Application of machine learning algorithms to mineral prospectivity

Numerous models and algorithms have been attempted for mineral prospectivity mapping in the past, and in this thesis we propose two new approaches. The first is a modified support vector machine algorithm which incorporates uncertainties on both the data and the labels. Due to the nature of geoscience data and the characteristics of the mineral ...

Mineral Prospectivity Prediction via Convolutional Neural …

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 …

From Predictive Mapping of Mineral Prospectivity to …

Introduction. Predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA) are two distinct predictive modeling …

(PDF) Regional-Scale Mineral Prospectivity …

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 using machine learning …

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 …

Mineral prospectivity mapping using a joint singularity

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 using a VNet convolutional …

The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses ...

A Spatial Data-Driven Approach for Mineral Prospectivity …

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

A Framework for Data-Driven Mineral Prospectivity …

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 …

IJGI | Free Full-Text | Mapping Mineral Prospectivity Using a …

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 …

Special Issue: Machine Learning-based Mapping for Mineral …

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 …

Mineral prospectivity mapping using a VNet convolutional …

Mineral prospectivity mapping using a VNet convolutional neural network. Michael McMillan; Eldad Haber; Bas Peters; Jennifer Fohring. Author and Article …

Selection of coherent deposit-type locations and their application …

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 …

Geodata Science-Based Mineral Prospectivity Mapping: …

This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between …

Data–driven prospectivity modelling of sediment–hosted Zn–Pb mineral

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 …

Random forest predictive modeling of mineral prospectivity …

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, …

A Framework for Data-Driven Mineral Prospectivity Mapping …

Mineral prospectivity mapping (MPM) aims to outline and categorize prospective areas for further exploration of undiscovered mineral deposits of the type of …

Minerals | Free Full-Text | 3D Mineral Prospectivity Modeling for the

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 …

Mineral prospectivity mapping using attention-based …

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 …

Geodata Science-Based Mineral Prospectivity Mapping: …

The workflow of geodata science-based mineral prospectivity mapping (GSMPM) (Fig. 2b) underli-nes the spatial correlations between geological, geochemical, geophysical, and remote sensing pat-terns with known mineral deposits. The identifica-tion of mappable layers is based on revealing the spatial associations between geoscience data and ...

(PDF) HANDBOOK OF EXPLORATION AND ENVIRONMENTAL GEOCHEMISTRY 11

LIFE CYCLE OF THE PHOSPHORIA FORMATION – FROM DEPOSITION TO THE POST-MINING ENVIRONMENT BIOGEOCHEMISTRY IN MINERAL EXPLORATION THE INDIAN OCEAN NODULE FIELD GEOCHEMICAL ANOMALY AND MINERAL PROSPECTIVITY MAPPING IN GIS Handbook of Explorat ion and Environment al …

Investigating the Capabilities of Various Multispectral …

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 …

Introduction to the Special Issue: Mineral prospectivity …

Mineral prospectivity analysis has been traditionally considered part of applied economic geology research. However, we argue that there is a strong need to develop mineral prospectivity analysis as an independent multidisciplinary research field that overlaps with and draws from fields as diverse as economic geology, mineral …

Regional-Scale Mineral Prospectivity Mapping: Support …

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 based on Support vector …

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 …

Vectorial fuzzy logic: a novel technique for enhanced mineral

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).

Mapping Mineral Prospectivity via Semi-supervised …

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 …

Application of GIS Processing Techniques for Producing Mineral …

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 …

Mineral Prospectivity Mapping based on Isolation Forest …

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) …