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Computation, Optimization, and Machine Learning in Seismology Paperback -
by Mallick, Subhashis (University of Wyoming, USA)
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Details
- Title Computation, Optimization, and Machine Learning in Seismology
- Author Mallick, Subhashis (University of Wyoming, USA)
- Binding Paperback
- Condition New
- Pages 416
- Volumes 1
- Language ENG
- Publisher American Geophysical Union
- Bookseller's Inventory # 35928994
- ISBN 9781119654469 / 1119654467
- Category Science
- Quantity available 5
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From the publisher
From the rear cover
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
