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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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References
Agterberg, F.P., 1971. A probability index for detecting favourable geological environments.
Canadian Institute of Mining and Metallurgy 10, 82–91.
Agterberg, F.P., 1973. Probabilistic models to evaluate regional mineral potential. Mathematical
methods in Geoscience, Symposium held at Pribram, Czechoslovakia, 3–38.
Agterberg, F.P., 1974. Automatic contouring of geological maps to detect target areas for mineral
exploration. Mathematical Geology 6(4), 373-395.
Agterberg, F.P., 1989. Systematic approach to dealing with uncertainty of geoscience information in
mineral exploration. Proceedings of the 21st APCOM Symposium, Las Vegas, USA, Chapter 18, 165-
178.
Agterberg F.P., 1992a. Combining indicator patterns in weights of evidence modeling for resource
evaluation. Nonrenewable Resources 1(1), 39-50.
Agterberg, F.P., 1992b. Estimating the probability of occurrence of mineral deposits from multiple
map patterns. In: D.F. Merriam, Kurzl, H. (Eds.), Microcomputers in Geology, Plenum Press, New
York, 73-92.
Agterberg, F.P., 2007. Mixtures of multiplicative cascade models in geochemistry. Nonlinear
Processes in Geophysics 14, 201–209
Agterberg, F.P., Bonham-Carter, G.F., 1990. Deriving weights-of-evidence from geoscience contour
maps for prediction of discrete events. Proceedings of the 22nd APCOM Symposium, Berlin,
Germany, v.2, 381-395.
Agterberg, F.P., Bonham-Carter, G.F., Wright, D.F., 1990. Statistical pattern integration for mineral
exploration. In: Gaal, G., Merriam, D.F. (Eds.), Computer Applications in Resource Estimation
Prediction and Assessment for Metals and Petroleum. Pergamon Press, Oxford-New York, 1-21.
Allègre, C.J., Lewin, E., 1995. Scaling laws and geochemical distributions. Earth Planet. Sci. Lett. 132,
1–13.
Althuwaynee, O.F., Pradhan, B., Park, H.J., Lee, J.H., 2014. A novel ensemble bivariate statistical
evidential belief function with knowledge-based analytical hierarchy process and multivariate
statistical logistic regression for landslide susceptibility mapping. Catena 114, 21-36.
Amiri, M.A., Karimi, M., Sarab, A.A., 2014. Hydrocarbon resources potential mapping using the
evidential belief functions and GIS, Ahvaz/Khuzestan Province, southwest Iran. Arabian Journal of
Geosciences, DOI:10.1007/s12517-014-1494-8.
Amiri, M.A., Karimi, M., Sarab, A.A., 2015. Hydrocarbon resources potential mapping using evidential
belief functions and frequency ratio approaches, southeastern Saskatchewan, Canada. Canadian
Journal of Earth Sciences, DOI:10.1139/cjes-2013-0193.
An, P., Moon, W.M., Rencz, A., 1991. Application of fuzzy set theory for integration of geological,
geophysical and remote sensing data. Canadian Journal of Exploration Geophysics 27, 1-11.
An, P., Moon, W.M., Bonham-Carter, G.F., 1994a. Uncertainty management in integration of
exploration data using the belief function. Nonrenewable Resources 3, 60-71.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
An, P., Moon, W.M., Bonham-Carter, G.F., 1994b. An object-oriented knowledge representation
structure for exploration data integration. Nonrenewable Resources 3, 132-145.
Aspinall, P.J., Hill, A.R., 1983. Clinical inferences and decisions-I: Diagnosis and Bayes' theorem.
Ophthalmic and Physiologic Optics 3, 295-304.
Bölviken, B., Stokke, P.R., Feder, J., Jössang, T., 1992. The fractal nature of geochemical landscapes. J.
Geochem. Explor. 43, 91–109.
Bonham-Carter, G.F., 1994. Geographic Information Systems for Geoscientists: Modeling with GIS.
Pergamon Press, Ontario, Canada, pp. 398.
Boser, B.E., Guyon, I.M., Vapnik, V.N., 1992. A training algorithm for optimal margin classifier, Fifth
ACM Annual Workshop on Computational Learning, Pittsburgh, PA, USA, 144-152.
