“Deep Learning and Its Application in Geochemical Mapping” is published on Earth-Science Reviews, Volume 192, May 2019. It is an achievement of Prof. ZUO Renguang’s team from the State Key Laboratory of Geological Processes and Mineral Resources. The first author and corresponding author of the paper is Prof. ZUO.
Machine learning algorithms have been applied widely in the fields of natural science, social science and engineering. It can be expected that machine learning approaches especially deep learning algorithms will help geoscientists to discover mineral deposits through processing of various geoscience datasets. This study reviews the state-of-the-art application of deep learning algorithms for processing geochemical exploration data and mining the geochemical patterns. Deep learning algorithms can deal with complex and nonlinear problems and, therefore, can enhance the identification of geochemical anomalies and the recognition of hidden patterns. Applied geochemistry needs more applications of machine learning and/or deep learning algorithms.
Fig. 1. Different deep learning architectures: (a) Stacked auto-encoder; (b) Deep belief network; (c) Convolutional neural network; (d) Generative adversarial network; and (e)Recurrent neural network.
Fig. 2. Schematic diagram of geochemical mapping and anomaly identification.
Fig. 3. Map showing geochemical anomaly map derived by local singularity analysis.
Full Text: https://doi.org/10.1016/j.earscirev.2019.02.023
