Deep learning and its application in geochemical mapping

By Renguang Zuo, Yihui Xiong, Jian Wang, Emmanuel John Muico Carranza. Earth-Science Reviews, 2019

365 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Mechanical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/77f8c8fa40d79d16933683272cb0021e5fbf935f

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In Artificial Intelligence

Citations in the corpus

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “The convolutional layer is responsible for automatically extracting features at various regions of the entire raw input or the intermediate feature maps with learnable ﬁlters (Zuo et al. 2019).”