125 results about "Earthquake map" patented technology

The invention relates to a Mama-based seismic image super-resolution reconstruction method, which comprises the step of constructing a local feature extraction module, a global feature extraction module and a high-quality seismic section reconstruction module so as to solve the problem of insufficient resolution caused by high-frequency attenuation and sparse acquisition. According to the invention, a Mama model is introduced, and a novel super-resolution reconstruction network is constructed. The local feature extraction module extracts shallow feature information through a convolutional layer; the global feature extraction module obtains global features through the multi-stage residual state space block; and the high-quality seismic image reconstruction module realizes reconstruction of a high-resolution seismic image through pixel recombination and convolution operation. In addition, a Charbonnier loss function is adopted, the gradient disappearance problem is effectively avoided, and the loss function of traditional Mamba is improved. According to the method, the seismic image resolution can be improved, and meanwhile, the calculation complexity and the time cost are remarkably reduced.

The invention provides a three-dimensional seismic fault identification method based on double-attention multi-scale fusion U-Net. The method comprises the following specific steps: constructing a U-shaped network architecture comprising an encoder, a decoder and jump connection; a constructed multi-scale feature fusion module is embedded in the first level of the encoder, and the extraction capability of fault features of different scales is enhanced through a multi-branch structure; introducing the constructed hole fusion modules into the second and third levels of the encoder, designing and expanding a receptive field by using multiple expansion rates, and capturing fault structures of different scales; a double-attention parallel mechanism is integrated in jump connection, and the sensitivity of channel attention and space attention to fault features is improved; constructing a combined loss function; and finally, performing three-dimensional seismic data training and reasoning based on the optimized model to realize high-precision fault identification. The method has high generalization and accuracy, and especially has good performance in the aspect of seismic image fault identification containing a large fault scale span.

The invention discloses a seismic data constrained direct current method three-dimensional inversion method, which relates to the field of geophysical exploration and comprises the steps of processing seismic data and performing inversion processing on resistivity data. The seismic wave layer velocity information is used for constraining high-density electrical method inversion, so that a supplement effect to a certain degree can be achieved, three-dimensional seismic image information can be used for supplementing the resolution ratio of resistivity inversion, the boundary of an underground electrical anomalous body is reflected, and meanwhile, through the comprehensive geological-geophysical information, the electrical anomalous body can be accurately determined. And boundary features of the anomalous body can be further depicted. Seismic data constraints are applied to three-dimensional resistivity inversion, numerical simulation and inversion processing are performed on electrical method data under rugged topography according to finite volume method numerical simulation and a Gaussian-Newton inversion method, and meanwhile, seismic anisotropic velocity tensor constraints are introduced to perform inversion, so that the problem of multiplicity of solutions of inversion of a single method is reduced, and the inversion accuracy is improved. A more accurate three-dimensional underground electrical structure is obtained, and the resolution of inversion to a longitudinal boundary is improved.

The invention relates to the technical field of seismic exploration, and particularly provides a seismic fault identification method based on adaptive multi-scale fusion and jump attention. The method comprises the following steps: constructing an earthquake fault identification model; in the encoder, constructing an adaptive multi-scale fusion module, and adaptively adjusting the number of network branches according to input data distribution and task complexity requirements; in the decoder, redesigning jump connection, constructing a jump attention module, and modeling a long-distance dependency relationship between a cross-space dimension and a depth dimension for compensating spatial continuity lost due to two-dimensional segmentation; according to the method, the seismic fault recognition model after the self-adaptive multi-scale fusion module and the jump attention module are constructed is used for recognizing the seismic fault, the spatial continuity of seismic data is improved through the self-adaptive multi-scale fusion module and the jump attention module, and then the quality of seismic image segmentation and the accuracy of seismic exploration are improved.

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

The invention provides a three-dimensional seismic fault identification method based on deep learning, and the method comprises the following steps: (1), carrying out the automatic modeling, generating three-dimensional seismic data and an accurate fault label, obtaining a three-dimensional seismic image data set, dividing the data set into a training set and a test set, and carrying out the recognition of the three-dimensional seismic fault; (2) training an improved 3D-Unet convolutional neural network of a two-channel structure or an AG-3D-Unet network fused with an attention mechanism and residual learning by taking the three-dimensional synthetic seismic data of the training set and a label of the three-dimensional synthetic seismic data as input, and verifying by using a test set to obtain a trained seismic fault recognition model, (3) inputting real seismic data, and obtaining a seismic fault recognition result. And outputting a fault identification result for the input to-be-predicted image through the identification network. According to the method, the definition, the fault continuity and the accuracy of fault identification are obviously improved, and the method has high practical value in actual seismic data fault interpretation.

