45 results about "Earthquake map" patented technology

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

The invention discloses a 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.

The invention belongs to the field of seismic data processing and geological modeling, and particularly discloses a seismic image super-resolution reconstruction method based on a multi-scale double-discriminator GAN, and the method comprises the following steps: constructing a synthetic seismic data set, and carrying out the preprocessing; a multi-scale double-discriminator generative adversarial network framework is constructed, the framework comprises a generator and a double-discriminator system, the generator adopts a network structure based on multi-scale residual groups, and each multi-scale residual group comprises parallel multi-branch expansion convolution, an enhanced residual dense block and an improved convolution attention mechanism; the double-discriminator system is composed of a structure discriminator and a frequency domain discriminator, and the authenticity of a spatial geologic structure and the physical rationality of a frequency domain signal are restrained respectively. The generator generates a high-resolution seismic image by minimizing a joint loss function of adversarial loss, reconstruction consistency loss and perception loss. According to the method, the full-scale geologic features can be accurately captured while noise interference is suppressed, and the reconstruction precision of complex structures such as thin layers and faults is improved.

The application discloses a kind of multi-information fusion's speed modeling method and device, the method includes, after seismic migration, the imaging data A of acquisition, data phase control type mask B, seismic structure interpretation horizon and fault data C, well logging velocity curve data D and seismic velocity field data E;Edge feature of imaging data A is extracted by edge detection technique, and the structural skeleton data F of seismic image is obtained;Phase control type mask B is fused with structural skeleton data F, and phase control structure guide data G is obtained;Seismic structure interpretation data C is added in phase control structure guide data G, and space interpolation guide data H is formed;Through two-step interpolation extrapolation, the space interpolation distance weight field Q corresponding to space interpolation guide data H and well logging velocity interpolation filling model P are obtained;Classification mask M is established for well logging velocity interpolation filling model P;Two-step filling is carried out to class mask M, and multi-information fusion model R is obtained.

A system and methods are disclosed. The method includes obtaining a seismic dataset including a plurality of recorded multiple events, generating a predicted multiple model using a multiple prediction method and the seismic dataset, and estimating a set of initial matching filters using a matching method, to match the plurality of estimated and recorded multiple events. The method further includes generating a tensor field based on the predicted multiple model, determining a set of structure-oriented matching filters based on the set of initial matching filters and the tensor field, generating a filtered multiple model based on the predicted multiple model and the set of structure-oriented matching filters, and generating a multiple-attenuated seismic dataset based on the filtered multiple model and the seismic dataset, forming a seismic image based, at least in part, on the multiple-attenuated seismic dataset, and determining a location of a hydrocarbon reservoir based on the seismic image.

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may correspond to a seismic survey that is divided into various bins in a predetermined bin grid. The method may further include determining a first multiblock bin within the seismic survey. The first multiblock bin may correspond to a source bin and a receiver bin among the bins. The method may further include determining traveltime table data using the seismic data and various multiblock bins that include the first multiblock bin. The method further includes determining migrated data using the seismic data, the traveltime table data, a velocity model, a migration function, and various parallel processors. The method further includes generating a seismic image of the geological region of interest using the migrated data.

The invention belongs to the technical field of geophysics and surveying and mapping science, and discloses an earthquake and level settlement association method and system based on spatial overlay analysis, and the method comprises the following steps: S1, obtaining level measurement data and earthquake directory data; s2, constructing a level line buffer area according to the leveling data; s3, processing the leveling data to obtain level settlement data, and generating a level settlement change point layer according to the level settlement data; s4, performing spatial interpolation according to the level settlement change point diagram layer and the level line buffer area to obtain a settlement change thermodynamic diagram; s5, generating a hierarchical symbolization seismic map layer according to the seismic directory data; s6, the settlement change thermodynamic diagram and the hierarchical symbolization seismic map layer are displayed in an overlapped mode, and an earthquake and level settlement correlation analysis result is obtained. The method disclosed by the invention intuitively reveals and evaluates the spatial correlation between the vertical deformation field and the seismic activity, and provides a brand new technical tool for the conjoint analysis of the earthquake and the level settlement.

