Applications of machine learning in earth sciences include geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of artificial intelligence (AI) that enables computer systems to classify, cluster, identify and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming.[1] Earth science is the study of the origin, evolution, and future[2] of the planet Earth. The Earth system can be subdivided into four major components including the solid earth, atmosphere, hydrosphere and biosphere.[3]
A variety of algorithms may be applied depending on the nature of the earth science exploration. Some algorithms may perform significantly better than others for particular objectives. For example, convolutional neural networks (CNN) are good at interpreting images, artificial neural networks (ANN) perform well in soil classification[4] but more computationally expensive to train than support-vector machine (SVM) learning. The application of machine learning has been popular in recent decades, as the development of other technologies such as unmanned aerial vehicles (UAVs),[5] ultra-high resolution remote sensing technology and high-performance computing units[6] lead to the availability of large high-quality datasets and more advanced algorithms.
^Mueller, J. P., & Massaron, L. (2021). Machine learning for dummies. John Wiley & Sons.
^Resources., National Academies Press (U.S.) National Research Council (U.S.). Commission on Geosciences, Environment, and (2001). Basic research opportunities in earth science. National Academies Press. OCLC 439353646.{{cite book}}: CS1 maint: multiple names: authors list (link)
^Miall, A.D. (December 1995). "The blue planet: An introduction to earth system science". Earth-Science Reviews. 39 (3–4): 269–271. doi:10.1016/0012-8252(95)90023-3. ISSN 0012-8252.
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^Si, Lei; Xiong, Xiangxiang; Wang, Zhongbin; Tan, Chao (2020-03-14). "A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face". Mathematical Problems in Engineering. 2020: 1–12. doi:10.1155/2020/2616510. ISSN 1024-123X.
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