881 results about "Seismic wave" patented technology

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 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 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 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 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 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.

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 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 discloses a seismic wave velocity-resistivity joint inversion method based on a time dimension, belongs to the technical field of geophysical exploration, and can solve the problem that the dynamic change of a geological structure cannot be determined in the prior art. The method comprises the following steps: constructing a seismic wave velocity distribution model according to seismic exploration data of a target area, and constructing a resistivity distribution model according to electromagnetic exploration data of the target area; determining an objective function of seismic wave velocity-resistivity joint inversion according to the seismic wave velocity distribution model and the resistivity distribution model; updating and iterating the seismic wave velocity distribution model and the resistivity distribution model according to the seismic exploration data and the electromagnetic exploration data of the plurality of periods until the objective function is minimized; and according to the corresponding seismic wave velocity distribution model and the resistivity distribution model when the target function is minimized, respectively determining seismic wave velocity inversion results and resistivity inversion results of the target area in multiple periods. The method is used for inverting the seismic wave velocity and the resistivity.

The invention provides a geological structure intelligent detection system and method for a deep special space, and relates to the technical field of geological exploration. The geological structure intelligent detection system and method for the deep special space comprises the steps that S1, a distributed sensor network is deployed in the deep special space, and seismic wave, electromagnetic field, sound wave, temperature and strain multi-physical field original detection data are synchronously collected; constructing a noise channel model based on a quantum computing principle, and modeling the original detection data; a quantum inspired adaptive filtering algorithm is applied, and noise characteristics are estimated in real time through variational optimization. Through synchronous acquisition of a distributed multi-physics field sensor network and an adaptive filtering technology of quantum inspiration, effective enhancement of weak characteristic signals is realized in a strong noise environment, and the signal-to-noise ratio and the target geological structure identification precision are significantly improved; and a neural network inversion model is combined with physical information constraint.

The invention discloses a stratigraphic water content layering inversion method based on a shallow seismic full waveform, and relates to the technical field of geophysical exploration data processing, and the method comprises the following steps: 1, under the same survey line condition, synchronously obtaining a seismic wave full waveform, complex impedance and an induced polarization frequency spectrum; 2, constructing a layered initial model containing a layer boundary line and an in-layer water content initial value; based on the layered initial model, executing dual-domain quantile alignment interface keeping iteration until convergence, and obtaining a final layer boundary line and an in-layer water content field; the double-domain quantile alignment interface maintaining iteration comprises the following steps: updating an in-layer moisture content field and a layer boundary line according to a seismic wave full waveform; and performing total variation regularization processing on the updated in-layer water content field to maintain the transition characteristic of the layer boundary line. According to the method, an efficient and reliable solution with actual guidance value is provided for mineral resource exploration, geological structure detection, groundwater survey and engineering geology evaluation.

The invention relates to the technical field of oil and gas geophysical exploration, and particularly discloses a tight sandstone gas reservoir fluid mobility gradient attribute prediction method and system.The method comprises the steps that a reflection coefficient expression containing a fluid mobility item is constructed, and a formula for calculating fluid mobility through frequency dispersion attributes is deduced; constructing a fluid mobility gradient attribute related to the frequency and the incident angle of the seismic wave; the method comprises the following steps: establishing a double-pore rock physical model, generating a synthetic seismic record, applying frequency variation AVO inversion to the synthetic seismic record, and extracting a bulk modulus dispersion attribute and a shear modulus dispersion attribute; and calculating the fluid fluidity changing along with the frequency and the incident angle, and calculating the gradient attribute of the fluid fluidity based on a gradient attribute calculation formula and the fluid fluidity. According to the method, seismic wave frequency dispersion and attenuation response caused by permeability and gas saturation are fully utilized, the seismic gradient attribute of the fluid fluidity and the calculation method are constructed, and high-precision prediction of the fluid fluidity by applying seismic signal elasticity and frequency dispersion rock physical information is realized.

