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Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, and analyze vast and complex data sets without the need for explicit programming to do so.[1] Earth science is the study of the origin, evolution, and future[2] of the Earth. The earth's 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 task. Some algorithms may perform significantly better than others for particular objectives. For example, convolutional neural networks (CNNs) are good at interpreting images, whilst more general neural networks may be used for soil classification,[4] but can be more computationally expensive to train than alternatives such as support vector machines. The range of tasks to which ML (including deep learning) is applied has been ever-growing in recent decades, as has the development of other technologies such as unmanned aerial vehicles (UAVs),[5] ultra-high resolution remote sensing technology, and high-performance computing.[6] This has led 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.
- ^ Bhattacharya, B.; Solomatine, D.P. (March 2006). "Machine learning in soil classification". Neural Networks. 19 (2): 186–195. doi:10.1016/j.neunet.2006.01.005. ISSN 0893-6080. PMID 16530382. S2CID 14421859.
- ^ Cite error: The named reference
:6was invoked but never defined (see the help page). - ^ 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.