A distributed acoustic sensing (DAS) system for determining an acoustic event may include an interferometer and an acoustic event detection processing device. The interferometer may measure DAS data from sensed signals from a sensing fiber deployed in a wellbore. The acoustic event detection processing device may determine an acoustic event in the wellbore from an out-of-band signal using the DAS data by performing operations. The operations can include determining a first acoustic event and a second acoustic event from the DAS data. The operations can include determining a first set of aliased frequencies from the first acoustic event and a second set of aliased frequencies form the second acoustic event. The operations can include determining, using an intersection of the first set of aliased frequencies and the second set of aliased frequencies, a frequency or amplitude of out-of-band signals that are usable to determine the at least one acoustic event.

An acoustic transducer includes a substrate element having a first side, and a second side opposite the first side. The acoustic transducer also includes first and second piezoelectric elements coupled to the first side, and third and fourth piezoelectric elements coupled to the second side. The first piezoelectric element has a first polarity, and the second piezoelectric element has a second polarity different than the first polarity. The third piezoelectric element has a third polarity, and the fourth piezoelectric element has a fourth polarity different than the third polarity.

Aspects of the disclosure provide for a method using clusters of sonic peaks from a logging tool to generate a log of an acoustic property of the formation as a function of depth.

Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

Methods and apparatuses for seismic data processing perform a least-squares reverse time migration method in which surface-attribute-independent coefficients for the surface attribute gathers are demigrated to reduce the computational cost.

Methods and apparatuses for seismic data processing perform a least-squares reverse time migration method in which surface-attribute-independent coefficients for the surface attribute gathers are demigrated to reduce the computational cost.

Displaying a spiral-shaped visualization of a geological time scale according to some aspects may include accessing a time-attributed data set representing a geological time scale of a subterranean region. The geological time scale may be segmented into a hierarchy of intervals (e.g., periods, epochs, and stages). The spiral-shaped visualization may include a path formed in a spiral formation. The path may begin at a center position of the spiral-shaped visualization and may end at an outer portion of the spiral-shaped visualization. The beginning of the path may represent a first time of the geological time scale. The ending of the path may represent a second time of the geological time scale. The spiral-shaped visualization may also be segmented to represent the hierarchy of intervals. Additionally, the spiral-shaped visualization may be interactive. Selecting an interval of the path may automatically cause the intervals of the spiral-shaped visualization to be filtered.

Displaying a spiral-shaped visualization of a geological time scale according to some aspects may include accessing a time-attributed data set representing a geological time scale of a subterranean region. The geological time scale may be segmented into a hierarchy of intervals (e.g., periods, epochs, and stages). The spiral-shaped visualization may include a path formed in a spiral formation. The path may begin at a center position of the spiral-shaped visualization and may end at an outer portion of the spiral-shaped visualization. The beginning of the path may represent a first time of the geological time scale. The ending of the path may represent a second time of the geological time scale. The spiral-shaped visualization may also be segmented to represent the hierarchy of intervals. Additionally, the spiral-shaped visualization may be interactive. Selecting an interval of the path may automatically cause the intervals of the spiral-shaped visualization to be filtered.

A method and system of machine learning-augmented geophysical inversion includes obtaining measured data; obtaining prior subsurface data; (a) partially training a data autoencoder with the measured data to learn a fraction of data space representations and generate a data space encoder; (b) partially training a model autoencoder with the prior subsurface data to learn a fraction of model space representations and generate a model space decoder; (c) forming an augmented forward model with the model space decoder, the data space encoder, and a physics-based forward model; (d) solving an inversion problem with the augmented forward model to generate an inversion solution; and iteratively repeating (a)-(d) until convergence of the inversion solution, wherein, for each iteration: partially training the data and model autoencoders starts with learned weights from an immediately-previous iteration; and solving the inversion problem starts with super parameters from the previous iteration.