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.

地球化学勘探数据在矿产勘探中很受欢迎，因为它在发现未知矿床方面起着显着的作用。在这项研究中，我们回顾了处理地球化学勘探数据和识别与矿化相关的地球化学异常的最先进的流行方法。在过去的几十年中，地球化学元素浓度的分布规律，包括正态分布、对数正态分布、幂律分布以及多峰和复数分布，得到了广泛的研究。因此，处理地球化学勘探数据的方法已经从经典统计、多元统计、地质统计学转向分形/多重分形模型和机器学习算法。地球化学勘探数据作为成分数据，存在闭合问题。我们首先需要使用对数变换打开它们。未来，深度学习算法将成为挖掘地球化学勘探数据和在矿产勘探中提取与成矿相关的目标的流行技术。