The invention discloses a seismic fault identification method based on a Dual-Attention mechanism and a multi-scale fusion network, and belongs to the technical field of seismic data interpretation, and the method comprises the following steps: S1, obtaining three-dimensional post-stack seismic data, and carrying out the slicing of a seismic data body, and obtaining multi-channel seismic data; s2, a DAMFault Net fault recognition model is constructed; s3, completing fault feature extraction in a coding stage; s4, obtaining an intermediate aggregation feature and an original same-scale feature from an encoder in a decoding stage; s5, adding a depth supervision mechanism to a tail end decoder; and S6, splicing and recovering prediction results to the size of the original seismic section. According to the seismic fault identification method based on the Dual-Attention mechanism and the multi-scale fusion network, excellent accuracy and fault continuity identification capability are shown in a fault identification task, and automatic, efficient and accurate identification of the fault in the seismic image is realized; and powerful technical support is provided for seismic analysis and research.

A method and system for forming a seismic image of a subterranean region are disclosed. The method includes determining an initial plan for a seismic survey with a value for each member of a set of acquisition parameters and acquiring a first seismic dataset from a first portion of the seismic survey based on the initial plan. The method further includes transmitting the first seismic dataset to a seismic processor, determining a first seismic image from the first seismic dataset by performing expedited seismic processing and determining a first updated plan for the seismic survey based on the first seismic image and acquiring a second seismic dataset from a second portion of the seismic survey based on the first updated plan. The method still further includes transmitting the second seismic dataset to the seismic processor and determining the seismic based on the first seismic dataset and the second seismic dataset.

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.

Systems and methods for noise-robust time-domain multi-scale full waveform inversion using convolved data are disclosed. The methods include obtaining, using a seismic acquisition system, an observed seismic dataset pertaining to a subsurface region of interest; obtaining, using a seismic processor, a seismic velocity model; and iteratively, using the seismic processor, until a stopping criterion is met: selecting a wavelet with a frequency parameter, wherein the frequency parameter increases with each iteration, forming a convolved seismic dataset based on a convolution of the wavelet with the observed seismic dataset, and updating, using a full waveform inversion, the seismic velocity model based, at least in part, on the convolved seismic dataset. The methods further include forming a seismic image of the subsurface region of interest using the updated seismic velocity model.

The invention relates to the technical field of seismic image recognition, in particular to a three-dimensional seismic data acquisition method and system. The method comprises the following steps: acquiring seismic source region data; performing real-time multi-source sensing data acquisition on the seismic source area data to obtain seismic source area multi-source sensing data; carrying out amplitude conversion on the multi-source sensing data of the seismic source region to generate a seismic source region amplitude conversion diagram; performing pre-earthquake potential easy-to-earthquake area positioning on the seismic source area amplitude conversion diagram to generate pre-earthquake easy-to-earthquake area positioning data; epicentral seismic waveform recording is carried out on the pre-earthquake earthquake-prone area positioning data, and earthquake-prone area epicentral seismic waveform data are generated; and carrying out earthquake instantaneous dynamic analysis on the epicenter seismic waveform data of the earthquake-prone area to generate epicenter instantaneous magnitude data. According to the invention, through real-time multi-source sensing data acquisition, dynamic epicenter analysis and three-dimensional modeling, the real-time performance and dynamic analysis capability of seismic data acquisition are improved.

A computer implemented method provides for detecting a geological object in a geological formation by processing a seismic 3D image, said seismic 3D image comprising a plurality of pixels representing seismic measurements performed on the geological formation. The method includes computing, based on the seismic 3D image, a geological-time (GT) isochronous surface of the geological formation, wherein the GT isochronous surface corresponds to the coordinates of pixels of the seismic 3D image which represent portions of the geological formation considered to have a same geological age, computing a seismic attribute 2D image representing at least one seismic attribute of the geological formation on the GT isochronous surface, and detecting, by image segmentation, pixels of the seismic attribute 2D image which represent the geological object.

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

A method is described for generating multispectral variance volumes from seismic data. The method may include receiving, at a computer processor, a seismic image; analyzing the seismic image to determine frequency content and vertical seismic resolution; performing structural oriented filtering to generate a filtered seismic volume; performing spectral enhancement of the filtered seismic volume using the determined frequency content and vertical seismic resolution to generate a plurality of frequency-dependent seismic volumes; calculating covariance of the frequency-dependent seismic volumes to generate a plurality of covariance matrices; combining the covariance matrices to create a multispectral variance volume. The method is executed by a computer system.

Methods and systems are disclosed. The methods may include obtaining vertical seismic profile (VSP) data and a seismic velocity model for a subterranean region of interest and determining, using the seismic velocity model and reverse time migration, a pre-stacked migrated seismic image based on the VSP data. The pre-stacked migrated seismic image includes seismic slices. The methods may further include, for each of the seismic slices in turn, determining a coherency map, determining a weighting map based on the coherency map, and determining a corrected seismic slice by applying the weighting map to each of the seismic slices. The methods may still further include determining a post-stacked corrected migrated seismic image based on the corrected seismic slices and determining a location of a geological feature within the subterranean region of interest based, at least in part, on the post-stacked corrected migrated seismic image.

