Mineral prospectivity mapping (MPM) is an essential element of MP, as it aids in the identification of areas with a higher likelihood of containing mineral deposits (Zuo and Carranza, 2011, Ye, 2014). The process of mineral prospectivity mapping can be classified into two categories: knowledge-driven and data-driven modeling.
1.Introduction. An exploration information system (EIS) is a framework comprising different steps that allow better integration between a mineral deposit …
1. Introduction. Mineral prospectivity mapping (MPM) is a multicriteria decision-making task that aims to outline and prioritize prospective areas for exploring …
Mineral prospectivity analysis is a predictive tool and typically used for exploration targeting at the broad regional to camp scale. It is less effective and less frequently used at the project to deposit scale where predictive tools compete with direct detection techniques (McCuaig & Hronsky, 2000, Hronsky & Groves, 2008, McCuaig et …
search, prospecting, or exploration of mineral deposits of increase progressively from regional- to local-scale. economic importance. Mineral prospectivity analysis, there …
In recent years, various geological activities and different mineral prospecting and exploration programs have been intensified along the Red Sea hills in order to elucidate the geological maps and to evaluate the mineral potentials. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (GIS) …
Conceptualization of prospectivity for mineral deposits in an area needs a thorough review of literature on characteristics and processes of formation of mineral …
Both mineral potential mapping by zones with RF and MaxEnt models have higher area under the ROC curve (AUC) values than the models performed in the study area and delineate 19% of the study area containing > 88% of the known deposit occurrences. Finally, according to the concentration–area (C-A) thresholds for …
Mineral prospectivity mapping is similar to probability prediction using multi-source geological data. However, the complexity of geological phenomena creates difficulties for research. 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 …
GIS-based mineral prospectivity mapping using machine learning methods: a case study from Zhuonuo ore district, Tibet @article{Cheng2023GISbasedMP, title={GIS-based mineral prospectivity mapping using machine learning methods: a case study from Zhuonuo ore district, Tibet}, author={Hongjun Cheng and Youye Zheng and Song Wu …
1. Introduction. Mineral prospectivity mapping (MPM) is a key tool for outlining and prioritizing prospective areas for mineral deposits exploration of the type …
value for the mineral prospectivity, Ytrain, ie. gold values in drilling. The task for gold assays any mineral prospectivity algorithm is to learn the mapping function, f, such that as Three Bluffs!"#;%&'()*+=-&'()* where # represent all the parameters which define the mapping. By optimizing these parameters and learning the mapping, one
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 …
In the recent Exploration in the House event at Parliament House in Sydney Dietmar provided an overview of the use of generative AI for assessing copper, nickel and cobalt prospectivity in the Lachlan fold belt, based on the Honours thesis of Nathan Wake, and work by Ehsan Farahbakhsh and Vera Nolte-Wilson.
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 …
Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas with mineral …
Mineral exploration is the necessary first step of any mining project. Mineral prospectivity analysis is a cost and time efficient exercise with for goals to delineate area of high prospectivity or to rank targets. The worldwide tendency in mineral exploration efforts is to focus on brownfield areas where large amount of data is available.
Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types. ... (1992). The study of magmatic evolution in the baghu area and relation with gold mineralization, SE Damghan (M.Sc. thesis). University of Tarbiat Moalem, Tehran, p. 324.
The goal is to parameterize the complex relationships between the data and the labels such that mineral potential can be estimated in under-explored regions using available …
Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types.
1. Introduction. Mineral prospectivity mapping (MPM) is a key procedure in the early stage of mineral exploration, and the fundamental purpose is to minimize prospecting cost and to reduce exploration risk (Chen and Wu, 2016).The MPM process was performed by integrating interpretations and observations from geologists and …
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 …
mineral deposits because many, or most, of the ea-sily accessible mineral deposits at or near surface have already been discovered and mined. Nowadays, mineral explorers are forced to explore new mineral 1State Key Laboratory of Geological Processes and Mineral Re-sources, China University of Geosciences, Wuhan 430074, China.
Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict …
The workflow of geodata science-based mineral prospectivity mapping (GSMPM) (Fig. 2b) underlines the spatial correlations between geological, geochemical, …
A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used …
Multi-source data integration for mineral prospectivity mapping (MPM) is an effective approach for reducing uncertainty and improving MPM accuracy. Multi-source data (e.g., geological, …
The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to …
The Axi low-sulfidation (LS) epithermal deposit in northwestern China is the result of geological controls on hydrothermal fluid flow through strike-slip faults. Such controls occur commonly in LS epithermal deposits worldwide, but unfortunately, these have not been quantitatively analyzed to determine their spatial relationships with gold …
The SVM classifications of mineral prospectivity have 5–9% lower total errors, 13–14% higher false-positive errors and 25–30% lower false-negative errors compared to those of the WofE prediction. The prospective target areas predicted by both SVM and WofE reflect, nonetheless, controls of Au deposit occurrence in the study area …
Geospatial information systems (GIS)-based mineral prospectivity mapping (MPM) is aimed at delineating prospective areas to explore a target deposit type by the integrated analysis of maps that are generated and weighted from spatial datasets (Bonham-Carter, 1994; Carranza, 2004; Chen, 2015; Yousefi and Carranza, 2015a; Tao …
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 …
Conceptualization of prospectivity for mineral deposits in an area needs a thorough review of literature on characteristics and processes of formation of mineral deposits of the class of interest, such as described in mineral deposit models (e.g., Cox and Singer 1986).A robust mineral prospectivity conceptual model is one that also …
Machine learning algorithms, including supervised and unsupervised learning ones, have been widely used in mineral prospectivity mapping. Supervised learning algorithms require the use of numerous known mineral deposits to ensure the reliability of the training results. Unsupervised learning algorithms can be applied to areas with rare …
This special issue was developed from a session on "Machine learning-based mineral prospectivity mapping (MPM)" chaired by Prof. Renguang Zuo and Prof. Emmanuel John M. Carranza at the 21st Annual Conference of the International Association for Mathematical Geosciences, held in Nancy, France, from August 29 to September 3, …
The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, …
The singularity index α and weight function ω can be calculated using the following steps (Fig. 1): (1) Construct buffer zones around each geological feature using appropriate buffer distances d; (2) Count the number of mineral deposits (N) occurring for each buffer distance (d) and calculate the distribution density (ρ) using the following …