1323 results about "Seismic wave" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a tunnel deep-buried drainage ditch rapid blasting excavation method based on vertical drilling, which comprises the following steps: step 1, detecting the geology in front of a tunnel by using vertical drilling coring, geological radar and TSP seismic wave technology, and constructing a three-dimensional model; 2, generating an asymmetric hole net layout based on a geological model and AI, and setting a drilling-free vibration isolation belt; the design of dense inside and sparse outside is adopted, and vibration propagation is blocked; thirdly, a three-stage air spacer is arranged at the bottom of the blast hole, segmented charging is implemented, the explosive energy release time is prolonged, and the single-segment explosive quantity peak value is reduced; fourthly, an electronic detonator is adopted for millisecond detonation, and peak vibration is weakened through the waveform superposition offset effect; 5, arranging fiber grating sensors in the tunnel, collecting data in real time and transmitting the data to the cloud AI platform; dynamically adjusting parameters based on the monitoring data; and 6, evaluating a blasting effect through a three-dimensional laser scanner, generating an evaluation report in combination with data, updating a BIM model database, and continuously optimizing an AI algorithm.

The invention provides a tunnel construction structure health on-line monitoring method and system, and the method comprises the steps: obtaining stress data, crack images and seismic wave signals of tunnel construction, and carrying out the preprocessing; obtaining and constructing stress-crack joint representation mapping, generating a first joint feature vector, and predicting a lining health state of a future # imgabs0 # time step; generating a second joint feature vector, and determining a stratum stress field of the future # imgabs1 # time step according to the second joint feature vector; constructing a tunnel lining damage probability model according to the stress data and the crack image, and determining an overall construction risk score; and dividing the construction risks into a plurality of grades. According to the method, data multi-modal fusion, structure-stratum joint modeling and AI enhanced prediction and early warning are achieved, the defects in the prior art are effectively overcome, the method can be widely applied to engineering scenes such as tunnel construction, subway construction and underground space development, and the construction safety and the structure health monitoring capacity are remarkably improved.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention relates to the technical field of building information, and discloses an ancient building anti-seismic performance analysis and evaluation system based on BIM modeling, and the system comprises a building parameter obtaining module which is used for dynamically collecting component material attributes, structure connection states and historical damage parameters, recognizing structure weak links, and triggering a collaborative evaluation instruction; the modal feature analysis module and the seismic response simulation module are used for respectively extracting vibration modal feature parameters and seismic wave energy distribution parameters to obtain modal feature integrity indexes and dynamic stability evaluation values; the multi-source fusion judgment module synthesizes the indexes to generate a structure reinforcing or component repairing signal; the optimization parameter generation module is matched with the anti-seismic optimization strategy and generates structure reinforcement and node connection repair parameters; the system further comprises a damage coupling analysis module and a model iteration updating module, and monitoring of historical damage evolution and closed-loop updating of the BIM model are achieved. According to the system, precise evaluation and dynamic optimization of the anti-seismic performance of the historic building are realized.

The invention provides a rainfall and earthquake coupled slope simulation method and system. The method comprises the following steps: constructing a coupling numerical model through multi-physics field data of a slope; the dynamic coupling effect of rainfall and earthquake is simulated by initializing a seepage field and loading earthquake power; the stability of the simulation result is comprehensively evaluated through iteration of a strength reduction method, and whole-process coupling from seepage to dynamic response of the slope is achieved. SeismoSignal is used for processing seismic waves, and the problem that the value of kinematics parameters is not zero when the seismic action is finished is avoided; the allowable maximum negative pore water pressure boundary is assigned to the slope surface of the side slope, the actual steady-state seepage field of the side slope is restored, and the seepage field in the side slope under different rainfall conditions is accurately calculated; by adopting an unsaturated soil dynamic seepage model for a seepage field, Rayleigh damping is introduced and an earthquake load is applied, so that cross-time scale coupling is realized. The method is simple and convenient to operate, high in calculation efficiency and accurate in calculation result, and has practical significance and value in research of the problem in the rock-soil field.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention relates to the technical field of geophysical exploration, in particular to an exploration surveying and mapping method based on remote sensing and a surveying and mapping instrument thereof. The exploration surveying and mapping method based on remote sensing comprises the following steps: step 1, multi-platform collaborative data dynamic acquisition; step 2, multi-modal data fusion and feature enhancement: vegetation noise filtering is performed on the laser radar point cloud data, and a surface terrain model under vegetation coverage is reconstructed; seismic wave speed data and ground penetrating radar dielectric constant data are combined to construct an underground medium physical property combined distribution model; associating the reflectivity characteristics of the multispectral image with the underground physical property parameters, and identifying a geological abnormal region; and step 3, hidden geologic body dynamic modeling and risk assessment. By means of the method, real-time monitoring and risk assessment of the complex geologic body can be achieved, and the geological disaster prediction and prevention and control capacity is greatly improved.

