797 results about "Seismic exploration" patented technology

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 seismic exploration, and discloses a pre-stack gather optimization method based on multi-dimensional constraint dynamic time warping, which remarkably improves the space continuity and stability of a time shift field. According to the method, the time shift field which is smoother and more continuous in a three-dimensional space can be generated, and the common abnormal jump and unreasonable jitter of the time shift field in a conventional DTW method are effectively inhibited. This better conforms to the gradient characteristics of the geologic structure. Mismatching and processing illusion are effectively avoided, the adaptability of a complex structure area is improved, the global optimization capacity of the DTW algorithm is enhanced through multi-dimensional constraint, and the phenomenon that different horizons or effective waves are wrongly matched with interference waves due to local waveform complexity or noise interference is reduced. Therefore, common processing illusions such as event dislocation and local distortion in a conventional DTW method can be remarkably reduced.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention discloses a geological modeling method for fusing remote sensing and seismic data based on a multi-mode auto-encoder, and the method comprises the steps: collecting a remote sensing image covering a target area and seismic exploration data, and converting the attribute of the seismic exploration data into a resolution consistent with the remote sensing image; respectively extracting remote sensing image features and seismic attribute features by using a parallel double-branch coding structure of a multi-mode encoder; a fusion module is arranged in the middle layer of the remote sensing image encoder and the seismic attribute encoder, fusion is carried out in the fusion module, remote sensing images and seismic data are reconstructed by using a decoder based on fusion features, and difference data between reconstructed data and the remote sensing images and the seismic data are calculated based on a combined loss function introducing regular terms. Training a multi-modal encoder by using the difference data to obtain an encoding feature and a difference heat map; and on the basis of the coding features and the difference heat map, key geological structure region identification and visual display are carried out, and key geological structures comprise faults and lithology.

The invention discloses a three-dimensional tomography method and system for seismic exploration data migration, and relates to the technical field of geological exploration, and the three-dimensional tomography method comprises the following steps: laying a layered heterogeneous sensing array of adaptive terrain coupling in an exploration target area, the layered heterogeneous sensing array comprises a shallow fiber grating sensing layer, a middle MEMS sensing layer and a deep distributed fiber sensing layer, sensors are all provided with terrain adaptive coupling assemblies, and sensing shafts all point to the radial direction of an underground medium. According to the seismic exploration three-dimensional tomography imaging method, an iterative imaging mechanism of real-time geological constraint feedback is constructed, the limitation of traditional static geological constraint one-time importing is broken through, the imaging process can dynamically fuse newly added local geological data in the exploration process, the imaging model and the actual geological conditions are continuously adapted and optimized, and the imaging efficiency is improved. The integrating degree of the imaging result and the actual geological condition is enhanced, and the high requirement of deep complex area exploration for the imaging accuracy is met.

The invention provides a three-dimensional geological structure modeling method, device and system based on drilling information. The method comprises the following steps: S1, collecting drilling data, seismic exploration data and remote sensing image data of a target area; s2, constructing a neural network model composed of a simple full-connection module, a complex convolution residual module and a hierarchical integration module which are connected in sequence; s3, training the initial three-dimensional geologic model by adopting a joint loss function; and S4, in the network training process, a plurality of geological scene models are generated, and each scene model corresponds to a group of different network parameter combinations. According to the three-dimensional geologic structure modeling method, device and system based on drilling information, a spatial receptive field for feature extraction is expanded through a three-dimensional cavity convolution residual block of a complex convolution residual module according to a symmetric expansion rate, input features and deep convolution features are added, processing is carried out through a hierarchical integration module, and the three-dimensional geologic structure modeling method, device and system based on drilling information are obtained. And geologic structure correlation characteristics across drill holes can be effectively captured.

The invention provides a shale reservoir fracture prediction method and system based on three-dimensional full-waveform inversion and a medium, and relates to the technical field of data analysis, and the method comprises the steps: arranging a multi-channel seismic exploration instrument in a shale reservoir region, and collecting full-waveform seismic data, including longitudinal waves, transverse waves and reflected waves; performing three-dimensional full-waveform inversion based on the logging data, the geological structure map and the full-waveform seismic data to generate a shale reservoir attribute model; a multi-scale inversion strategy is constructed, fracture sensitivity analysis and fracture form dynamic prediction are carried out, and reservoir fracture distribution characteristics and reservoir fracture form characteristics are obtained; discrete fracture point connection and fracture network reconstruction are carried out, a reservoir three-dimensional fracture network model is constructed, and fracture prediction visualization display is carried out. The technical problems that in the prior art, only inference based on limited data can be provided, comprehensive capture of a complex fracture network is lacked, distribution and form of fractures cannot be comprehensively and accurately described, and reservoir analysis and mining decisions are affected are solved.

