Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting

Journal Pre-proofs Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting Oliver P. Kreuzer, Mahyar Yousefi, Vesa Nykänen PII: DOI: Reference:

S0169-1368(20)30040-8 https://doi.org/10.1016/j.oregeorev.2020.103391 OREGEO 103391

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Ore Geology Reviews

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Please cite this article as: O.P. Kreuzer, M. Yousefi, V. Nykänen, Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting, Ore Geology Reviews (2020), doi: https://doi.org/10.1016/j.oregeorev.2020.103391

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Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting

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Oliver P. Kreuzer1,2,*, Mahyar Yousefi3,*, Vesa Nykänen4

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1Corporate 2Economic

Geology Research Centre (EGRU), College of Science & Engineering, James Cook University, Townsville, QLD 4811, Australia

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3Faculty

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Geoscience Group (CGSG), PO Box 5128, Rockingham Beach, WA 6969, Australia

4 Information

of Engineering, Malayer University, Malayer, Iran

Solutions, Geological Survey of Finland, Rovaniemi, Finland

*Corresponding authors: E-mail addresses: [email protected] (O.P. Kreuzer); [email protected] (M. Yousefi).

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Abstract

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Ore deposits are diverse with much of their diversity attributable to the complex interplay of ore-

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forming processes with a variety of geological environments, over a range of scales and both in space

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and time. This diversity makes it difficult for geoscientists to accurately predict the location of

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undiscovered ore deposits. Improving our understanding of the processes that are critical to ore deposit

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formation would help us to hone our predictive capabilities. However, this task is difficult to achieve

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as we cannot observe these genetic processes first-hand and different parameters and ingredients are

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important at different scales. Modelling offers a means of simulating and analysing ore-forming

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processes and their mappable expressions. This knowledge can then be used to build a predictive model

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by translating key process components into spatial proxies that can be mapped or recognized in mineral

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exploration data. Modelling and analysis of ore-forming processes are therefore critical for the future

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success of mineral exploration. Currently underutilized in exploration targeting, the application of

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statistical and mathematical concepts can help steer geoscientists towards a better understanding of the

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complex geological processes critical in the formation of mineral deposits and, ultimately, improved

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exploration success rates. This editorial article presents a brief introduction to the main concepts that

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support a collection of articles published in a virtual special issue (VSI) of Ore Geology Reviews

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entitled “Spatial modelling and analysis of ore-forming processes in mineral exploration targeting”.

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The articles examine three critical themes: (1) Translating the expressions of ore-forming processes and

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critical parameters of mineral systems into mappable spatial proxies; (2) identifying mineral deposit

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footprints through geochemical and geophysical data analysis; and (3) targeting and improving the

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discovery chance of mineral deposits by way of spatial data analysis.

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Keywords

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Geographic Information Systems (GIS), Spatial Modelling, Ore-Forming Processes, Exploration

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Targeting, Mineral Systems

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1. Introduction

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This editorial article introduces and discusses the implications of a collection of diverse, yet interlinked,

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research and review articles, published in a virtual special issue (VSI) of Ore Geology Reviews entitled

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“Spatial modelling and analysis of ore-forming processes in mineral exploration targeting”.

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2. Underlying subject areas

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2.1. Mineral exploration targeting

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Mineral exploration is undertaken in stages (Lord et al., 2001), with each stage designed to arrive at the

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next decision point of whether or not to keep exploring a particular area based on the results obtained.

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As a general rule, each consecutive exploration stage is more expensive due to the progressively more

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intensive nature of the work required (Kreuzer et al., 2015). According to Hronsky and Groves (2008),

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the initial predictive targeting stage, referred to here as mineral exploration targeting, presents a major

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geoscientific challenge that has important implications for the following direct-detection stages of

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exploration. In other words, if the initial ground selection is done poorly, it is irrelevant how efficient

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and effective the subsequent work may be carried out (Hronsky and Groves, 2008).

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As argued by Hronsky (2004), mineral exploration targeting is a scientific endeavour that requires the

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integration of genetic ore deposit models with data, information and knowledge derived from other

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fields, such as geophysics, geochemistry, remote sensing, spatial analysis, mineral economics, decision

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science and probability theory, to deliver a successful business outcome.

