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Sparse laterally constrained inversion of surface-wave dispersion curves via minimum gradient support regularization

Julien Guillemoteau, Giulio Vignoli, Jeniffer Barreto and Guillaume Sauvin
Sparse laterally constrained inversion of surface-wave dispersion curves via minimum gradient support regularization
Geophysics (June 2022) 87 (3): R281-R289

Abstract

We have developed a 1D laterally constrained inversion of surface-wave dispersion curves based on the minimum gradient support regularization, which allows solutions with tunable sharpness in the vertical and horizontal directions. The forward modeling consists of a finite-elements approach incorporated in a flexible nonparametric gradient-based inversion scheme, which has already demonstrated good stability and convergence capabilities when tested on other kinds of data. Our deterministic inversion procedure is performed in the shear-wave velocity log space as we noticed that the associated Jacobian indicates a reduced model dependency, and this, in turn, decreases the risks of local nonconvexity. We show several synthetics and one field example to demonstrate the effectiveness and the applicability of the proposed approach.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 87
Serial Issue: 3
Title: Sparse laterally constrained inversion of surface-wave dispersion curves via minimum gradient support regularization
Affiliation: Universitaet Potsdam, Institut fuer Geowissenschaften, Potsdam, Germany
Pages: R281-R289
Published: 202206
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 36
Accession Number: 2022-021789
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table
N58°00'00" - N71°00'00", E04°00'00" - E31°00'00"
Secondary Affiliation: University of Cagliari, Cagliari, ITA, ItalyNorwegian Geotechnical Institute, Oslo, NOR, Norway
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 2022
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