56 results about "Earthquake map" patented technology

There is provided herein a system and method of acquiring, processing, and imaging transient Controlled Source ElectroMagnetic (t-CSEM) data in ways that are similar to those used for seismic data. In particular, the instant invention exploits the time-distance characteristics of t-CSEM data to permit the design and execution of t-CSEM surveys for optimal subsequent processing and imaging. The instant invention illustrates how to correct t-CSEM data traces for attenuation and dispersion, so that their characteristics are more like those of seismic data and can be processed using algorithms familiar to the seismic processor. The resulting t-CSEM images, particularly if combined with corresponding seismic images, may be used to infer the location of hydrocarbon reservoirs.

The invention discloses an earthquake image structure guiding noise reduction method based on regularization mixed norm filtering. The earthquake image structure guiding noise reduction method includes the following steps that a gradient structure tensor is solved for an input three-dimensional earthquake image; regularization mixed norm filtering is conducted on the gradient structure tensor; a diffusion tensor is designed according to the eigenvalue and eigenvector of the filtered gradient structure tensor; continuity factors are calculated, the continuity factors at the position of a boundary fault feather edge and the like are close to zero, and the maintain performance of the structure is achieved; a sobel operator serves as a derivation operator so that divergence can be calculated. By means of the earthquake image structure guiding noise reduction method based on regularization mixed norm filtering, the textured edge information of earthquake-related data can be reserved, Gaussian noise, ultra Gaussian noise and sub Gaussian noise can be effectively suppressed, and therefore an efficient noise reduction method is achieved.

Reflected-transmitted non-converted and converted seismic waves as used for seismic imaging which is not limited by the angle of inclination of the seismic interfaces. These wave signals are recorded at the surface and are later used to obtain seismic imaging of geological discontinuities and the physical properties of the medium. Known duplex waves are used with transmitted waves for obtaining seismic images from much wider angles, ranging from horizontal to vertical. Two types of migration procedures are used: the first one being based on migration of transmitted compressional and converted duplex waves, and the second one being based on the joint migration of two interrelated fields, such as interferometric principles; for example, the first field of primary waves corresponding to the one time reflected monotypic compressional (non-converted) type of waves and a second wavefield corresponding to secondary waves of more complex origin, such as reflected waves and converted waves.

The maximum output of a seismic source array may be reduced by activating the individual seismic sources within these seismic source array in a pattern that is extended in time rather than by the presently employed conventional simultaneous activation of a large number of individual seismic sources. Methods are disclosed which take data shot with patterned sources and may use a sparse inversion method to create data with the about same image quality as that of conventional sources. In this manner the output of the maximum impulse of a seismic source array may be reduced by an amplitude factor of about 10 in the examples shown here, corresponding to a reduction of about 20 dB while maintaining virtually the same seismic image quality. The disclosed methods may be used in combination with any simultaneous sourcing technique. In addition, the disclosed methods may be used with a plurality of source arrays.

The invention discloses a method for interpreting logging curve and seismic map data by using artificial intelligence, and data entry and result output are both displayed in the form of a network page, so that remote deployment and data sharing are facilitated. The method comprises the steps that a part of collected sample data sets of known lithofacies classification are used as training data, automatic identification of lithofacies is carried out by using machine learning and deep learning methods, and then the lithofacies in stratums of unknown regions is divided. The invention comprises anartificial intelligence interpretation method, module and system. The server deployed with the method displays the following functions: a mutual communication interface of a webpage end comprises a data entry part, a data verification part, a data preprocessing part, a model establishment part, a model training part, a model iteration part, a model use part, a result display part and the like. According to the method, the calculation result of artificial intelligence is displayed on a network page by using a Python framework.

