Inversion with exponentially encoded seismic data can include exponentially encoding acquired seismic data and associated synthetic seismic data, storing the exponentially encoded acquired seismic data and the exponentially encoded associated synthetic seismic data, determining a one-dimensional (1D) Wasserstein distance between the exponentially encoded acquired seismic data and the exponentially encoded associated synthetic seismic data, and generating an adjoint source based on the 1D Wasserstein distance. The example method also includes adapting a dynamic weight implementation of a sensitivity kernel to the adjoint source to build a gradient associated with the acquired seismic data and the associated synthetic seismic data, and iteratively inverting a waveform associated with the exponentially encoded acquired seismic data and the exponentially encoded associated synthetic seismic data based on the gradient. An image of a subsurface location can be generated based on results of the iterative inversions.

Systems, computer readable, and methods concern receiving seismic data representing a subsurface volume. The method also includes determining, for the seismic data, analysis coordinates as a function of time. One or more of the analysis coordinates may vary in position over time. The method includes performing at least one of an interpolation or regularization process on the seismic data based at least partially on the analysis coordinates. The method also includes outputting a result of the at least one of the interpolation or regularization process.

A device and method to compress and store ultrasound data from an ultrasound logging device. The logging device is deployed in a well or pipe to be logged with one or more ultrasound transducers, preferably an array of transducers. On-board the device, are processors and memory units for convolving the ultrasound data with a wavelet transformation to generate wavelet coefficients and then compress the wavelet coefficients to generate compressed wavelet coefficients. The compressed wavelet coefficients are stored on the memory units and transferred to a remote computer once the device leaves the well or pipe.

A process for seismic imaging of a subterranean geological formation includes generating a source wavefield from seismic data representing a subterranean formation. The process includes generating a receiver wavefield from the seismic data representing the subterranean formation. The process includes decomposing the source wavefield to extract a source depth component and decomposing the receiver wavefield to extract a receiver depth component. The process includes applying a transform to each of the source depth component and the receiver depth component. The process includes combining the source depth component and the receiver depth component to generate an imaging condition. The process includes extracting a low-frequency term from the imaging condition to generate a wave-path tracking data, generating a wave path from the wave-path tracking data, and rendering a seismic image of at least a portion of the subterranean geological formation from the generated wave path.

A method of obtaining a relative amplitude preserved seismic volume acquired in a time-domain for a subterranean region of interest and transforming it into a low-frequency monospectral amplitude volume. The method further determines a seismic attenuation volume from the relative amplitude preserved seismic volume acquired in the time-domain. Furthermore, the method generates a low-frequency monospectral amplitude map for a surface of interest by averaging the low-frequency monospectral amplitude volume over a depth-window around the surface of interest, and generates a seismic attenuation map for a surface of interest by averaging the seismic attenuation volume over a depth-window around the surface of interest. The method further determines an attribute map based on the seismic attenuation map and the low-frequency monospectral amplitude map for the surface of interest, and determines a presence of gas in the subterranean region of interest based on the attribute map.

A system for seismic surveying, method for performing seismic and a non-transitory computer readable medium having instructions stored therein that, when executed by one or more processors, cause the one or more processors to perform a method for performing seismic surveying including emitting seismic waves into a substrate, receiving seismic waves reflected from discontinuities within the substrate, converting the seismic waves into seismic traces, and representing the seismic traces by superimposed multiple tone sinusoidal waves using a parameter estimation. An optimized residual of the modelling is compressed using entropy coding or quantization coding techniques, and the optimized residual and the parameter sets are transmitted to a remote processing station for reconstruction and analysis of the discontinuities.

A method of generating diffraction images based on wave equations includes generating a source wavefield and a receiver wavefield. Based on the source wavefield, a first source wavefield propagating in a first direction and a second source wavefield propagating in a second direction are generated. Based on the receiver wavefield, a first receiver wavefield propagating in the first direction and a second receiver wavefield propagating in the second direction are generated. A first seismic image is generated based on the first source wavefield and the first receiver wavefield. A second seismic image is generated based on the second source wavefield and the second receiver wavefield. A final seismic image is generated based on the first seismic image and the second seismic image.

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.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for generating Angle Domain Common Image Gathers (ADCIGs). One computer-implemented method includes receiving, at a data processing apparatus, a set of seismic data associated with a subsurface region wherein the set of seismic data includes receiver signal data at a plurality of time steps; for each time step in the plurality of time steps: calculating a receiver wavefield based on the receiver signal data at the respective time step; separating a first direction receiver wavefield and a second direction receiver wavefield of the receiver wavefield using Hilbert transformation of the receiver signal data at the respective time step; and applying an optical flow process on the first direction receiver wavefield to calculate wavefield directions; and generating an Angle Domain Common Image Gather (ADCIG) based on the wavefield directions.

A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.