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IntroductiontotheSpecialIssue:GIS-basedmineralpotentialmodellingandgeologicaldataanalysesformineralexploration

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Introduction to the Special Issue: GIS-based mineral potential modelling andgeological data analyses for mineral exploration

Alok Porwal, Emmanuel John M. Carranza

PII: S0169-1368(15)00105-5DOI: doi: 10.1016/j.oregeorev.2015.04.017Reference: OREGEO 1500

To appear in: Ore Geology Reviews

Received date: 13 April 2015Accepted date: 19 April 2015

Please cite this article as: Porwal, Alok, Carranza, Emmanuel John M., Introduction tothe Special Issue: GIS-based mineral potential modelling and geological data analyses formineral exploration, Ore Geology Reviews (2015), doi: 10.1016/j.oregeorev.2015.04.017

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Introduction to the Special Issue: GIS-

based mineral potential modelling and

geological data analyses for mineral

exploration

Alok Porwal1,2, Emmanuel John M. Carranza3

1. CSRE, Indian Institute of Technology Bombay, Powai 400076 Mumbai India

2. Centre for Exploration Targeting, University of Western Australia, Crawley 6009, WA,

Australia

3. Department of Earth and Oceans, James Cook University, Townsville, Queensland, Australia

Abstract

This introduction provides an overview of the procedures involved in mineral potential modelling.

The papers included in this Special Issue are also summarized.

1.0 Introduction

Model-based mineral prospectivity mapping is a predictive desktop tool for narrowing down target

areas for ground exploration at different scales ranging from the regional to the deposit. A mineral

prospectivity model is essentially an integration function that relates a set of geological features

(input variables) to the presence of the targeted mineral deposits (output variable). The input

geological features are considered spatial proxies of the mineralization processes and are termed

predictor or evidential maps. The integration functions that are used in mineral prospectivity

modelling vary from simple arithmetic or logical operators to complex mathematical functions.

Models are classified into data-driven or knowledge-driven depending on whether the function

parameters are estimated heuristically based on expert-knowledge or empirically based on the

spatial statistical relationships between the known deposits of the targeted type and the predictor

maps. The modelling is usually implemented using geographic information system (GIS) tools.

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Historically, model-based approaches to mineral prospectivity modelling have their origin in the

works of mathematical geologists such as Harris (1965, 1969), Sinclair and Woodsworth (1970),

Agterberg (1971, 1973, 1974), Singer (1972) and others. The early approaches involved data-driven

regression modelling of the association between known mineral deposits and geological features in

well-explored control areas in the region of interest, and applying the models to determine the

prospectivity of poorly explored or unexplored parts of the region. Duda et al. (1978a), on the other

hand developed a knowledge-driven expert system known as Prospector for evaluating mineral

prospects at the Stanford Research Institute. Prospector used fuzzy inference system in conjunction

with Bayesian probability for classifying deposits by type and evaluating their prospectivity based on

the attributes supplied by a geologist. The system was framed in natural language so that an

exploration geologist can directly interact with it and generate results. The original Prospector was

not a mineral prospectivity mapping system because it was not designed to handle spatial data.

However, later workers modified the system for incorporating exploration data and used it for

modelling the prospectivity of various deposit types (e.g., Duda et al., 1978b; Campbell et al., 1982;

Katz, 1991; Reddy et al., 1992).

The data- and knowledge-driven approaches described above formed the basis of subsequent

advancements in the field. A major spurt in model-based mineral prospectivity modelling was

provided by the development of easy-to-use commercial GIS software in the late 1980s that could be

run on desktop computers. The closing years of 1980s are watershed in the research on mineral

prospectivity modelling marked by the development of the weights-of-evidence (WofE) model by

F.P. Agterberg and G.F. Bonham-Carter along with their co-workers (Agterberg, 1989; Agterberg and

Bonham-Carter, 1990; Agterberg et al., 1990). The WofE is a probabilistic model that uses the theory

of conditional probability to quantify the spatial association between a set of predictor maps and

known mineral deposits of the targeted type. The spatial association is expressed in terms of

conditional probability measures (termed weights-of-evidence), which are used to update the prior

probability of occurrence of mineral deposits using Bayes’ rule in a log-linear form under an

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assumption of conditional independence of the maps to derive posterior probability of occurrence of

mineral deposits. The WofE model was originally developed for non-spatial applications, particularly

in the field of quantitative medical diagnosis (e.g., Lusted, 1968; Aspinall and Hill, 1983; Reggia and

Perricone, 1985; Spiegelhalter, 1986).

