structure are determined; then, a training set (TrainX, TrainY) is determined, and the training set (TrainX, TrainY) is input into a width-adjustable basic spline partial least square response surface model (WiBS-PLS-RSM) for training; a test set (TestX, TestY) is input into the trained WiBS-PLS-RSM, verification is carried out through a decision coefficient R2, and finally, a total data set input variable sample TotalX is input into the verified WiBS-PLS-RSM to obtain a damage index considering design parameter uncertainty. According to the method, the problem of over-fitting or under-fitting of a traditional response surface model in earthquake risk assessment which is high in nonlinearity, multi-dimension and strong in data correlation and considers parameter uncertainty is effectively solved.

The invention relates to the field of oil and gas pipeline risk assessment, in particular to an oil and gas pipeline earthquake risk factor grade and weight determination method and system, and the method comprises the steps: collecting a plurality of data groups related to the oil and gas pipeline earthquake risk assessment; each data group comprises indexes of oil and gas pipeline earthquake risk factors and corresponding risk conditions; establishing a decision tree model by taking the various indexes as features and taking the risk conditions corresponding to the various indexes as targets; according to a node branch condition and a path length in the decision tree model, determining an oil and gas pipeline earthquake risk factor grade; and according to the importance of the nodes in the decision tree model, determining the earthquake risk factor weight of the oil and gas pipeline. According to the invention, the risk factor level of the pipeline under the earthquake condition can be accurately determined, and the earthquake risk factor weight of the oil and gas pipeline can be objectively determined, so that the efficiency and accuracy of pipeline earthquake risk management can be improved.

The invention relates to an RSM-based bridge anti-seismic multi-objective optimization method, equipment, a medium and a program product, and belongs to the technical field of bridge engineering anti-seismic design. The method comprises the following steps: determining design parameters and value ranges of bridge piers; designing a test by adopting a CCD method; establishing a finite element model; establishing a vulnerability curve; establishing an earthquake risk curve; the anti-seismic reliability is calculated through a PEER total probability anti-seismic risk assessment framework, the seismic oscillation randomness and the structural parameter uncertainty can be comprehensively considered, and the scientificity and the reliability of a subsequent optimization result are ensured; constructing an anti-seismic RSM model and each coefficient estimation value; and carrying out multi-objective optimization analysis by adopting an NSGA-II algorithm. According to the method, the problem that the calculated amount is too large in traditional anti-seismic optimization design is effectively solved, the optimization efficiency is remarkably improved while the precision is guaranteed, balance optimization of the anti-seismic reliability and the construction cost is achieved, the method has important practical value for improving the anti-seismic performance of the bridge and reducing the engineering construction cost, and multiple feasible optimization schemes are provided for engineering design.

The invention provides a fracture blocking rate inversion method based on GNSS earth crust deformation observation and elastic dislocation theory constraint, and the method comprises the steps: constructing a fracture three-dimensional model and an elastic dislocation constraint model, and calculating a fracture blocking rate. Through multi-period result comparison, strain field verification and earthquake activity verification, a high-risk region of pregnancy and earthquake can be identified. According to the method, the GNSS earth crust deformation observation elastic dislocation theory is adopted to constrain the negative dislocation model, and the precision and stability of fracture blocking rate inversion are remarkably improved. According to the invention, multiple periods of GNSS data are integrated to realize dynamic inversion, and the evolution trend of the locking state can be captured. According to the method, secondary blocks are finely divided, strain rate field constraints are introduced, locking distribution of a local strain enhancement area is accurately identified, the reliability of judgment of a large earthquake risk area is improved, a fracture high-locking-rate area is defined, through theoretical constraint and multi-source data fusion, the fracture locking rate inversion precision and the dynamic capture capability are improved, and the method is suitable for large-scale popularization and application. And reliable technical support is provided for earthquake risk assessment.

