Joint Application of Fractal Analysis and Weights-of-Evidence Method for Revealing the Geological Controls on Regional-Scale Tungsten Mineralization in Southern Jiangxi Province, China

Abstract

Abstract:The Southern Jiangxi Province (SJP) hosts one of the best known districts of tungsten deposits in the world. Delineating spatial complexities of geological features and their controls on regional-scale tungsten mineralization by using an integrated fractal and weights-of-evidence (WofE) method can provide insights into the understanding of ore genesis and facilitate further prospecting in this area. The box-counting fractal analysis shows that most of the tungsten occurrences are distributed in regions with high fractal dimensions of faults and fault intersections, suggesting ore-forming favorability of areas with highly complex structural patterns. The WofE-derived indices are employed to quantitatively measure the controls of analyzed features on mineralization, which illustrate that tungsten anomalies, faults, Yanshanian granites, and manganese anomalies have high contrast values, implying a spatially strong correlation of these features with tungsten occurrences. In particular, high manganese anomalies in host rock may provide a novel indication for mineral prospecting in this area. A predictive map is extracted based on the combination of fractal and WofE results, providing intuitive guides for future prospectivity in this area. Regions identified by high posterior probability in conjunction with high fractal dimensions of both faults and fault intersections are evaluated as the most favorable targets.

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

“…Proximity to outcropped Yanshanian intrusions was employed as an evidential map representing the source component of the ore-forming system (Figure 5a). According to a spatial analysis conducted in this area [65], NE-and EW-trending regional faults exhibit a positive relationship with W occurrences, whereas NW-trending faults were likely formed post-mineralization, and, show no obvious spatial association with mineral occurrences. Therefore, density maps of NE-and EW-trending faults and their intersections were used as evidences to represent structural controls on mineralization (Figure 5b,c).…”

“…The metal source of Mn/Fe from the wall rocks can explain why Mn anomalies exert a significant influence on W prospectivity modelling in this study. The spatial correlation of Mn anomalies and W mineralization in the study area is also revealed by a weights-of-evidence analysis [65]. It is also noted that Fe anomaly provides little evidential information for predicting W mineralization, which may be ascribed to (i) majority of hubnerite (MnWO 4 ) in the wolframite ores from W-Sn deposits that dominate the W-polymetallic mineralization in the study area [87], and (ii) interference from widely distributed Fe-enriched Cretaceous red beds formed after W mineralization in the study area.…”

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

“…In the case of the data-driven methods, the weight of each of the geoscientific criteria to be used in the predictive modeling is determined by assessing how they spatially correlate with respect to known locations of the mineral occurrences within the study area [ 46 ]. The use of data-driven methods in predicting the spatial occurrence of a natural resource or a geohazard is mostly carried out by the weight of evidence [ 47 , 74 ], frequency ratio [ 16 , 53 , 69 ], weighting factor [ 9 , 23 , 35 , 36 ], statistical information [ 35 , 52 ], information value [ 6 , 27 ], shannon entropy [ 8 , 77 ], certainty factor [ 56 , [80] , [80a] ], evidence belief function [ 40 , 62 ], neural networks [ 55 , 57 ], logistic regression [ 29 , 58 ], support vector machine [ 28 , 82 ], and random forest [ 65 , 80 ] techniques. It should also be emphasized that, data-driven methods do not work well in situations where the known locations of the sought-after mineral is limited or absent.…”

Fractal analysis of spatio-temporal changes of forest cover in Istanbul, Turkey

“…The rapid developments in GIS have enabled spatial data to be examined with more innovative perspectives. In this context, fractal analysis, an effective tool for identifying and analyzing the irregularities of objects, events, and phenomena, has recently been integrated into GIS to study the spatial pattern of objects in many subjects such as urbanization (Li et al 2011;Tannier et al 2011;Terzi and Kaya 2011;Ozturk 2017;Purevtseren et al 2018;Man and Chen 2020), transportation (Lu and Tang 2004;Sun et al 2007;Dasari and Gupta 2020;Karpinski et al 2020;Sahitya and Prasad 2020), and geology (Wang et al 2012;Pourghasemi et al 2014;Ni et al 2017;Sun et al 2017;Yang et al 2019). Although fractal analysis has the potential to improve the accuracy of measurement and identification of forest areas (Lorimer et al 1994), very few studies have been conducted on the use of fractal analysis in forest areas.…”