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2.2. Ore-forming processes

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The last two decades have seen growing acceptance of holistic approaches to mineral exploration

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targeting based on a greater awareness and improved understanding of the range of geological processes

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required to form and preserve ore deposits at all scales, both in space and time (Hronsky, 2004; Kreuzer

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et al., 2008; Hronsky and Groves, 2008; McCuaig et al., 2010; McCuaig and Hronsky, 2014; Hagemann

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et al., 2016). The emergence of the mineral systems concept (Wyborn et al., 1994; Knox-Robinson and

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Wyborn, 1997), an adaptation of the previously proposed petroleum systems concept (Magoon and

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Dow, 1994), has arguably had the greatest influence on mineral exploration targeting, in particular the

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approach taken by many national and state geological survey organisations and university research

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groups (McCuaig et al., 2010; Hagemann et al., 2016).

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Whilst holistic ore deposit models are not a new concept (cf. Kirkham, 1993), the minerals system

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concept is far broader in its reach than the traditional, forensic source–transport–trap analysis, typically

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undertaken at the deposit scale, in that it considers ore deposit formation in the framework of much

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larger lithospheric-scale processes (Hagemann et al., 2016). In this context, an ore deposit can be

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thought of as the product of five critical genetic processes (Wyborn et al., 1994; Knox-Robinson and

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Wyborn, 1997):

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

or fluids, metals and ligands) from their crustal and/or mantle sources;

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  

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Deposition: All geological processes required for efficient extraction of metals from melts or fluids passing through the traps; and

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Trap: All geological processes required for focusing melt or fluid flow into physically and/or chemically responsive sites that can accommodate significant volumes of ore and gangue;

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Transport: All geological processes required for driving the melt- or fluid-assisted transfer of the ore components from source to trap;

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Source: All geological processes required for extracting the necessary ore components (melts

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Preservation: All geological processes required to preserve the accumulated metals through time.

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Where one or more of these processes is missing, ore formation is precluded. As such, the mineral

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systems concept is essentially a probabilistic concept in that if the probability of occurrence of any of

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the critical processes became zero, then no deposit would have formed. This principle is one of the key

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strengths of the mineral systems approach. By integrating mineral systems models into a probabilistic

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framework, a prior probability of success can be calculated for discovery of a potentially economic

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mineral deposit within a particular search area. This thinking has been applied to measuring exploration

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success (Lord et al., 2001), exploration decision-making and target ranking (Kreuzer et al., 2008),

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economic risk analysis (Partington, 2010) and prospectivity analysis (e.g., Nykänen and Salmirinne,

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2007; González-Álvarez et al., 2010; Kreuzer et al., 2010; Joly et al., 2012; Yousefi and Carranza, 2015

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a,b; Chudasama et al., 2018).

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2.3. Spatial modelling and analysis

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The advent of commercial geographic information systems (GIS) in the mid- to late 1980s, in

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combination with significant advancements in computing technology, not only enabled the management

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and spatial querying of ever increasing amounts of data but also promoted the development of new,

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powerful statistical and soft computational models, facilitating pattern recognition and predictive spatial

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modelling (Porwal and Kreuzer, 2010; McCuaig and Hronsky, 2014; Hagemann et al., 2016; Yousefi

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and Nykänen, 2017). These developments have had profound impact on mineral exploration targeting

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workflows today.

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Mineral prospectivity modelling (MPM) has been specifically developed to improve the effectiveness

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of exploration targeting by augmenting the geoscientist’s expertise with automated tools for efficient

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and reproducible data processing and the integration of multi-source and multi-scale datasets (Carranza,

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2008; Yousefi and Carranza, 2015a; Almasi et al., 2017; Hagemann et al., 2016). MPM is a multi-step

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process that, in general terms, entails the (1) identification, capture and weighting of the mappable

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expressions (also known as proxies or targeting criteria) of the targeted mineral system, (2) generation

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of predictor or evidence maps representing the proxies, (3) combination of the predictor maps through

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computational functions, and (4) generation of prospectivity maps designed to inform mineral

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exploration targeting and target ranking (Bonham-Carter, 1994; Carranza, 2008; Chudasama et al.,

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2018).

