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COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026)

COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026)

COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026)
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COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026) Paperback -

by MALLICK S

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USA Edition . New. Brand New! Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Details

  • Title COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026)
  • Author MALLICK S
  • Binding Paperback
  • Edition USA Edition
  • Condition New
  • Pages 416
  • Volumes 1
  • Language ENG
  • Publisher American Geophysical Union
  • Bookseller's Inventory # CBS 9781119654469
  • ISBN 9781119654469 / 1119654467
  • Category Science
  • Quantity available 1

About XLANCEBOOKS L.L.C. Wyoming, United States

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Reader reviews for COMPUTATION OPTIMIZATION AND MACHINE LEARNING IN SEISMOLOGY (PB 2026)

From the publisher

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

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

About the author

Subhashis Mallick, University of Wyoming, USA

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