Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

Abstract

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model. This method uses three techniques (window offset, scaling, and rotation) to enhance the number of training data for the model. A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits. Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area. This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset, meaning that the deep-learning method can be effectively used for deposit prospecting prediction. Using soil active geochemical measurement data, this method was applied in the Daqiao area, Gansu Province, for which seven favorable gold prospecting target areas were predicted. The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3–4 g/t. In 2020, the project team drilled and verified the K prediction area, and found 66 m gold mineralized bodies. The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.© 2022 China Geology Editorial Office.

Keywords

• Soil geochemistry
• Spatial feature matching
• Gold deposit
• Deep learning
• Mineral prospecting prediction model
• Data augmentation mineral exploration