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This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences. .

Fossil fuels. --- Engineering geology. --- Engineering—Geology. --- Foundations. --- Hydraulics. --- Computational intelligence. --- Geology—Statistical methods. --- Physical geography. --- Fossil Fuels (incl. Carbon Capture). --- Geoengineering, Foundations, Hydraulics. --- Computational Intelligence. --- Quantitative Geology. --- Earth System Sciences. --- Geography --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Flow of water --- Water --- Fluid mechanics --- Hydraulic engineering --- Jets --- Architecture --- Building --- Structural engineering --- Underground construction --- Caissons --- Earthwork --- Masonry --- Soil consolidation --- Soil mechanics --- Walls --- Engineering --- Civil engineering --- Geology, Economic --- Fossil energy --- Fuel --- Energy minerals --- Flow --- Distribution --- Details --- Geology --- Data processing.

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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.

Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Seismology --- Geology --- Neural networks (Computer science) --- Machine learning --- Data processing --- Technological innovations