Partly because of the lucid exposition of the model by GF Bonham-Carter and FP Agterberg

(Agterberg, 1989; Agterberg and Bonham-Carter, 1990; Agterberg et al., 1990; Bonham-Carter,

1994) and partly because it is intuitive and easy to implement and interpret, the WofE model soon

became immensely popular amongst the mathematically oriented mineral exploration researchers.

The model has been and remains one of the most widely applied mathematical models in mineral

prospectivity modelling.

This special issue marks the circa 25th anniversary of the above landmark publications that inspired a

whole generation of young researchers (including the Guest Editors of this special issue) to develop

new research in the field and to turn it into a mainstream research discipline.

2.0 Mineral prospectivity modelling: the work-flow

Mineral prospectivity modelling involves the following three procedures: (i) conceptual genetic

modelling of the targeted mineral deposits and identification of input predictor maps; (ii) processing

of available relevant exploration datasets within a GIS to derive appropriate predictor maps; and (iii)

integrating the predictor maps using mathematical models, either within or outside a GIS.

2.1 Conceptual genetic modelling: mineral deposit models versus mineral systems approach

Traditionally mineral deposit models (e.g., Cox and Singer, 1992) have been used to identify input

predictor maps for mineral prospectivity modelling. Descriptive mineral deposit models document

the geological attributes of different deposit-types and sub-types, and are valuable for

understanding the structural, chemical, and mineralogical footprints of mineralization. Deposit

model analogues have been widely used to target new deposits in both greenfields and brownfields

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areas. However, there are two problems involved in using deposit models for the selection of input

predictor maps.

The first problem is that mineral prospectivity modelling based on mineral deposit models focuses

on the features of deposits rather than on the processes that generate those features. As a result,

areas whose geological setting does not have the features of the deposit model would be modelled

as non-prospective even though those features may not be critical indicators of mineralization

processes (McCuaig et al., 2007; McCuaig and Hronsky, 2014). Conversely, the model may generate

many ‘false positives’ (McCuaig et al., 2009).

The second problem is that mineral deposit models often focus on deposit-scale features. Mineral

prospectivity models, on the other hand, are generally implemented at the camp-scale using

regional-scale public-domain datasets, in which deposit-scale features may not even respond. (The

exceptions are 3D mineral prospectivity models that are generally implemented at the deposit

scale.) Therefore, mineral deposit models have limited applications to the camp-scale mineral

prospectivity modelling (cf. Sillitoe, 2004; Simmons et al., 2005; Sillitoe and Thompson, 2006).

To address the limitations of the deposit-model based approaches, Wyborn et al. (1994) proposed a

systems approach to mineral deposit formation. Drawing from the petroleum systems approach

(Magoon and Dow, 1994) used by the petroleum industry. A mineral system is defined as “all

geological factors that control the generation and preservation of mineral deposits, and stresses the

processes that are involved in mobilising ore components from a source, transporting and

accumulating them in more concentrated form and then preserving them throughout the

subsequent history” (Wyborn et al., 1994). Although the idea of source-pathways-trap as key

geological factors controlling mineral deposit formation goes back a long time, Wyborn et al. (1994)

provided the first formal definition and a systemic exposition of the concept. In addition to source,

pathways and traps, they also recognized energy source for mass transfer and post-formation

preservation as critical geological processes. The conceptual basis of the mineral systems approach is

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that mineral deposits are focal points of much larger systems of energy and mass transfer brought

about by various earth processes that operated conjunctively in time and space (Wyborn et al.,

1994; Hronsky, 2004; Hronsky and Grove, 2008; McCuaig and Hronsky, 2014). With the mineral

systems approach, the focus shifted away from rigid deposit models that are based on deposit-scale

features to generic mineral systems models that are based on the underlying mineralization

processes that operate across geographic scales ranging from the crustal-scale to the deposit-scale

