 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
|
|
|
Advances in Subsurface Data Analytics
◆日本地質学会 第131年学術大会 特別割引セール開催中！：2024年10月18日(金)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Publisher: Elsevier USA ISBN: 9780128222959 Cover: PAPERBACK Date: 2022年05月 DESCRIPTION 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. TABLE OF CONTENTS Part 1: Traditional Machine Learning Approaches 1. User Vs. Machine Seismic Attribute Selection for Unsupervised Machine Learning Techniques: Does Human Insight Provide Better Results Than Statistically Chosen Attributes? Karelia La Marca and Heather Bedle 2. Relative Performance of Support Vector Machine, Decision Trees, and Random Forest Classifiers for Predicting Production Success in US unconventional Shale Plays Cedric Michael John, Jessica Wevill, Alex Bromhead, Kate Evans and Jeffrey Yarus Part 2: Deep Learning Approaches 3. Recurrent Neural Network: application in facies classification Sumit Verma and Miao Tian 4. Recurrent Neural Network for Seismic Reservoir Characterization Mingliang N. Liu Sr., Dario Grana, Philippe Nivlet and Robert Smith 5. Application of Convolutional Neural Networks for the Classification of Siliciclastic Core Photographs Rafael Augusto Pires de Lima and FNU Suriamin 6. Convolutional Neural Networks for Fault Interpretation - Case Study Examples around the World Hugo Garcia Part 3: Physics-based Machine Learning Approaches 7. Scientific Machine Learning for Improved Seismic Simulation and Inversion Lei Huang 8. Prediction of Acoustic Velocities using Machine Learning Lian Jiang, John Castagna and Pablo Guillen 9. Regularized Elastic Full Waveform Inversion using Deep Learning Zhendong Zhang and Tariq Alkhalifah 10. A Holistic Approach to Computing First-arrival Traveltimes using Neural Networks Umair Bin Waheed Part 4: New Directions 11. Application of Artificial Intelligence to Computational Fluid Dynamics Shahab D. Mohaghegh, Ayodeji Aboaba, Yvon Martinez, Mehrdad Shahnam, Chris Guenther, Yong Liu
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
