Machine learning in earth sciences - Wikipedia

Application of machine learning in earth sciences is the use of computer systems to classify, cluster, identify and analyze vast and complex data in earth science study, for example, geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of Artificial Intelligence (AI) that allows computer systems to interpret data while eliminating the need for explicit instructions and programming. 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 Network (ANN) performs well in soil classification[4] but more computationally expensive to train than Support Vector Machine (SVM) learning.