Using EVT for Geological Anomaly Design and Its Application in Identifying Anomalies in Mining Areas

Abstract

A geological anomaly is the basis of mineral deposit prediction. Through the study of the knowledge and characteristics of geological anomalies, the category of extreme value theory (EVT) to which a geological anomaly belongs can be determined. Associating the principle of the EVT and ensuring the methods of the shape parameter and scale parameter for the generalized Pareto distribution (GPD), the methods to select the threshold of the GPD can be studied. This paper designs a new algorithm called the EVT model of geological anomaly. These study data on Cu and Au originate from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed EVT model of the geological anomaly is applied to identify anomalies in the Jiguanzui Cu-Au mining area. The results show that the model can effectively identify the geological anomaly region of Cu and Au. The anomaly region of Cu and Au is consistent with the range of ore bodies of actual engineering exploration. Therefore, the EVT model of the geological anomaly can effectively identify anomalies, and it has a high indicating function with respect to ore prospecting.

Modeling GPDA and Its Application in Deep Mineral Prediction in the Jiguanzui Cu‐Au Mining Area in Hubei, China

“…., x n that obey a common distribution and are independent, if there is a sufciently large observed value μ, the excess of sample data (x j , −, μ, j � 1, 2,. . ., n) satisfes the following expression [10,20]:…”

“…Te anomalies of geochemical element contents are often located at the tail of the observed distribution, which characteristically follows a GPD [10]. Te GPD considers the distribution of the maximum values of observation data [11].…”

“…Te GPD considers the distribution of the maximum values of observation data [11]. Te mathematical foundation of geochemical anomaly analysis was described as an extreme value distribution by Chen et al [12], a famous Academician of CAS, and thus, we can use a GPD to extract anomaly information on geochemical element contents [10]. However, if the threshold estimation of the GPD is too large, then it will lead to a decrease in the anomaly area of the geochemical element, and hence, the anomaly area will not cover the actual ore body.…”

“…However, if the threshold estimation of the GPD is too large, then it will lead to a decrease in the anomaly area of the geochemical element, and hence, the anomaly area will not cover the actual ore body. Conversely, if the threshold value is too small, it will lead to an increase in the anomaly area of the geochemical element; the anomaly area will far exceed the scope of the ore body, and this is not conducive to mineral prediction [10]. Unfortunately, there is no exact method for threshold estimation.…”

“…Graphical methods and computational methods are widely used to estimate thresholds. Te mean excess function (MEF) is a graphical method as it estimates the threshold according to the linearity of ftting observational data [13,14], but the MEF lacks theoretical support; i.e., the threshold is determined only according to the linearity of a scatter plot [10]. Calculation methods also have some drawbacks in threshold estimation; for example, the exponential regression model [15] and the sequential method [16] are only suitable for medium-sized sample data, while the subsample bootstrap method [17] is suitable for large amounts of sample data.…”