A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.

Generally, seismic data may provide valuable information with regard to the description such as the location and/or change of hydrocarbon deposits within a subsurface region of the Earth. The present disclosure generally discusses techniques that may be used by a computing system to analyze a data set including weak-coherence signals (e.g., non-coherent blending noise). In particular, a computing system may detect portion of the weak-coherence signals of a gather due to the overlap of selected seismic source excitations and use a mask to isolate coherent signals and the other weak-coherence signals from the masked portion of weak-coherence signals. The coherent signals and other weak-coherence signals may be iteratively processed and used to predict values of the masked weak-coherence signals.

A sonic logging method is provided that transmits acoustic signals using a high order acoustic source and processes waveform data to identify a set of arrival events and time picks by automatic and/or manual methods. Ray tracing inversion is carried out for each arrival event over a number of possible raypath types that include at least one polarized shear raypath type to determine two-dimensional reflector positions and predicted inclination angles of the arrival event for the possible raypath types. One or more three-dimensional slowness-time coherence representations are generated for the arrival event and raypath type(s) and evaluated to determine azimuth, orientation and raypath type of a corresponding reflector. The method outputs a three-dimensional position and orientation for at least one reflector. The information derived from the method can be conveyed in various displays and plots and structured formats for reservoir understanding and also output for use in reservoir analysis and other applications.

The present invention is related to a method for reconstructing at least one trace in a seismic image of a common receiver and time domain, the image comprising traces in time domain with seismic data and one or more traces to be reconstructed. A first aspect of the invention is a method that is characterized by a specific use of a convolutional neural network trained under an unsupervised learning approach with a modified receptive field. A second aspect of the invention is a deblending method based on the use of a reconstructing method according to the first aspect of the invention applied to a denoising step of a deblending process allowing a very effective data acquisition while keeping a high quality output data sets after being processed according to the first and/or second aspects of the invention.

Embodiments of the present disclosure provide a method and system for recognizing a mine microseismic event, and belong to the field of mine data processing. The method includes: converting historical microseismic data monitored by a mine microseismic monitoring system into a microseismic waveform image, and then, converting the microseismic waveform image into a four-neighborhood microseismic waveform graph structure; performing area defining on the microseismic waveform graph structure, and extracting a similar feature layer of any node in the microseismic waveform graph structure based on the defined area; and taking the microseismic waveform image as an input layer of an improved convolutional neural network model, and sequentially connecting the input layer with the similar feature layer as well as a convolutional layer, a pooling layer, a fully connected layer and an output layer which are pre-configured for the improved convolutional neural network model to form a recognition model for recognizing the mine microseismic event. By using the recognition model designed in the present disclosure, the similar feature layer can be extracted, so that the mine microseismic event is effectively recognized.

A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.

Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.

A machine-implemented method, at least one non-transitory computer-readable medium storing instructions, and a computing system are provided for attenuating noise. A computing system receives a seismic image and generates a first image using a first neural network configured to identify low-frequency ground roll in a seismic image, and a second image using a second neural network configured to identify reflections in the seismic image. A combined image is generated by combining the first image and the second image. The first neural network and the second neural network are adjusted to reduce a difference between the combined image and the seismic image using frequency constraint to guide separation of the seismic image into the first image and the second image.

Aspects described herein provide for methods and apparatus for characterizing azimuthal heterogeneities in a barrier installed outside an outer casing in a borehole traversing a formation in a cased hole configuration including an inner and outer casing. The approach is based on specific attributes in sonic signals acquired with an azimuthal and axial array receiver system located inside the inner casing. The methods include slowness-time-coherence (STC) processing based on specific arrivals identified in data acquired by axial arrays associated with multiple azimuthal sections of the receiver system. The specific arrivals contain STC signatures that can be examined in terms of coherence amplitude and localization within STC maps. Based on specific attributes in the sonic signals, an azimuthal coverage of the outer casing annular contents can be created.

A system for seismic imaging of a subterranean geological formation, the system includes a receiver configured to obtain seismic data comprising a data volume representing a post-stacked image. The system includes a filtering module configured to: apply frequency-wavenumber (F-K) filter to the data volume extract a negative-dip structure image and apply the frequency-wavenumber (F-K) filter to the data volume extract a positive-dip structure image. The system includes a diffraction rendering module configured to: multiply the positive-dip structure image with the negative-dip structure image and generate a diffraction-enhanced seismic image representing a geological formation of the data volume.