Breiman, L., 2001. Random forests. Machine Learning 45, 5-32.
Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A., 1984. Classification and Regression Trees. CRC
Press.
Brown, W.M., Gedeon, T.D., Groves, D.I., Barnes, R.G., 2000. Artificial neural networks: a new
method for mineral prospectivity mapping. Australian Journal of Earth Sciences 47, 757-770.
Bui, D.T, Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., 2012. Spatial prediction of landslide hazards
in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief
functions and fuzzy logic models. Catena 96, 28-40.
Campbell, A.N., Hollister, V.F., Duda, R.O., 1982. Recognition of a hidden mineral deposit by an
artificial intelligence program. Science 217, 927–929.
Carranza, E.J.M., 2002. Geologically-Constrained Mineral Potential Mapping (Examples from the
Philippines), Ph.D. Thesis, Delft University of Technology, The Netherlands, ITC (International
Institute for Geo-Information Science and Earth Observation) Publication No. 86, Enschede, 480 pp.
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook
of Exploration and Environmental Geochemistry, v. 11. Elsevier.
Carranza, E.J.M., 2009a. Mapping of anomalies in continuous and discrete fields of stream sediment
geochemical landscapes. Geochemistry: Exploration, Environment, Analysis 10, 171–187
Carranza, E.J.M., 2009b. Controls on mineral deposit occurrence inferred from analysis of their
spatial pattern and spatial association with geological features. Ore Geology Reviews 35, 383-400.
Carranza, E.J.M., 2010. Improved Wildcat Modelling of Mineral Prospectivity. Resource Geology
60(2), 129-149
Carranza, E.J.M., 2011. From predictive mapping of mineral prospectivity to quantitative estimation
of number of undiscovered prospects. Resource Geology 61, 30-51.
Carranza, E.J.M., 2015. Data-driven evidential belief modelling of mineral potential using few
prospects and evidence with missing values. Natural Resources Research, DOI:10.1007/s11053-014-
9250-z.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
Carranza, E.J.M., Hale, M., 2001. Logistic regression for geologically-constrained mapping of gold
mineralization potential, Baguio district, Philippines. Exploration and Mining Geology Journal 10(3),
165-175
Carranza, E.J.M., Hale, M. 2002. Wildcat mapping of gold potential, Baguio district, Philippines.
Trans. Inst. Mining Metallurgy (B Appl. Earth Sci.), 111, 100–105.
Carranza, E.J.M., Hale, M., 2003. Evidential belief functions for geologically constrained mapping of
gold potential, Baguio district, Philippines. Ore Geology Reviews 22, 117-132.
Carranza, E.J.M., Woldai, T., Chikambwe, E.M., 2005. Application of data-driven evidential belief
functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia.
Natural Resources Research 14(1), 47-63.
Carranza, E.J.M., Castro, O.T., 2006. Predicting lahar-inundation zones: case study in west Mount
Pinatubo, Philippines. Natural Hazards 37, 331-372.
Carranza, E.J.M., Hale, M., Faassen, C., 2008a. Selection of coherent deposit-type locations and their
application in data-driven mineral prospectivity mapping. Ore Geology Reviews 33(3-4): 536-558.
Carranza, E.J.M., Van Ruitenbeek, F.J.A., Hecker, C.A., Van der Meijde, M., Van der Meer, F.D.,
2008b. Knowledge-guided data-driven evidential belief modelling of mineral prospectivity in Cabo de
Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation 10, 374-387.
Carranza, E.J.M, Wibowo, H., Barritt, S.D., Sumintadireja, P., 2008c. Spatial data analysis and
integration for regional-scale geothermal potential mapping, West Java, Indonesia. Geothermics 33,
267-299.
Carranza, E.J.M., Owusu, E., Hale, M., 2009. Mapping of prospectivity and estimation of number of
undiscovered prospects for lode-gold, southwestern Ashanti Belt, Ghana. Mineralium Deposita 44,
915-938.
Cheng,Q., 1999. Spatial and scaling modelling for geochemical anomaly separation, Journal of
Geochemical Exploration, 65, 175–194
Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of
undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews 32, 314–324
Chung, C.F., Agterberg, F.P., 1980. Regression models for estimating mineral resources from
geological map data. Mathematical Geology 12(5), 472-488.