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The first of the above steps is highly contingent upon the conceptual model of the targeted ore deposit

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type in that the quality of the conceptual model and its translation into a targeting model ultimately

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determine the quality of the resulting prospectivity maps (cf. McCuaig et al., 2010; Joly et al., 2012;

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Yousefi and Carranza, 2015a; Chudasama et al., 2018). Whilst much effort has been directed by the

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MPM research community toward refining, improving and validating the computational modelling

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techniques at the core of MPM (e.g., weights of evidence, fuzzy logic, artificial neural networks: Porwal

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and Kreuzer, 2010) and methods for weighting and integrating predictor maps (e.g., Yousefi, 2017;

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Yousefi and Nykänen, 2017), little work has gone into qualifying and quantifying the potential impact

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of the underlying conceptual model on the MPM results (cf. Kreuzer et al., 2015), and how to best

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translate a conceptual model into an effective targeting model (cf. McCuaig et al., 2010; Czarnota et

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al., 2010).

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3. Spatial modelling and analysis of ore-forming processes in mineral exploration targeting

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3.1. Translating the expressions of ore-forming processes and critical parameters of mineral systems into mappable spatial proxies

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Ore deposits reflect the tectonic environment in which they formed and, thus, are diagnostic of this

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environment (Jenkin et al., 2015; Huston et al., 2016). Ore-forming processes, on the other hand, are

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complex and not directly observable. Instead, they must be inferred from geological observations and

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the interpretation of geochemical, geophysical and remote sensing data. Just like in forensic science,

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geoscientists draw from various scientific disciplines to help them to piece together data, information

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and knowledge in order to uncover and evaluate the mappable evidence of particular ore-forming

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processes. Despite many significant conceptual advancements, in particular the advent of plate tectonics

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and recognition of the supercontinent cycle, our understanding of ore-forming processes and

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appreciation of their mappable expressions remains limited and incomplete.

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Given these limitations, translating our incomplete understanding of ore-forming processes into an

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effective targeting system that it is focused on criteria that are mappable in available or obtainable

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spatial datasets (cf. McCuaig et al., 2010) is one of the most difficult tasks in exploration targeting. In

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their landmark paper “Translating the mineral systems approach into an effective exploration targeting

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system”, McCuaig et al. (2010) described a methodology of how to best approach such a translation.

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However, to date, detailed case studies on this subject are very limited in the literature (e.g., Chudasama

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et al., 2018). The first theme of this VSI, entitled “Translating the expressions of ore-forming processes

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and critical parameters of mineral systems into mappable spatial proxies”, is aimed at alleviating this

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deficiency by presenting a series of contributions that address this subject matter area:

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Skirrow et al. (2019) describe a method of mapping iron oxide copper-gold (IOCG) potential in several regions of Australia adopting a holistic, multi-scale mineral systems approach. The

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authors provide a detailed account of how the components of the IOCG system can be translated

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into mappable criteria and IOCG potential maps can be generated by integrating diverse and

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rich input data sets. The results of the knowledge-driven analyses of IOCG potential not only

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successfully predict the majority of the known IOCG deposits but also highlight possible new

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greenfields plays.

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Ford et al. (2019) present a detailed workflow for translating a mineral system model to

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mappable spatial proxies for mineral potential mapping, using case studies from the southern

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New England Orogen, Australia. These studies serve to illustrate the importance of developing

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a clear understanding of the targeted mineral system and generating high-quality data that

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accurately map the system. Both aspects are prerequisites for producing geologically

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meaningful mineral potential maps.

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Kreuzer et al. (2019) describe an approach to generating new exploration targets at the

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approximately 12 Moz Au Sigma-Lamaque gold mine, Val d’Or district, Quebec. The case

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study presented in this article illustrates how a predictive model can be generated by translating

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components of a mineral system into an exploration targeting model and how spatial proxies of

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ore-forming processes can be recognized and mapped in the available mineral exploration data.

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The work presented in this article is based on the authors’ third prize-winning submission to

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the high-profile Integra Gold Rush Challenge, a global crowdsourced exploration targeting

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challenge.

