Advances in Subsurface Data Analytics

This PDF book is become immediate popular in Computers genre. Advances in Subsurface Data Analytics is written by famous author Shuvajit Bhattacharya and Ready to Download in ePUB, PDF or Kindle formats. Released by Elsevier in 2022-05-27. Click Download Book button to get book file and read directly from your devices. Here is a quick description and cover image of Advances in Subsurface Data Analytics book.

Advances in Subsurface Data Analytics
Author :
Publisher : Elsevier
Release Date :
ISBN : 9780128223086
Pages : 376 pages
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Advances in Subsurface Data Analytics by Shuvajit Bhattacharya Book PDF Summary

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences


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