Chung, C.F., Fabbri, A.G., 1993. The representation of geoscience information for data integration.
Nonrenewable Resources 2, 122-139.
Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning 20, 273-297.
Cox, D.P., Singer, D.A., 1992. Mineral deposit models, U.S. Geological Survey Bulletin, v. 1693.
Dempster, A.P., 1967. Upper and lower probabilities induced by a multivalued mapping. Annals of
Mathematical Statistics 38, 325-339.
Dempster, A.P., 1968. A generalization of Bayesian inference. Journal of the Royal Statistical Society
B30, 205-247.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
Duda, R.O., Hart, P.E., Barrett, P., Gasching, J.G., Konolige, K., Reboh, R., Slocum, J., 1978a.
Development of the Prospector consultation system for mineral exploration. Final Report, SRI
Projects 5821 and 6415, Menlo Park: Artificial Intelligence Center, SRI International, pp. 193.
Duda, R.O., Hart, P.E., Nilsson, N.J., Sutherland, G.L., 1978b. Semantic network representations in
rule-based interference systems. In: Waterman, D.A., Hayes-Roth, F. (Eds.), Pattern-Directed
Inference Systems. Academic Press, 203–221.
Fallara, F., Legault, M., Rabeau, O., 2006. 3-D integrated geological modeling in the Abitibi
Subprovince (Québec, 617 Canada): techniques and applications. Exploration and Mining Geology.
15(2), 27–41.
Gordon, J., Shortliffe, E.H., 1984. The Demspter-Shafer theory of evidence. Rule-Based Expert
Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project 3, 832-838.
Gumiel, P., Sanderson, D.J., Arias, M., Roberts, S., Martin-Izard, A., 2010. Analysis of the fractal
clustering of ore deposits in the Spanish Iberian Pyrite Belt. Ore Geol. Rev. 38, 307–318.
Hagemann, S.G., Cassidy, K.F., 2000. Archean orogenic lode-gold deposits. In: Hagemann, S.G.,
Brown, P.E. (Eds.), Gold in 2000: Reviews in Economic Geology, 13, 9–68.
Harris, D.P., 1965. An application of multivariate statistical analysis to mineral exploration.
Unpublished Ph.D. Dissertation, Pennsylvania State University, pp. 261.
Harris, D.P., 1969. Alaska's base and precious metals resources: a probabilistic regional appraisal.
Quarterly of the Colorado School of Mines 64, 295–327.
Hronsky, J.M.A., 2004. The science of exploration targeting. In: Muhling, J. (Ed.), SEG 2004—
Predictive Mineral Discovery Under Cover: University of Western Australia and Centre for Global
Metallogeny Publication, 33, 129–133.
Hronsky, J.M.A., Groves, D.I., 2008. Science of targeting: definition, strategies, targeting and
performance measurement. Australian Journal of Earth Sciences 55, 3–12.
Joly, A., Porwal, A., McCuaig, T.C., 2012, Exploration targeting for orogenic gold deposits in the
Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis, Ore
Geology Reviews 48, 349-383
Katz, S.S., 1991. Emulating the Prospector expert system with a raster GIS. Computers & Geosciences
17, 1033–1050.
Kelepertsis, A., Argyraki, A., Alexakis, D., 2006. Multivariate statistics and spatial interpretation of
geochemical data for assessing soil contamination by potentially toxic elements in the mining area of
Stratoni, north Greece. Geochemistry 6 (4), 349–355.
Knox-Robinson, C.M., Wyborn, L.A.I., 1997. Towards a holistic exploration strategy: using geographic
information systems as a tool to enhance exploration. Australian Journal of Earth Sciences 44, 453-
463.
Kreuzer, O.P., Markwitz, V., Porwal, A., McCuaig, T.C.M., 2010). A continent-wide study of Australia's
uranium potential. Part I: GIS-assisted manual prospectivity analysis. Ore Geology Reviews 38, 334-
366
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
Lee, S., Hwang, J., Park, I., 2013. Application of data-driven evidential belief functions to landslide
susceptibility mapping in Jinbu, Korea. Catena 100, 15-30.
Lusted, L.B., 1968. Introduction to Medical Decision Making. Charles Thomas, Springfield, pp. 271.
