Computational Seismology, Optimization, and Machine Learning - Couverture souple

Livre 8 sur 8: AGU Advanced Textbooks

Mallick, Subhashis

 
9781119654469: Computational Seismology, Optimization, and Machine Learning

Synopsis

A textbook applying fundamental seismology theories to the latest computational tools

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

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l?auteur

Subhashis Mallick, University of Wyoming, USA

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.