Computation, Optimization, and Machine Learning in Seismology

Subhashis Mallick (University of Wyoming, USA)

$215.95

Paperback

Forthcoming
Pre-Order now

QTY:

English

American Geophysical Union
03 October 2025

Volcanology & seismology; Machine learning

Series: AGU Advanced Textbooks

Computation, Optimization, and Machine Learning in Seismology

The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models.

Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data.

Volume highlights include:

Mathematical foundations and key equations for computational seismology Essential theories, including wave propagation and elastic wave theory Processing, mapping, and interpretation of prestack data Model-based optimization and artificial intelligence methods Applications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problems Exercises applying the main concepts of each chapter

By: Subhashis Mallick (University of Wyoming USA)
Imprint: American Geophysical Union
Country of Publication: United States [Currently unable to ship to USA: see Shipping Info]
Dimensions: Height: 249mm, Width: 175mm, Spine: 20mm
Weight: 771g
ISBN: 9781119654469
ISBN 10: 1119654467
Series: AGU Advanced Textbooks
Pages: 416
Publication Date: 03 October 2025
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Forthcoming

Subhashis Mallick, University of Wyoming, USA