1216 results about "Seismic exploration" patented technology

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The invention relates to the technical field of seismic exploration, in particular to a seismic facies recognition semantic segmentation method based on a LightGBM algorithm. The method comprises the following steps: performing multi-dimensional attribute preprocessing on a three-dimensional post-stack seismic body to obtain a geologic body standardized feature matrix; performing region labeling based on the geologic body standardized feature matrix to obtain region labeling data; performing training-verification set division on the region labeling data to obtain seismic model training data and seismic model verification data; carrying out dynamic expansion on the seismic model training data, and carrying out geologic model constraint to obtain seismic constraint feature data; therefore, through the steps of multi-dimensional attribute processing, region labeling, dynamic expansion, noise processing, model optimization and the like on the seismic data, the problems of data isomerism, noise interference and insufficient model generalization ability in traditional seismic data analysis are solved, and the precision and reliability of seismic facies segmentation and geologic body modeling are improved.

The invention discloses a coal seam frequency resonance seismic exploration system and method based on deep learning. The method comprises the following steps: S1, arranging passive source seismic equipment to collect background waveform data; s2, performing spatial autocorrelation spectrum analysis, and extracting a frequency dispersion curve; s3, speed inversion is carried out, and a speed profile map is output; s4, constructing a DeepLabV < 3 + > model to identify geological anomalies; s5, performing image preprocessing to generate standardized input; s6, performing model reasoning to obtain a segmentation result graph; s7, generating a structure map and a risk map in combination with the coordinates; and S8, exporting an interpretation result in a structured format. According to the method, a seismic data processing flow combining a spatial autocorrelation method and a DeepLabV3 + model is adopted, and automatic extraction of coal seam frequency resonance information and intelligent recognition and visual interpretation of a geological structure are achieved.

The invention provides an active and passive source metal mine seismic exploration method, which comprises the following steps of: acquiring geological exploration data of a target area, and analyzing and judging to obtain a metallogenic favorable part of the target area; obtaining seismic geologic features of the target area and distribution features of natural field sources; according to the acquired geological exploration data, the passive source seismometers are arranged in a grid mode in a target area to form a passive source detection grid; the active source seismographs are arranged in a cross shape at the favorable metallogenic part to form two active source detection lines; artificial source seismic waves are excited, active source seismic data of the active source seismograph and passive source seismic data of the passive source seismograph are collected, and after the artificial source seismic waves are excited, the passive source seismic data of the passive source seismograph are continuously collected. The problem of active and passive source seismic data fusion acquisition is solved, the exploration cost is reduced, and the working efficiency and the detection precision are improved.

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 discloses a brine type potassic salt ore detection method based on a comprehensive geophysical technology, and relates to the field of geophysical detection and strategic mineral resources, the method comprises the following steps: adopting an audio magnetotelluric sounding technology to obtain audio magnetotelluric sounding data; determining a stratum resistivity profile of the audio magnetotelluric sounding line according to the audio magnetotelluric sounding data; acquiring shallow seismic exploration data of the same survey line by adopting a shallow seismic exploration technology; determining a seismic stack section according to the shallow seismic exploration data; the stratum resistivity profile and the seismic stack profile are combined to jointly reveal the stratum structure and construction characteristics of the research area; based on the stratum structure and construction characteristics, according to the low resistance abnormity of the stratum resistivity profile and the strong reflection event characteristics of the seismic stack profile, the sylvite mineralization favorable area and the brine reservoir interval are comprehensively detected, and the brine type sylvite ore identification precision can be improved.

The invention provides a deep coal seam fracture density azimuth seismic inversion method and device, and relates to the technical field of seismic exploration, and the method comprises the steps: obtaining pre-stack seismic trace set data and logging data of a research work area; constructing an elastic stiffness matrix of which each stratum is a VTI medium based on the logging data; processing the porosity of the coalbed methane reservoir in the logging data and an elastic stiffness matrix of the coalbed methane reservoir as a VTI medium according to a linear slip theory to obtain an elastic stiffness matrix of the coalbed methane reservoir as an orthotropic medium so as to determine an initial model parameter vector; combining a reflection coefficient approximation formula based on pre-stack seismic trace set data and orthoanisotropy to construct an objective function used for inverting model parameters; solving the target function to obtain a target model parameter vector of the research work area; and the target model parameters of the coal bed gas reservoir are processed by using the target prediction model to obtain the fracture density of the coal bed gas reservoir, so that an effective technical means is provided for inversion of the fracture density of the deep coal bed.