(Knox-Robinson and Wyborn, 1997; McCuaig et al., 2010; Porwal and Kreuzer, 2010). In the context

of mineral potential modelling, this implies that the same mineral system model can be used to

identify input predictor maps at different scales, although the relative importance of the various

mineralization processes, targeting criteria and predictor maps will obviously change with scale

(Hronsky and Groves, 2008; McCuaig et al., 2010). Similarly, a given mineral systems model can be

used for prospectivity modelling of genetically related deposit types that are defined by similar

proxies (Wyborn et al., 1994; Knox-Robinson and Wyborn, 1997; Hagemann and Cassidy, 2000;

Porwal and Kreuzer, 2010).

In practical model-based exploration targeting, the mineral systems modelling involves generating a

matrix of essential components of a mineral system (sources for energy, fluids/melts, ligands and

metals; pathways for focussed fluid flow; physical throttle for trapping fluids; and chemical

scrubbers for precipitation of metals), the respective mappable targeting criteria for each

component, and respective predictor maps for each criterion (e.g., Joly et al., 2012; Porwal et al.,

2015 – this special issue).

2.2 GIS-based data processing: mapping processes by proxy

Identifying and deriving geologically consistent and representative predictor maps for each mineral

system component are arguably the most important stages in mineral prospectivity modelling. It

requires GIS and statistical skills, but more importantly a sound understanding of the geology of the

targeted mineral systems. For example, the key components of surficial uranium systems are

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sources of uranium and fluids; driving force for fluid flow; transportation pathways; physical traps;

and chemical scrubbers. These systems generally form in palaeochannels in arid and flat desertic to

semi-desertic terranes. Although uranium-rich granites are the main sources for uranium, which can

easily be mapped from geological and geochemical/radiometric data, it is not the total uranium

content but the leachable uranium content of a granite and fluid-rock interaction that makes it a

good source. The factors that influence the leachability of uranium include fluid-rock ratio, granite

geochemistry (peralkaline versus peraluminous) and Eh conditions (oxidizing environment). All these

factors can be mapped from publicly available exploration datasets. For example, the fracture

density and intensity of weathering over granites can be mapped from structural, topographic and

remote sensing data, respectively, and used as proxies for fluid-rock ratio. Eh conditions can be

mapped using information about mineral assemblages in rocks, which can be extracted from public

domain geology data (Kreuzer et al., 2010). Shallow groundwater is the main fluid involved in

surficial uranium systems, and can be mapped using public-domain aquifer data; other proxies that

can be used include sand-filled palaeochannels, surface drainage density, topographic slopes, etc.

Hydraulic gradient is the main driving force for ground water flow in shallow aquifers and

palaeochannels. The flow directions in flat and low-lying regions can be mapped using topographic

trends. Palaeochannels are the main transportation pathways for uranium-bearing ground waters;

they may not always be exposed on the surface, but they can be detected and mapped either from

topographic data or from their response in remote sensing data. Because they are filled with highly

porous sediments that are good aquifers and contain water, they show good thermal contrasts with

the surrounding terrane on remotely sensed night-time thermal infrared data (Porwal et al., 2015 –

this special issue). Kinetic temperature and emissivity data derived from thermal infrared data can

also be used to map the channel morphology and slopes of the valley floor, which are good proxies

for hydraulic gradient (Porwal et al., in prep.). Calcrete deposits in palaeochannels are the physical

traps for surficial uranium; they can be extracted from public-domain regolith data, or mapped from

remote sensing data. Finally uranium precipitation in surficial systems is brought about by a change

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in pH towards neutral, evaporation, and disassociation of the soluble uranyl carbonate or uranyl

sulphate complexes. Soil pH maps and ground water hydrology data can help map the pH gradients

in soil and ground water. Uranyl complexes are dissociated by changes in the partial pressures of

carbon dioxide or sulphur dioxide as ground water migrates to near surface environments in

palaeochannels, the loss of confining pressure leads to exsolution of carbon dioxide and

precipitation of calcium carbonates along with uranium as carnotite. The presence of calcrete can be

mapped from surface regolith data. Similarly gypsum in playa-lake environments is a good proxy for

uranyl sulphate disassociation. Evaporation is another key precipitation mechanism, and can be

mapped using meteorological data.