The invention discloses a tunnel cross-sectional image analysis method based on image processing, and aims to solve the problems that a surface image is not clear in correspondence with a transient seismic wave method, a geological radar and a resistivity imaging section space, and anomalies at a certain distance in front are difficult to map to a tunnel face. According to the method, anisotropic reforming is carried out by adopting a Fourier neural operator, cross-modal Transform registration of a micro attention mask based on sector geometry, curve mileage and section polar coordinate position coding is combined, analytic geometry mapping and uncertainty propagation are matched, tunnel face structure traces and water seepage texture evidences are fused, and the tunnel face structure traces and the water seepage texture evidences are combined. The technical effects of accurate positioning of abnormity on the tunnel face, position confidence range estimation, risk grading early warning, stripe artifact suppression, abnormal boundary reservation and output of structured results of mileage stake numbers, azimuth angles, distance intervals and the like are achieved.

The invention provides a stratigraphic structure and ground pressure distribution detection method based on wave velocity tomography, and relates to the technical field of wave velocity tomography. The method comprises the following steps: firstly, arranging dense linear detector arrays in a monitoring area at an interval of 5m, and collecting the distribution condition of an aquifer, a lithology detection result and a drill core diagram of the monitoring area; recording seismic wave data, carrying out noise reduction processing on the seismic wave data, picking up arrival time, and associating the arrival time with a waveform event; performing path tracking on an observation result by adopting a wave velocity inversion algorithm, generating a wave velocity tomography image and substituting the wave velocity tomography image into the wave velocity-ground pressure evaluation model; and finally generating a ground pressure distribution diagram.

The invention discloses a seismic wave velocity model inversion method based on time-space coupling deep learning, and the method achieves a time-space coupling mechanism through a Fourier neural operator framework of a wavelet transform-attention mechanism, and supports the reconstruction of a spatial two-dimensional velocity model from a time sequence track of multiple seismic sources. Firstly, the local time-frequency representation of each seismic time-domain trajectory is extracted by adopting continuous wavelet transform, and then a wavelet time-frequency spectrogram is coded through a convolution feature extractor. Then, information from multiple sources is fused at each receiver point location through a multi-head attention mechanism, enhancing correlation between multiple sources. And finally, projecting the fused features into a two-dimensional Fourier neural operator to efficiently learn space mapping and reconstruct a bottom-layer velocity model based on sound velocity. According to the method, the time sequence characteristic and the spatial distribution characteristic of the seismic channel are fully utilized, the precision and robustness of an inversion result are improved, and a more reliable speed model is provided for geological structure interpretation and acoustic imaging.

The invention discloses a coal mine tunneling roadway advanced geological exploration and ground stress inversion method, particularly relates to the technical field of seismic signal processing in geophysical exploration, and is used for solving the problems of geological exploration signal distortion and insufficient stress inversion precision caused by strong noise interference of tunneling equipment in the prior art. Rock mass vibration signals are collected through an optical fiber sensor arranged on a roadway wall, equipment noise characteristics are identified through spectral analysis, a quantitative mapping relation between working condition parameters and noise characteristics is established, a noise reference time period is determined according to the quantitative mapping relation, a spatial coherence characteristic template is constructed, high-coherence noise components are inhibited through template comparison, and the noise reference time period is determined according to the spatial coherence characteristic template. Finally, the geological structure and stress field distribution in front of the working face are inverted based on the purified seismic wave signals, a stress concentration area and a disaster risk area are identified, the signal-to-noise ratio of the seismic signals in the strong noise environment and the reliability of the geological inversion result are effectively improved, and accurate technical support is provided for safe mining of a coal mine.

The invention relates to a geological radar advanced forecasting system for TBM shield construction and a detection method, which are characterized in that a transmitting antenna and a receiving antenna are arranged in a tool bit gap of a TBM shield machine, rotation of a tool bit is not affected, measurement can be carried out when the tool bit rotates, and a three-dimensional structure section right in front of the tool bit is further acquired; a method of forming obvious reflection based on geological body physical property dielectric constant difference is adopted, the TBM shield tunneling advancing direction is detected and imaged, a geological structure image is obtained, then geological information such as faults, rich water, cavities and boulders is provided, the influence of geological difference is small, and testing is more accurate compared with a seismic wave method; the receiving antenna and the transmitting antenna are arranged in the tool bit gap of the TBM, the receiving antenna and the transmitting antenna are not affected by the blade, rotation of the tool bit is not affected, and stability and accuracy of rotation measurement of the whole set of forecasting system can be guaranteed.

