470 results about "Geophysical prospection" patented technology

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 geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

The invention relates to the technical field of mine safety monitoring and geophysical exploration, in particular to a mine water disaster intelligent alarm system responding to multi-dimensional physical field parameter abnormity, which comprises a full-space data acquisition module for acquiring monitoring area observation data and calling a background geological physical model; the ideal reference reconstruction module is used for constructing an ideal physical field reference in a non-abnormal state; the double difference extraction module is used for calculating an observation residual error of observation data relative to a reference and a theoretical residual error of simulation response relative to the reference; the inversion coupling verification module is used for driving the theoretical residual error to approach to the observation residual error and extracting parameter sensitivity characteristics; the intelligent alarm judgment module is used for analyzing sensitivity characteristics and convergence states; generating an alarm instruction if the feature is determined to be the entity fluid abnormal feature; if it is determined that the target abnormal interference feature is not the target abnormal interference feature, signal suppression processing is executed; according to the invention, the false alarm rate of mine water disaster detection is greatly reduced.

The invention relates to a geotechnical engineering geological investigation system and an investigation method thereof, which are used for solving the problems of drilling redundancy, repeated model correction and long period caused by traditional uniform hole distribution. The system comprises a geophysical exploration module, a rock-soil body structure feature extraction module, a geological abnormal area identification module, a survey detection point dynamic optimization module, a drilling sampling analysis module and a geotechnical engineering stability evaluation module. Earth material exploration is carried out on representative points to obtain a comprehensive geophysical prospecting data set, inversion is carried out according to the comprehensive geophysical prospecting data set to obtain a three-dimensional rock-soil structure, an abnormal area is recognized, accordingly, the drilling layout is dynamically optimized, and stability evaluation is completed at a time after a small amount of directional drilling sampling is carried out. By adopting the system, a high-precision geologic model and a stable evaluation result can be obtained with fewer drilled holes in a preliminary exploration stage, the cost is remarkably reduced, and the period is shortened.

The invention discloses a three-dimensional intelligent seismic velocity modeling method based on a two-dimensional self-attention U-shaped network and low-rank adaptation, and belongs to the technical field of seismic services of geophysical exploration. The problems that in the prior art, a traditional three-dimensional intelligent seismic velocity modeling method is low in efficiency and insufficient in model generalization ability are solved. The method comprises the following steps: generating a three-dimensional hybrid geological velocity model, obtaining multi-shot seismic record data based on a wave equation, obtaining a three-dimensional initial velocity model through Gaussian filtering, and obtaining an imaging result by adopting reverse time migration; carrying out dimension reduction processing on the three-dimensional data, and constructing a multi-dimensional pre-training sample set; carrying out pre-training by utilizing the training set to obtain an optimized LAUNet framework; and performing fine tuning by adopting a dual-stage collaborative optimization mechanism, and obtaining a fine-tuned LAUNet framework through a LoRA * method. The method effectively improves the model inversion efficiency, and can be applied to the establishment of a three-dimensional intelligent seismic velocity model.

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.

The invention relates to the technical field of geophysical exploration equipment, and particularly discloses a double-top umbrella type multi-angle adjustable transient electromagnetic transmitting coil array detection device which comprises a main supporting system, a framework supporting system, a coil system, a multi-angle adjusting mechanism and a rapid storage system. The device has the beneficial effects that accurate adjustment in a continuous angle range is realized, and the adaptability of the device to different exploration environments is improved; a double-coil system design is adopted, so that two typical working modes are realized; in structural design, a double-top umbrella type folding structure is combined with a light-weight material, so that the weight of equipment is reduced. According to the device, conversion of multiple detection modes is achieved, traditional multiple fixed angle devices are replaced, the equipment investment cost is reduced, and the equipment operation efficiency is improved.