The above mentioned conceptual approach to the identification of spatial proxies or predictor maps

is complemented by empirical analyses of the spatial association between known mineral deposits

and geologic features, particularly in brownfields exploration. Empirical statistical analyses provide

objective measures of spatial associations that are not biased by the belief system of the modeller,

and hence are useful for getting new insights into mineralization processes and controls. However,

empirical approaches are also biased by the quality and distribution of data. Nevertheless,

significant progress has been made in the development and application of statistical techniques,

both spatial and non-spatial, to exploration data processing and understanding the empirical spatial

association between mineral deposits and geologic features. Some of the established and widely

used techniques include fractal- and multi-fractal analysis (Bölviken et al, 1992; Allègre and Lewin,

1995; Cheng, 1999, 2007; Agterberg, 2007; Raines, 2008; Carranza, 2009a; Zuo et al., 2009; Gumiel

et al., 2010) and principal components, independent components and factor analyses (Carranza,

2002; Kelepertsis et al., 2006; Reimann et al., 2002; Carranza, 2010; Cheng et al., 2011; Wang et al.,

2014). These data mining and knowledge discovery techniques help in optimizing geological

information extraction from the exploration datasets. They are particularly useful in geochemical

data processing, analysis and anomaly extraction. Carranza (2008) provides an exhaustive exposition

of GIS-based techniques for geochemical anomaly mapping along with mineral prospectivity

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modelling.

2.3 Mathematical modelling: integrating predictor maps

There are four main methods to mineral potential modelling: probabilistic, regression-based,

artificial-intelligence-based (AI-based) and Dempster-Shafer-belief-theory-based methods.

Probabilistic methods are based on Bayes’ theory of probability and involve estimation of posterior

probability of mineral deposit occurrence in a given unit area given the presence or absence of

various geologic features. The WofE is the most widely used probabilistic method, in which geologic

features are assumed to be conditionally independent with respect to the targeted mineral deposits.

The assumption of conditional independence allows modular estimations of conditional probabilities

(or WofE), which represent spatial associations of predictor maps with known mineral deposits, that

can be combined log-linearly to estimate posterior probabilities.

Regression-based methods are based on the estimation of the ‘best-fit’ function relating the

targeted mineral deposit (dependent variable) to a set of input predictor maps (explanatory

variables). The coefficients of explanatory variables in the best-fit equation represent the spatial

association of known mineral deposits with the predictor maps. Logistic regression is the most

commonly used regression-based method in mineral potential modelling (Agterberg, 1974, 1992a,

1992b; Chung and Agterberg, 1980; Carranza and Hale, 2001). In logistic regression, the dependent

variable is binary and its predicted values are constrained between 0 and 1, and therefore the

output of a logistic regression model for a given unit area can be interpreted as the probability of

occurrence of a deposit in that unit area.

AI-based or soft-computation methods of mineral prospectivity modelling can be broadly classified

into two groups: (i) fuzzy-set-theory-based expert systems, which aim at capturing the cognitive

reasoning of the exploration geologist in explicit if-then type of statements written in natural

language (An et al., 1991; Porwal et al., 2015 – this special issue) and (ii) and machine learning

systems, which include a whole range of algorithms developed mainly by computer scientists for

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pattern recognition and classification tasks. Some of the most commonly used machine learning

algorithms in mineral prospectivity modelling are: neural networks (Singer and Kouda, 1999; Brown

et al., 2000; Porwal et al., 2003); decision trees (Breiman et al., 1984); support vector machines

(Boser et al., 1992; Cortes and Vapnik, 1995; Zuo and Carranza, 2011); and random forests (Breiman,

2001; Rodriguez-Galiano et al., 2014; Carranza, 2015 – this special issue). Rodriguez-Galiano et al.

(2015 – this special issue) provide a detailed exposition and review of the above machine learning

algorithms.

