Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future

Jon M. A. Hronsky, Oliver P. Kreuzer

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence.

It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.

Original languageEnglish
Pages (from-to)647-653
Number of pages7
JournalOre Geology Reviews
Volume107
DOIs
Publication statusPublished - Apr 2019

Cite this

@article{7713df80f40f4d44ab36c2ac3447e5c2,
title = "Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future",
abstract = "Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence.It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.",
keywords = "Mineral prospectivity modelling, Exploration targeting, Artificial intelligence, Intelligence amplification, NORTH-CENTRAL NEVADA, MINERAL SYSTEMS, FOLD BELT, GOLD, DEPOSITS, MODELS, RISK, GIS",
author = "Hronsky, {Jon M. A.} and Kreuzer, {Oliver P.}",
year = "2019",
month = "4",
doi = "10.1016/j.oregeorev.2019.03.016",
language = "English",
volume = "107",
pages = "647--653",
journal = "Ore Geology Reviews",
issn = "0169-1368",
publisher = "Pergamon",

}

TY - JOUR

T1 - Applying spatial prospectivity mapping to exploration targeting

T2 - Fundamental practical issues and suggested solutions for the future

AU - Hronsky, Jon M. A.

AU - Kreuzer, Oliver P.

PY - 2019/4

Y1 - 2019/4

N2 - Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence.It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.

AB - Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence.It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.

KW - Mineral prospectivity modelling

KW - Exploration targeting

KW - Artificial intelligence

KW - Intelligence amplification

KW - NORTH-CENTRAL NEVADA

KW - MINERAL SYSTEMS

KW - FOLD BELT

KW - GOLD

KW - DEPOSITS

KW - MODELS

KW - RISK

KW - GIS

U2 - 10.1016/j.oregeorev.2019.03.016

DO - 10.1016/j.oregeorev.2019.03.016

M3 - Article

VL - 107

SP - 647

EP - 653

JO - Ore Geology Reviews

JF - Ore Geology Reviews

SN - 0169-1368

ER -