Abstract: We have collected in China many high-quality, multi-element and multi-scale geochemical exploration datasets, which provide an effective data support for mineral exploration and environmental studies. It is of great interest to further explore these datasets to identify geochemical anomalies associated with mineralization, in supporting breakthroughs in the next round of mineral exploration to alleviate current mineral shortage distress. However, deep buried or covered mineral deposits pose significant challenge to mineral exploration, what we are currently facing is how to identify and evaluate weak geochemical anomalies in the field of exploration geochemical data processing. In this paper, we reviewed the state-of-the-art methods and models for exploration geochemical data processing and geochemical anomaly identification. We also discussed the data closure problem and its solutions, as well as application of big data and machine learning methods to geochemical data processing. It is shown that the hybrid method, which combines big data thinking and machine learning methods under the constraints of geological settings, is a powerful tool to explore geochemical patterns and identify geochemical anomalies. The hybrid method takes into account the correlations between geochemical patterns and locations of mineral deposit, involving all geochemical variables, and can reveal non-linear characteristics of geochemical patterns.

Key words: geochemical prospecting, weak geochemical anomalies, fractal, machine learning