The emergence of expert systems during the 1970s through to the 1990s resulted in a rapid growth

of interest within the AI community in issues relating to the management of uncertainty and

evidential reasoning. The Dempster-Shafer theory of evidence, based on belief functions and

plausible reasoning (Dempster, 1967, 1968; Shafer, 1976), was developed independent of AI but it

has been strongly considered for managing uncertainty in expert systems (Gordon and Shortliffe,

1984). However, the Dempster-Shafer theory of evidence has also attracted significant attention as

an appropriate method for combining evidence and fusion of data. The representation of geoscience

information for data integration based on interpretation of the Dempster-Shafer theory of evidential

belief has been described by Chung and Fabbri (1993), whereas An et al. (1994a) demonstrated the

management or representation of uncertainty in the integration of exploration data using Dempster-

Shafer evidential belief functions (EBFs): belief, disbelief, uncertainty and plausibility. The earliest

applications of EBFs to mineral prospectivity modelling were knowledge-driven (e.g., Moon, 1990,

1993; Moon et al., 1991; An et al., 1994a, 1994b; Chung and Fabbri, 1993). As two independent EBFs

(belief and disbelief; or belief and uncertainty) must be estimated together and assigned to spatial

evidence for a proposition being evaluated (e.g., mineral prospectivity), the application of

knowledge-driven EBFs to mineral prospectivity modelling is not as appealing as the application of

the fuzzy logic theory. However, Carranza (2002) has developed equations for data-driven

estimation of EBFs for mineral prospectivity modelling, which have been demonstrated in several

case studies (e.g., Carranza and Hale, 2003; Carranza et al., 2005, 2008a,b, 2009; Carranza 2009,

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2011). These data-driven EBFs have now also been demonstrated for predictive modelling of

landslide susceptibility (Carranza and Castro, 2006; Park, 2011; Althuwaynee et al., 2014; Bui et al.,

2012; Lee et al., 2013; Pradhan et al, 2014), groundwater potential (Nampak et al., 2014; Park et al.,

2014; Pourghasemi and Beheshtirad, 2014), hydrocarbon potential (Amiri et al., 2014, 2015) and

geothermal prospectivity (Carranza et al., 2008c; Moghaddam et al., 2013).

While several models exist for mineral prospectivity mapping, there is no single best model that can

be effectively used in all situations (Carranza, 2002; Porwal, 2006). In actual practice, the

performance of a model depends largely on the quality of the conceptual genetic model and how

well the input predictor maps capture the mineralization processes. This is an outstanding major

issue in model-based mineral prospectivity modelling because mineralization processes operate in a

4D space-time while the predictor maps that are traditionally used to represent them are in 2D.

3.0 Mineral prospectivity modelling in three dimensions

In order to address the above limitation, Joly et al. (2012) used innovative techniques to

represent 4D mineralization processes in the form of 2D GIS layers. However, since the

advent of easy-to-use commercially available 3D GIS, there has been a spurt of research on

3D prospectivity modelling in the last half a decade. Most of the 2D GIS based prospectivity

modelling methods can now be implemented within 3D GIS (Fallara et al., 2006; Sprague et

al., 2008; Wang et al., 2012, 2013; Mejía-Herrera et al., 2014). The main limitation is that 3D

data are generally available on deposit- to project-scales only, and this limits the application

of 3D GIS-based prospectivity modelling to deposit-scale. At this scale, however, direct

detection techniques are often more efficient and reliable than prospectivity modelling,

although the latter complements the former with spatial information where to focus

exploration (McCuaig and Hronsky, 2000; Hronsky and Groves, 2008; McCuaig et al., 2010).

The lack of regional-scale 3D data can be overcome by using 3D geophysical modelling

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techniques such as forward modelling or inversion to develop a 3D geological model (Joly et

al., 2012; Perrouty et al., 2014; Wang et al., 2015 – this special issue). The 3D geological

model can be incorporated into 3D GIS using the 3D voxel model (Perrouty et al., 2014). The

3D GIS model is then used to generate 3D predictor maps, which can be combined using any

of the above described mathematical models to estimate the prospectivity of each voxel in

the model (e.g., Wang et al., 2015 – this special issue).