The invention discloses a deep learning seismic source mechanism inversion system and method based on physical driving. The system is characterized in that a seismic wave acquisition module is used for acquiring an actually measured seismic wave waveform; the data preprocessing module preprocesses the data; the data feature extractor is used for extracting seismic waveform data features; the physical constraint optimizer is used for constructing a comprehensive loss item function; the model training module is used for training the seismic source mechanism prediction model; the seismic source mechanism prediction module is used for result inversion; the method comprises the following steps: simulating and generating a synthetic seismic waveform, and extracting spectrum features; preprocessing the data; extracting multi-level enhanced features from actually measured and synthesized seismic waveforms by using a data feature extractor; introducing the fault physical mechanism, the stress tensor conservation and the seismic source tensor constraint into a loss function to form a comprehensive loss item, and constructing a seismic source mechanism prediction model; and performing inversion of an actual seismic event by using the seismic source mechanism prediction model. According to the system and the method, efficient and accurate seismic source mechanism inversion can be carried out on unknown seismic events.

According to the micro-seismic source intelligent positioning method and the model training method and system, physical laws such as a seismic wave equation are integrated, physical loss and data loss are comprehensively considered, the adaptability of the model to a complex geological environment is improved, micro-seismic source positioning challenges in the complex geological environment can be effectively dealt with, and the positioning accuracy of the micro-seismic source in the complex geological environment is improved. Noise immunity and positioning precision are improved, and more reliable technical support is provided for safety monitoring of underground engineering.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention discloses a mine earthquake intelligent identification method based on multi-mode deep learning and signal processing, and belongs to the technical field of mine earthquake identification. Aiming at the problems that a traditional mine earthquake signal processing method cannot fully utilize spatial relevance and frequency domain characteristics of signals in a sensor network, the automation level is relatively low, the response speed is slow, and the emergency processing effect is influenced, seismic waveform data and multi-modal characteristic input are adopted, so that the emergency processing effect is influenced. Based on text semantic features generated by a large language model (LLMs), frequency domain features extracted by Fourier transform and time sequence features after obspy processing, a dynamic graph recognition model of a fusion graph neural network (GNN) and Transform is constructed; graph structure edge weights are dynamically updated through cross correlation between signals, and the structure change of complex environments such as mine roadways is self-adapted.

The invention discloses a coal seam passive source seismic survey system and method based on deep learning, and relates to the technical field of coal mining safety monitoring and early warning, and the method comprises the following steps: S1, collecting the passive source seismic data of a coal seam area; s2, deep fusion features are extracted; s3, constructing a semi-supervised self-attention prediction network model; s4, extracting coal seam structure prediction parameters; s5, the corrected coal seam structure parameters are extracted; s6, constructing a fast seismic wave forward model, and calculating and outputting an initial model of the coal seam underground structure; s7, constructing a Transform optimization network, and extracting a depth feature vector; and S8, constructing a coal seam structure inversion model, and training iteration by using the improved batch depth Bayesian optimization algorithm. The method overcomes the limitation of manual analysis and slow response in a traditional coal seam seismic survey method, and provides an efficient and accurate solution for safe production of a coal mine.