A method for improving seismic images by correction of distortions in the underlying seismic data caused by a near-surface anomaly that produces a non-hyperbolic move-out component of the seismic reflection below the anomaly includes the steps of:a. redatuming the input seismic data to go from the surface to a target horizon using true one-way traveltime operators to provide a first new redatuming dataset;b. redatuming the input seismic data using hyperbolic one-way travel time operators to provide a second new redatuming dataset; andc. redatuming the combination of a first causal part of the first new redatuming dataset and an anti-causal second part of the second redatuming dataset to go from the target horizon back to the surface using hyperbolic one-way traveltime operatorsto provide a dataset that is referenced to the surface without an imprint of the anomaly.

The invention discloses a seismic noise suppression method based on an unbalanced depth expectation block logarithm likelihood network, belongs to the technical field of machine learning and seismic image processing, and aims to solve the problem that strong noise cannot be suppressed thoroughly due to the fact that regular term parameters in an expectation block logarithm likelihood algorithm only change along with overall noise variance. The invention provides the seismic noise suppression method based on an unbalanced depth expected block logarithm likelihood network, and the end-to-end denoising network consists of an expected block log-likelihood denoising main network and an unbalanced multilayer perceptron parameter estimation network, and takes a noisy seismic image as an input end. The clean seismic image is used as an output-end learning network parameter. Futhermore the multilayer perception paramter estimation parameter with unbalanced block signal-to-noise ratio is firstlyadopted. According to the method, accurate regular term parameters can be estimated for each block in the seismic image, the denoising intensity of each block can be better controlled, the block denoising effect is improved. The method is superior to a traditional seismic denoising algorithm in the aspects of desert strong noise suppression and signal detail maintenance.

A method for computing amplitude independent gradient for seismic velocity inversion in a frequency domain includes receiving seismic data associated with a region. The region comprises one or more earth subsurface layers represented by a plurality of points, and each point is associated with a seismic velocity. Seismic velocities at the plurality of points are determined by iteratively updating the seismic velocities based on a plurality of gradient values, where each gradient value corresponds to a point and is determined by evaluating a gradient of an objective function at a location of thepoint. A seismic image of the one or more earth subsurface layers is displayed based on the determined seismic velocities.

Automatic propagation of real-world parent seismic images to efficiently generate a collection of realistic synthetic child training images to train a model for accurate automatic seismic interpretation. A 3D structural model in a present-day geological space (e.g., GB) depicting subsurface locations of particles (e.g., in region B) may be transformed by a 3D coordinate space transformation (e.g., uvtB) to a depositional space (e.g., G*B) depicting past depositional locations of those particles (e.g., corresponding depositional region B). A real-world parent image depicting subsurface locations of particles (e.g., in region A) may be transformed, via a forward transformation (e.g., uvtA), to a depositional seismic image in the depositional space of the three-dimensional structural model (e.g., G*A=G*B). A reverse transformation (e.g., uutB−1) may transform the depositional seismic image from the depositional space into synthetic child training images in the present-day geological space (e.g., GB) for training the model.

The invention discloses a rigidity matrix calculation method for point source seismic response for seismic prediction, and the method comprises the steps: supposing that a point source dislocation layer is fixed, and solving a dynamic response and a fixed end face counterforce in the dislocation source layer; reversely applying counter-force of the fixed end face to the layered half space to obtain dynamic response of the fixed end face; superposing the dynamic response in the dislocation source layer and the dynamic response of the fixed end surface to obtain a total dynamic response; and combining the total dynamic response with a seismic source time function, carrying out Fourier-Hankel inverse transformation, and obtaining a seismic map of three components in a time-space domain. The method is a semi-analytical method based on combination of an accurate dynamic stiffness matrix and integral transformation, and has the advantages that the precision is not limited by the layer thickness, and error accumulation does not exist.

The invention discloses a seismic image completion method based on an information entropy norm. The method is applied to the field of geophysical seismic data processing. The problem that a low-rank matrix obtained through recovery based on a matrix rank reduction method is not a real low rank due to the limitation of an existing L1 norm is solved. According to the method, the objective function based on the information entropy norm replaces a nuclear norm adopted by original SVD, then the information entropy norm is used for regularizing a rank minimization item of the objective function, andfinally an iterative ADMM estimation method is used for approximate solution to obtain a low-rank matrix, so that steady recovery of a missing image is obtained.

