TY - GEN digital ID - 131869448 TI - A Primer on Machine Learning in Subsurface Geosciences PY - 2021 SN - 9783030717681 9783030717698 9783030717674 PB - Cham Springer International Publishing DB - UniCat KW - Geology. Earth sciences KW - Mining industry KW - Fuels KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Physical geography KW - neuronale netwerken KW - fuzzy logic KW - cybernetica KW - KI (kunstmatige intelligentie) KW - geologie KW - fysische geografie KW - aarde (astronomie) KW - fossiele brandstoffen UR - https://www.unicat.be/uniCat?func=search&query=sysid:131869448 AB - 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. . ER -