Magoon, L. B., and W. G. Dow, 1994. The petroleum system. In: Magoon, L. B, Dow, W. G. (Eds.), The
Petroleum System—from Source to Trap. AAPG Memoir 60, p. 3-24.
McCuaig, T.C., Hronsky, J.M.A., 2000. The current status and future of the interface between the
exploration industry and economic geology research. In: Hagemann, S.G., Brown, P.E. (Eds.), Gold in
2000: Reviews in Economic Geology, 13, pp. 553–559.
McCuaig, T.C., Hronsky, J.M.A., 2014. The mineral system concept: the key to exploration targeting.
Society of Economic Geologists Special Publication, 18, 153-175.
McCuaig, T.C., Kreuzer, O.P., Brown, W.M., 2007. Fooling ourselves — dealing with model
uncertainty in a mineral systems approach to exploration. Mineral Exploration and Research—
Digging Deeper. 9th Biennial SGA Meeting, Dublin, Proceedings, 1435–1438.
McCuaig, T.C., Porwal, A., Gessner, K., 2009. Fooling ourselves: recognizing uncertainty and bias in
exploration targeting. Centre for Exploration Targeting Quarterly News, The University of Western
Australia, 2(7), 1 and 5–8.
McCuaig, T.C., Beresford, S., Hronsky, J., 2010. Translating the mineral systems approach into an
effective exploration targeting system. Ore Geology Reviews 38, 128-138.
Mejía-Herrera, P., Royer, J.J., Caumon, G., Cheilletz, A., 2014. Curvature attribute from surface
restoration as predictor variable in Kupferschiefer copper potentials. An example from the Fore-
Sudetic Region. Natural Resources Research DOI: 10.1007/s11053-014-9247-7
Moghaddam, M.K., Noorollahi, Y., Samadzadegan, F., Sharifi, M.A., Itoi, R., 2013. Spatial data analysis
for exploration of regional scale geothermal resources. Journal of Volcanology and Geothermal
Research 266, 69-83.
Moon, W.M., 1990. Integration of geophysical and geological data using evidential belief function.
IEEE Transactions on Geoscience and Remote Sensing 28, 711-720.
Moon, W.M., Chung, C.F., An, P., 1991. Representation and integration of geological, geophysical
and remote sensing data. Geoinformatics 2, 177-188.
Nampak, H., Pradhan, B., Manap, M., 2014. Application of GIS based data driven evidential belief
function model to predict groundwater potential zonation. Journal of Hydrology 513, 283-300.
Park, I., Kim, Y., Lee, S., 2014. Groundwater productivity potential mapping using evidential belief
function. Groundwater 52, 201-207.
Park, N.W., 2011. Application of Dempster-Shafer theory of evidence to GIS-based landslide
susceptibility analysis. Environmental Earth Sciences 62, 367-376.
Perrouty, S., Lindsay, M.D., Jessell, M.W., Aillères, L., Martin, R., Bourassa, Y., 2014. 3D modeling of
the Ashanti Belt, southwest Ghana: evidence for a lithostratigraphic control on gold occurrences
within the Birimian Sefwi Group. Ore Geol. Rev. 63, 252–264.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
Porwal, A., 2006. Mineral potential mapping with mathematical geological models. ITC Ph.D.
Dissertation, University of Utrecht, ISBN 90-6164-240-X, pp. 289.
Porwal, A., Carranza, E.J.M., Hale, M., 2003. Artificial neural networks for mineral potential mapping:
A case study from Aravalli province, western India. Natural Resources Research 12(3), 155-177.
Porwal, A., Kreuzer, O.P., 2010. Introduction to the Special Issue: Mineral prospectivity analysis and
quantitative resource estimation. Ore Geology Reviews 38, 121-127
Pourghasemi, H.R., Beheshtirad, M., 2014. Assessment of a data-driven evidential belief function
model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto
International, DOI: 10.1080/10106049.2014.966161.
Pradhan, B., Abokharim, M.H., Jebur, M.N., Shafapour, M., 2014. Land subsidence susceptibility
mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards
73, 1019-1042.
Raines, G.L., 2008. Are fractal dimensions of the spatial distribution of mineral deposits meaningful?