The invention relates to the technical field of earthquake risk analysis, in particular to a structural response danger curve determination method based on simulated earthquake, which comprises the following steps of: 1, dividing an earthquake potential source based on an earthquake activity model of a target site, and generating a random earthquake event directory in target observation time; 2, carrying out random seismic oscillation simulation on the seismic events in the random seismic directory, and constructing a simulated seismic oscillation database; 3, comparing the simulated seismic oscillation data with the seismic oscillation intensity index of the seismic oscillation experience prediction equation, and screening out a qualified time history subset meeting the physical consistency requirement; 4, inputting the qualified time history subset into a finite element model of a target structure to obtain a sample set of structural engineering demand parameters; 5, generating a structure response probability risk curve based on the engineering demand parameter sample set; and key scenes such as near-field large earthquakes can be included, the regional adaptability and authenticity are better, and the seismic oscillation input is more comprehensive.

This invention discloses a water-injection-induced earthquake monitoring and fault activation detection system, belonging to the field of earthquake monitoring and fault activation detection. The method includes: an earthquake observation module for obtaining earthquake monitoring data; a seismic phase data extraction module for identifying microseismic events based on the earthquake monitoring data to obtain seismic phase data; an initial earthquake location module for obtaining an initial earthquake catalog; a fine earthquake location module for performing secondary location to obtain a finely located earthquake catalog; a fault and structure modeling module for integrating the finely located earthquake catalog into a three-dimensional geological structure model for spatial location and visualization of seismic activity; and a fault activation and seismic hazard analysis module for analyzing the temporal variation trend of seismic activity, b-value changes, fault activation, and seismic hazard based on the three-dimensional geological structure model. This invention solves the problem of existing methods lacking monitoring capabilities for fault activation and weakening in shale gas development areas, as well as water-injection-induced earthquakes.

The invention discloses a high-resolution earthquake risk information analysis method and system, and relates to the technical field of earthquake risk analysis, and the method comprises the steps: collecting and preprocessing multi-source data of a target region, and forming a standardized multi-source data set; extracting multi-dimensional features for representing earthquake risks by using the standardized multi-source data set; on the basis of causal inference, identifying a feature subset having essential causal association with earthquake occurrence from the multi-dimensional features; the method comprises the following steps: determining a background seismic activity level based on historical seismic records, and obtaining a confidence coefficient weight reflecting local cognition by quantifying the sparseness of available data and the complexity of a geologic structure at each space lattice point; the dynamic information contained in the characteristic subsets associated with the essential causality is combined with the confidence coefficient weight reflecting the local cognition. According to the method, the whole-process refined analysis of the earthquake risk from data fusion, feature screening and uncertainty quantification to dynamic evaluation is realized.

The application provides a vector primary aftershock risk analysis model construction and early warning method and system, establishes a primary aftershock database of a target engineering structure; at least one characteristic signal related to each primary aftershock is obtained from the primary aftershock database; data analysis is performed on the target engineering structure by using the characteristic signal, and each analysis result is obtained; each analysis result is combined and calculated to determine a vector primary aftershock vulnerability surface and a vector primary aftershock danger surface; the vector primary aftershock vulnerability surface and the vector primary aftershock danger surface are integrated and calculated to determine a vector primary aftershock risk analysis model. The problem that a traditional earthquake risk analysis process cannot consider aftershock events is solved, and a three-dimensional primary aftershock risk surface is obtained. The vector primary aftershock risk analysis method can be used to establish a risk analysis model of a target engineering site under the action of a primary aftershock sequence and evaluate the earthquake risk of the target engineering site under the action of the primary aftershock sequence.

The invention discloses a multi-scale decomposition and abnormal mode detection system for a non-stationary time sequence, and relates to the technical field of earthquake monitoring and early warning, and the system comprises a data obtaining module which obtains earthquake directory data and a spatial topological graph of a target area; the time sequence processing module is used for obtaining the seismic frequency time sequence of each space unit through multi-scale decomposition and screening out components related to seismic precursor; the state vector generation module is used for obtaining a state vector of each space unit based on the space-time diagram neural network; the symbodynamic analysis module is used for calculating a dynamic orderliness index of each space unit; the abnormal flag generation module is used for judging whether the dynamics orderliness is lower than an abnormal threshold value or not and generating an abnormal flag bit; the feature fusion module fuses the flag bit and the state vector to obtain an enhanced state vector; the abnormal propagation recognition module recognizes an abnormal propagation mode based on the enhanced state vector; according to the invention, prospective and high-confidence decision support can be provided for earthquake risk assessment.