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 belongs to the technical field of seismic exploration and artificial intelligence, and relates to a physical perception collaborative fusion-based full waveform inversion neural network construction method, which comprises the steps of required model data construction, full waveform inversion neural network construction, loss function construction, network pre-training, and network fine tuning and application. Physical feature perception and collaborative fusion are carried out through a U-Net network and an adaptive residual learning module, so that the problem that traditional full-waveform inversion depends on synthetic data training and is not matched with an actually measured data field is solved. The designed pre-training strategy not only can get rid of dependence on a large-scale synthetic data set, but also can effectively relieve the domain offset problem through fine adjustment of measured data, thereby improving the generalization ability of the model. And the adaptive residual learning module enables the neural network to adaptively and dynamically optimize feature expression, and improves inversion precision through collaborative fusion of a physical perception mechanism. According to the method, the physical consistency and imaging reliability of speed model prediction can be remarkably improved while the labor cost is reduced.

The invention discloses a seismic image super-resolution reconstruction method based on a double-branch multiple attention network, and belongs to the technical field of seismic exploration, and the method comprises the steps: constructing a data sample library for model training; adopting a super-resolution backbone network and a gradient branch auxiliary network to construct a double-branch multiple attention network model; and training the double-branch multiple attention network model by adopting the data sample library and the mixed loss function to obtain a network model for realizing seismic image super-resolution reconstruction and high-frequency texture information reconstruction, and performing reconstruction based on the trained network model to obtain a seismic image subjected to super-resolution reconstruction. According to the method, a gradient branch is introduced as an auxiliary network, so that the super-resolution reconstruction effect is enhanced. Meanwhile, a mixed loss function is adopted to train the network, and a loss function for constraining high-frequency information is particularly introduced, so that the common structural deformation problem in a super-resolution task is effectively solved, the generation of artifacts is prevented, and a new technical approach is provided for high-precision seismic interpretation.

One or more embodiments of the invention provide a Scholte wave frequency dispersion curve adaptive inversion method and a related device, and the method comprises the steps: obtaining and processing the original seismic data of a target seabed, and obtaining a Scholte wave frequency dispersion curve; constructing an initial shear wave velocity model through linear inversion based on a Schlte wave frequency dispersion curve; and performing fitting optimization on the Schlte wave frequency dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile. According to the technical scheme, the problem that a traditional linear inversion method depends on an initial model is solved, the calculation efficiency is remarkably improved, the problem of local extremum is avoided, and an automatic high-precision inversion solution suitable for batch data processing is provided for submarine seismic exploration.

The invention provides a method for identifying a middle-level boundary of a geological envelope based on artificial intelligence. The method comprises the following steps: determining seismic exploration data and logging data of a target work area; extracting seismic reflection features from the seismic exploration data based on the trained first neural network model, and extracting logging sequence features from the logging data by using the trained second neural network model; according to the seismic reflection characteristics and the logging sequence characteristics, geological characteristic representation of the target work area is generated; based on formation contact relation constraint and lithologic sequence constraint which are constructed for a target work area, according to geological characteristic representation, determining implicit topological structure representation of a representation geological envelope body in the target work area; and processing the implicit topological structure representation based on the trained third neural network model to predict the horizon boundary of the geological envelope in the target work area. According to the invention, the prediction result of the horizon boundary of the geological envelope body better conforms to the actual geological law.

The invention relates to the technical field of seismic exploration, in particular to a seismic offset angle gather generation method and device, equipment and a medium. The method comprises the following steps: acquiring pre-stack seismic data of a work area after preprocessing; performing pre-stack time migration on the pre-stack seismic data, and performing grouping accumulation according to inclination angles in the pre-stack time migration process to generate an inclination angle gather; according to the inclination angle gather, picking up Fresnel zone boundaries of reflection events of all imaging points according to preset intervals; stable-phase offset is carried out by using the picked Fresnel zone boundary, and an offset angle gather of stable-phase control is generated according to grouping accumulation of incident angles in the offset process. The problems that an existing method for generating an angle gather through pre-stack depth migration is difficult in interval velocity modeling and high in calculation cost are solved.

