Abstract
Geochemical mapping based on machine learning algorithms has been proven to significantly improve the efficiency of geological mapping related to mineral exploration. This process is generally implemented by interpolating discrete geochemical data into spatially continuous fields and comparing chemical composition and spatial distribution to a reference. However, the use of geochemical survey data for geological mapping is challenging because of discontinuous geochemical sampling and inferior model performance owing to the restriction of insufficient training samples and spatial feature representations. Geochemical data interpolation is subject to uncertainty that also deserves be quantified. This study addresses the above challenges by using a direct sampling (DS) multiple-point statistical technique in conjunction with a convolutional neural network (CNN) algorithm. Specifically, the DS technique is designed to produce spatially continuous and sufficient samples by reconstructing unsampled locations from sparse geochemical survey data; and CNN facilitates automatic lithological feature learning and classification based on multilevel convolutional operations that considers the spatial information within neighboring samples. The proposed framework is illustrated in a case study mapping lithological units in Daqiao district, western China, and compared with the deterministic interpolation approach visually and quantitatively. Most lithological units were correctly identified with an overall accuracy of 94%, providing feasible and practical insight into geological mapping using stream sediment geochemical survey data.
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Acknowledgements
The authors would like to thank two reviewers for their comments and suggestions which improved this study. This research was jointed supported by the National Natural Science Foundation of China (Nos. 41972303 and 42102332) and the China Postdoctoral Science Foundation (No. 2021M692988).
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Wang, Z., Zuo, R. & Yang, F. Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data. Math Geosci 55, 1035–1058 (2023). https://doi.org/10.1007/s11004-022-10023-z
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DOI: https://doi.org/10.1007/s11004-022-10023-z