The invention belongs to the technical field of geophysical exploration in an extremely complex tunnel environment, and provides an air-ground and hole-tunnel multi-angle combined tunnel unfavorable geology advanced detection system and method.A first main control unit is used for establishing a unified time reference and controlling an air-ground capacitive induced polarization detection unit to collect air-ground electric field data under the unified time reference; the second main control unit is used for controlling the hole-tunnel induced electric field and natural electric field combined detection unit to collect underground induced polarization response signals and continuous natural electric field signals and controlling the tunnel seismic electric field detection unit to collect seismic electric field signals and seismic wave signals; and the second main control unit is used for receiving the air-ground electric field data sent by the first main control unit, uniformly integrating the air-ground electric field data with an underground induced polarization response signal, a continuous natural electric field signal, a seismic electric field signal and a seismic wave signal which are acquired by the second main control unit to obtain a multi-field data set, and performing interpretation and inversion to obtain a potential unfavorable geologic body identification result. And the disaster source detection precision and efficiency in the extremely complex tunnel environment are obviously improved.

The invention discloses a microseismic source positioning method, device and system, and a storage medium. The method comprises the following steps: dividing a microseismic data sample data set into a training set and a test set; according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder, and according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder; the micro-seismic forward modeling subnet realizes seismic wave field continuation by inputting a micro-seismic source position and a speed model and utilizing a recurrent neural network structure and a convolution operator, and establishes a forward modeling subnet based on a wave equation; constructing an inversion-forward modeling closed-loop neural network according to the micro-seismic inversion subnet and the forward modeling subnet based on the wave equation; and inputting the test set into the inversion-forward closed-loop neural network to carry out micro-seismic source positioning. By adopting the technical scheme of the invention, the limitations on physical constraint, feature modeling and anti-noise capability in the prior art are overcome.

The invention relates to the technical field of rock mass earthquake dynamic response, and discloses a layered rock mass earthquake dynamic response multi-scale monitoring system and method, and the system comprises a rock mass three-dimensional geological module, a simulation setting module and an earthquake simulation module. The method comprises the following steps: establishing a layered rock mass three-dimensional geologic model; the method comprises the following steps: identifying a dangerous rock mass structural surface, monitoring slope displacement, and installing sound emission monitoring equipment at a key position to form a three-dimensional monitoring network; a layered rock mass and a broken area in the layered rock mass three-dimensional geological model are divided into discrete element areas, and a continuous medium area is divided into finite difference areas; coupling the discrete element region and the finite difference region; inputting the seismic wave as a dynamic boundary condition into the finite difference region, transmitting a response to the discrete element region through a coupling interface, and simulating a rock stratum and rock mass dynamic fracture process; and the crack propagation particle size and the original stress propagation are transmitted to the cloud platform. The geometric morphology and the internal structure of the rock mass can be accurately reflected.

The invention discloses a boundary-preserving seismic wave impedance inversion method, device, equipment and medium, and relates to the technical field of geological prospecting. Historical seismic data of a target area and logging curve data including logging impedance parameters are acquired, and seismic wavelet data are extracted from the historical seismic data; carrying out data processing on the seismic wavelet data to obtain target seismic wavelet data, constructing an initial impedance parameter model according to the logging curve data, carrying out forward modeling on the initial impedance parameter model, constructing a composite objective function comprising a boundary preserving constraint term and a prior information constraint term, and carrying out the forward modeling on the initial impedance parameter model based on the composite objective function. And the initial impedance parameter model is updated to obtain the target impedance parameter model, and the output impedance parameter inversion curve is obtained through the target impedance parameter model, so that the inversion precision and efficiency are improved, and the inversion stability and the geological reduction capability are improved.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The invention provides a three-dimensional interface inversion method fusing geological constraints and a level set function, and relates to the technical field of geophysical inversion and imaging, and the method comprises the steps: obtaining geological prior information, carrying out the parametric modeling of an underground structure of a research region through a Gaussian random field, and generating a model parameter vector; and simulating the propagation process of seismic waves in a seismic wave speed model by using a forward modeling calculation method, carrying out iterative optimization on model parameter vectors based on an EKI set Kalman inversion method, recovering an underground three-dimensional structure in a research area, and carrying out interpretation and verification in combination with drilling data to obtain structural characteristics and resource distribution of the research area. According to the method, unified parametric modeling is performed on speed disturbance and interface forms through a Gaussian random field, and a level set function is introduced to represent discontinuous layering, fault cutting, inclusion embedding and other complex geological interfaces, so that collaborative inversion of a speed field and a structure boundary under the same optimization framework is realized; and geometric expressive power and geological consistency of the model are improved.