The invention provides a seismic exploration path planning method, system and device and a medium, and relates to the technical field of seismic exploration assistance, and the method comprises the steps: a laser radar module obtains laser radar data of a target region; the fusion correction module obtains current attitude information and current positioning information of the unmanned aerial vehicle, and determines target three-dimensional point cloud data according to the current attitude information, the current positioning information and the laser radar data; the data processing module determines earth surface types and terrain gradients respectively corresponding to different sub-regions in the target region through a machine learning algorithm based on the target three-dimensional point cloud data; constructing a three-dimensional trafficability map of the target area based on the earth surface type and the terrain gradient of each sub-area; and the path planning module performs path planning by adopting a preset path planning algorithm based on the three-dimensional trafficability map to obtain a target exploration path, and the target exploration path is a transportation path of the target equipment. According to the invention, the planning precision and the planning efficiency are improved.

The invention discloses a scattered wave seismic acquisition and imaging method based on a full random observation system, which belongs to the technical field of seismic exploration and comprises a quasi-Monte Carlo random observation array design step, a scattered wave intelligent identification and enhancement step, a sparse wave field reconstruction step and a multi-target optimization imaging step. The limited equidistant property is ensured through the random observation array design based on a low-difference sequence, intelligent separation and enhancement of scattered waves are realized by using a physical constraint neural network, a sparse reconstruction problem is solved by using an alternating direction multiplier algorithm, a multi-target optimization imaging framework is constructed to realize high-resolution imaging, and high-resolution imaging is realized. The four core steps form a closed-loop cooperative system, sparse reconstruction quality evaluation feedback optimizes observation array design, imaging quality measurement feedback adjusts scattered wave identification parameters, and cooperative interaction is achieved.

The invention relates to a DAS-VSP seismic data noise suppression method based on a double-domain generative adversarial network, and belongs to the field of seismic exploration data denoising and deep learning. The method comprises the following steps: constructing a double-domain generative adversarial network, determining an optimal hyper-parameter combination by Bayesian optimization, adding actual noise to a pure seismic signal obtained by a forward modeling method to construct a complete training set, training the double-domain generative adversarial network, and testing the double-domain generative adversarial network. According to the method, DAS-VSP data which is low in signal-to-noise ratio and contains various kinds of complex noise can be effectively processed, the denoised seismic signals are clearer in structure, better in continuity, higher in signal-to-noise ratio and more thorough in noise suppression, effective signals are reserved to the maximum extent, high-quality basic data are provided for subsequent seismic data processing and explanation, and the method is suitable for large-scale popularization and application. The method meets the high-precision requirement of current seismic exploration, and has a wide application prospect in the field of oil and gas resource exploration and development.

The invention relates to the technical field of oil-gas exploration, in particular to a micro-amplitude structure seismic exploration detection method and system based on pre-stack depth migration, and the method comprises the steps: generating an equivalent speed model set, calculating speed uncertainty data, probabilistically detecting a micro-amplitude structure, and fusing and displaying a result. Compared with the prior art that an absolutely correct speed model is blindly pursued, but the influence of model errors on tiny structure recognition cannot be overcome finally, the method has the advantages that the speed model errors are converted into valuable detection signals from interference needing to be minimized; by actively generating multiple sets of global equivalent speed models and analyzing the uncertainty of the speed models, the most unstable areas in the modeling process become direct evidences for indicating the existence of the micro-amplitude structure, normal form transfer from error elimination to error utilization is realized, and the detection capability of the micro-amplitude structure is remarkably improved.

The invention belongs to the technical field of seismic exploration, and particularly relates to a coal mine underground hydraulic hammering type controllable seismic source vehicle device. The device comprises a crawler, a rotating platform and a hydraulic hammering system, the rotating platform, a four-column type lifting hydraulic support installed on the rotating platform and a hydraulic support in the hydraulic hammering system are hydraulically controlled, and the hydraulic hammering system can conduct 360-degree horizontal rotation, height stepless adjustment and horizontal stretching and retracting. And meanwhile, the crawler-type vehicle body and remote control operation are adopted, so that the crawler-type underground coal mine hammering vehicle is suitable for a complex walking environment in an underground coal mine, and the construction efficiency and the safety are improved.

The invention belongs to the technical field of seismic exploration and artificial intelligence, and relates to a seismic data high-resolution reconstruction and random noise suppression method, which comprises the following steps of: performing feature extraction and reconstruction on low-resolution noisy seismic data through a classic generator to generate a high-resolution denoised seismic image; and discriminant learning is carried out on real seismic data and a generation result through a quantum enhanced double-path discriminator, and the reconstruction capability of the generator is continuously optimized. According to the quantum enhanced double-path structure designed by the invention, the modeling capability of the model on complex seismic signals can be improved, the reconstructed image is finer, the structure is clearer, the robustness of the network on random noise can be enhanced, and more effective noise suppression is realized. According to the invention, the residual learning mechanism enables the feature interaction between the layers to be more sufficient, and facilitates the transmission and fusion of the seismic features at different layers, thereby improving the overall reconstruction quality. According to the method, the suppression effect on random noise can be remarkably improved while high-resolution reconstruction of seismic data is realized.