The methods may include defining cells that represent a subterranean region of interest (SROI) and obtaining seismic data and well data pertaining to the SROI. Each cell is associated with a location within the SROI and seismic constraint among the seismic data. The cells include informed cells and uninformed cells. Each informed cell is associated with each well datum. The methods may further include obtaining a process-based geological model (PBGM) of the SROI and determining a corresponding simulated seismic image (CSSI) based on the PBGM. Each cell is further associated with a facies among the PBGM and simulated seismic constraint among the CSSI. The methods may further still include determining a probability based on the location, seismic constraint, PBGM, CSSI, and well data for each uninformed cell, determining a seismic-conditioned PBGM based on the probabilities, and identifying a predicted location of hydrocarbons within the SROI based on the seismic-conditioned PBGM.

Methods and systems are disclosed for automatically integrating subsurface structural maps with strike and dip information measured in subsurface wells. The method includes obtaining a seismic image volume for a subsurface region of interest and a well log for each of a plurality of wellbores penetrating the subsurface region of interest. Further, the method includes determining a seismic map of a geological surface from the seismic image volume, wherein the seismic map comprises an estimated depth and an estimated vector normal to the seismic map at a plurality of horizontal locations and determining an intersection point for each of the plurality of wellbores with the geological surface. Additionally, the method includes forming a cost function based, at least in part, on the seismic map and the intersection points of the plurality of wellbores and constructing a subsurface map by solving a constrained optimization problem based on the cost function.

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.

The invention discloses a deep learning-based intelligent quantitative evaluation method and system for amplitude preservation of a front trace gather. Constructing an unsupervised deep learning model; inputting a to-be-detected work area seismic data slice seismic image into the unsupervised deep learning model for training to obtain a pre-offset gather amplitude preservation intelligent quantitative evaluation model; and inputting two-dimensional pre-offset gather data to be evaluated into the pre-offset gather amplitude preservation intelligent quantitative evaluation model to obtain corresponding quantitative evaluation data. According to the method, quantitative evaluation can be carried out on the amplitude preserving performance of geological data under the unsupervised condition, a qualified label-free training preamble gather amplitude preserving performance intelligent quantitative evaluation model is established, and the established preamble gather amplitude preserving performance intelligent quantitative evaluation model can be used for evaluating the amplitude preserving performance of the geological data under the condition of not depending on any prior information. Amplitude preservation quantization parameters are intelligently predicted in a data driving mode, and then the precision of lithologic oil and gas reservoir prediction is improved.

Systems and methods for time preserving full waveform inversion are disclosed. The methods may include acquiring, using a seismic acquisition system, a seismic dataset and, using a seismic processor, obtaining, from the seismic dataset, an event in two-way traveltime (TWT) and a velocity model in TWT, and converting the velocity model in TWT into a velocity model in depth. The methods further include producing a final velocity model in depth, where the final velocity model is produced using a full waveform inversion (FWI), the velocity model in depth, and a TWT-preserving method preserves the event in TWT at each iteration of the FWI; and forming a seismic image in depth using the final velocity model in depth and the seismic dataset. The methods further include identifying, using a seismic interpretation workstation and based on the seismic image in depth, a drilling target; and planning a borehole path to the drilling target.

The invention relates to a seismic data reconstruction method based on dual-domain feature collaborative enhancement, and the method comprises the following steps: obtaining an original seismic data set, carrying out the size normalization and down-sampling processing, and obtaining a lacked-channel seismic data set; the missing channel seismic data set comprises a complete seismic image and missing channel seismic data corresponding to the complete seismic image and missing distance, the missing channel seismic image serves as input of the dual-domain feature-based collaborative enhancement neural network, and the corresponding complete seismic image serves as a label; the method comprises the following steps: constructing a double-domain feature collaborative enhancement neural network, taking a UNet class model as a trunk architecture, performing multiple times of down-sampling on lacked-channel seismic data in the Unet class model through nested connection, and embedding a double-domain feature collaborative enhancement module in each layer of jump connection part; and training the network by using the missing-channel seismic data set, and performing missing-channel seismic data reconstruction by using the trained double-domain feature collaborative enhancement neural network. And high-efficiency reconstruction of a large-area missing region in complex seismic data is realized.

Systems and methods for parallel sorting of seismic data are disclosed. The methods include obtaining a seismic dataset comprising a plurality of shot gathers; dividing headers from the plurality of shot gathers among a plurality of computer nodes; sorting the headers separately on each of the plurality of computer nodes; saving the sorted headers from the plurality of computer nodes to a temporary file; sorting the sorted headers saved to the temporary file into a cross spread gather ordering; and sorting seismic traces according to the cross spread gather ordering to create cross spread gathers. The methods further include applying a seismic imaging algorithm to the cross spread gathers to create a seismic image.

A method may include obtaining various cross-spread gathers regarding a geological region of interest. The method may further include determining traveltime table data based on the cross-spread gathers. The method may further include transmitting the cross-spread gathers to various parallel processing groups in a parallel processing network. A respective cross-spread gather among the cross-spread gathers may be transmitted to a respective parallel processing group among the parallel processing groups. The method may further include determining, by various parallel processing nodes in the respective parallel processing group, a first subset of a migrated dataset using the respective cross-spread gather, a subset of the traveltime table data, a velocity model, and a migration function. The method may further include generating, based on various subsets of the migrated dataset that includes the first subset, a first seismic image of the geological region of interest.

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.