4.0 Final Thoughts

Porwal and Kreuzer (2010) argue that that there is a strong need to develop mineral

prospectivity modelling as an independent multidisciplinary research field that overlaps with

and draws from fields as diverse as economic geology, mineral economics, spatial science,

statistics, soft computation, and cognitive psychology. We strongly concur with the above

ideas. Mineral prospectivity modelling is now an established exploration targeting technique

widely used in academia and also in the industry.

5.0 Organization of the special issue

This special issue is a compilation of 19 papers, which can be divided into two groups.

5.1 Group I: Case studies documenting applications of WofE to 2D and 3D mineral

prospectivity modelling

The work presented in the paper ‘Prospectivity for epithermal gold-silver deposits in the

Deseado Massif, Argentina’ by Andrade de Palomera et al. involves regional- and district-

scale prospectivity modelling for low- and intermediate-sulphidation epithermal deposits in

the Deseado Massif, Argentina using the WofE model. The authors also compare the

regional- and district-scale prospectivity models with respect to their capability in

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identifying prospective target areas.

The paper ‘Evaluation of uncertainty in mineral prospectivity mapping due to missing

evidence: a case study with skarn-type Fe deposits in southwestern Fujian Province, China’

by Zuo et al. describes the uncertainty induced by missing data in fuzzy-WofE modelling. The

analyses help in ranking the input predictor layers in terms of their predictive capability and

in establishing the main source of uncertainty in the model.

In the paper 'Comparing prospectivity modelling results and past exploration data: a case

study of porphyry Cu–Au mineral systems in the Macquarie Arc, Lachlan Fold Belt, New

South Wales', Kreuzer et al. generated a map of porphyry Cu-Au prospectivity using WofE

and compared the prospectivity map to a map of exploration expenditures that serves as a

proxy for porphyry Cu–Au potential as perceived by the minerals exploration industry. Their

analyses confirmed that despite more than a century of exploration and mining history,

much of the prospective ground within the study area remained untested. This study

demonstrates that spatial and statistical comparative analyses are important for assessing

the effectiveness of exploration investment and explanation maturity and, thus, exploration

decision-making in the future.

In the paper 'Chatham Rise nodular phosphate — modelling the prospectivity of a lag

deposit (off-shore New Zealand): a critical tool for use in resource development and deep

sea mining', Nielsen et al. used WofE to quantitatively define the most important predictive

parameters for phosphate mineralisation over an area with highest data concentration as

well as covering the most sampled seabed sedimentary units. The results of WofE modelling

were used to guide regional-scale fuzzy logic modelling to elucidate where future

exploration should be targeted to give the best chance of success in expanding the known

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resource. This study shows that combining the WofE and fuzzy logic prospectivity models

with a map of statistical confidence in the results can be used to limit exploration to areas

where exploration will give the most return, limiting expenditure as well as environmental

impact.

In the paper 'From 2D to 3D: prospectivity modelling in the Taupo Volcanic Zone, New

Zealand', Payne et al. generated a regional-scale 2D WofE prospectivity model that was, in

turn, was used to target areas that would be appropriate to apply a 3D prospectivity model.

For the latter, they generated a multi-class index overlay prospectivity model for the

Ohakuri prospect in the Taupo Volcanic Zone. The study highlighted the main issues that

need to be resolved before 3D prospectivity modelling becomes standard practise in the

mineral exploration industry. The study also helped develop a work flow that incorporates

preliminary 2D spatial data analysis, for example by WofE modelling, into 3D predictive

analysis.

The paper ‘3D prospectivity modelling of orogenic gold in the Marymia Inlier, Western

Australia’ by Nielsen et al. aims at establishing the depths at which potential targets can be

located in the Marymia Inlier, Western Australia. The authors built a 3D-geological model

based largely on surface geology extended into the subsurface using geophysical data. They

implemented a 2D WofE prospectivity model initially created to constrain the 3D predictive

maps integrated into the 3D prospectivity model, and finally generated a 3D model using a

ranked fuzzy logic technique.