The invention discloses a seismic image completion method of a fast double-interpolation POCS applied to the field of seismic data processing, and in order to solve the problem of low calculation efficiency in the prior art, a specific linear combination of first two iterative process results is used as an iterative contraction operator, and then double interpolation is carried out, so that complete seismic data is recovered; compared with a traditional POCS method, the method provided by the invention has a faster convergence speed, and the high efficiency and effectiveness of the fast dual-interpolation algorithm are verified on real seismic data.

The invention discloses a lithofacies threshold-based mixed-order anisotropic diffusion seismic image denoising method, and the method combines a low-order anisotropic diffusion equation and a high-order anisotropic diffusion equation, estimates a diffusion threshold based on gradient statistical information of stratum lithofacies, and controls weights of two anisotropic diffusion items through the lithofacies threshold. According to the method, the defect that the boundary is excessively smooth or a local block model is generated when the seismic image is denoised through a traditional anisotropic diffusion method is overcome, noise interference of the seismic image can be effectively removed through the method, and structural detail information can be recovered while the boundary of theimage is protected.

The invention provides a seismic imaging method, which belongs to the technical field of geophysical exploration. The seismic imaging method comprises the steps of acquiring time domain seismic post-stack data of a to-be-studied area and depth domain well data of a plurality of test wells in the to-be-studied area; establishing a structural frame model through time domain seismic post-stack data;preprocessing each piece of depth domain well data to convert the depth domain well data into time domain well data; correspondingly interpolating each piece of time domain well data into the construction framework model, and calculating to obtain an initial speed model; and optimizing and updating the initial velocity model, and performing pre-stack depth migration processing on the pre-stack seismic data to obtain a seismic image. By means of the seismic imaging method, the imaging precision can be higher.

A method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; performing a machine learning algorithm on the seismicdataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method may be executed by a computer system.

The invention provides a method and a system for obtaining seismic parameter estimation, and the method comprises the steps: carrying out the compressed sensing of a sparse seismic signal in seismic map data, and constructing a seismic map database; performing space division on the complete signal of the seismic map database, and establishing a first index; performing compressed sensing on sparseseismic signals in the seismic wave travel time information, and constructing a seismic wave travel time table; performing space division on the complete signal of the seismic travel time table, and establishing a second index; inputting seismic event information, and performing parallel search in the seismic map database and the seismic travel time table through the first index and the second index to obtain a search result set; and determining matched seismic map data and seismic wave travel time information from the search result set, and obtaining seismic parameter estimation of the seismic event from the matched seismic map data and seismic wave travel time information. According to the method, the seismic map database and the seismic wave travel time table are constructed by adoptinga compressed sensing technology, the influence of model difference on a search result can be reduced, and the estimation of seismic parameters is obtained.

The invention provides an adversarial neural network high-resolution seismic fault detection method and system, and the method comprises the steps: training a target adversarial neural network based on a preset training sample set, and obtaining a trained target adversarial neural network, wherein the preset training sample set comprises seismic data and fault labels; the target adversarial neural network comprises a segmentation module, a feature fusion module and a discriminator module; the segmentation module is a module for obtaining fault features based on a preset training sample set; the feature fusion module is used for fusing fault features and seismic data into a global feature map, and performing seismic fault detection on the target seismic image based on the trained target adversarial neural network. The technical problem that in the prior art, seismic fault detection precision and resolution are low is solved.

