LEADER 02958nam 2200481 450 001 9910484054903321 005 20220505234456.0 010 $a3-030-71768-2 024 7 $a10.1007/978-3-030-71768-1 035 $a(CKB)4100000011912018 035 $a(DE-He213)978-3-030-71768-1 035 $a(MiAaPQ)EBC6587710 035 $a(Au-PeEL)EBL6587710 035 $a(OCoLC)1250085222 035 $a(PPN)255885008 035 $a(EXLCZ)994100000011912018 100 $a20220113d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA primer on machine learning in subsurface geosciences /$fShuvajit Bhattacharya 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XVII, 172 p. 130 illus., 118 illus. in color.) 225 1 $aSpringerBriefs in Petroleum Geoscience & Engineering,$x2509-3126 311 $a3-030-71767-4 327 $aIntroduction -- Brief Review of Statistical Measures -- Basic Steps in Machine Learning and Deep Learning Models -- Brief Review of Popular Machine Learning and Deep Learning Algorithms -- Applications of ML/DL in Geophysics and Petrophysics Domain -- Applications of ML/DL in Geology Domain -- Multi-scale Data Integration and Analytics -- The Road Ahead. 330 $aThis book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences. . 410 0$aSpringerBriefs in Petroleum Geoscience & Engineering,$x2509-3126 606 $aGeology$xData processing 615 0$aGeology$xData processing. 676 $a550.285 700 $aBhattacharya$b Shuvajit$0866391 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484054903321 996 $aA Primer on Machine Learning in Subsurface Geosciences$91933722 997 $aUNINA