mineral prospectivity in thesis forment

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 …

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) …

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 …

search, prospecting, or exploration of mineral deposits of increase progressively from regional- to local-scale. economic importance. Mineral prospectivity analysis, there-fore, aims to predict where undiscovered mineral deposits of a certain class exist in order guide mineral exploration. Conceptual Modeling of Mineral Prospectivity.

91 Citations. Explore all metrics. Abstract. This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on …

1. Introduction. Mineral prospectivity mapping (MPM) is a multicriteria decision-making task that aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of the type sought (Carranza and Laborte, 2015, Yousefi and Carranza, 2015b).This task is challenging, because mineral deposits are end …

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 …

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 …

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) the synthetic minority...

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 …

In mineral prospectivity mapping, the GNNWLR, GWLR, GWR and GWSVR models, which incorporate the first law of geography, generate mineral prospectivity mapping values with relatively smooth transitions, without the abrupt changes observed in pixel-level classification methods such as RF and SVM. The resulting maps are more …

Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types. The stochastic type is usually linked ...

Mineral prospectivity mapping (MPM) based on the principle of geometric mean was applied to stream sediment geochemical, fault density, and aeromagnetic data from Tagmout basin, Morocco to ...

Predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA) are two distinct predictive modeling processes with a common aim of deriving information that is essential for strategic planning in mineral exploration and development. Despite this common goal, PMMP has not been a …

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 ...

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, geophysical, geochemical, remote sensing, and drilling) should first be identified as evidence layers that represent ore-prospecting-related features. …

Abstract. Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based …

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 …

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 Prospectivity Mapping (MPM) is a geoscientific process that involves assessing and predicting the likelihood of discovering economically viable …

Semi-supervised learning scheme is especially applicable to mapping of mineral prospectivity. Mineral deposits can be regarded as end products of rare geo …

One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity ...

1 Introduction. Mineral prospectivity mapping (MPM) is concerned with quantifying and mapping the likelihood that mineral deposits are present at a certain location, which requires the application …

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 ...

Prospectivity based on sampling history. The number of onshore samples taken for the purpose of diamond indicator testing (including diamond-only) was counted for each prospectivity region with results lodged in the supplementary data appendix (Supplementary Table 1).Samples that contained diamond-indicator minerals were also …

The development of mineral prospectivity mapping (MPM), which aims to outline and prioritize mineral exploration targets, has been spurred by advances in data-driven machine learning algorithms. …

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 …

mineral prospectivity in thesis forment ; mineral prospectivity in thesis forment. Clustering of mineral prospectivity area as an This paper describes the usage of clustering methods including selforganizing map (SOM) and fuzzy cmeans (FCM) which are applied to prepare mineral prospectivity map Different evidential layers, including …

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 …

quantitative mineral resources prediction by applying the STOAD model has a good performance, where the value of Area Under Curve (AUC) is 0.97. Finally, three main mineral exploration targets are delineated for further investigation. Keywords: 3D mineral prospectivity mapping; geological and geochemical quantitative prediction

The ability of this map for accurately guiding mineral exploration is assessed using the training and validation deposits/ occurrences to measure its success and prediction rates, respectively. Details of the procedure for quantifying the success and prediction rates of a data-driven mineral prospectivity map can be found in Carranza (2009).

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.

Mineral Prospectivity Mapping (MPM) is a pivotal technology that identifies specific types of geo-anomalies which are indicative to occurrence of mineralization by integrating ore-forming evidence from geological exploration data. ... Ph.D. thesis. Chang'an University. Xi'an. In Chinese. Google Scholar. Lopez-Paz and Oquab, 2016. …

Mineral prospectivity mapping (MPM) is a key tool for outlining and prioritizing prospective areas for mineral deposits exploration of the type sought by …

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random …

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 …

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. In recent years, the random forest (RF), which is recognized as a significant model for ensemble machine learning algorithms, has been widely used in MPM due to its advantages of high performance and robustness. Nevertheless, the RF method does not …

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 …

This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct models are presented for comparison: one employing RF and three using SVM with different kernel functions—namely linear, Radial Basis …