A dip angle-steering median filtering method based on a niche differential evolution algorithm, comprising the following steps: dividing a data to be processed into a series of overlapping time-space windows; obtaining an event energy curve in a time-space window and obtaining an event position according to a local maximum value of the event energy curve; obtaining event dip angles and coherence values of the event dip angles through the niche differential evolution algorithm at the event position; filtering the event dip angles according to the event dip angles and the coherence values of the event dip angles; and performing a median filtering sequentially along a filtering dip angle. The disclosure can simultaneously obtain all dip angles of intersecting events and a true three-dimensional feature enable the present disclosure to obtain a better filtering effect.

Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

A dip angle-steering median filtering method based on a niche differential evolution algorithm, comprising the following steps: dividing a data to be processed into a series of overlapping time-space windows; obtaining an event energy curve in a time-space window and obtaining an event position according to a local maximum value of the event energy curve; obtaining event dip angles and coherence values of the event dip angles through the niche differential evolution algorithm at the event position; filtering the event dip angles according to the event dip angles and the coherence values of the event dip angles; and performing a median filtering sequentially along a filtering dip angle. The disclosure can simultaneously obtain all dip angles of intersecting events and a true three-dimensional feature enable the present disclosure to obtain a better filtering effect.

A method for enhancing properties of geophysical data with deep learning networks. Geophysical data may be acquired by positioning a source of sound waves at a chosen shot location, and measuring back-scattered energy generated by the source using receivers placed at selected locations. For example, seismic data may be collected using towed streamer acquisition in order to derive subsurface properties or to form images of the subsurface. However, towed streamer data may be deficient in one or more properties (e.g., at low frequencies). To compensate for the deficiencies, another survey (such as an Ocean Bottom Nodes (OBN) survey) may be sparsely acquired in order to train a neural network. The trained neural network may then be used to compensate for the towed streamer deficient properties, such as by using the trained neural network to extend the towed streamer data to the low frequencies.

Methods and systems for determining a spectral ratio log using a time domain seismic image and a seismic velocity model are disclosed. The method includes determining a first mono-spectral seismic image and a second mono-spectral seismic image from the time domain seismic image. The method further includes determining a time domain spectral ratio image from the first mono-spectral seismic image and the second mono-spectral seismic image and transforming the time domain spectral ratio image into a depth domain spectral ratio image using the seismic velocity model. The method still further includes defining a wellbore path through the depth domain spectral ratio image and determining a spectral ratio log along the wellbore path from the depth domain spectral ratio.

The techniques and device provided herein relate to receiving, via a processor, image data representative of a borehole of a well. The technique may include generating dequantized image data based on the image data, such that the dequantized image data filters one or more artifacts present in a Hough transformed version of the image data. One or more dip orientations (inclination and azimuth) associated with one or more formation dips present in the image data may be determined based on the dequantized image data. The technique may also include performing an a-contration validation algorithm for for the one or more formation dips to verify whether at least a formation dip having the or one of the possible dip orientation is present at a predetermined measured depth in the image data..

A method for enhancing properties of geophysical data with deep learning networks. Geophysical data may be acquired by positioning a source of sound waves at a chosen shot location, and measuring back-scattered energy generated by the source using receivers placed at selected locations. For example, seismic data may be collected using towed streamer acquisition in order to derive subsurface properties or to form images of the subsurface. However, towed streamer data may be deficient in one or more properties (e.g., at low frequencies). To compensate for the deficiencies, another survey (such as an Ocean Bottom Nodes (OBN) survey) may be sparsely acquired in order to train a neural network. The trained neural network may then be used to compensate for the towed streamer deficient properties, such as by using the trained neural network to extend the towed streamer data to the low frequencies.

A method of subsurface exploration includes generating a representation of a portional volume of the Earth from a seismic measurement of a physical parameter. The method includes providing observed seismic dataset having three distinct nonzero data values derived from three distinct nonzero seismic measured values of said portional volume of the Earth, generating a predicted seismic dataset having three distinct nonzero data values, generating a nontrivial convolutional filter including three nonzero filter coefficients, generating a convolved observed dataset by convolving the convolutional filter with said observed seismic dataset, generating primary objective functions to measure the similarity between said convolved observed dataset and said predicted dataset, maximizing and/or minimizing said primary objective functions by modifying at least one filter coefficient of the convolutional filter, generating predetermined reference filters having at least three reference coefficients generating secondary objective functions to measure the similarity between filter coefficients for the nontrivial filter and reference coefficients for the predetermined reference filters, and minimizing and/or maximizing said secondary objective functions by modifying a model coefficient of a subsurface model of a portion of the Earth to produce an updated subsurface model of a portion of the Earth.

Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

Systems and methods for modeling subsurface rock formations based on well log data are provided. Systems include a downhole tool for acquiring data from which borehole dips may be picked and a processor including machine-readable instructions for curvature analysis based on inputs generated from the picked borehole dips data and which may be independent of 2D cross section model orientation. Methods (which may be incorporated in the machine-readable instructions corresponding to the systems) include pre-processing borehole dips data to generate inputs such as true stratigraphic thickness index, Local Constant Dips, borehole structural dip, and attributes for structural dip projections which may be used in a curvature analysis process for generating curvature logs such as standard, curvature along axis and curvature normal to axis logs from for smoothed dips, short zone structural dips and/or long zone structural dips.