Lithological Mapping Using a Convolutional Neural Network based on Stream Sediment Geochemical Survey Data.

Mapping of lithological units is a significant challenge for geological tasks. Stream sediment geochemical survey data contain abundant geological information that can help delineate lithological units. In this study, a convolutional neural network (CNN) was applied to map the lithological units in the Daqiao gold District, West Qinling Orogen, China, based on stream sediment geochemical data, in which each sample includes the concentrations of 15 trace elements (Cu, Pb, Zn, Ag, Mo, Sn, W, Mn, Ba, As, Sb, Bi, Cd, Au, and Hg). The training samples were firstly constructed with a certain window size by randomly selecting locations within each lithological unit. A CNN model was then established based on AlexNet to classify the lithologic categories. The classification map showed that 7 lithological units were correctly distinguished with an overall classification accuracy of 90.0%, suggesting that (1) stream sediment geochemical survey data of only trace element concentrations are useful for lithological mapping, and (2) a CNN can extract effectively geochemical characteristics from geochemical survey data. This study confirms the potential of a CNN as an effective method for geological mapping based on geochemical survey data. [ABSTRACT FROM AUTHOR]