In the paper ‘3D geological modelling for prediction of subsurface Mo targets in the

Luanchuan district, China’, the authors Wang et al. aimed at identifying potential targets for

Mo exploration in the Luanchuan district, China. They used geophysical inversion technique

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to develop a 3D geological model and to map the 3D controls on mineralization, and

implemented WofE and concentration-volume fractal analysis techniques to identify and

classify Mo deposits.

In the paper 'GIS-based 3D prospectivity mapping: a case study of Jiama copper-polymetallic

deposit in Tibet, China', Li et al. employed WofE in 3D to estimate the subsurface

prospectivity for Cu (Mo) orebodies in the study area, resulting in the identification of three

prospective deep‐seated exploration targets. This study demonstrates the value of 3D

modelling and a quantitative data analysis workflow to improve exploration targeting of

concealed deposits.

5.2 Group II: Case studies documenting applications of other methods to 2D mineral

prospectivity modelling

The paper entitled ‘Predictive mapping of prospectivity for orogenic gold, Giyani greenstone

belt (South Africa)’ by Carranza et al. emphasizes the importance of using accurate input

predictor maps for efficient mineral prospectivity modelling. Two prospectivity maps for

orogenic gold in the Giyani greenstone belt, South Africa were derived using EBFs, one using

updated lithological maps and spatially coherent mineral deposits, and the other using old

lithological maps and all known Au occurrences. The result shows that the output model

from updated lithological maps and spatially coherent mineral deposits was more effective

in terms of goodness-of-fit and prediction rates as compared to those derived from old

lithological maps and all known Au occurrences.

In the paper ‘Application of the tectono-geochemistry method to mineral prospectivity

mapping: a case study of the Gaosong tin-polymetallic deposit, Gejiu district, SW China’,

Zhao et al. attempted mineral prospectivity mapping using the tectono-geochemistry

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method, which involves analysing element migration and concentration adjacent to

geological structures. The method involves factor analysis and multifractal singularity

mapping to identify geochemical distribution patterns of elements around structures. The

results of the analyses aid in detection of geochemical anomalies that can be related to

mineralization.

In the paper ' GIS-based mineral potential modelling by advanced spatial analytical methods

in the southeastern Yunnan mineral district, China', Wang et al. used the singularity-theory-

based spatial analysis to extract geo-anomalies related to intrusions, fault intensity, and

wall-rocks alterations indicative of hydrothermal mineralization from geophysical,

geochemical, and geological datasets. They then used PCA to integrate the extracted

predictors of mineral potential. In addition, the authors employed geographically-weighted

regression analysis to investigate the spatially non-stationary controlling effects of geo-

processes on mineralization, which helped to improve understanding of local metallogeny in

the study area.

Chen's paper describes 'Mineral potential mapping with a restricted Boltzmann machine',

which can be trained to encode and reconstruct training samples from a training sample

population with an unknown complex probability distribution. The study showed that (a)

the performance of a restricted Boltzmann machine that is trained for mineral prospectivity

mapping is comparable with those of the WofE and logistic regression models, (b) a not-too-

large number of training epochs, such as 100 epochs in the case study, are adequate for a

restricted Boltzmann machine to map mineral prospectivity, and further training does not

improve the model's performance, and (c) mapping mineral prospectivity does not require a

restricted Boltzmann machine to be well-trained.

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In the paper 'Prospectivity of Western Australian iron ore from geophysical data using a

reject-option classifier', Merdith et al. used a multivariate analysis of geophysical datasets to

develop a methodology that utilizes machine learning algorithms to build and train two-

class (e.g., present-absent) classifiers for provincial-scale, greenfield minerals exploration.

They applied a classifier with reject-option to create a discriminant function that best

separates sampled data into two classes while simultaneously “protecting” against new

unseen data by “closing” the domain in feature space occupied by the target class. This

shows a substantial 4% improvement in mineral prospectivity classification performance in

the study area.

Carranza and Laborte describes ‘Data-driven predictive mapping of gold prospectivity,

Baguio district, Philippines: application of random forests algorithm’. The study involves(a)

assessing the efficiency and sensitivity of the Random Forest (RF) algorithm for Au

prospectivity mapping, and (b) comparing the results of the RF algorithm with other data-

driven prospectivity modelling methods, viz, weights-evidence, evidential belief and logistic

regression modelling based on examining the success rates and prediction rates. The results

demonstrate the capability of the RF algorithm in establishing spatial relationships between

predictor maps and training data, and that it performs better than the other algorithms in

term of success and prediction rates.