Nat. Resour. Res. 17, 87–97.
Reddy, R.K.T., Bonham-Carter, G.F., Galley, A.G., 1992. Developing a geographic expert system for
regional mapping of volcanogenic massive sulphide (VMS) deposit potential. Nonrenewable
Resources 2, 112–124.
Reggia, J.A., Perricone, B.T., 1985. Answer justification in medical decision support systems based on
Bayesian classification. Computers in Biology and Medicine 15(4), 161-167.
Reimann, C., Filzmoser, P., Garrett, R.G., 2002. Factor analysis applied to regional geochemical data:
problems and possibilities. Appl. Geochem. 17(3), 185–206.
Rodriguez-Galiano, V.F., Chica-Olmo, M., Chica-Rivas, M., 2014. Predictive modelling of gold
potential with the integration of multisource information based on random forest: a case study on
the Rodalquilar area, Southern Spain. International Journal of Geographical Information Science 28,
1336-1354.
Shafer, G., 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton, N.J.
Sillitoe, R.H., 2004. Musings on future exploration targets and strategies in the Andes. Society of
Economic Geologists Special Publication, v. 11, 1–14.
Sillitoe, R.H., Thompson, J.F.H., 2006. Changes in mineral exploration practice: Consequences for
discovery. Society of Economic Geologists Special Publication 12, 193-219.
Simmons, S.F., White, N.C., John, D.A., 2005. Geological characteristics of epithermal precious and
base metal deposits. In: Hedenquist, J.W., John, F.H., Thompson, J.F.H., Goldfarb, R.J., Richards, J.P.
(Eds.), Economic Geology 100th Anniversary Volume 1905-2005, 485-522.
Sinclair, A.J., Woodsworth, G.L., 1970. Multiple regression as a method of estimating exploration
potential in an area near Terrace, B.C. Economic Geology 65, 998–1003.
Singer, D.A., 1972. Multivariate statistical analysis of the unit regional value of mineral resources.
Unpublished Ph.D. Dissertation, Pennsylvania State University, pp. 211.
ACC
EPTE
D M
ANU
SCR
IPT
ACCEPTED MANUSCRIPT
Singer, D.A., Kouda, R., 1999. A comparison of the weights of evidence method and probabilistic
neural networks. Natural Resources Research 8(4), 287-298.
Spiegelhalter, D.J., 1986. Uncertainty in expert system. In: Gale, W.A. (Ed.), Artificial Intelligence and
Statistics. Addison-Wesley, Reading, MA, 17-55.
Sprague, K., Kemp, E., Wong, W., 2008. Spatial targeting using queries in a 3-D GIS environment with
application to mineral exploration. Computers & Geosciences. 32, 396–418.
Wang, G., Zhu, Y., Zhang, S., Yan, C., Song, Y., Ma, Z., Hong, D., Chen, T., 2012. 3D geological
modeling based on gravitational and magnetic data inversion in the Luanchuan ore region, Henan
Province, China. Journal of Applied Geophysics. 80, 1-11.
Wang, G., Pang, Z., Boisvert, J.B., Hao, Y., Cao, Y., Qu, J., 2013. Quantitative assessment of mineral
resources by combining geostatistics and fractal methods in the Tongshan porphyry Cu deposit
(China). Journal of Geochemical Exploration 134, 85-98
Wang, W., Zhao, J., Cheng, Q., Carranza, E.J.M., 2014. GIS-based mineral potential modeling by
advanced spatial analytical methods in the southeastern Yunnan mineral district. China, Ore Geology
Reviews, DOI:10.1016/j.oregeorev.2014.09.032
Wyborn, L.A.I., Heinrich C.A., Jaques A.L., 1994. Australian Proterozoic mineral systems: essential
ingredients and mappable criteria. Australian Institute of Mining and Metallurgy Annual Conference,
Melbourne, Proceedings, 109-115
Zuo, R., Cheng, Q., Xia, Q., 2009. Application of fractal models to characterization of vertical
distribution of geochemical element concentration. J. Geochem. Explor. 102, 37–43.
Zuo, R., Carranza, E.J.M., 2011. Support vector machine: A tool for mapping mineral prospectivity.
Computers & Geosciences 37, 1967-1975.
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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.