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 deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

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 geophysical exploration, and particularly relates to a method and a system for quantitatively measuring intrinsic parameters of bedrock fractures based on all-angle domain amplitude and phase joint inversion. The method comprises the following steps: acquiring geological radar reflection data of a target fracture at a plurality of incident angles; synchronously extracting data sequences of amplitude change along with angles and phase change along with angles; establishing a fracture reflection theoretical model; constructing a target function jointly constrained by the amplitude residual and the phase residual; and adopting a global optimization algorithm to minimize the target function, and synchronously inverting to obtain the fracture opening, the filler dielectric constant and the occurrence parameter. According to the invention, through the combined constraint of the amplitude and the phase, the inversion multiplicity is effectively inhibited; accurate system effect correction is adopted, so that the reliability of large-angle data is ensured; according to the method, multi-parameter synchronous inversion without prior hypothesis is realized, the inversion precision is small in relative error, the method is obviously superior to a traditional method, and the method has important engineering application value.

The invention discloses a consistency detection method, device and system for large-scale seismic signal acquisition equipment, and relates to the technical field of geophysical exploration. The method is executed by computer equipment in a large-scale seismic signal acquisition equipment consistency detection system, and after background noise original data continuously and synchronously acquired by a plurality of seismic signal acquisition equipment is received, an accumulated power spectrum variance value of each acquisition equipment is obtained through preprocessing and multi-step calculation in sequence; and finally, according to the accumulated power spectrum variance value of each acquisition device and the distance from each acquisition device to the layout center position, determining the acquisition device with an abnormal relationship in the relationship dimension of the accumulated power spectrum variance and the acquisition time or the relative distance. The purpose of efficient, reliable and automatic consistency detection on large-scale seismic signal acquisition equipment can be achieved, and then the detection result can accurately reflect the performance state of a seismic instrument.

The invention discloses a hidden ore body multi-source data fusion prediction method and system, and belongs to the technical field of geophysical exploration data processing, and the method comprises the steps: carrying out the standardization of obtained multi-source exploration data, and generating standardized data; quantifying errors of the standardized data in spatial positions and attribute values to generate a multi-source data set; constraining the multi-source data set according to a physical rule to obtain physical constraint embedded data; performing multi-scale feature extraction on the physical constraint embedded data to generate a multi-source feature map; performing cross-source and cross-scale association on the multi-source feature map to generate a fusion feature vector; and based on the fusion feature vector, predicting the mineralization probability, the physical property parameters and the uncertainty distribution, and generating a prediction result map. According to the method, multi-source data standardization, physical law constraint embedding, multi-scale feature fusion and uncertainty quantification technologies are adopted, and a three-dimensional mineralization prediction result with reasonable geology and controllable risk can be generated.

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 geomagnetic vector measurement, and discloses an error correction method of a cross-shaped magnetic gradient tensor measurement system. According to the method, a coupling model containing a sensitivity error, a non-orthogonal error, a zero offset error and an array non-alignment error of a single fluxgate sensor is established, and a fitness function is designed to solve error parameters by utilizing the characteristics of high global search capability and high convergence speed of a firefly optimization algorithm (FA). Compared with the existing particle swarm optimization and the least square method, the method has the advantages that the correction precision of zero offset and non-alignment errors is remarkably improved, the single-sensor zero offset error estimation deviation is smaller than 2%, the array non-alignment angle error deviation is smaller than 1.5%, expensive equipment support is not needed, and real-time on-site implementation can be achieved. The method is suitable for the fields of magnetic anomaly detection, geophysical exploration and the like, and has the advantages of low cost, simplicity and convenience in operation and remarkable correction effect.

The invention belongs to the technical field of geophysical exploration, and discloses a transient electromagnetic vertical component detection data one-dimensional real-time accurate inversion method and system. Processing the established stratum model, training the constructed Seq2seq network by using a back propagation algorithm, and training transient electromagnetic optimal inversion models with different numbers of layers; performing background field extraction on all measurement points of the actually measured data, constructing simulated background field data, and extracting pure abnormal field ratio data; multiplying the obtained actually measured pure abnormal field ratio data by the obtained simulated background curve to reconstruct brand new total field data; the reconstructed brand-new total field data is corrected to be consistent with the time domain of the sample library; an inversion model of the corresponding layer number is selected, and the resistivity-thickness of the stratum is calculated in real time; and carrying out visual display on an inversion result. According to the invention, inversion precision is improved, and real-time inversion of transient electromagnetic detection data is realized.

