PyFWI: A Python package for full-waveform inversion and reservoir monitoring

Full-waveform inversion (FWI) of seismic data is a technique that can be used to image the subsurface as well as to monitor time-lapse changes in the subsurface (TL-FWI). PyFWI is a package that has been designed to carry out FWI and TL-FWI efficiently on GPU for research purposes. Several time-laps...

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Main Authors: Amir Mardan, Bernard Giroux, Gabriel Fabien-Ouellet
Format: Article
Language:English
Published: Elsevier 2023-05-01
Series:SoftwareX
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352711023000808
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author Amir Mardan
Bernard Giroux
Gabriel Fabien-Ouellet
author_facet Amir Mardan
Bernard Giroux
Gabriel Fabien-Ouellet
author_sort Amir Mardan
collection DOAJ
description Full-waveform inversion (FWI) of seismic data is a technique that can be used to image the subsurface as well as to monitor time-lapse changes in the subsurface (TL-FWI). PyFWI is a package that has been designed to carry out FWI and TL-FWI efficiently on GPU for research purposes. Several time-lapse strategies are implemented in PyFWI, such as parallel, double-difference, cascaded, central-difference, cross-updating, simultaneous, and weighted-average. An important challenge of TL-FWI is the crosstalk between parameters across different vintages. To alleviate this problem, PyFWI allows using different parameterizations. PyFWI is written in Python and relies on OpenCL for enabling calculations on GPUs, which leads to significant reduction of computation time compared to CPU implementation. Using OpenCL makes PyFWI portable across systems built with GPUs from different manufacturers.
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spelling doaj.art-dd9e589f763d41a68474f3691a28637a2023-05-27T04:25:57ZengElsevierSoftwareX2352-71102023-05-0122101384PyFWI: A Python package for full-waveform inversion and reservoir monitoringAmir Mardan0Bernard Giroux1Gabriel Fabien-Ouellet2INRS-ETE, 490 Rue de la Couronne, Québec, QC, G1K 9A9, Canada; Corresponding author.INRS-ETE, 490 Rue de la Couronne, Québec, QC, G1K 9A9, CanadaPolytechnique Montréal, 2900 Boulevard Edouard-Montpetit, Montréal, Quebec H3T 1J4, CanadaFull-waveform inversion (FWI) of seismic data is a technique that can be used to image the subsurface as well as to monitor time-lapse changes in the subsurface (TL-FWI). PyFWI is a package that has been designed to carry out FWI and TL-FWI efficiently on GPU for research purposes. Several time-lapse strategies are implemented in PyFWI, such as parallel, double-difference, cascaded, central-difference, cross-updating, simultaneous, and weighted-average. An important challenge of TL-FWI is the crosstalk between parameters across different vintages. To alleviate this problem, PyFWI allows using different parameterizations. PyFWI is written in Python and relies on OpenCL for enabling calculations on GPUs, which leads to significant reduction of computation time compared to CPU implementation. Using OpenCL makes PyFWI portable across systems built with GPUs from different manufacturers.http://www.sciencedirect.com/science/article/pii/S2352711023000808Full-waveform inversionReservoir monitoringTime-lapse seismicPythonGPU programming
spellingShingle Amir Mardan
Bernard Giroux
Gabriel Fabien-Ouellet
PyFWI: A Python package for full-waveform inversion and reservoir monitoring
SoftwareX
Full-waveform inversion
Reservoir monitoring
Time-lapse seismic
Python
GPU programming
title PyFWI: A Python package for full-waveform inversion and reservoir monitoring
title_full PyFWI: A Python package for full-waveform inversion and reservoir monitoring
title_fullStr PyFWI: A Python package for full-waveform inversion and reservoir monitoring
title_full_unstemmed PyFWI: A Python package for full-waveform inversion and reservoir monitoring
title_short PyFWI: A Python package for full-waveform inversion and reservoir monitoring
title_sort pyfwi a python package for full waveform inversion and reservoir monitoring
topic Full-waveform inversion
Reservoir monitoring
Time-lapse seismic
Python
GPU programming
url http://www.sciencedirect.com/science/article/pii/S2352711023000808
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