A method for predicting a property of a subsurface formation, includes: drilling one or more wellbores into the subsurface formation at a well location; conducting a seismic survey at the well location to construct a primary seismic image volume, the primary seismic image volume dataset comprises at least a pressure wave velocity vp, a shear velocity vs, and a density rho, in both time and frequency domains; conducting well logging at the one or more wellbores to generate logging information at the well location; generating an auxiliary seismic image volume for seismic-guided property prediction using Kriging at the well location; generating a prediction operator based on the primary seismic image volume, the auxiliary seismic image volume and the well logging information; feeding a seismic image volume and the prediction operator into a property prediction engine; and generating the predicted property of the subsurface formation.

This invention provides a method and system for automatic vertex picking of DAS-based microseismic events, relating to the field of microseismic location technology. The method includes acquiring multi-channel data corresponding to microseismic events and performing demodulation analysis to form multi-channel waveform data; performing correlation analysis on the waveform data from different channels to establish an automatic picking correlation model; performing image transformation based on the automatic picking correlation model to form corresponding grayscale images; and performing location discrimination analysis based on the grayscale images to determine the event vertex channel. This method improves the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata.

System methods for generating a seismic image of a subterranean feature is provided. The method includes receiving an input gather representing the subterranean feature. The method further includes applying a pseudo offset header to the input gather to generate a pseudo-offset gather. Finally, the seismic image of the subterranean feature is generated by performing domain processing of the pseudo-offset gather. The method results in an improvement of structural imaging of obscured areas.

The application discloses a salt-underground seismic image quality evaluation method and device, equipment and a storage medium. The salt-underground seismic image quality evaluation method comprises the following steps: acquiring a salt-underground seismic image to be evaluated, and uniformly setting at least two quality detection points in the salt-underground seismic image; determining a linear quality score relationship between an expert score result of a quality detection point and an optimal image attribute based on the expert score result of the quality detection point and an attribute value of the optimal image attribute corresponding to the quality detection point; and determining a predicted expert score result of the quality detection point based on the linear quality score relationship, and performing quality grade evaluation on the salt-underground seismic image to be evaluated. Through the technical solution, objective, rapid and accurate evaluation of a large amount of seismic images is realized.

The invention discloses an OVT domain self-supervision seismic data reconstruction method and device, electronic equipment and a medium. The method comprises the following steps: taking seismic data with data missing as an original data set, and establishing a training data set, a verification data set and a test data set; constructing a convolutional neural network, training the convolutional neural network through the training data set, and performing verification and testing through the verification data set and the test data set to obtain a final convolutional neural network; and inputting seismic data to be reconstructed into the final convolutional neural network to obtain reconstructed OVT domain data. According to the method, the seismic data are converted into the OVT domain to serve as network input, the seismic image features of the OVT domain are extracted, the ability of the network in learning different information of the seismic data is enhanced, local detail features can be well reconstructed while main information is reserved, the reconstruction quality of the seismic data is improved, and the method is suitable for being used for the field of seismic data reconstruction. And the method is of great significance to high-precision seismic data reconstruction processing.

A computer-implemented method for processing a seismic image obtained from seismic measurements performed on a geological formation includes determining a relative geological time (RGT) image based on the seismic image; obtaining setpoints for the RGT image; retrieving an RGT surface of the RGT image for each of the setpoints, wherein each point of an RGT surface is defined by a vertical position and at least one horizontal position; and determining a reference RGT surface by combining the RGT surfaces determined for the setpoints. The vertical position of a point of the reference RGT surface is obtained by combining the vertical positions of the points of the RGT surfaces having the same at least one horizontal position as the considered point of the reference RGT surface. The combining of the vertical positions uses weighting coefficients.

A computer-implemented method for determining modeled seismic data of a subsurface region includes receiving observed seismic data from the subsurface region captured by one or more seismic receivers as one or more reflected seismic signals; determining, based on a velocity model of the subsurface region and the observed seismic data, a traveltime function associated with the subsurface region; migrating, using the traveltime function and a Kirchhoff migration operator, the observed seismic data to produce a migrated seismic image; and performing reverse-time demigration on the migrated seismic image, using the velocity model and a solved full wave-equation, to produce modeled seismic data.