The invention discloses a magnetic method data physical property inversion method and system based on a deep learning terrain disturbance layer, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set, generating an underground three-dimensional magnetic anomaly model through a mixed hexahedron model and a terrain disturbance function, and obtaining corresponding magnetic anomaly data through forward modeling, each group of magnetic anomaly data corresponds to different terrain parameter combinations; constructing a deep learning inversion network model based on UNet; and training the deep learning inversion network model based on the magnetic anomaly data, taking the trained training set parameters as the weight of the deep learning inversion network, verifying the effect by using the verification set after the training is finished, and predicting the effect according to the test set. According to the method, a traditional same-dimensional random projection inversion method is combined with a deep learning network structure, and dynamic transformation of terrain parameters is realized by using a terrain disturbance layer, so that the network keeps high inversion precision and stability under different terrain fluctuation conditions.

The invention discloses a magnetic data boundary identification method and system based on a physical prior selection module, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set through underground target magnetic anomaly data with different parameters; generating magnetic measurement data through a magnetic anomaly forward modeling calculation formula, and forming a magnetic measurement data set; constructing a boundary recognition network model based on UNet + +, and integrating the physical prior selection module to the boundary recognition network model; inputting the training set sample, the verification set sample and the test set sample into a physical prior selection module for operator transformation, and generating a corresponding transformation result graph; and calculating the structural similarity index of each transformation result graph and the corresponding sample label, and splicing the transformation result graph with the maximum structural similarity index and the corresponding sample according to the channel dimension to generate a corresponding multi-channel feature. According to the method, the boundary recognition network model is established and the physical prior selection module is added, so that the robustness of the boundary recognition network model to complex noise is improved.

The invention provides an electromagnetic detection system based on a sea-bottom crawler, belonging to the field of geophysical exploration technology, comprising: a survey vessel, a sea-bottom crawler, a transmitting antenna, a receiving node array, a control chip, and a power supply; the survey vessel is connected to the sea-bottom crawler through a traction rope, and the tail of the sea-bottom crawler is provided with a drag cable, on which the transmitting antenna and the receiving node array are mounted, and both the control chip and the power supply are configured in the integrated electronic cabin of the sea-bottom crawler; the power supply provides electrical energy to the sea-bottom crawler, transmitting antenna, receiving node array, and control chip, and the control chip is electrically connected to the transmitting antenna, the receiving node array, and the power system of the sea-bottom crawler; the control chip is configured to have an electromagnetic detection interference compensation module for performing interference elimination and compensation on reflected waves received by the receiving node array. It solves the technical problem of insufficient detection accuracy in existing technologies.

The invention belongs to the technical field of geophysical exploration transient electromagnetic detection, particularly relates to an ultra-wide coal face roof electrical tomography system and method, and is suitable for high-precision detection of a hidden geological structure of a working face roof with the width exceeding 300 meters and the depth within 100 meters in an underground coal mine. A transient electromagnetic perspective detection technology is adopted, and full-coverage imaging of an electrical structure of a working face roof is realized through alternate arrangement of transmitting-receiving roadways and dynamic data fusion; a time-space mapping relation is established in combination with a smoke ring effect diffusion model, and omnibearing imaging of the top plate is achieved; aiming at the refined detection of the hidden geologic structure of the top plate of the ultra-wide coal face (the width is greater than or equal to 300 meters), bidirectional detection and grid subdivision are adopted, and a single-side detection blind area is eliminated through roadway transposition of a transmitting end and a receiving end, so that grid full-coverage imaging in a detection range is realized.

The invention belongs to the technical field of geophysical exploration, and relates to an underground water magnetic resonance longitudinal relaxation time detection method, which comprises the following steps: laying a magnetic resonance detection coil on the ground, fixing a short emission time, changing a current amplitude, emitting a plurality of short pulses, and collecting a short pulse observation signal generated by each short pulse; fixing a long emission time, re-emitting a series of long pulses with different current amplitudes, and collecting a long pulse observation signal generated by each long pulse; inverting the short pulse observation signal to obtain water content and transverse relaxation time distribution of underground water at different depths; and taking the water content and the transverse relaxation time distribution of the underground water at different depths as prior data of Bayesian inversion, and analyzing the long pulse observation signal by adopting Bayesian inversion to obtain longitudinal relaxation time distribution. According to the invention, a complex excitation sequence form is not needed, and a longitudinal relaxation signal of a short relaxation sample with a fast attenuation speed can be captured. The method is suitable for a high-power scene of field ground magnetic resonance detection.