In the paper 'Data- and knowledge-driven mineral prospectivity maps for Canada's North',

Harris et al. applied a RF supervised classifier in data-driven mineral prospectivity modelling

and then compared its performance with that of weighted-index overlay modelling, which is

a commonly used knowledge-driven method of mineral prospectivity modelling. The RF

classification outperformed the knowledge-based model with respect to prediction of the

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known Au occurrences.

The paper entitled ‘Machine learning predictive models for mineral prospectivity: an

evaluation of neural networks, random forest, regression trees and support vector

machines’ by Rodriguez et al. compared the applications of those data-driven machine

learning algorithms and assessed their efficiency for mineral prospectivity modelling. The

study runs comparative analyses based on the accuracy and sensitivity of the above

mentioned algorithms in identifying prospective areas for epithermal Au in the Rodalquilar

district, Spain. The results identify the RF algorithm to be the most efficient and successful in

mapping highly prospective areas.

Asadi et al. discuss 'Exploration feature selection applied to hybrid data integration

modelling: targeting copper-gold potential in central Iran'. They implemented the hybrid

“adaptive neuro-fuzzy inference system” or ANFIS, which is a Sugeno-type fuzzy inference

system (FIS), in the framework of an adaptive neural network to map Cu–Au prospectivity in

the study area. They used the ANFIS to optimize the fuzzy membership values of input

predictor maps and the parameters of the output functions using the spatial distribution of

known mineral deposits. As a consequence, the application of ANFIS outperforms

conventional fuzzy modelling.

Porwal et al. discuss 'Fuzzy inference systems for prospectivity modelling of mineral systems

and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area, Western

Australia'. They implemented a Mamdani-type FIS for mineral prospectivity modelling,

which is a type of knowledge-driven symbolic artificial intelligence that is transparent,

intuitive and is easy to construct by geologists because they are built in natural language

and use linguistic values. A key aspect of the described FIS-based modelling is the

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identification and generation of accurate proxies for the constituent processes of the

targeted mineral systems. They demonstrated the model by an application to surficial

uranium prospectivity modelling of the Yeelirrie area, Western Australia.

Nykänen et al. demonstrate ‘Receiver Operating Characteristics (ROC) as validation tool for

prospectivity models – a magmatic Ni-Cu case study from the Central Lapland greenstone

belt, Northern Finland’. They generated a prospectivity model for magmatic Ni-Cu deposits

in Central Lapland Greenstone Belt (Northern Finland) using the fuzzy logic technique and

they validated the model using the ROC method. The paper emphasizes on the importance

of validation of prospectivity models and identifies ROC as a suitable validation technique.

Acknowledgement

We appreciate the efforts and unselfish time given by the following individuals in reviewing

papers included in this special issue, some of who have reviewed more than one paper:

Laurent Ailleres (3x), Pablo Andrada de Palomera, Adrian Baddeley, Biplab Banerjee, Avik

Bhattacharya, Frank Bierlein, Karol Czarnota, Tim Chalke, Yongqing Chen, Jose Escavy,

Arianne Ford (2x), Mark Gettings, Ignacio González-Álvarez, Matthew Greenwood, Jeff

Harris, Jon Hronsky, David Huston, Oliver Kreuzer, Mark Lindsay (2x), Vladimir Lisitsin,

Ahmed Madani, Antony Mamuse (2x), Greg Partington (2x), Stephane Perrouty ,Gilipin

Robinson, Victor Rodriguez-Galiano (2x), Martiya Sadeghi, Helmut Schaeben, Don Singer,

Andrew Skabar, Abera Tessema, Qingfei Wang, Mahyar Yousefi, Zuoheng Zhang, Nora

Rubinstein, Vesa Nykänen, Bijal Chudasama. We thank all the authors for contributions. AP

thanks Bijal Chudasama for useful discussions.

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Conflict of interest: No conflict of interest.

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Highlights:

This is the editorial of the special issue on mineral potential modelling.