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DeWolfe et al. (2019) present a new 3D volcanic reconstruction of the giant Kidd Creek

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volcanogenic massive sulphide (VMS) deposit, Canada, generated by translating complex

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geological data acquired through core logging and underground mapping indicates. The model

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illustrates that the volcanic setting of, and ore-forming environment at, Kidd Creek is much

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larger than previously thought. In addition, the new volcanic reconstruction highlights primary

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volcanic controls on hydrothermal fluid flow, and therefore ore formation, and how these

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features can be translated into mappable spatial proxies that can be used in exploration

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targeting.

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Holden et al. (2019) describe a text mining algorithm, GeoDocA, designed for fast extraction

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of geological knowledge from open-file mineral exploration reports. The results of the study

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demonstrate the effectiveness of GeoDocA, supporting fast, automated analysis of large text

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repositories, such as exploration reports, to quickly locate and extract information about the

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targeted mineral systems and associated geological environments in particular areas of interest.

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3.2. Identifying mineral deposit footprints through geochemical and geophysical data analysis

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Exploration geochemical data are a powerful, cost-effective and scalable exploration tool that has been

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widely applied by mineral explorers and government organisations alike (Agnew, 2004; Cohen et al.,

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2010). Despite a multitude of caveats relating to, for example, the sample media (e.g., stream sediments,

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soils, till, rock chips), sampling methods and analytical techniques (Grunsky and de Caritat, 2019),

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geochemical datasets are invaluable with respect to: (1) Better constraining lithological units and

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defining stratigraphy; (2) better understanding whole-rock fertility, hydrothermal fluid pathways and

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potential trap sites; and (3) identifying element dispersion patterns and/or geochemical anomalies and,

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thus, potential exploration targets (Agnew, 2004; Cohen et al., 2007; Grunsky and de Caritat, 2019;

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Brauhart, 2019).

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Geophysical (e.g., gravity, magnetic, electromagnetic and radiometric) data are particularly useful for

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mapping the subsurface and developing a better understanding of the geology and structure of a search

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area, especially where outcrop is poor. Lithostructural (solid) geology interpretations of geophysical

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data serve as indirect targeting tools, highlighting potentially favourable lithologies and structural

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settings such as possible fluid pathways and traps. However, given the right conditions and physical

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rock properties, exploration geophysics have also proven highly effective as direct-detection tools with

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respect to targeting, for example, diamondiferous kimberlite, unconformity-related uranium,

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volcanogenic massive sulphide or IOCG deposits (Ford et al., 2007).

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In short, both tools are crucial in mineral exploration targeting: They offer means to understand and

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map expressions of ore-forming processes and identify ore deposit footprints and signatures. As such,

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it is not surprising that both methods have played significant roles in numerous green- and brownfields

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discoveries, including of many major and giant ore deposits (e.g., Paterson, 1966; Cox and Curtis, 1977;

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Rutter and Esdale, 1985; Crebs, 1996; Carlile et al., 1998; Craven et al., 2000; Sillitoe, 2000; Collins,

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2001; Baker and Waugh, 2005; Bennett et al., 2014; Witherly and Mackee, 2015; Hope and Andersson,

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2016). Given their significance, the second VSI theme entitled “identifying mineral deposit footprints

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through geochemical and geophysical data analysis” focuses on the use of exploration geochemistry

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and geophysics in spatial data analysis and how these methods can aid in better defining mappable

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expressions of ore-forming processes and generating more reliable exploration targeting models:

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Byrne et al. (2019) use geophysical models to illustrate a spatial relationship between magnetic

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susceptibility signatures and wall-rock alteration assemblages at Highland Valley, Canada’s

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largest porphyry copper mining district. Based on their models of magnetic susceptibility data,

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the authors define a geophysical footprint around the porphyry copper deposits that extends 1–

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4 km away from the intrusive centres and correlates with porphyry-related vein and alteration

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domains. Byrne et al. conclude that variability in magnetic susceptibility provides a proxy for

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mapping key ore-forming processes that can be used to target additional porphyry copper

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mineralisation.