A computer-implemented method for determining modeled seismic data of a subsurface region includes receiving observed seismic data from the subsurface region captured by one or more seismic receivers as one or more reflected seismic signals; determining, based on a velocity model of the subsurface region and the observed seismic data, a traveltime function associated with the subsurface region; migrating, using the traveltime function and a Kirchhoff migration operator, the observed seismic data to produce a migrated seismic image; and performing reverse-time demigration on the migrated seismic image, using the velocity model and a solved full wave-equation, to produce modeled seismic data.

This invention relates to a desert earthquake noise suppression method based on a multi-scale attention interaction network, belonging to the fields of machine learning and seismic image processing. Addressing the problem of low-frequency noise suppression in desert earthquake images, this invention proposes a multi-scale attention interaction network. This network first downsamples the input seismic data, then adaptively learns the complex features of the multi-scale seismic data using dual-branch convolutional layers with different kernel sizes. It then employs multiple attention mechanisms, including permutation attention and coordinate attention, to integrate and interact with multi-scale features across channel, spatial, and coordinate dimensions to obtain continuous seismic signals. This method uses a hybrid loss function of mean squared error and mean cosine similarity, combining the multi-scale strategy with the attention mechanism to improve the denoising effect of desert earthquake images. Compared with single-scale denoising convolutional neural networks, this invention significantly suppresses desert noise and outperforms single-scale denoising convolutional neural networks in restoring the continuity of the phase axis.

A method (600) and a system (1000) for generating an adaptive migration taper for a pre-stack seismic dataset are disclosed. The method (600) includes obtaining the pre-stack seismic dataset (602) and a seismic velocity model of a subterranean region (604). The method (600) also includes generating the adaptive migration taper based, at least in part, on the pre-stack seismic dataset (606), and forming a migrated seismic image using a migration function, the seismic velocity model, the pre-stack seismic dataset, and the adaptive migration taper (608). The method (600) further includes determining a location of a hydrocarbon reservoir based, at least in part, on the migrated seismic image (610).

The invention provides a step-by-step frequency expanding method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-stack CRP gather; performing interpretive CRP gather superposition on the pre-stack CRP gather to obtain superposed seismic data; performing spectrum equalization processing of a scale domain on the superposed seismic data to obtain a reconstructed seismic image; carrying out smooth filtering processing on the reconstructed seismic image by adopting anisotropic diffusion filtering; selecting a scale range with a high signal-to-noise ratio in the reconstructed seismic image after smooth filtering processing to extrapolate other scale segments to obtain seismic data of a plurality of scale segments; and performing signal reconstruction on the seismic data of all the scale segments to obtain broadband seismic data. According to the method, the relative effective frequency band of seismic data can be remarkably widened, stratum details are well depicted, the resolution of the seismic data is effectively improved, and the method has high industrial practical value and application prospects in oil and gas seismic exploration.

The invention belongs to the field of seismic data processing and geological modeling, and particularly discloses a seismic image super-resolution method and system based on a residual feature distillation generative adversarial network, and the method comprises the following steps: carrying out the preprocessing of paired high-resolution seismic image and low-resolution seismic image samples; a generative adversarial network framework is constructed and comprises a generator and a discriminator, the generator adopts an enhanced residual characteristic distillation network, and the enhanced residual characteristic distillation network is formed by cascading a plurality of enhanced residual characteristic distillation blocks. Each enhanced residual feature distillation block comprises a residual feature distillation mechanism and a parallel channel attention and space attention mechanism; a generator and a discriminator are optimized in an alternate training mode, and the generator generates a high-resolution seismic image by minimizing a joint loss function of pixel reconstruction loss, perception loss and adversarial loss. According to the method, the reconstruction precision of the seismic image can be effectively improved while the model parameter quantity and the calculation quantity are reduced.

The application provides a seismic wave impedance inversion method and system based on deep learning, comprising the following steps: S1, constructing and preprocessing a data set; S2, constructing and training a DA-TransUNet model; extracting features of an input seismic image through a convolutional neural network of the DA-TransUNet model, extracting local features, and generating skip connection features; extracting global features through a Transformer network based on the local features; S3, performing upsampling processing through the convolutional neural network of the DA-TransUNet model, fusing the skip connection features with features of a current layer of a decoder, and obtaining a fusion result as an upsampling result; and S4, constructing a final seismic image prediction result. Through the DA-TransUNet network architecture, the application solves the problems of insufficient precision and low efficiency of traditional seismic wave impedance inversion technology in processing complex geological data.

