Application of a Maximum Entropy Model for Mineral Prospectivity Maps

Abstract

The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model’s performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen’s maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity.

3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China

“…The MaxEnt model is constructed according to this principle, together with a log-linear ML-based model. This model can set constraints flexibly and has been successfully applied in mineral resource prediction [49,50]. 4 Geofluids within a fixed section.…”

“…Machine learning (ML) has provided a new means of building models based on large data volumes [43][44][45]. ML algorithms that consider relationships between prediction and response variables through direct modeling have proved effective in capturing complex nonlinear relationships between geochemical models and mineralization [44,46], including the use of logic regression [43], neural networks [47], support vector machine [45], random forest [48], and maximum-entropy (MaxEnt) model [49,50]. A MaxEnt model is a high-performance statistical model often used in probabilistic estimation.…”

“…It is suitable for classification problems and shows a performance superior to that of other methods, especially with limited sample data [51,52]. Max-Ent modeling has been widely applied in such fields as natural language processing [53,54], economic prediction [55], the geographical distribution of animal and plant species [56], and mineral resource prediction [49,50].…”

Geochemical prospectivity mapping through factor analysis and maximum entropy model: a case study in the Mila Mountain copper deposit, southern Tibet

“…Maximum entropy model is simple to calculate, can quickly and efficiently process data, and has high prediction accuracy, which is suitable for solving classification problems (Phillips et al 2006;Phillips and Jane 2013). This model has been successfully applied in various fields, such as natural language processing (Dong et al 2012), economic prediction (Xu et al 2014), environmental evaluation Biazar and Ferdosi 2020;Aghelpour et al 2020;Jahangir et al 2021;Yang et al 2021;Yang et al 2020;Azareh et al 2019), geographical distribution of animal and plant species (Wang et al 2017), and mineral exploration (Liu et al 2018;Li et al 2019Li et al , 2021. Liu et al (2018) applied the maximum entropy model to the potential distribution of orogenic gold deposits based on quantitative critical metallogenic processes in the Tangbale-Hatu belt, western Junggar, China.…”

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

“…In the past few decades, a variety of data-driven modelling methods have led to advancements in the field of MPM [9][10][11][12][13][14][15]. Probabilistic methods such as weights of evidence and logistic regression have gained great popularity and remain widely used algorithms due to their lucid expression of models and simplicity of interpretation [3,11,16,17].…”