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 provides a seismic source positioning method and system based on three-dimensional seismic reflection data and electronic equipment, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the initial positioning of a seismic source according to the three-dimensional geological reflection data of a target research region, and obtaining a first seismic source position; extracting a reflected wave time sequence array mapped to the detection point array from the three-dimensional geological reflection data; taking the first seismic source position as an optimization starting point, and performing correlation analysis on the three-dimensional geological reflection data and the reflected wave time sequence array to obtain seismic source space-time parameters; pre-constructing a three-dimensional seismic wave velocity model; and carrying out inversion iteration by taking the seismic source space-time parameters as initial conditions and taking the three-dimensional seismic wave velocity model as iteration constraints, and determining the position of a target seismic source. The technical problem of low positioning precision under a complex geological structure due to insufficient consideration of seismic wave propagation characteristics and reflection characteristics in the prior art is solved, and the technical effect of improving the seismic source positioning precision is achieved.

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 seismic surface wave suppression method based on EWT-Curvelet, and belongs to the technical field of seismic exploration data processing. The method comprises the following steps: firstly, performing empirical wavelet transform (EWT) on post-stack seismic data channel by channel, extracting a frequency localized modal component through adaptive spectrum segmentation, and constructing a surface wave noise model; then converting the model to a curvelet domain, performing physical constraint filtering at a specific scale and in a nearly horizontal direction, and combining pixel connectivity analysis to suppress a continuous noise region; and finally, reconstructing and optimizing noise through inverse transformation and removing the noise from the original data. The method has the advantages that the modal aliasing problem is solved through channel-by-channel adaptive spectrum segmentation, a surface wave energy dense region is accurately locked by curvelet domain scale-direction combined filtering, isolated effective signals are protected in combination with connectivity analysis, high-precision suppression of surface waves is efficiently achieved in a non-iterative mode, meanwhile, the structural integrity of reflected waves is kept, and high-precision suppression of the surface waves is achieved. The seismic section signal-to-noise ratio and the geological interpretation precision are remarkably improved, and the method is suitable for land seismic exploration data processing.

The invention provides a diffracted wave separation method and device under non-uniform space-time sampling, and relates to the technical field of seismic prospecting, the method comprises the following steps: obtaining common offset seismic data of a target area, the common offset seismic data comprising fluctuation signals of a plurality of sampling points, the position intervals among the sampling points of different seismic traces are non-uniform, and / or the time intervals among different sampling points of the same seismic trace are non-uniform; calculating the plane wave slope of each sampling point of the common offset seismic data through a finite difference method to obtain plane wave slope data; determining a reflected wave travel time curve of each sampling point according to the plane wave slope data; and diffracted wave separation of the common offset seismic data is carried out according to the reflected wave travel time curve of each sampling point. According to the method, accurate diffracted wave separation under non-uniform space-time sampling is realized, and the seismic data analysis accuracy is improved.

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

The invention is suitable for the technical field of coal seam seismic exploration, and particularly relates to a coal seam seismic exploration acquisition method and system, and the method comprises the steps: delimiting an exploration region where a coal seam earthquake needs to be carried out, dividing the exploration region into a mined region and an unmined region, obtaining a daily mining task in the mined region, and positioning a blasting point location; the unmined area is divided into a plurality of levels, the deployment position of an optical fiber sensor is positioned, the linear distance between the deployment position and a blasting point position is calculated, mapping between the linear distance and the deployment density is established, look-up tables are constructed, each level corresponds to one look-up table, and each look-up table corresponds to a blasting point position; the lookup table is composed of a linear distance item and a deployment density item. According to the method, the coal seam earthquake can be quickly acquired by generating the snapshot, so that the speed and the precision of coal seam seismic exploration can be greatly improved, and the safety management and the resource utilization of an exploration area can be greatly optimized.

