Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.

A method, article and system are provided for processing and interpreting acoustic data. The method and system includes providing a number of acoustic processing elements, each element being associated with an acoustic mode of a number of acoustic modes of a sonic measurement tool adapted to acquire data representing acoustic measurements in a borehole. In addition the method and system includes providing a user interface to organize a processing chain of the number of acoustic processing elements such that the acoustic processing elements process the acquired data according to a predefined workflow.

Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.

Methods to estimate formation shear wave slowness from multi-firings of different types of acoustic sources and multi-mode dispersion estimation systems are presented. The method includes obtaining waveform data of waves traversing through a downhole formation, where the waves are generated from multi-firings of different types of acoustic sources. The method also includes performing a multimode dispersion analysis of the waveform data for each firing of the multi-firings, and removing one or more tool waves generated from the multi-firings. The method further includes determining a formation type of the formation the waves traverse based properties of the waves and determining an initial shear wave slowness estimate of the waves. The method further includes generating a modeling of the waves, and reducing a mismatch between the modeling of the waves and a slowness dispersion of the waves to improve the modeling of the waves.

Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Methods for determination of mechanical properties of geological formations using deep learning include receiving, by a computer system, data acquired during drilling a geological formation. The computer system generates features of the data acquired during drilling. The features are indicative of mechanical properties of the geological formation. The computer system segments the features of the data acquired during drilling into sequences readable by a trained temporal convolutional network (TCN). The computer system determines the mechanical properties of the geological formation using the TCN based on the sequences obtained from the features of the data. A display device of the computer system generates a graphical representation of the mechanical properties of the geological formation.

A method of determining a shear-wave attenuated vertical component vertical seismic profile (VSP) dataset is disclosed. The method includes, obtaining a multi-component VSP dataset, including a vertical and a horizontal component, transforming the vertical component into a vertical spectrum and the horizontal component into a horizontal spectrum, and designing a band-pass filter based, at least in part, on an energetic signal of the horizontal spectrum. The method further includes determining a muted vertical amplitude spectrum by applying the pass-band filter to an amplitude spectrum of the vertical spectrum, determining an estimated noise model based on the muted vertical amplitude spectrum and the vertical spectrum; and determining the shear-wave attenuated vertical component VSP dataset by adaptively subtracting the estimated noise model from the vertical component of the multi-component VSP dataset. A system including a seismic source, a plurality of seismic receivers, and a seismic processor for executing the method is disclosed.

A method can include receiving a digital operational plan that specifies computational tasks for seismic workflows, that specifies computational resources and that specifies execution information; dispatching instructions that provision the computational resources for one of the computational tasks for one of the seismic workflows; issuing a request for the execution information; receiving the requested execution information during execution of the one of the computational tasks using the provisioned computational resources; and, based on the received execution information indicating that the execution of the one of the computational tasks deviates from the digital operational plan, dispatching at least one additional instruction that provisions at least one additional computational resource for the one of the computational tasks for the one of the seismic workflows.

Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Methods of providing digital images of living tissue that may include: obtaining data of a propagating wavefield through living tissue; obtaining a reference digital image of the living tissue; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; and determining a quantitative difference measure between the reference digital image and the new digital image based on the changing of one or more parameters selected from the group consisting of field sampling, imaging sampling, and image quality.