The invention discloses a magnetic target boundary recognition method and system based on a magnetic gradient tensor, and relates to the technical field of geophysical exploration and industrial nondestructive testing, and the method comprises the following steps: collecting spatial magnetic gradient tensor data through a magnetic gradient sensor array, and obtaining nine components of the magnetic gradient tensor; calculating the vertical guide modulus of the vertical component of the magnetic gradient tensor; calculating a Z-direction analysis signal ASAz and a vertical guide thereof; a boundary recognition amount is generated by a ratio operation, and a boundary position of the magnetic target is determined based on a maximum value position of the boundary recognition amount. According to the method, a vertical component derivative and analytic signal derivative dual-derivative coupling mechanism is combined for the first time, the limitation that a traditional method depends on a single parameter is overcome, the vertical component derivative accurately captures the change rate of the boundary in the vertical direction, and the analytic signal derivative effectively suppresses background noise.

The invention belongs to the technical field of seismic data processing in geophysical exploration, and relates to a Marchenko multiple suppression method for correcting wave field mismatch based on Wasserstein distance, which comprises the following steps: acquiring a pulse reflection sequence through preprocessing and deconvolution; a pulse reflection sequence is input, and Marchenko iteration initialization is carried out; and remodeled Marchenko iteration for correcting the wave field mismatch based on the Wasserstein distance is carried out. According to the method, the optimal transmission theory is systematically introduced into the Marchenko multiple wave suppression field for the first time, the adaptive capacity of the Marchenko method to actual data noise, defect and amplitude distortion is remarkably improved while the advantage that the Marchenko method does not need a speed model is kept, and therefore higher-precision multiple wave suppression and more reliable primary wave recovery are achieved, and the method is suitable for popularization and application. The method is used for high-precision seismic imaging in a complex land exploration environment. The method has remarkable advantages in the aspects of constraint mechanism, error control and global feature protection.

The invention discloses a method for data inversion by utilizing a multipoint geological statistical structure constraint direct current electric method, which belongs to the field of geophysical exploration, and comprises the following steps: based on geological drilling data, establishing a model containing geological structure information by adopting a multipoint geological statistical algorithm, and constructing an underground stratum structure operator by utilizing the model; a structure operator is added to a regularization term, an initial resistivity model is given to calculate a data residual term, and an inversion objective function with structure constraint is formed; in the inversion process, a finite memory quasi-Newton optimization algorithm is adopted to carry out iterative optimization on a target function until the difference between forward modeling simulation data and actually measured data reaches a set threshold value, and constraint inversion is completed; and finally, outputting the resistivity inversion model. According to the method, the problems of fuzzy stratum boundary, reduced resolution and the like caused by regularization smooth constraint in a traditional method are reduced, and the method is suitable for subsurface stratum structure detection and fine imaging in a complex geological environment and has wide application value.

The invention relates to the technical field of gold mine geological modeling exploration, in particular to a gold mine modeling and reserve estimation method under multi-source information constraint. Geological images and exploration data of the target mining area are collected; performing interpolation operation according to the geological image in combination with the elevation data and the drilling data to establish a geological model; establishing a fracture model by adopting a deterministic modeling method according to the geological image; constructing a lithologic model by adopting an envelope surface modeling method according to the drilling data and the geophysical exploration data; constructing a gold ore body model by adopting an envelope surface modeling method according to the elevation data and the geophysical exploration data; obtaining mineralization information to construct a variation function model, and establishing a grade model by adopting a Kriging difference method according to the variation function parameter value of each fault; and estimating the gold ore reserves of the target mining area based on the established model. According to the method, the model can be established in combination with multi-source information, and the ore body structure can be accurately represented, so that the estimation precision of the gold ore is effectively improved.