The invention provides a method for analyzing anti-seismic performance of a steel reinforced concrete frame structure in a construction process, which comprises the following steps: S1, establishing a time-varying calculation model of the steel reinforced concrete frame structure in the construction process, the time-varying calculation model being used for simulating structural incompleteness and boundary condition change in a construction stage; s2, analyzing the anti-seismic performance of the steel reinforced concrete frame structure in the construction process by adopting a time-history analysis method, wherein the time-history analysis is based on a seismic wave acceleration time-history curve; s3, according to the time history analysis result, determining an earthquake response change rule of the steel reinforced concrete frame structure in the construction process; and S4, according to the earthquake response change rule, a measure for improving the anti-seismic safety of the steel reinforced concrete frame structure in the construction process is determined.

The invention discloses a method for automatically inverting seismic slip distribution based on deep learning. The method comprises the following steps: collecting random slip distribution corresponding to different seismic events and synthesized three-component seismic HR-GNSS waveform data, and preprocessing to obtain a sample group; building a SlipNet neural network based on a Transform-CNN model, and carrying out the training through the sample group; and inputting to-be-processed three-component seismic HR-GNSS waveform data into the trained SlipNet neural network, and generating a corresponding inverted seismic fracture area binary distribution matrix and an inverted seismic fracture slippage distribution matrix. The method solves the problem that a traditional seismic slip inversion method is low in calculation efficiency and lacks physical constraints due to single dependence on seismic waves or deformation data, and is difficult to meet the requirements of complex seismic source characterization. The method can be applied to three-component seismic HR-GNSS waveform data of large earthquakes of different types and different magnitudes.

The invention relates to a three-component high-dimensional seismic data reconstruction method and device based on vector tensor reduced rank, a medium and equipment, and the method comprises the steps: firstly, placing three-component high-dimensional seismic data at three imaginary part positions of a quaternion array in a time domain through employing a quaternion tool, and setting a real part of the quaternion array to be 0 value; and realizing vector joint representation of the quaternion-based three-component seismic data. Quaternion representation can protect the nonlinear orthogonal relation of three component seismic data in the space direction, and vector field structural characteristics of seismic waves are kept. And secondly, transforming a time domain quaternion array into a frequency domain by using quaternion Fourier transform, extracting a spatial high-dimensional quaternion array corresponding to each frequency domain slice, and performing missing data restoration and reconstruction on the high-dimensional quaternion array by using a tensor reduced-rank reconstruction technology based on a quaternion parallel matrix decomposition algorithm. According to the method, the memory occupation amount is remarkably reduced, the calculation amount is small, the calculation cost is low, and the reconstruction calculation efficiency is high.

The invention belongs to the technical field of geotechnical engineering disaster prevention and reduction and earthquake engineering tests, and relates to a vibration table test method considering time-varying deterioration of anti-seismic performance of an anchored rock slope. The method comprises the following steps: firstly, constructing a dynamic performance time-varying deterioration model based on an anchoring system interface bonding force and a free section steel bar yield resistance attenuation rule; physical equivalent simulation of interface degradation and component yield resistance reduction is realized by regulating and controlling the proportion of rock mass similar materials and utilizing a graded rigidity anchoring system. The core of the method is that before a vibration table applies seismic waves, attenuation of the anchoring performance of a supporting structure is actively simulated in advance, so that a real evolution path that the bearing capacity of an anchoring system is reduced firstly, slope stress redistribution and accumulated damage are caused, and then sudden seismic loads are suffered is reproduced; the limitation that only the static low bearing capacity is simply simulated in a traditional test is broken through. According to the method, space-time coupling of static time-varying degradation accumulation and transient earthquake dynamic action is effectively achieved, the problems of interface degradation simulation distortion and the like are solved, and a reliable test method is provided for dynamic stability evaluation and reinforcement design of a long-term service anchoring slope.