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Chen et al. (2019) base their research on the premise that ore-forming processes can result in

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both element enrichment and depletion, and that simultaneous consideration of both processes

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is advantageous in geochemical anomaly modelling. To address the above, the authors adapt

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the cosine similarity measure method, a tool for pattern recognition of ore-related geochemical

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anomalism, to facilitate the simultaneous modelling of both enrichment and depletion of ore-

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related pathfinder elements. The performance of this new approach is tested on porphyry copper

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exploration data from the Manzhouli belt, China, illustrating its efficiency with respect to

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geochemical anomaly recognition and the definition of more reliable geochemical exploration

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targets.

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Zekri et al. (2019) compare the use of joint singular value decomposition and semi-discrete

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decomposition and non-negative matrix factorisation with univariate analysis of raw data, to

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detect multi-element patterns in soils related to geochemical dispersion from Mississippi

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Valley-Type lead-zinc deposits in the Irankuh district of central Iran. The study illustrates that

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matrix decomposition techniques are effective in modelling geochemical patterns associated

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with ore-forming processes and help generate more reliable mineral potential maps.

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Nguyen and Vu (2019) combine a number of approaches, namely the Robust Mahalanobis

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Distance, spatial autocorrelation analysis, and robust statistics, into a new method for

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identifying multivariate geochemical anomalies, the effectiveness of which is illustrated in a

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case study of the Jiurui ore district, China. Here, the spatial variability of the geochemical data

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and their degree of spatial autocorrelation is measured at different scales to identify and model

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both negative and positive ore-related geochemical anomalies. The resulting geochemical

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evidence map can be integrated with other evidence maps for use in MPM.

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Wang et al. (2019) apply a machine learning method known as maximum margin metric

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learning to analysing geochemical exploration data from a polymetallic belt in the southwestern

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Fujian Province, China. An adaptive coherence estimator detector is used to identify and better

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constrain geochemical anomalies. Comparison of the resulting anomaly maps with maps

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derived by other means using statistical analyses tools confirms the superior performance of

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the maximum margin metric learning algorithm, suggesting this method can deliver credible

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results.

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Ghasemzadeh et al. (2019) describe an approach to geochemical data analysis at the district

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scale. The authors evaluate and compare two methods, spatial U-statistic and concentration-

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area fractal modelling, using stream sediment data from the Baft porphyry copper district, Iran,

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as the input. The case study demonstrates that the concentration-area fractal approach more

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effectively decomposes geochemical anomalies and yields a more precise geochemical

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targeting model.

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Gaillard et al. (2020) adopt a lithogeochemical approach to delineating hydrothermal fluid

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pathways in the Malartic district, Québec. Ore-associated pathfinder elements delineate broad

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enrichment patterns around the deposit and are used to understand hydrothermal fluid

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circulation. A statistical approach based on the comparison of the mass balance results with the

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background composition provides robust constraints on the magnitude and extent of the

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lithogeochemical halos. The results of this study demonstrate that whole-rock

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lithogeochemistry can provide a valuable tool with which to define vectors toward gold

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mineralization in a regional exploration context.

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3.3. Targeting and improving the discovery chance of mineral deposits by way of spatial data analysis

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The volume of geoscience and mineral exploration data generated, stored and examined today has

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increased substantially compared to previous decades, and so has data diversity, resulting in greater

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challenges for spatial data analysis and a requirement for more efficient analytical procedures. For

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example, there is a strong need for new approaches to handling and analysing subsurface data as

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collected during drilling programs and geophysical surveys. There is also strong need for new tools

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capable of pre-processing exploration data and translating these data into appropriate formats

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facilitating data analysis and their integration with other relevant data. Geographic information systems

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(GIS) provide an ideal platform for the development and application of such tools, as illustrated, for

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example, by the emergence of GIS-based spatial data mining technology, driven by powerful algorithms

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(e.g., Goyal et al., 2017).

