Solving Larger Seismic Inverse Problems with Smarter Methods

doi:10.1017/9781009180412.016

15 - Solving Larger Seismic Inverse Problems with Smarter Methods

from Part III - ‘Solid’ Earth Applications: From the Surface to the Core

Published online by Cambridge University Press: 20 June 2023

Lars Gebraad,

Dirk-Philip van Herwaarden,

Solvi Thrastarson and

Edited by

Dylan Jones and

Alik Ismail-Zadeh: Affiliation:
Karlsruhe Institute of Technology, Germany
Fabio Castelli: Affiliation:
Università degli Studi, Florence
Dylan Jones: Affiliation:
University of Toronto
Sabrina Sanchez: Affiliation:
Max Planck Institute for Solar System Research, Germany

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Summary

Abstract: The continuously increasing quantity and quality of seismic waveform data carry the potential to provide images of the Earth’s internal structure with unprecedented detail. Harnessing this rapidly growing wealth of information, however, constitutes a formidable challenge. While the emergence of faster supercomputers helps to accelerate existing algorithms, the daunting scaling properties of seismic inverse problems still demand the development of more efficient solutions. The diversity of seismic inverse problems – in terms of scientific scope, spatial scale, nature of the data, and available resources – precludes the existence of a silver bullet. Instead, efficiency derives from problem adaptation. Within this context, this chapter describes a collection of methods that are smart in the sense of exploiting specific properties of seismic inverse problems, thereby increasing computational efficiency and usable data volumes, sometimes by orders of magnitude. These methods improve different aspects of a seismic inverse problem, for instance, by harnessing data redundancies, adapting numerical simulation meshes to prior knowledge of wavefield geometry, or permitting long-distance moves through model space for Monte Carlo sampling.

Keywords

Earth structure high-performance computing inverse theory numerical simulation seismic tomography seismology uncertainty quantification wave propagation

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Type: Chapter
Information: Applications of Data Assimilation and Inverse Problems in the Earth Sciences , pp. 239 - 251
DOI: https://doi.org/10.1017/9781009180412.016 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2023

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