The invention relates to an observation signal amplitude measurement method based on natural seismic source radiation intensity correction, and aims to solve the problem that the amplitude measurement is difficult to perform by using a recording signal of a large-magnitude natural earthquake on a seismic station due to the influence of the intensity directivity of a seismic source, and a seismic signal amplitude-magnitude relation model of a specific path from a seismic source to a seismic station is difficult to establish. The method is based on a known seismic source mechanism of a natural earthquake, a theoretical seismic map is generated by using a theoretical seismic map calculation method, a theoretical seismic map waveform containing a plurality of path arrival signals is obtained, and a plurality of path arrival signals are obtained by measuring an average value of the theoretical seismic maps of different azimuth angles and measuring a theoretical seismic map signal amplitude on a seismic station. And the average value is normalized to obtain an amplitude correction coefficient corresponding to the seismic station so as to correct the radiation intensity of the seismic source.

The invention discloses a seismic data interpolation method combining Gabor feature extraction and support vector regression. The method comprises the steps of pre-interpolation, Gabor feature extraction, feature vector extraction, regression function training and seismic image reconstruction which are executed in sequence. The method comprises the following steps: performing pre-interpolation complementation on a missing seismic image lacking a seismic trace to obtain a low-resolution seismic image, performing Gabor filtering on the low-resolution seismic image to obtain a feature image, obtaining a prediction feature vector by utilizing the low-resolution seismic image and feature image transformation, and training a regression function by training the feature vector and a label; and finally, inputting the prediction feature vector into a trained regression function, and obtaining a reconstructed seismic image through regression reconstruction and transformation. According to the invention, the seismic data interpolation algorithm combining Gabor filtering and SVR is designed, the feature image obtained by Gabor feature extraction and the regression reconstruction capability of SVR are fully utilized, and a clear and complete seismic image is obtained.

The invention discloses a seismic data orderliness assessment method and device. The method comprises the following steps: acquiring a three-dimensional seismic data volume to be assessed; performing point-by-point scanning on the three-dimensional seismic data according to the depth direction, the main survey line direction and the connection survey line direction, and calculating the image entropy of each sampling point to obtain an entropy data volume of the three-dimensional seismic data volume; and according to the entropy data volume, performing order evaluation on the three-dimensional seismic data volume. According to the method, the reliability of the seismic data generating the fracture attribute is evaluated based on the seismic image entropy, and then the seismic data is optimized by using the evaluation result, so that the purposes of adjusting the fracture attribute and balancing the fracture attribute are achieved, and the response form of the fracture attribute is optimized.

The invention is a method applicable to oil and gas exploration and exploitation for automatically detecting geological objects belonging to a given type of geological object in a seismic image, on a basis of a priori probabilities of belonging to a type of geological object assigned to each of samples of the image to be interpreted. The image is transformed into seismic attributes applied beforehand, followed by a classification method. For each of the classes, an a posteriori probability of belonging to a type of geological object is determined for each of the samples of the class according to the a priori probabilities, of the class, of belonging, and according to a parameter α describing a confidence in the a priori probabilities of belonging. Based on the class of the sample, the determined a posteriori probability of belonging to a type of geological object is assigned for the samples of the class. The geological objects belonging to the type of geological object are detected based on determined of the a posteriori probabilities of belonging to the type of geological object for each of the samples of the image to be interpreted.

The embodiment of the invention provides a time-lapse seismic observation data analysis method and device, and the method comprises the steps: detecting the site conditions of an observation site, anddetermining the arrangement types of an excitation point and a receiving point according to the site conditions of the observation site; performing a site experiment to obtain basic parameters; whenthe arrangement type is a linear type, arranging the positions of the excitation point and the receiving point according to the basic parameters; when the arrangement type is a regular grid type, calculating observation parameters according to the basic parameters, and arranging the positions of the excitation point and the receiving point according to the observation parameters; selecting different time points to excite the excitation point, and obtaining seismic waves received by the receiving point; calculating seismic images of the observation site at different times according to the seismic waves received by the receiving point; and acquiring site data, establishing a corresponding relationship according to the site data and the seismic images, and performing data analysis through thecorresponding relationship. By the adoption of the method, the seismic data of an expansive soil bank slope and a dam landslide can be reflected in time and space, and then abnormity can be accurately recognized.