The invention relates to the technical field of oil and gas geophysical exploration, in particular to a dolomite reservoir fracture-cavity system prediction method and device, equipment and a medium. The method comprises the steps that large-scale fractures of a reservoir in a research area are described by extracting seismic attributes, small-scale fractures of the reservoir in the research area are described by solving attenuation gradient attributes of different azimuth angles, and hole bodies of the reservoir in the research area are described by conducting pre-stack total porosity inversion. Therefore, large-scale and small-scale crack and hole body depicting results are obtained; and the dolomite reservoir fracture-cavity system prediction result in the research area is obtained through fusion. The problems that when a dolomite fracture-cavity system is depicted through logging data or seismic data in the prior art, fracture-cavity development characteristics cannot be described and predicted on the whole, fine depiction of fracture-cavity body seismic response outer contours cannot be achieved, and the accuracy of hole recognition and fracture depiction is relatively low are solved.

The invention discloses a seismic strong background noise removal method based on a two-channel network, and relates to the field of geophysical exploration data processing, and the method comprises the steps: selecting a noiseless signal and background noise when the noiseless signal and the background noise are not excited from a single-channel seismic record after a seismic source is excited, carrying out the superposition, generating noisy data, and carrying out the normalization processing; constructing a dual-channel constraint denoising network composed of a global constraint sub-network and a denoising sub-network; optimizing network parameters through the training sample set; and performing global constraint denoising and slice local denoising on the noisy seismic data by using the trained model, and finally splicing to obtain a complete denoising result. According to the method, the splicing effect problem caused by traditional blocking processing is effectively solved, the strong background noise removal effect is remarkably improved, and seamless high-quality denoising with the complete edge is achieved.

The invention discloses an earthquake intelligent filtering method based on multi-scale information fusion and structural feature enhancement, and belongs to the technical field of geophysical exploration data processing, and the method comprises the following steps: obtaining original earthquake data, carrying out the normalization processing of the data, and obtaining the preprocessing earthquake data; performing anisotropic diffusion filtering processing on the original seismic data based on an anisotropic diffusion equation to obtain structure enhanced seismic data; constructing a parallel convolutional neural network, and performing feature extraction on the preprocessed seismic data and the structure enhanced seismic data to obtain a multi-scale seismic feature image; based on a feature fusion neural network, performing cross-scale fusion on the multi-scale feature image to generate a fused feature image; and constructing and training a residual neural network MA-ResCNN based on multi-scale fusion and an attention mechanism. The method can effectively remove the noise interference of the exploration seismic data in a complex structure region, and improves the resolution and the signal-to-noise ratio of the seismic data.

The invention discloses a gravity anomalous body inversion method based on a PMU-Net deep learning network, and belongs to the field of gravity inversion, and the method comprises the steps: increasing the number of observation equation sets through the joint inversion of two different types of gravity anomalous data, and comprehensively presenting an underground geologic structure. Data are processed by means of two independent channels, one channel uses a Mama module to capture dependency between observation points, and the other channel uses a spatial pre-training model to capture position association between the observation points and underground density. And the processed features are fused and input into the U-shaped neural network constructed by one-dimensional convolution, so that the calculation amount is reduced and the efficiency is improved. The system further comprises a simulation data generation module, and the problem of weak interpretability of deep learning in gravity inversion is solved by embedding priori knowledge. According to the invention, the accuracy and reliability of inversion under complex geological conditions are effectively improved, and an efficient and accurate new method is provided for geophysical exploration.

The invention provides a fluid-structure interaction wave field simulation method based on a distance weighted momentum exchange strategy, and belongs to the crossing field of geophysical exploration and computational acoustics. According to the method, a distance weighting strategy is introduced into an LSM-LBM coupling algorithm, and a dynamic adaptive multi-node momentum exchange mechanism is established, so that the simulation capability of the algorithm on a complex geological structure is remarkably improved, and a new solution is provided for high-precision wave field simulation in geophysical exploration of oil and gas. Comprising the following steps: step 1), constructing a geologic model and setting physical property parameters; 2) establishing discrete forms of a solid medium lattice spring model and a fluid medium lattice Boltzmann model; step 3), designing a fluid-solid boundary momentum exchange strategy based on a distance weighting thought; step 4), carrying out iterative calculation and parameter updating on the coupled wave field; and step 5), verification and application.