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As discussed by Nykänen et al. (2008a,b), analysing the spatial relationships between mineral deposits

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and exploration data can reveal trends and patterns that may aid in guiding exploration targeting and,

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ultimately, making new discoveries. The third theme of this VSI, entitled “targeting and improving the

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discovery chance of mineral deposits by way of spatial data analysis”, taps into this thematic. It presents

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a series of papers describing cutting-edge approaches to spatial data analysis. The case studies presented

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under this umbrella represent a broad spectrum in that they use both knowledge- and data-driven

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methods (Bonham-Carter, 1994), adopt both supervised and unsupervised approaches and cover both

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2D and 3D realms:

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

Ferrier et al. (2019) describe a novel exploration tool integrating field and ASTER SWIR and

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TIR satellite imagery that provides an enhanced means of resolving surface expressions of

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epithermal-porphyry systems. Alteration zones associated with such systems exposed on the

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island of Lesvos, Greece, were clearly resolved by the remote sensing data and an intimate

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spatial relationship between strongly altered rocks and topographic highs was identified at a

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number of locations. A knowledge-based, integrated MPM approach implemented in this study

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yielded results that closely match the hydrothermal alteration mapped in the field, supporting

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the accuracy of this methodology.

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

Niiranen et al. (2019) describe a knowledge-driven fuzzy logic approach to MPM targeting

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orogenic gold deposits in northern Finland. The modelling is implemented in a stepwise manner

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simulating successive stages of mineral exploration, each marked by a reduction in search space

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and increase in data resolution. The regional scale prospectivity map outlines the central part

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of the Central Lapland Greenstone belt as the most prospective area for orogenic gold in

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northern Finland. The belt scale model identifies several camp sized targets, whilst the camp

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scale model produces prospect size targets. The authors conclude that remodelling of the most

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prospective targets at progressively finer scales is a worthwhile undertaking and delivers

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important new detail even if the input data are the same.

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

Nielsen et al. (2019) describe the development of a 3D mineral potential model for the Tampia gold deposit, Yilgarn Craton, Western Australia, and how this model helped constrain resource

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estimation, better understand grade distribution and continuity, and predict the location of new

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gold mineralisation for future exploration drilling to expand the gold resource at Tampia.

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Results from a closely-spaced infill drilling program undertaken after the modelling, confirmed

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the continuity of the predicted post probability values, suggesting that the mineral potential

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model predicts the location and distribution of gold mineralisation within the area drilled. The

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results were also better and more continuous than predicted by an independent resource

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estimate.

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

Zhang et al. (2019) employ total horizontal derivative, multi-scale edge detection (worms) and

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two-dimensional empirical mode decomposition of district-scale gravity and magnetic data to

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extract geophysical information reflecting deep-seated structures and concealed intrusions in

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the Wulong gold district, Liaodong Peninsula, China. In addition, deposit-scale 3D modelling

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and controlled-source audio-frequency magnetotelluric data are used to identify potential ore-

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controlling structures. The results of this integrated, multi-scale geological and geophysical

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study suggest that both the Early Cretaceous intrusions and gold deposits are spatially and

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genetically associated with a network of NE-SW- and NW-SE-striking faults.

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

Torppa et al. (2019) present an MPM case study of the Central Lapland Greenstone Belt in

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northern Finland that combines the unsupervised clustering together and fuzzy logic integration

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method. The study also illustrates the benefits of applying empirical (data-driven) methods to

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MPM. The authors argue that increased uptake of empirical computational methods in MPM

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would lead to a more efficient and methodical use of the input data and reduce the subjectivity

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surrounding expert knowledge and judgement.

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

Uchôa et al. (submitted) present a multi-scale, mineral systems-driven approach to MPM,

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using the orogenic gold deposits in the Rio das Velhas Greenstone Belt, Brazil, as a case study.

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Multivariate statistical techniques and the fuzzy logic method are employed to enhance and

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map expressions of the gold mineral system at the province, district and camp scales. A key

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observation is that the gold prospective areas predicted by the models vary according to the

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scale of the analysis with progressively smaller targets identified as data quality and density

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improve from the province to camp scale. A novel factor in the approach is the assessment of

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the targeting criteria and their proxies according to their spatial resolution and the presentation

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in form of multi-scale maps.