The invention provides a geological disaster identification method based on seismic wave dynamics and kinematics characteristics, the geological disaster is mountain torrent or debris flow, and the method comprises the following steps: collecting seismic signals in a geological disaster potential risk area; the seismic signals are preprocessed; extracting early warning parameters from the pre-processed seismic signal, wherein the early warning parameters comprise a long and short time window energy ratio, a time-frequency spectrum parameter, a power spectrum density and a seismic signal duration; establishing an earthquake prediction model of the single-measuring-point geological disaster according to the extracted early warning parameters; fusing the earthquake prediction models of the geological disasters at multiple measuring points, and judging whether the earthquake signals are earthquake signals triggered by the geological disasters or not; and when it is judged that the seismic signal is triggered by the geological disaster flow, real-time early warning information issuing is carried out on the downstream risk area. The method can be applied to monitoring and early warning of geological disasters caused by artificial or natural activities such as mountain torrents, dam break, landslides, collapse and the like, and has wide engineering technology application and scientific research prospects.

The invention discloses a joint imaging method and device for DAS-VSP data and ground seismic data, and the method comprises the steps: collecting the ground seismic data, and collecting the DAS-VSP data; performing wave field separation on the DAS-VSP data of the left well and the right well to obtain first seismic wave field information; obtaining second seismic wave field information by adopting a Marchenko iterative algorithm and the ground seismic data; screening same seismic source data from the ground seismic data, the first seismic wave field information and the second seismic wave field information; and performing normalization adjustment on the same-source data to obtain same-source three-dimensional exploration seismic data, performing Kirchhoff prestack migration processing on the same-source three-dimensional exploration seismic data, outputting a migration result, and respectively processing the DAS-VSP data and the ground seismic data to obtain a detection point which is located in the ground surface and the wells on the two sides, and a detection point which is located in the ground surface and the wells on the two sides. Omnibearing three-dimensional exploration data on an underground virtual calibration surface is used for prestack depth migration, and exploration imaging precision is improved.

The invention discloses a multi-source information fusion and intelligent interpretation technology in hot dry rock fracture identification, and the technology specifically comprises the following steps: 1, data collection: collecting multi-source data including seismic reflected waves, logging lithologic parameters, electromagnetic abnormal data and thermal infrared remote sensing images, and building a hot dry rock reservoir physical property parameter database, and 2, data preprocessing: carrying out denoising and offset correction on seismic data, normalizing logging data, inverting resistivity distribution of electromagnetic data, and extracting a temperature abnormal region of a thermal infrared image. The invention relates to the technical field of geothermal development. According to the multi-source information fusion and intelligent interpretation technology in hot dry rock fracture identification, by integrating multi-modal information such as geology, geophysics, remote sensing and drilling data and combining a feature level and decision level fusion strategy, a sensing blind area of a single sensor is effectively made up, the data are preprocessed, and the recognition accuracy is improved. And the capability of effectively inhibiting complex environmental factors such as seismic wave noise and electromagnetic interference is improved.

The invention discloses a physical simulation method and device for seismic response certainty of an urban underground rail transit network, and the method comprises the steps: constructing a mixed seismic source model based on the active fault parameters and kinematic parameters of a target region, the seismic source model integrates a low-wavenumber deterministic slip component and a high-wavenumber random slip component on a fault fracture surface to generate a broadband seismic wave field source reflecting the physical process of a seismic source; the constructed seismic source model is used as input, a regional crustal velocity structure model is combined, propagation of seismic waves in a layered geological medium is simulated through a frequency-wavenumber domain method, and a seismic oscillation space-time field is obtained through calculation; and establishing a finite element model of the urban underground rail transit network, applying the seismic oscillation time-space field as non-consistent seismic oscillation input to the finite element model, and solving through dynamic time-history analysis to obtain the dynamic response of the whole underground rail transit network in the earthquake process. According to the invention, a complete physical chain from seismic source fracture to structural response can be reproduced.