Today's era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this …
Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive …
Although mineral prospectivity modeling (MPM) has undergone decades of development, it has not yet been widely adopted in the global mineral exploration industry. Exploration geoscientists encounter challenges in understanding the internal working of many mineral prospectivity models due to their black box nature. Besides, their …
Keywords: remote sensing; mineral prospectivity mapping; machine learning; random forest; gold mineralization; Sudan 1. Introduction The prediction of mineral prospectivity is one of the substantial practices in mineral exploration, which is used to fulfill the growing demand for mineral resources in industrial development …
The Middle–Lower Yangtze River Metallogenic Belt is an important copper and iron polymetallic metallogenic belt in China. Today's economic development is inseparable from the support of metal mineral resources. With the continuous exploitation of shallow and easily identifiable mines in China, the prospecting work of deep and …
Introduction. Mineral potential mapping can be used to help explorers generate projects, identify targets, and increase the efficiency of their exploration programs. This Story Map presents an example of a typical weights of evidence mineral potential mapping workflow followed by Kenex, using the Bundarra porphyry Cu-Au project in …
Data-driven prospectivity mapping can be undermined by dissimilarity in multivariate spatial data signatures of deposit-type locations. Most cases of data-driven prospectivity mapping, however, make use of training sets of randomly selected deposit-type locations with the implicit assumption that they are coherent (i.e., with similar …
Mineral prospectivity mapping (MPM) aims to outline and categorize prospective areas for further exploration of undiscovered mineral deposits of the type of …
This article presents a case study of mineral prospectivity mapping based on support vector machine and random forest algorithm, two powerful machine learning methods, for the Ashele copper–zinc deposit in Xinjiang, NW China. The results show that the proposed approach can effectively identify the most promising areas for mineral …
Machine learning methods have recently been used widely for mineral prospectivity mapping. However, few studies have focused on the determination of variables for mineral prospectivity prediction using such methods. Here, we present a comparative study using supervised and unsupervised methods to determine predictive …
The successful application of geographic information system (GIS)-based mineral prospectivity mapping (MPM) essentially relies on two factors: one is reasonable evidential layers that conform to geological cognition, and the other is excellent models that can extract critical prospecting information from evidential layers.
Mineral Prospectivity Mapping (MPM) is a geoscientific process that involves assessing and predicting the likelihood of discovering economically viable …
This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between …
Known mineralized locations and randomly chosen non-mineralized locations are used traditionally as training samples in data-driven mineral prospectivity mapping (MPM). In this paper, we took advantage of (a) the variable importance and partial dependence plot, which enable interpretation of random forest (RF) modeling, and (b) …
1. Introduction. Predictive modeling of mineral prospectivity entails the analysis and synthesis of various layers of spatial evidence derived from various relevant geoscience spatial datasets in order to delineate and rank areas that are prospective for exploration of mineral deposits of the type sought (Bonham-Carter, 1994, Carranza, …
With the decrease in surface and shallow ore deposits, mineral exploration has focused on deeply buried ore bodies, and large-scale metallogenic prediction presents new opportunities and challenges. This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of …
Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We …
Regions with low prospectivity scores and high uncertainty should also be considered during mineral exploration decision–making since at least some of the evidence is favourable in these H3 cells. Covered areas with mixed evidence and thus high uncertainty tend to be associated with prospective pathways that were identified using …
Called vectorial fuzzy logic, it differs from existing methods in that it displays prospectivity as a continuous surface and allows a measure of confidence to be incorporated. With this technique, two maps are produced: one displays the calculated prospectivity and the other shows the similarity of input values (or confidence).
Mineral prospectivity mapping as a computer-based approach to delineate targeted areas for a specific type of mineral deposit has changed from being knowledge driven to data driven to today's big data analytics. There are increasing applications of machine learning algorithms in mapping mineral prospectivity and identifying …
In this study, a deep regression neural network was built to map the mineral prospectivity in the Daqiao Gold Mine in Gansu Province, China. The neural network was trained using multi-source data including geological, geophysical, and geochemical data for the study area. The proposed deep regression neural network reveals the complex ...
Highlights. •. Parameters used for training models strongly impact the predictions. •. RF model outperforms ANN and SVM models in predictive accuracy and …
Introduction. Predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA) are two distinct predictive modeling …
Abstract. Data-driven mineral prospectivity mapping (MPM) based on deep learning methods has become a powerful tool for mineral exploration targeting in the past years. Convolutional neural networks (CNNs) have shown great success in this field because of their powerful ability to capture the complex spatial geo-anomalies related to …
A generalized fuzzy model for mineral prospectivity mapping can be defined as follows. If X is a set of n predictor maps Xi (i =1 to n) with r patterns (or classes) denoted generically by xij (j= 1 to r), then n fuzzy sets Ãi in X, containing 'favorable indicators for the targeted mineral deposit-type', can be defined as follows: ~ ¼ x ...
A three-pronged approach to gold prospectivity analysis is put into practice in the Western GTO, which comprises: (1) a "manual" analysis and delineation of exploration targets based on knowledge gained from a new 4D model of the orogen (Joly et al., 2010) complemented by a detailed review of orogenic gold mineral systems …
Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning ...
The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot topic in mineral exploration. However, few studies have focused on recurrent neural networks (RNNs) in terms of integrating different evidential layers to map mineral potential. In this study, a gated recurrent unit (GRU) model was employed …
