Computational Seismology, Optimization, and Machine Learning - Brossura

Mallick, Subhashis

Brossura

ISBN 10: 1119654467 ISBN 13: 9781119654469

Casa editrice: Amer Geophysical Union, 2025

Riassunto

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

Su questo libro

Informazioni sull?autore

Subhashis Mallick, University of Wyoming, USA

Dettagli dell'edizione

• Editore: Amer Geophysical Union
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1119654467
• ISBN 13: 9781119654469
• Rilegatura: Copertina flessibile
• Numero di pagine: 400
• Contatto del produttore: American Geophysical Union; https://www.agu.org/; ; 2000 Florida Avenue, N.W.; Washington; DC; 20009; U.S.A.
• Persona responsabile: non disponibile