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

Ramezanali et al. (2019) propose the application of a hybrid methodology, combining the

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Best-Worst Method and Additive Ratio Assessment approaches to MPM, using vein-type

326

copper mineralisation in the Kuhsiah-e-Urmak district, Iran, as a case study. The results of the

327

proposed hybrid methodology were statistically compared to those obtained through alternative

328

methods, demonstrating the superiority of the newly proposed hybrid approach. Subsequent

329

field investigation of the most prospective targets led to the identification of outcropping copper

330

mineralisation, further corroborating the approach.

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

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Sun et al. (2019) employ several machine learning algorithms, including support vector

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machine, artificial neural networks and random forest, in their approach to MPM of copper

333

skarn systems in the Tongling ore district, eastern China. As indicated by the model

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performance statistics, the random forest model outperforms the other models in terms of

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predictive accuracy and efficiency. That is, the random forest model captures most of the known

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deposits within the smallest prospective tracts. 

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Keykhay-Hosseinpoor et al. (2020) present a machine learning-based approach to MPM

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targeting porphyry copper-gold systems in the Dehsalm district, eastern Iran, integrating the

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outputs of restricted Boltzmann machine and random forest models. The target areas delineated

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in the combined model occupy 4.2% of the study area and contain 82% of the known copper-

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gold occurrences. The most prospective areas coincide with structures that are spatially

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associated with known copper-gold occurrences and, thus, provide clear targets for future

343

exploration. 

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Swain et al. (2019) analyse 23 drill holes from the Gadag goldfield, India, using fractal analysis

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to better constrain possible fluid pathways. The fractal dimension values obtained in this

346

analysis range from 0.014 to 3.000 with the higher values taken to indicate the presence of

347

interconnected fault-fracture networks representing permeable fluid pathways. The lower

348

values are taken to indicate unfavourable fluid pathways. Given the spatial correlation between

349

higher-grade gold ores and structures represented by steep fractal gradients, the results have

350

implications for exploration targeting in less well and unexplored areas. 

351

Sengar et al. (2020) discuss the effectiveness of spectroscopic techniques in identifying

352

hydrothermal alteration zones. The authors use the SWIR bands of ASTER to map phyllic,

353

argillic and propylitic alteration associated with the Mundiyawas-Khera copper deposit, Alwar

354

Basin, India, and corroborate their results with results obtained from petrographic studies and

355

XRD analyses. The integrated approach resulted in a comprehensive remotely sensed

356

hydrothermal alteration map illustrating the spatial distribution of the copper-related

357

hydrothermal alteration zones and has implications for copper exploration in the greater Alwar

358

basin.

359

4. Putting the above into context

360 361

4.1.

362

Hronsky and Kreuzer (2019) emphasise that despite many decades of development, MPM is not yet

363

widely used or accepted by the global mineral exploration industry. According to the authors, practical

364

issues that limit the effectiveness of MPM include the failure (1) of input data to uniformly and

365

objectively represent the search space of interest; (2) to include as input data critical targeting-relevant

366

geoscientific elements not included or easily mapped in the available or obtainable datasets; (3) to

367

appropriately match the scale of input geological data to the prospectivity model applied; (4) to

Applying MPM to exploration targeting: Fundamental practical issues and suggested solutions for the future

10

368

appropriately define the boundaries of the study, both in terms of extent and geological events; and (5)

369

to recognise strong dependency between input layers and misuse of the mineral systems concept. It is

370

considered that these problems are not in principle barriers to the eventual successful deployment of

371

MPM as a targeting technology. However, future approaches to spatial prospectivity modelling need to

372

explicitly address these concerns. It is suggested that the most effective method may be a hybrid of

373

subjective human geological interpretation and objective, machine-based analysis, that captures the best

374

aspects of these alternative approaches; that is, an intelligence amplification (IA) rather than an artificial

375

intelligence (AI) approach.

376 377

4.2.