The invention discloses an electric spark source multi-wave micro-logging system and method, and relates to the technical field of seismic exploration, in particular to an electric spark source multi-wave micro-logging system which comprises a cable, a transverse wave converter, a seismic recorder, an electric spark source, a first three-component detector, a second three-component detector and a longitudinal and transverse wave data processing system. An electric spark electrode column is longitudinally arranged in an inner cavity of the transverse wave converter; after being started, the electric spark source discharges through an electric spark electrode column, water molecules at the position where the electric spark electrode column is located are electrolyzed to generate gas through discharging of the electric spark electrode column, the gas impacts a stratum to generate seismic waves, and a first three-component detector and a second three-component detector receive and record seismic wave signals and transmit the seismic wave signals to a longitudinal and transverse wave data processing system; the depth of the transverse wave converter and the depth of the electric spark electrode column are increased by pulling the mooring rope, so that the electric spark electrode column can excite a seismic source at a plurality of observation points, and the purpose of exciting the seismic source for multiple times is achieved.

The invention discloses a reservoir fracture parameter quantitative inversion method and system and a medium. Relates to the technical field of seismic exploration. A shale reservoir rock physical model and a nonlinear relation between fracture parameters and seismic azimuth difference data are constructed, an inversion data set is synthesized by taking the nonlinear relation between the fracture parameters and the seismic azimuth difference data as a constraint condition based on the shale reservoir rock physical model, and an inversion framework driven by the physical model is realized. According to the framework, a labeling sample containing real physical response is generated through rock physical modeling, a network learning process is constrained, multi-parameter collaborative optimization is realized in combination with seismic response, and a solution idea is provided for breaking through the bottleneck of a crack quantitative inversion technology.

The invention discloses a cooperative regulation and control method for seepage and heat transfer in a heterogeneous medium, and relates to the technical field of heterogeneous media, and the method comprises the following steps: S1, through a logging technology, a seismic exploration technology and a drilling sampling experiment, obtaining spatial distribution parameters of a target heterogeneous medium, constructing a heterogeneous parameter field, and obtaining a heterogeneous parameter field; the spatial distribution parameters comprise permeability distribution data, heat conductivity coefficient distribution data and medium deformation data; and S2, based on the permeability distribution data, the heat conductivity coefficient distribution data and the medium deformation data, establishing a seepage, heat transfer and medium deformation three-field coupling model. Three fields of seepage, heat transfer and medium deformation are coupled and incorporated into a regulation and control system, a mathematical model containing seepage and heat transfer control equations is established, the multi-field interaction process is accurately simulated, the limitation of isolated regulation and control of a traditional method is avoided, and a quantitative theoretical basis is provided for coordinated regulation and control.

A reflective focusing-based depth-velocity modeling method, relating to the field of seismic exploration, comprising: determining an actual seismic record and an initial velocity model (S1); on the basis of a seismic observation system, performing forward simulation on the initial velocity model, and obtaining forward wave field data (S2); on the basis of the actual seismic record and the forward wave field data, calculating a focusing function of a reflected wave residual response, calculating a Green's function of the reflected wave residual response on the basis of the focusing function, and, on the basis of the Green's function and the forward wave field data, calculating a gradient of an objective function with respect to a longitudinal wave velocity (S3); determining an updated velocity model on the basis of the gradient of the objective function with respect to the longitudinal wave velocity and an update step size (S4); and on the basis of the actual seismic record and the updated velocity model, determining whether a termination condition has been met (S5), if so, using the finally obtained updated velocity model as a depth-velocity model, and if not, re-performing forward simulation and the following steps. Further related is a reflective focusing-based depth-velocity modeling apparatus.

The invention discloses an offshore field two-dimensional seismic data rapid imaging method, and relates to the technical field of seismic exploration data processing, and the method comprises the steps: obtaining SEGD format original seismic data of a target region from a seismic collection system, combining the data of each shot of each measuring line, decoding the data into internal available data, and obtaining a to-be-processed data body; and loading an observation system and assigning seismic trace header parameters according to actual acquisition parameters based on the decompiled to-be-processed data volume. According to the invention, through a multi-stage denoising and signal protection strategy, effective suppression of various sea interferences is realized, a frequency division multi-channel statistical abnormal amplitude suppression technology is adopted, zero-phase high-pass filtering is combined, low-frequency effective signals are protected while low-frequency strong-energy background noise is removed, and the noise suppression effect is improved. The direct wave cutting step further eliminates strong energy interference under a long cable condition, highlights an effective reflection signal of middle and far offset data, forms a progressive signal-to-noise ratio improvement mechanism from original data to final imaging, and improves the interpretability of seismic data.

