Three-dimensional mineral prospectivity mapping based on natural language processing and random forests: A case study of the Xiyu diamond deposit, China
Guanyun Zhou, Jianping Chen, Wentong An, Chang Liu, Wei Li
Abstract
The advent of the big data era has gradually brought about a new demand for the integration of geological text big data in three-dimensional (3D) mineral prospectivity mapping (3DMPM). Here, we report a novel workflow suitable for the application of natural language processing in 3DMPM. Our study area is the Xiyu kimberlite-type diamond deposit in Shandong, China, which has significant prospecting potential in its deep portion. In this study, exploration criteria served as a bridge connecting geological text big data and 3DMPM, and exploration criteria for the Xiyu diamond deposit were constructed employing text mining technologies. A comparative evaluation of conventional and text-mining-based exploration criteria was also conducted. Considering the text-mining-based deposit exploration criteria, we reconstructed 3D spatial anomalies and quantitatively analyzed ore-controlling factors. A random forests classification model, created using the deposit exploration criteria obtained through text mining, was applied to prospecting predictions in the Xiyu deposit, with superior results being obtained. The prediction results confirmed the applicability of the workflow and demonstrated its capacity for effective dual coupling of geological data and knowledge.