The invention discloses a gas permeability monitoring method and system based on carbon dioxide geological sequestration, and relates to the technical field of gas permeability monitoring, and the method comprises the following steps: obtaining geological data and a first constraint condition of a target stratum; disturbing the seismic wave frequency to generate a plurality of seismic monitoring control sets; constructing a three-dimensional geological model and a wave velocity change simulation model of the sequestration area, and performing forward simulation to obtain wave velocity change response data under different frequency combinations; wave velocity disturbance characteristic distribution caused by carbon dioxide injection under different earthquake monitoring control sets is obtained, and the set evaluation model is weighted and trained; constructing a second set according to an evaluation result of the set evaluation model and a corresponding earthquake monitoring control set; and fitting a multi-dimensional curved surface according to the second set, and screening an optimal seismic monitoring control set for seismic data acquisition and inversion, thereby solving the problem that traditional single-frequency-band inversion cannot dynamically match a stratum structure and permeability characteristics.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The invention relates to the technical field of seismic source analysis and inversion, and discloses a seismic source analysis and inversion system based on mine earthquake monitoring, which is characterized in that energy parameters are generated through waveform feature extraction, mine earthquake event identification and accurate positioning are realized by using a space-time aggregation rule, and seismic source mechanism parameters are inverted in combination with a seismic wave initial motion parameter and amplitude relationship. On the basis, a regional stress field initial inversion model is constructed, a stress gradient is calculated in combination with a working face track and stratum information, a principal stress rotation composite index is determined through interlayer mechanical difference and a spatial change rate, and self-adaptive deformable partitions are generated. And finally, secondary stress field inversion is carried out in the subareas, the corrected maximum principal stress direction is output, high-precision dynamic analysis of a mine earthquake mechanism and stress distribution is realized, and the mine disaster prediction and early warning capability is remarkably improved.

A seismic wave identification method based on deep learning comprises the following steps: step 1, acquiring seismic wave signals to obtain original data, and performing center interception and normalization processing to obtain unified signals; 2, decomposing a unified signal by using CEEMDAN to obtain a plurality of orders of intrinsic mode functions and residual components, carrying out coarse graining processing on each order of mode function according to a preset time scale parameter to divide into subsequences, constructing a similarity criterion through a fuzzy membership function, calculating fuzzy entropy values of the subsequences, and integrating the fuzzy entropy values into a feature matrix; and 4, inputting the feature matrix after the dimension reduction into a CTCM-1D-CNN model optimized by a tribe competition and member cooperation algorithm, predicting the type of an output seismic wave, and completing seismic wave identification. Therefore, the design has good seismic wave identification precision and stability.

The invention discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions are remarkably improved.

A 3D inversion method of DC method using seismic data constraints is disclosed, comprising processing of seismic data and inversion processing of resistivity data; by utilizing seismic wave interval velocity information to constrain the high-density electrical inversion, which can play a complementary role to a certain extent, it is beneficial to supplement the resolution of resistivity inversion with 3D seismic image information, reflect a boundary of subsurface electrical anomalous body, and further characterizes the boundary characteristics of anomalous body through comprehensive geological-geophysical analysis. Seismic data constraints are applied to 3D resistivity inversion, performing numerical simulation and inversion of electrical data under undulating terrain using the finite volume method and Gauss-Newton inversion method. Furthermore, the seismic anisotropic velocity tensor constraint is introduced for inversion to mitigate the non-uniqueness inherent in single-method inversion, yielding a more accurate 3D subsurface electrical structure and improving the resolution of longitudinal boundaries.

