Abstract: A method includes acquiring blended seismic data representing a subsurface volume of interest from a plurality of seismic sources, estimating a signal mode using one or more first priors by applying sparse inversion to the blended seismic data, predicting multi-source interference in the blended seismic data based at least in part on the estimated signal mode, removing the estimated signal mode and the predicted multi-source interference from the blended seismic data, such that a residual signal is left, and estimating a coherent signal from the residual signal by solving a sparse inversion.

Abstract: A method can include receiving seismic survey data of a subsurface environment from a seismic survey that includes a source arrangement of sources that is spatially denser than a receiver arrangement of receivers; processing the seismic survey data using the principle of reciprocity for performing interpolation across the receivers to generate processed seismic survey data; and generating an image of at least a portion of the subsurface environment using the processed seismic survey data.

Abstract: A method can include receiving data acquired via survey equipment disposed in an environment; analyzing at least a portion of the data via an iterative adaptive approach that implements matching pursuit to generate analysis results; and identifying at least one feature in the environment based at least in part on a portion of the analysis results.

Abstract: A method can include receiving data acquired via survey equipment disposed in an environment; analyzing at least a portion of the data via an iterative adaptive approach that implements matching pursuit to generate analysis results; and identifying at least one feature in the environment based at least in part on a portion of the analysis results.

Abstract: Various implementations described herein are directed to methods for processing seismic data, including estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors. The methods may include receiving a shot record of multi-measurement seismic data in time-domain, partitioning the shot record into overlapping time-space windows, and computing a frequency-domain spectrum for each time-space window. The methods may include computing a signal presence probability for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window.

Abstract: Computing systems, methods, and computer-readable media for noise mitigation in seismic multimeasurement data are disclosed.

Abstract: Systems, methods, and computer-readable media for estimating a component of a seismic wavefield. The method may include accessing marine seismic data comprising a plurality of discrete measurements of a seismic wavefield; processing the marine seismic data to determine a relationship between a plurality of components of the seismic wavefield and each of the discrete measurements; and estimating from the marine seismic data processed via the one or more processors, each component of the seismic wavefield separated from each of the other plurality of components of the seismic wavefield and evaluated at a predetermined position.

Abstract: Computing systems, methods, and computer-readable media for noise mitigation in seismic multimeasurement data are disclosed.

Abstract: Various implementations described herein are directed to methods for processing seismic data, including estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors. The methods may include receiving a shot record of multi-measurement seismic data in time-domain, partitioning the shot record into overlapping time-space windows, and computing a frequency-domain spectrum for each time-space window. The methods may include computing a signal presence probability for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window.