This application relates to a seismic wavefield extension method, apparatus, device, storage medium, and computer program. The method includes: acquiring a seismic image at any time within a reference time period; performing image denoising on the seismic image to obtain a target seismic image; calculating the dispersion relation of the wave equation corresponding to the target seismic image according to a preset spatiotemporal finite difference method; performing a Fourier transform on the wave equation according to the dispersion relation to obtain the frequency-wavenumber domain seismic wavefield corresponding to the target seismic image; selecting the negative frequency component in the wavenumber domain seismic wavefield; and performing wavefield extension on the frequency-wavenumber domain seismic wavefield according to the negative frequency component to obtain the target seismic wavefield. This application improves the efficiency of seismic wavefield extension.

The invention discloses a three-dimensional seismic image super-resolution reconstruction method based on an improved generative adversarial network, and belongs to the technical field of seismic images, and the method comprises the steps: constructing a data volume comprising three-dimensional low-resolution seismic data and three-dimensional high-resolution seismic data; based on the data volume, constructing a Gan-CMDEP network as a generative adversarial network; constructing a loss function including discriminator loss and generator loss, and training a Gan-CMDEP network based on the data volume and the loss function; and performing three-dimensional seismic image super-resolution reconstruction based on the trained Gan-CMDEP network to obtain a three-dimensional super-resolution seismic image. According to the method, the global spatial dependency relationship is enhanced, the trend and distribution of the underground geologic structure can be mastered as a whole, the attention of the network to spatial features is dynamically adjusted, discriminator loss and generator loss are designed, it is ensured that the network can effectively learn the distribution features of real seismic data, and the accuracy of super-resolution reconstruction is improved.

The invention belongs to the field of geophysical exploration and the technical field of artificial intelligence, and particularly relates to a seismic image self-supervision super-resolution reconstruction method based on diffusion model data augmentation, which comprises the following steps of: firstly, carrying out data augmentation, constructing a data set and initializing model parameters, and inputting a small amount of high-quality seismic images into a diffusion model for training; selecting a network model parameter with the minimum loss function value as an optimal solution; performing data augmentation by using the stored optimal network model to generate a new high-quality seismic image, and verifying the validity of the augmented data; constructing a data set of a super-division network model through downsampling and filtering, and designing the super-division network model; the method comprises the following steps: performing parameter initialization and training detail setting, inputting augmented data into SRUNet for training and storage, finally testing a stored optimal model in a test set, verifying the accuracy of the model through indexes such as structural similarity, and applying the model to seismic data resolution improvement in a larger range.

The application provides a seismic data random noise blind denoising method based on a generative adversarial network. First, the original data set is proportionally divided into training data and test data, and Gaussian noise and unknown random noise are added in the training and test data respectively; during training, the data is sent into an improved residual module for first coarse feature extraction, so as to ensure the correlation between adjacent image pixels; then, an improved channel attention mechanism is used for second fine extraction of the feature vector, so as to ensure the completeness of the image itself; finally, the improved gradient penalty function is calculated to enlarge the gap between the real samples and the false samples, so as to not only save the training time, but also enhance the discrimination ability of the discriminator and improve the feature extraction ability of the generator. The network has good generalization and high accuracy, and has good performance in the denoising of some complex unknown noise seismic images.

The invention discloses a method and device for predicting oil and gas reserves, electronic equipment and a storage medium. The method comprises the following steps: acquiring to-be-predicted data; the to-be-predicted data is used for representing seismic wave data of the oil and gas Acquiring seismic feature parameters and seismic image feature vectors according to the to-be-predicted data; fusing the seismic feature parameters and the seismic image feature vectors to obtain fused feature vectors; and inputting the fusion feature vector into a preset oil and gas reserves prediction model to obtain oil and gas predicted reserves. Therefore, the seismic feature parameters and the seismic image feature vectors are extracted and fused from the to-be-predicted data representing the seismic wave data of the oil and gas well, so that the fused feature vectors obtained through fusion can more comprehensively and accurately represent the multi-dimensional features of the seismic wave data; and after the fusion feature vector is input into the preset oil and gas reserve prediction model, more accurate oil and gas predicted reserves can be obtained, and the accuracy of oil and gas reserve prediction is improved.