The invention discloses a three-dimensional fine geological modeling method for conventional and unconventional oil and gas reservoirs, which comprises the following steps of: firstly, performing multi-source data integrated management: integrating heterogeneous data such as drilling data (lithology and logging curves), a geological profile map, seismic exploration data (seismic waves and induced polarization), construction dynamic data (tunnel face sketches and laser point clouds) and the like; unified storage and dynamic calling are realized; by integrating multi-source data of earthquake, logging, geochemistry and the like, a high-precision three-dimensional model is established, such as a drilling data regularization processing algorithm and a point cloud hole filling algorithm, the problems of inconsistent data formats and space missing are effectively solved, and the geometric precision and topological integrity of the model are improved. Therefore, the problems of difficulty in multi-source heterogeneous data fusion and insufficient model precision in existing oil and gas reservoir three-dimensional geological modeling are solved; through the multi-dimensional data fusion and intelligent modeling technology, the precision of the three-dimensional geologic model of conventional and unconventional oil and gas reservoir development is improved.

The invention relates to the technical field of safety engineering, in particular to a tunnel unfavorable geologic body recognition system and method based on fused geological information, and the method comprises the steps: data collection and preprocessing, geological information quantification, sample set construction and enhancement, Faster-RCNN model construction of fused geological information, model training and optimization, and unfavorable geologic body intelligent recognition. The intelligent recognition of the unfavorable geologic body of the tunnel is realized by fusing the geological information and the seismic wave method imaging data and utilizing the convolutional neural network model, the recognition precision and robustness are effectively improved, and effective technical support is provided for tunnel construction safety early warning.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

The invention discloses a mining area disaster control-oriented dual-stage scheme construction method, and relates to the technical field of mine safety engineering, and the method comprises the following steps: S100, constructing a high-resolution multi-mode joint detection system, and carrying out the cooperative arrangement of a seismic wave array and an electrical method profile, and synchronously collecting the wave impedance response and electrical property anomaly characteristics, and obtaining a physical anomaly information set covering full depth. According to the method, through multi-mode joint detection and heterogenous data fusion, the precision and integrity of deep structure recognition are improved, and in combination with geological evolution reasoning and dynamic inversion, recognition blind areas are complemented, and accurate positioning of high-risk disaster-causing units is realized. Based on matching of a disaster index model and a parameterization strategy, automatic mapping of disaster partition and a treatment scheme is achieved for the first time, scientificity, targeting and resource allocation efficiency of disaster treatment are remarkably improved, and the method has important engineering application and popularization value.

The invention provides a high-pile wharf anti-seismic toughness evaluation method and system based on finite elements, and relates to the technical field of high-pile wharfs, and the method comprises the steps: firstly constructing a finite element soil body model and a finite element wharf model at a high-pile wharf, and forming a finite element coupling model after coupling for simulating an earthquake situation under a real condition; after simulated seismic waves are applied to the finite element coupling model, the damage condition of the soil body, the damage index of the wharf, the energy dissipation of the wharf and the ductility coefficient of the wharf under the action of the seismic waves are analyzed respectively; wherein the damage index, the energy dissipation coefficient and the ductility coefficient are used for evaluating the damage degree, the energy absorption and consumption capacity, the deformation capacity and the ductility of the wharf respectively, fitting analysis is conducted on analyzed parameters, a comprehensive toughness evaluation index for reflecting the anti-seismic capacity of the high-pile wharf is generated, and finally the anti-seismic grade of the wharf structure is output. Workers are assisted to complete shock resistance toughness evaluation of the wharf, and the working efficiency is greatly improved.