The invention relates to a near-surface longitudinal wave velocity modeling method and device based on micro-motion exploration, and the method comprises the steps: obtaining a surface survey-based ground geological map of a target work area, and correcting a geological boundary of the surface survey-based ground geological map; according to stratum, fault and stratum occurrence information provided by the corrected ground geological map, a geological cross section is drawn along a seismic survey line, and a shallow ground structure model of the target work area is obtained; obtaining the shallow surface shear wave velocity of each preset measuring point in the target work area, and converting the shallow surface shear wave velocity of each preset measuring point into a longitudinal wave velocity; with the shallow ground structure model as a constraint, spatial interpolation is carried out on the longitudinal wave velocity of each preset measuring point to obtain a near-surface longitudinal wave velocity model based on the shallow ground structure model and a shallow velocity field based on the shallow ground structure model, the inversion velocity is close to the stratum velocity, and the precision is high; and a good near-surface longitudinal wave velocity model is provided for subsequent pre-stack depth migration.

The invention discloses a salt cavern ammonia reservoir stability evaluation system based on multi-parameter fusion and intelligent perception, and aims to solve the problem of stability evaluation of a salt cavern ammonia reservoir. According to the system, the geological condition is determined through a three-dimensional seismic exploration system and a drilling exploration technology, and sonar measurement and a sensor are used for monitoring data such as the cavity internal pressure and the volume shrinkage condition of the ammonia storage library in real time. Multi-source parameters such as the internal pressure, the cavity volume shrinkage rate and the safety pillar width are fused, micromechanical parameters and macrogeological conditions are incorporated into a stability coefficient, and the judgment accuracy is improved. The Internet of Things based on a deep learning algorithm is used for collecting data through a sensor network and transmitting the data to a large platform for analysis, so that real-time judgment and intelligent perception of stability can be realized, an operation risk can be predicted, and a response mechanism can be made in time. Meanwhile, the method provides mature thoughts and references for stability evaluation of similar underground reservoirs.

A method can include receiving seismic survey data of a subsurface environment from a seismic survey that includes a source arrangement of sources that is spatially denser than a receiver arrangement of receivers; processing the seismic survey data using the principle of reciprocity for performing interpolation across the receivers to generate processed seismic survey data; and generating an image of at least a portion of the subsurface environment using the processed seismic survey data.

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 discloses a fast scanning seismic travel time calculation method based on a process function equation, and relates to the field of seismic travel time calculation, and the method comprises the steps: firstly, reading a speed model and related parameter information; then carrying out seismic source initialization, setting a slowness fixed item, calculating a travel time fixed item, and setting a travel time disturbance item according to the position of a seismic source point; next, Gauss-Seidel iterative calculation is carried out, in four alternating directions, a Gauss-Seidel type iterative method is used for solving an eikonal equation of factorization, a proper travel time disturbance term is selected through a causal condition, and finally, a travel time calculation result meeting a termination condition is output. According to the high-precision seismic travel time calculation method based on the factorization eikonal equation, the problem of seismic source singularity is well solved, high precision is guaranteed, meanwhile, the algorithm has high calculation efficiency, and the accuracy of subsequent seismic exploration data processing is improved.

The invention discloses a mining-while-digging earthquake advanced detection system based on distributed optical fiber acoustic sensing. Relates to the technical field of seismic exploration. On the basis of a distributed optical fiber acoustic sensing technology, mining-while-digging seismic detection of a working face is realized by utilizing vibration generated by cutting a coal rock stratum by mining machinery. In the production process, the detector collects vibration data of mining machinery and transmits the vibration data to the server through a network; optical cables are arranged on two sides of a coal face upper and lower crossheading or two sides of a driving face roadway; the demodulator emits pulse laser and transmits the pulse laser to the working face optical cable through the industrial looped network or the independent fiber core, vibration of measuring points at equal intervals of the optical cable is measured, and a measuring data packet is returned to the demodulator through the industrial looped network or the independent fiber core; the demodulator demodulates vibration waveform data and uploads the vibration waveform data to the server; server data processing software carries out noise reduction processing on the waveform, then stores and images the waveform, carries out real-time intelligent interpretation, and realizes detection of a geological structure and an abnormal area in front of a working face.

