Title: Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression

Abstract

Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress themmore » before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.« less

Authors:

Lindstrom, Peter ^[1]; Chen, Po ^[2]; Lee, En-Jui ^[3]

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+ Show Author Affiliations

1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Univ. of Wyoming, Laramie, WY (United States)
3. National Cheng Kung Univ., Tainan City (Taiwan)

Publication Date:

Thu May 05 00:00:00 EDT 2016

Research Org.:

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:

1258513

Alternate Identifier(s):

OSTI ID: 1358859

Report Number(s):

LLNL-JRNL-678678
Journal ID: ISSN 0098-3004

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Accepted Manuscript

Journal Name:

Computers and Geosciences

Additional Journal Information:

Journal Volume: 93; Journal ID: ISSN 0098-3004

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING