Shearwater Geoservices Software Inc.

Conocophillips Company

Alcatel Lucent

Chengbo Li

Charles Clinton Mosher

Joel D Brewer

Frank D Janiszewski

Laurence S Williams

Robert G Keys

Chuck Mosher

Sam T Kaplan

Leo Ji

Peter M Eick

Hong Jiang

Paul Albin Wilford

Yu Zhang

Kim N Matthews

Feng Chen

Jianxing Hu

Jeffrey Malloy

Jon Anderson

Bradley Bankhead

Yong Ma

Zhengxue Li

Yunqing Shen

Joel Brewer

Method for acquiring seismic data is described. The method includes obtaining undersampled seismic data acquired from a non-uniform sampling grid. Attenuating multiples from the undersampled seismic data.

Offshore application of non-uniform optimal sampling survey design

Leveraging migration and demigration, here we propose a learning-based approach for fast denoising with applications to fast-track processing. The method is designed to directly work on raw data without separating each noise type and character. The automatic attenuation of noise is attained by performing migration/demigration guided sparse inversion. By discussing examples from a Permian Basin dataset with very challenging noise issues, we attest the feasibility of this learning-based approach as a fast turnaround alternative to conventional denoising methodology.

Seismic denoising

Method for acquiring seismic data is described. The method includes determining a non-uniform optimal sampling design that includes a compressive sensing sampling grid. Placing a plurality of source lines or receiver lines at a non-uniform optimal line interval. Placing a plurality of receivers or nodes at a non-uniform optimal receiver interval. Towing a plurality of streamers attached to a vessel, wherein the plurality of streamers is spaced apart at non-uniform optimal intervals based on the compressive sensing sampling grid. Firing a plurality of shots from one or more seismic sources at non-uniform optimal shot intervals. Acquiring seismic data via the plurality of receivers or nodes.

Non-uniform optimal survey design principles

A method for 2D seismic data acquisition includes determining source-point seismic survey positions for a combined deep profile seismic data acquisition with a shallow profile seismic data acquisition wherein the source-point positions are based on non-uniform optimal sampling. A seismic data set is acquired with a first set of air-guns optimized for a deep-data seismic profile and the data set is acquired with a second set of air-guns optimized for a shallow-data seismic profile. The data are de-blended to obtain a deep 2D seismic dataset and a shallow 2D seismic dataset.

Use NUOS technology to acquire optimized 2D data

A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.

Methods for simultaneous source separation

Various aspects described herein relate to a machine learning based signal recovery. In one example, a computer-implemented method of noise contaminated signal recovery includes receiving, at a server, a first signal including a first portion and a second portion, the first portion indicative of data collected by a plurality of sensors, the second portion representing noise; performing a first denoising process on the first signal to filter out the noise to yield a first denoised signal; applying a machine learning model to determine a residual signal indicative of a difference between the first signal and the first denoised signal; and determining a second signal by adding the residual signal to the first denoised signal, the second signal comprising (i) signals of the first portion with higher magnitudes than the noise in the second portion, and (ii) signals of the first portion having lower magnitudes than the noise in the second portion.

Machine learning based signal recovery

Use nuos technology to acquire optimized 2D data

Computer-implemented method for determining optimal sampling grid during seismic data reconstruction includes: a) constructing an optimization model, via a computing processor, given by min&#x2225;Su&#x2225;s.t. &#x2225;Ru&#x2212;b&#x2225;&#x2266;&#x3c3; wherein S is a discrete transform matrix, b is seismic data on an observed grid, u is seismic data on a reconstruction grid, and matrix R is a sampling operator; b) defining mutual coherence as wherein C is a constant, S is a cardinality of Su, m is proportional to number of seismic traces on the observed grid, and n is proportional to number of seismic traces on the reconstruction grid; c) deriving a mutual coherence proxy, wherein the mutual coherence proxy is a proxy for mutual coherence when S is over-complete and wherein the mutual coherence proxy is exactly the mutual coherence when S is a Fourier transform; and d) determining a sample grid r=arg min&#x3bc;(r).

Compressive sensing

The present invention relates to method and apparatus for arbitrary resolution video coding using compressive measurements. The method includes receiving at least one measurement of a set of measurements that was generated at an encoder. The set of measurements represents encoded video data. The method further includes determining a display resolution, where the display resolution is the same or different than an original display resolution. The method further includes determining an expansion matrix based on at least a number of pixels for the determined display resolution, and reconstructing the video data using the determined expansion matrix such that the original display resolution is resized to the determined display resolution if the determined display resolution is different than the original display resolution. The expansion matrix includes a pattern of values.

Method and apparatus for arbitrary resolution video coding using compressive sampling measurements

The present invention relates to an apparatus and method for video coding using compressive measurements. The method includes receiving video data including frames, and determining at least one temporal structure based on a series of consecutive frames in the video data. The temporal structure includes a sub-block of video data from each frame in the series. The method further includes obtaining a measurement matrix, and generating a set of measurements by applying the measurement matrix to the at least one temporal structure. The measurement matrix includes an assigned pattern of pixel values and the set of measurements is coded data representing the at least one temporal structure.

Video coding using compressive measurements

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Houston, TX, United States of America

Chengbo Li