A new full waveform inversion method based on shifted correlation of the envelope and its implementation based on OPENCL

Wu, Zedong; Alkhalifah, Tariq; Zhang, Zhendong; Alonaizi, Faisal; Almalki, Majed

doi:10.1016/j.cageo.2019.04.007

Cited by 13 publications

(3 citation statements)

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“…[17], [29]), to employ more robust objective functions to compare the recorded and simulated wavefield ( e.g. [2], [30], [31], [59]) or to implement signal-and/or gradient-based preconditioning or regularization techniques (e.g. [3], [55]).…”

Section: B Seismic Data Inversionmentioning

confidence: 99%

Toward a Practical Appraisal for Waveform Tomography of Band- and Offset-Limited Marine Seismic Data

Gras¹,

Tejero

Sallarès

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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We present a generalized workflow to retrieve highresolution P-wave velocity (VP ) models of complex Earths subsurface structures from traditional marine near-vertical seismic reflection experiments. These records have typically offsets too short to map refraction phases and lack low frequency information. The workflow is composed of three steps: (1) downward continuation (DC) of seismic records to the seafloor to recover diving wave information; (2) travel-time tomography (TTT) of first arrivals obtained from DC data, to retrieve a kinematically correct model, and (3) full-waveform inversion (FWI) of the original streamer data set, starting with the model obtained with TTT and sequentially introducing higher wavenumber details into the model. We show that the TTT allows overcoming the issues associated to the non-linearity intrinsic to FWI. We also disentangle envelope and phase from the waveform to choose the objective function most suitable for FWI. We assess the accuracy of initial models and predict the quality of the FWI results by quantifying the early-arrival cycle skipping between original and simulated data. The efficiency of the workflow is tested with a challenging synthetic target model, containing vertical boundaries with a strong velocity contrasts and velocity inversions embedded in a checkerboard-like pattern. We show that workflow steps (1) and ( 2) provide a TTT model that is not cycle-skipped at the frequencies available in most marine seismic experiments, and thus allow step (3) FWI to obtain highresolution VP models of the subsurface using band-and offsetlimited field data sets, traditionally collected in marine airgun and streamer acquisitions.

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Section: B Seismic Data Inversionmentioning

confidence: 99%

Toward a Practical Appraisal for Waveform Tomography of Band- and Offset-Limited Marine Seismic Data

Gras¹,

Tejero

Sallarès

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…In the OpenCL execution architecture, the host-side program is used to uniformly manage and schedule multiple computing devices that support OpenCL [31]. When the host side submits the kernel to the computing device, OpenCL defines the organizational structure of the work-item through the index space and defines how the kernel operates on the computing device in a mapping manner on the computing device [32], [33]. In the OpenCL abstract model, each instance of the execution kernel is called a work-item, which is represented by its coordinates in the NDRange.…”

Section: A Opencl Parallel Computing Platformmentioning

confidence: 99%

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

et al. 2021

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The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multilayer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and workitem. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.INDEX TERMS weighted mean filtering; Gaussian noise; Graphic Processing Unit (GPU); Open Computing Language (OpenCL); parallel algorithm.

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“…The L 2 norm objective function requires that the maximum mismatch of the predicted and observed data should not exceed a half-cycle for all arrivals; otherwise, the adjoint source is cycle-skipped. The correlationbased objective function (Van Leeuwen andMulder, 2008, 2010;Routh et al, 2011;Choi and Alkhalifah, 2012;Chi et al, 2015;Zhang et al, 2018a;Wu et al, 2019) or adaptive full waveform inversion (AWI) (Warner and Guasch, 2014) can compare the arrivals globally within a time window and in some cases are free of cycle-skipping. Third, find a simplified representation of the complex data.…”

Section: Introductionmentioning

confidence: 99%

Local-crosscorrelation elastic full-waveform inversion

Zhang

Alkhalifah

2019

GEOPHYSICS

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Full-waveform inversion (FWI) in its classic form is a method based on minimizing the [Formula: see text] norm of the difference between the observed and simulated seismic waveforms at the receiver locations. The objective is to find a subsurface model that reproduces the full waveform including the traveltimes and amplitudes of the observed seismic data. However, the widely used [Formula: see text]-norm-based FWI faces many issues in practice. The point-wise comparison of waveforms fails when the phase difference between the compared waveforms of the predicted and observed data is larger than a half-cycle. In addition, amplitude matching is impractical considering the simplified physics that we often use to describe the medium. To avoid these known problems, we have developed a novel elastic FWI algorithm using the local-similarity attribute. It compares two traces within a predefined local time extension; thus, is not limited by the half-cycle criterion. The algorithm strives to maximize the local similarities of the predicted and observed data by stretching/squeezing the observed data. Phases instead of amplitudes of the seismic data are used in the comparison. The algorithm compares two data sets locally; thus, it performs better than the global correlation in matching multiple arrivals. Instead of picking/calculating one stationary stretching/squeezing curve, we used a weighted integral to find all possible stationary curves. We also introduced a polynomial-type weighting function, which is determined only by the predefined maximum stretching/squeezing and is guaranteed to be smoothly varying within the extension range. Compared with the previously used Gaussian or linear weighting functions, our polynomial one has fewer parameters to play around with. A modified synthetic elastic Marmousi model and the North Sea field data are used to verify the effectiveness of the developed approach and also reveal some of its limitations.

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A new full waveform inversion method based on shifted correlation of the envelope and its implementation based on OPENCL

Abstract: A new full waveform inversion method based on shifted correlation of the envelope and its implementation based on OPENCL. Computers & Geosciences 129: 1-11.

Cited by 13 publications

References 45 publications

Toward a Practical Appraisal for Waveform Tomography of Band- and Offset-Limited Marine Seismic Data

Toward a Practical Appraisal for Waveform Tomography of Band- and Offset-Limited Marine Seismic Data

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

Local-crosscorrelation elastic full-waveform inversion

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