Embodiments deal with a method, a computer program as well as an apparatus for detecting one or more objects in the sea floor. The method comprises obtaining a receiver signal. The receiver signal is based on a scattering of multiple acoustic signals at the one or more objects in the sea floor. The receiver signal is generated by a plurality of receivers. The method further comprises grouping portions of the receiver signal to points of a detection grid. The detection grid represents a grid at the points of which the one or more objects are being localized. The method further comprises performing a travel time correction of the portions of the receiver signal with respect to the points of the detection grid. The method further comprises combining the travel time corrected portions of the receiver signal at the points of the detection grid. The method further comprises detecting the one or more objects at the points of the detection grid based on the combination of the travel time corrected portions of the receiver signal. The detection of the one or more objects is based on the scattering of the multiple acoustic signals at the one or more objects.

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.

A seismic time-frequency analysis method based on generalized Chirplet transform with time-synchronized extraction, which has higher level of energy aggregation in the time direction and can better describe and characterize the local characteristics of seismic signals, and is applicable to the time-frequency characteristic representation of both harmonic signals and pulse signals, comprising the steps of processing generalized Chirplet transform with time-synchronized extraction for each seismic signal to obtain a time spectrum by: carrying out generalized Chirplet transform, calculating group delay operator and carrying out time-synchronized extraction on seismic signals, thereby the boundary and heterogeneity structure of the rock slice are more accurately and clearly shown and subsequence seismic analysis and interpretation are facilitated.

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving seismic data acquired by at least one receiver of a geologic survey system configured to perform a geologic survey of a subterranean formation, wherein the seismic data is associated with reflected acoustic signals generated by at least one source of the geologic survey system; calculating a ground force signal by stacking the acoustic signals generated by the least one source; calculating, using the ground force signal, a time and depth variant quality factor (Q) of the subterranean formation; and compensating, based on the time and depth variant Q, attenuation in the seismic data.

Methods and systems for identifying a multiple artifact are disclosed. The method includes obtaining a post-stacked seismic image of a subterranean region and identifying a horizon with the post-stacked seismic image. The method further includes determining a spectral section over the horizon by applying spectral decomposition to the post-stacked seismic image. The method still further includes detecting a frequency anomaly within the spectral section by comparing the spectral section to a reference spectral section and identifying the multiple artifact based on the frequency anomaly.

A symmetry attribute is described that may be used for determining seismic stratigraphic features in a formation. In one example, seismic input data from a formation is processed to determine an attribute by selecting a center trace, assigning a first cluster of the traces to a left image and a second cluster of the traces to a right image, and determining symmetry about the center trace between the left and the right images.

Methods and systems, including computer programs encoded on a computer storage medium can be used to preserve edges while performing Gaussian grid smoothing of noise components in subsurface grids to generate geological maps. A subsurface grid is generated from data indicating properties of subsurface formations. A weighting grid is generated by: i) receiving seismic data representing the subsurface formations; ii) generating seismic attributes associated with discontinuities in the subsurface formations; and iii) assigning a particular weight value to weighting grid points that the seismic attributes associated with discontinuities in the subsurface formations indicate the presence of a discontinuity. The subsurface grid is processed by iteratively computing local averages of grid points in the subsurface grid using a compact Gaussian filter weighted by values in the weighting grid. A geological map of subsurface formations is generated based on the filtered subsurface grid.

Acoustic imaging tools and methods use refracted wave amplitudes to generate borehole images, thereby providing a method and tool that is highly sensitive to borehole discontinuities.

Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.