A reservoir earthquake risk evaluation method based on fusion of a physical mechanism and probability prediction comprises the following steps: step 1, constructing a three-dimensional model of a reservoir, simulating spatio-temporal evolution of a stress field and a pore water pressure field under reservoir water level change, and according to a spatio-temporal evolution result, calculating a reservoir earthquake risk evaluation result; calculating coulomb stress and pore pressure variation of each section of the target fault; a second step of jointly inputting the coulomb stress variation, the pore pressure variation and the tectonic strain cumulant into a Bayesian-machine learning hybrid prediction model based on historical earthquake example training, and obtaining conditional probability density functions of each segment corresponding to different earthquake magnitude grades at different time; and a third step of dynamically determining a multi-mechanism weight according to a real-time water level and a fault stress state, generating a standardized space-time risk index according to weighted fusion probability density, and dividing risk levels according to the standardized space-time risk index to obtain a reservoir earthquake risk evaluation result. Therefore, according to the design, dynamic and quantitative evaluation of the reservoir-induced earthquake risk can be realized.

The invention relates to the technical field of earthquake monitoring and early warning, in particular to an active fault earthquake risk dynamic assessment early warning method and system, and the method comprises the steps: constructing and optimizing an active fault earthquake detection system according to the historical information of an active fault monitoring region; establishing a three-dimensional equidistant grid model of the active fault, and screening information acquisition points in the active fault seismic detection system according to the acquisition point error analysis model to determine a target monitoring point of the system; performing information monitoring on the active fault through the detection system and the target monitoring point to obtain active fault earthquake monitoring data; and predicting the relative displacement condition of the active fault based on the monitoring data and the three-dimensional equidistant grid model, and realizing the evaluation and early warning of the active fault earthquake by combining the relative displacement condition and the active fault earthquake monitoring data. The active fault can be monitored in real time, the motion condition of the active fault can be analyzed, evaluation and early warning of active fault earthquakes are achieved, and the occurrence possibility and the influence range of the earthquakes are accurately predicted.

The invention belongs to the technical field of earthquake risk assessment, and provides a multi-factor calculation method for constructing a space distribution function of a potential source area, which comprises the following steps: selecting a plurality of factors for evaluating the earthquake risk of the potential source area; determining a factor combination participating in calculation of a space distribution function for different earthquake magnitude grades; performing quantitative assignment on each selected factor of each potential seismic source region; and according to the factor combination, carrying out average calculation on the normalized value of each factor in each potential seismic source region to obtain a space distribution function value of the potential seismic source region corresponding to the specific seismic magnitude grade. According to the method, different factor combinations are adopted for calculation for different earthquake magnitudes. The design is based on the understanding of seismic activity control factors of different earthquake magnitudes, so that the calculation result can more finely reflect the relative danger degree of the potential seismic source area on different earthquake magnitude levels, and reliable technical support is provided for compiling a more accurate seismic oscillation parameter zoning map.

The application discloses a structural seismic optimization design method, belonging to the field of civil engineering and seismic design, and comprising the following steps: step 1, selecting a representative seismic intensity based on a site seismic hazard curve; step 2, obtaining layer stiffness and layer shear strength of an initial building; step 3, establishing a multi-degree-of-freedom layer model by using OpenSees software; step 4, evaluating component damage and economic loss by using a HAZUS database; step 5, constructing a cost constraint model and an equivalent implementation; step 6, jointly optimizing the shear strength and stiffness of each layer of the structure by using a particle swarm optimization algorithm; and step 7, outputting the optimization result: outputting optimal layer shear strength and stiffness distribution. The application realizes the reduction of economic loss under the premise of ensuring that the construction cost is unchanged by combining site hazard, component damage and loss evaluation and a swarm intelligent algorithm.

The invention discloses a dense array earthquake real-time monitoring and risk analysis method and system, and belongs to the field of energy development and production. The method comprises the following steps: performing format conversion and segmentation processing on original seismic data acquired in real time; seismic facies pickup, seismic correlation, seismic relocation and seismic magnitude calculation are sequentially carried out based on the preprocessed seismic data, finally, seismic risk quantitative evaluation is carried out based on results of the four stages by two evaluation methods, and finally, evaluation results are visualized and fed back to a user side. According to the invention, automation from data acquisition, real-time processing to risk analysis is realized, and the timeliness of induced earthquake monitoring and the reliability of risk assessment are remarkably improved.