The invention relates to the technical field of seismic exploration, in particular to a method for determining structural parameters and sweep frequency signals of a controllable impact seismic source, which is used for carrying out an indoor physical test of a controllable impact seismic source model machine and ensuring the effectiveness of a design scheme of the controllable impact seismic source. Constructing and verifying a multi-body dynamics numerical model of single impact, performing single variable sensitivity analysis on key structure parameters, and determining an optimal structure parameter combination by taking a frequency domain of output seismic waves as an evaluation index; constructing a multi-impact model, performing a simulation test on the sweep frequency control parameter, and determining an optimal sweep frequency signal parameter based on the main lobe amplitude and the main lobe-side lobe amplitude ratio of the output signal autocorrelation function; through forward modeling and cross-correlation-scattered wave inversion imaging processing, an application method of a controllable impact seismic source is established, and validity is verified. The technical problems that a traditional trial and error method is low in efficiency, main lobe energy of seismic source output signals is dispersed due to an unknown parameter influence mechanism, side lobe interference is large, and the signal-to-noise ratio is low are solved.

The invention discloses a mine geology advanced detection and real-time monitoring system based on a six-component seismograph, and relates to the technical field of coal mine underground seismic exploration. The six-component seismographs are respectively arranged on coal walls of a coal face and a driving face, synchronously collect original data at the same time, and upload the collected original data to the background server through the wireless repeater and the industrial looped network; the background server converts the original data into a geophysical inversion result; and the background server carries out real-time dynamic interpretation and artificial intelligence dynamic early warning on parameters of the stratum form, the geological structure and the rock mechanical property according to a geophysical inversion result, uploads early warning information to a control center and distributes the early warning information to personnel and mechanical equipment of a working face. Prediction and early warning of parameters such as stratum form, geologic structure and rock mechanical property are realized, and dynamic high-precision spatio-temporal information is provided for an intelligent mine transparent geologic support system.

Systems and methods for building a near surface velocity model for a subsurface formation include obtaining seismic data representing a subsurface formation; forming seismic gathers based on the seismic data; and determining uphole vertical velocities for the subsurface formation based on uphole seismic survey data. A training dataset is formed including input features that include a subset of the seismic gathers and labeled output data that includes the uphole vertical velocities corresponding to the subset of seismic gathers. A machine learning model is trained using the training dataset; and a near surface velocity model is generated for the subsurface formation using the machine learning model that takes as input the seismic gathers.

The invention provides a seismic data bidirectional frequency band expansion method based on carrier modulation, which relates to the field of seismic exploration, and comprises the following steps: processing original seismic data through time window segmentation, calculating an autocorrelation function and carrying out time domain continuation; solving a prediction coefficient based on entropy maximization constraint to generate an amplitude spectrum; calculating a boundary attenuation rate through boundary frequency point positioning; constructing an attenuation compensation function to generate a frequency domain carrier function; applying sparse constraint to solve a weighting factor and iteratively calculating a modulation amplitude spectrum; and finally, carrying out combined inverse transformation with an original phase spectrum to obtain expanded data. According to the method, the resolution and the signal-to-noise ratio of the seismic data are effectively improved, and the seismic interpretation precision is improved.

The invention belongs to the technical field of seismic exploration data processing, and discloses a seismic data reconstruction method and system based on dynamic region perception. According to the method, a seismic data reconstruction model is established, a dynamic region sensing basic module is introduced into a model encoder, a known original mask is utilized to carry out hard division on a feature map, independent normalization and affine transformation are respectively carried out on a complete region and a missing region, pollution of invalid information of the missing region to feature distribution of the complete region is blocked, and the accuracy of feature distribution is improved. And the precision and reliability of data reconstruction are improved. A dynamic region perception learning module is used in a model decoder, a learnable double-path pooling and convolution operation is used for dynamically generating a spatial response diagram with geological semantics, self-adaptive region division is achieved, the network can capture missing feature distribution which is left after multi-layer convolution and has fuzzy boundaries in a deep layer, and therefore the spatial response diagram with geological semantics is obtained. The problem of fuzzy mask division in a deep network is solved, and finer and more robust reconstruction of complex geological structures and weak signals is realized.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.