378

Yousefi et al., (2019) describe how exploration decision-making has become much more complex in

379

the wake of big data, in particular with respect to questions about how to best manage and use the data

380

to obtain information, generate knowledge and gain insight. The authors note that one of the ways in

381

which the mineral exploration industry works with big data is by using GIS. For example, GIS platforms

382

are often used for integration, interrogation and interpretation of diverse geoscience and mineral

383

exploration data with the goal of refining and prioritising known and identifying new targets. In this

384

paper, Yousefi et al. (1) briefly discuss the importance of carefully translating conceptual ore deposit

385

models into effective exploration targeting maps, (2) propose and describe what we term exploration

386

information systems (EIS): a new idea for an information system designed to better integrate the

387

conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral

388

system) with data available to support exploration targeting, and (3) discuss how best to categorise

389

mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. The authors’ vision

390

for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted

391

mineral system are translated and combined into a set of weighted evidence (or proxy) maps

392

automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked

393

exploration targets. Yousefi et al. do not envisage the EIS replacing human input and ingenuity; rather

394

they envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying

395

system in which humans are making use of machines to achieve the best possible results.

396

5. Concluding Remarks

397

This VSI was borne out of the Guest Editors’ desire to showcase and advance the science of MPM and

398

promote greater uptake of MPM by the global mineral exploration industry. One way this could happen

399

is through the discovery of a significant ore deposit as a direct result of MPM. In the meantime, greater

400

dissemination and accessibility of the tools and skills required for MPM may help stimulate broader

401

uptake as may the resolution of the failures highlighted by Hronsky and Kreuzer (2019). These failures

402

are not related to limitations in the MPM algorithms, but rather to some fundamental issues in the typical

403

use of input data and prospectivity maps.

Exploration information systems–A proposal for the future use of GIS in mineral exploration targeting

11

404

A key outcome and direct result of our working on this VSI was the idea of a new information system

405

for mineral exploration targeting designed to better integrate the conceptual mineral deposit model with

406

data available to support exploration targeting. The idea of an exploration information systems (EIS),

407

summarized and discussed in Yousefi et al. (2019), encapsulates all aspects we believe are required for

408

a step change in MPM and the broader field of mineral exploration targeting using GIS. Future

409

implementation of an EIS as a framework for converting data to information, information to knowledge,

410

and knowledge to insight, would facilitate problem-solving in mineral exploration targeting and provide

411

a platform where mineral systems insight can be converted into mappable criteria and the prediction of

412

undiscovered mineral deposits. Such an information system would also help address and alleviate the

413

knowledge gap surrounding ore-forming processes.

12

414

Acknowledgements

415

The Guest Editors thank Franco Pirajno, the Editor-in-Chief of Ore Geology Reviews, for his ongoing

416

support and making this Special Issue possible. In the same vein, we extend our gratitude to Mary

417

Ayyamperumal, the Journal Manager, and Emily Wan, the Special Issues Coordinator, for their

418

unwavering support and assistance. A very warm thank you goes to the authors who responded to our

419

call for papers. In addition, we are extremely grateful to the reviewers listed below (in alphabetical

420

order) for their time, dedication and valuable feedback, which ensured the quality and success of this

421

Special Issue: Maysam Abedi, Peyman Afzal, James Austin, Amin Bassrei, Frank Bierlein, Richard

422

Blewett, Alazar Yosef Billay, Matt Bruce, Antonella Buccianti, Kris Butera, John Carranza, Thais

423

Andressa Carrino, David Clark, Li Zhen Cheng, Alvaro Crosta, Pablo Andrada de Palomera, Pasi Eilu,

424

Moslem Fatehi, Leonardo Feltrin, Arianne Ford, Steve Gardoll, Ignacio Gonzalez-Alvarez, Eric

425

Grunsky, Mark Hannington, Jeff Harris, Shawn Hood, Jon Hronsky, Simon Jowitt, Mark Lindsay, Yue

426

Liu, Anthony Mamuse, Philippa Mason, Václav Metelka, Johann Mitloehner, Ahmad Reza Mokhtari,

427

Giovanni Mongelli, Simon Nielsen, Gregor Alan Partington, Alok Porwal, Anthony Reid, Ronald Reid,

428

Pierre-Simon Ross, Majid Tangestani, Johanna Troppa, Jian Wang, Wenlei Wang, Andy Wilde, Tom

429

Wise, and Renguang Zuo. In addition, we would like to point out here that many of the above reviewed

430

more than one manuscript. Special thanks also go to the anonymous reviewers who dealt with the

431

contributions authored by the Guest Editors.

13

433

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Highlights No applicable

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Graphical abstract Not applicable

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