The invention discloses a modeling method and device of a velocity model, computer equipment and a storage medium, and belongs to the technical field of seismic exploration. The method comprises the following steps: determining a first optimized speed model corresponding to land according to first seismic data, and determining a second optimized speed model corresponding to ocean according to second seismic data; part of data is intercepted from the first optimization speed model and the second optimization speed model to serve as a third initial speed model corresponding to the sea-land transition zone, and after the third initial speed model is optimized, it can be ensured that the accuracy of the obtained third optimization speed model corresponding to the sea-land transition zone is high; and therefore, the imaging effect is relatively good when earthquake imaging is carried out on the sea-land transition zone subsequently.

The invention relates to the technical field of seismic exploration, and provides a three-dimensional seismic exploration method and system based on multi-source geological data fusion. The method comprises the following steps: acquiring geological data through a detection device, constructing a first tensor based on multi-source geological data, extracting principal components of the first tensor in a preset dimension, and performing fusion to generate a fusion tensor; generating global features based on tensor slices and hidden states in the fusion tensor, and screening first features with authenticity from the global features through a generative adversarial network; performing three-dimensional modeling based on the first feature and the seismic wave field data to generate a parameter cube; a Bayesian network is constructed based on the parameter cube and drilling data, and stratum evolution information is obtained through prediction by fusing a long-term and short-term memory network so as to dynamically generate a geologic model. A tensor is constructed based on multi-source geological data to extract global features, three-dimensional modeling is carried out in combination with seismic wave field data to obtain a parameter cube, then drilling data and stratum evolution information are fused to dynamically generate a geological model, and the prediction accuracy is improved.

The present invention relates to the technical field of seismic exploration. Provided are a three-component seismic data vector anti-aliasing interpolation reconstruction method and an apparatus. The method comprises: representing three-component seismic data in a time domain as pure quaternions for vector joint; transforming the pure quaternion data in a time-space domain to a frequency-space domain to obtain frequency-space domain data; transforming the frequency-space domain data into a frequency-wave number domain; in the frequency-wave number domain, using an inclination angle scanning strategy to construct a mask matrix; performing vector anti-aliasing interpolation reconstruction on the frequency-space domain data and the mask matrix, so as to obtain a frequency-space domain reconstruction result; and inversely transforming the frequency-space domain reconstruction result to the time-space domain to obtain a time domain three-component three-dimensional data anti-aliasing reconstruction result. The present invention can effectively solve the problem of vector anti-aliasing reconstruction for three-component seismic data that contain both irregular and regular missing traces, thus improving the accuracy of effective wave event identification.

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 belongs to the field of seismic exploration of petroleum and natural gas, and relates to a comprehensive interpretation method and system for low-measurement data based on tomographic inversion. According to the method, a micro-logging routine interpretation result and a micro-logging tomography inversion result are obtained through the micro-logging first arrival time. And obtaining a small-refraction conventional interpretation result and a small-refraction tomography inversion result through the small-refraction first arrival time. And obtaining a comprehensive interpretation result by taking a micro-logging tomography inversion result and a small refraction tomography inversion result from a shallow layer and taking a micro-logging routine interpretation result and a small refraction routine interpretation result from a deep layer. According to the method, the advantages of high shallow layer precision of tomography inversion and stability of a deep layer conventional interpretation method are integrated, personal errors are eliminated, the detection capability of small refraction data is fully excavated, the precision of comprehensive interpretation of low measurement data is improved, the precision and stability of the low measurement data are improved, and through constraint tomography inversion used for multiple work areas, the accuracy and the stability of the low measurement data are improved. Therefore, the extremely shallow layer better conforms to the actual geological condition, and the imaging quality is improved.

The invention provides a low-frequency noise elimination method and device for distributed optical fiber data in a well, and belongs to the field of seismic exploration. The method comprises the steps of converting input DAS time domain data into DAS frequency domain data, and then carrying out threshold-adjustable notch filtering processing on the DAS frequency domain data. According to the method, the DAS data are processed by adopting the notch filtering method with the adjustable threshold, low-frequency noise of the DAS data can be effectively eliminated, and the signal-to-noise ratio of the DAS data is improved.