Computation, Optimization, and Machine Learning in Seismology : AGU Advanced Textbooks - Subhashis Mallick

Computation, Optimization, and Machine Learning in Seismology

By: Subhashis Mallick

Paperback | 27 May 2025 | Edition Number 1

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Computational Seismology is defined as theoretically simulating seismic wave propagations on computer for various subsurface earth models and matching the computed responses with the field observations to find the actual subsurface rock properties. As the computations advanced from serial to vector with eventually parallel computing technology emerging between 1980 and 2000, this manual matching procedure was automated on the computer, leading to a new field to what is called Seismic Inversion or Optimization, which is since a routine practice in almost every field of geophysics. From the beginning of this millenium, a new computing technology based on artificial intelligence has been developed, which involves, starting from the artificial neural networks, a variety of intelligent methods which are now broadly classified as Machine-learning (ML). Application of ML in geophysics is broad, such as geological interpretation of seismic sections via pattern recognition, signal-to-noise-ratio enhancement on data, seismic depth imaging, and much more.  

Computational Seismology, Optimization, and Machine Learning encompasses proper use of seismic data that requires (1) thorough understanding of the seismic wave theory and how they can be efficiently implemented on the computer, (2) how the seismic theory can be combined with the optimization theory to estimate subsurface geological structure, lithology, and fluid properties and (3) how recent developments of machine learning can aid in efficient implementations of these optimization methods in high-performance computing environments. While there are some excellent textbooks available separately covering each of these topics, a single textbook integrating them into a comprehensive framework is necessary. Thus this will be a primary textbook for seismic inversion/optimization courses taught at different Universities around the world. In addition, this book will also serve as an excellent reference book to the professionals at the academic, research, and industry communities.

Volume highlights include:

  • A comprehensive account of the seismic wave-theory, different optimization, and machine learning methods in an integrated and unified framework
  • Provides language-independent pseudo-codes for each method with detailed discussion that will allow developing group projects for the class, and these pseudo-codes will allow researchers to easily implement the methods in practice
  • Discussions on multi-objective optimization method in geophysics, which is a unique inclusion in this textbook
  • Although machine learning is, in theory, an optimization, a unified treatment of classical optimization theory and machine learning tools will be discussed in the book, this is not currently available in any textbook
  • Well thought out exercises to reinforce the concepts and methods developed in every chapter

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