The processing methods of geochemical exploration data: past, present, and future

• Published in: Applied geochemistry Vol. 132; p. 105072
• Main Authors: Zuo, Renguang, Wang, Jian, Xiong, Yihui, Wang, Ziye
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2021
• Subjects: Classic statistics; Fractal; Geochemical anomaly; Geochemical exploration; geochemistry; geostatistics; Machine learning; mineralization; multivariate analysis; Classic statistics; Fractal; Geochemical anomaly; Machine learning; Geochemical exploration
• ISSN: 0883-2927; 1872-9134
• DOI: 10.1016/j.apgeochem.2021.105072

Geochemical exploration data is popular in mineral exploration in that it plays a notable role in discovering unknown mineral deposits. In this study, we review the state-of-the-art popular methods for processing geochemical exploration data and for identifying geochemical anomalies associated with mineralization. The distribution laws of geochemical elements concentrations, including normal, log-normal, power-law, and multimodal and complex distributions, have been extensively studied over the past several decades. Accordingly, methods for processing geochemical exploration data have shifted from classic statistics, multivariate statistics, geostatistics, to fractal/multifractal models and machine learning algorithms. Geochemical exploration data, as compositional data, suffer from the closure problem. We need first to open them using logratio transformation. In the future, deep learning algorithms will become a popular technique for mining geochemical exploration data and for extracting targets associated with mineralization in mineral exploration. •The distribution laws of geochemical elements include normal, log-normal, nonlinear and complex distributions.•Classic statistics and EDA techniques can explore the frequency distribution characteristics of geochemical exploration data and to identify geochemical anomalies.•Fractal/multifractal models can consider both the frequency and spatial characteristics of geochemical exploration data.•Machine learning algorithms can deal with nonlinear and complex geochemical patterns and enhance the identification of geochemical anomalies.