Systems and methods for creating a plurality of dip-constrained pseudo-angle common image gathers (DPACIGs) to pick a residual moveout are disclosed. The method includes obtaining a plurality of plane-wave CIGs and an initial seismic velocity model, stacking the plane-wave CIGs to estimate a structural dip and applying a vector median filter (VMF) to attenuate a dip noise contained in the structural dip. The method also includes smoothing the plurality of plane-wave CIGs by applying a structural oriented smoothing (SOS) to create a plurality of dip-constrained plane-wave CIGs and applying a non-linear transform to the plurality of dip-constrained plane-wave CIGs to create a plurality of DPACIGs. The method further includes performing a migration velocity analysis (MVA) on the DPACIGs to pick a residual moveout, determining an updated seismic velocity model based on the residual moveout and creating a seismic image using the updated seismic velocity model and the seismic dataset.

This invention belongs to the field of seismic image signal processing, and provides a method and related apparatus for digitizing short-period seismograph observation maps. The method includes: removing noise from scanned short-period seismograph observation map images to obtain denoised images; using a correction formula based on the length of the recording pen, correcting for arc distortion in the denoised images to obtain arc-distortion-corrected images; since the images also have tilt distortion, based on the working principle of short-period seismographs, deriving an inherent displacement formula for the recording pen, and using this formula to correct for tilt distortion in the arc-distortion-corrected images to obtain distortion-free seismic waveform images; and digitizing the distortion-free seismic waveform images. This application solves the problem mentioned in the background art that previous solutions only completed the electronic scanning and storage of simulated seismic observation maps, without realizing the digitization of the electronic image waveforms, by performing image preprocessing and digitization processing on simulated seismic observation maps.

The invention discloses a time sequence seismic data high-resolution processing method based on an improved U-shaped network, and belongs to the technical field of seismic image processing, and the method comprises the steps: generating low-resolution sample data and high-resolution label data through employing a forward modeling method; adding the SE block into the U-Net network, and simultaneously realizing the functions of a residual module and an attention mechanism to obtain an SEU-Net network of the improved U-type network; an MSE-MAE loss function is constructed, and an SEU-Net network is trained based on the sample data, the label data and the MSE-MAE loss function; and obtaining high-resolution three-dimensional seismic data based on the trained SEU-Net network. According to the method, the problems of low resolution and multiple noises of seismic data are solved, the frequency band width is widened, the main frequency of the seismic data is improved, detail texture information is enriched, meanwhile, a good suppression effect on the noises is achieved, and the resolution of the seismic data is effectively improved.

This invention provides a method and system for signal protection and multiple suppression in large-volume seismic data processing areas, relating to the field of seismic data processing technology. The method includes: preprocessing seismic data to form preprocessed seismic data; performing multiple prediction on the preprocessed seismic data to form a multiple prediction model; combining the multiple prediction model and the preprocessed seismic data to determine subtraction parameters; performing interpolation processing on the multiple prediction model to obtain an interpolation model; performing subtraction suppression on the interpolation model and the subtraction parameters to form subtracted post-processed data; and merging the subtracted post-processed data with the subtraction parameters to form suppressed post-processed data. This method improves the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata.

Methods and systems are disclosed. The method include generating a simulated seismic training dataset, where the simulated seismic training dataset includes upgoing signals and downgoing signals, each recorded on hydrophones and geophones and training a deep learning network, using the simulated seismic training dataset, to separate the upgoing signals from the downgoing signals. The method also includes obtaining field hydrophone data and field geophone data pertaining to a subsurface region of interest, recorded by a seismic acquisition system including hydrophones and geophones, and separating the field hydrophone data and field geophone data into predicted upgoing signals. The method further includes generating a seismic image of the subsurface region of interest based, at least in part, on the predicted upgoing signals.