Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province
Chang Liu, Jianping Chen, Shi Li, Tao Qin
Abstract
With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs.