ORCID Profile
0000-0003-2614-2069
Current Organisations
University of Western Australia
,
CSIRO
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Geology | Geophysics not elsewhere classified | Stochastic Analysis and Modelling | Structural Geology | Tectonics | Geophysics | Statistics | Basin Analysis | Decision Support and Group Support Systems | Knowledge Representation and Machine Learning | Geodynamics | Applied Statistics | Artificial Intelligence and Image Processing not elsewhere classified | Natural Resource Management | Geology not elsewhere classified |
ANZSRC Socio-Economic Objective (SEO)
Mineral Exploration not elsewhere classified | Primary Mining and Extraction of Mineral Resources not elsewhere classified | Expanding Knowledge in the Earth Sciences | Environmental Management Systems | Service Industries Standards and Calibrations | Energy Exploration not elsewhere classified | Environmental and Natural Resource Evaluation not elsewhere classified | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Copper Ore Exploration
Related Links
Publications
Inversion and Geodiversity: Searching Model Space for the Answers
Publisher: Springer Science and Business Media LLC
Date: 17-05-2014
Proterozoic accretionary tectonics in the east Kimberley region, Australia
Publisher: Elsevier BV
Date: 06-2016
Improving assessment of geological structure interpretation of magnetic data: An advanced data analytics approach
Publisher: Elsevier BV
Date: 02-2016
The topology of geology 2: Topological uncertainty
Publisher: Elsevier BV
Date: 10-2016
Rift structures and magmatism focus VMS and gold mineralisation in the Paleoproterozoic Bryah Rift Basin, Australia
Publisher: Elsevier BV
Date: 08-2021
Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization
Publisher: Copernicus GmbH
Date: 06-04-2018
Abstract: Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which s les from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, s le analysis and statistical consistency of the s led distribution. Pole vector s ling is proposed as a more rigorous alternative than dip vector s ling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.
The provision and utility of earth science to decision-makers: synthesis and key findings
Publisher: Springer Science and Business Media LLC
Date: 05-07-2019
Locating and quantifying geological uncertainty in three-dimensional models: Analysis of the Gippsland Basin, southeastern Australia
Publisher: Elsevier BV
Date: 06-2012
Hf isotopic fingerprinting of geodynamic settings
Publisher: Elsevier BV
Date: 09-2019
Paleoproterozoic basin development on the northern Yilgarn Craton, Western Australia
Publisher: Elsevier BV
Date: 10-2017
Mineral systems prospectivity modelling for gold and nickel in the Halls Creek Orogen, Western Australia
Publisher: Elsevier BV
Date: 12-2020
Gravity inversion using wavelet-based compression on parallel hybrid CPU/GPU systems: application to southwest Ghana
Publisher: Oxford University Press (OUP)
Date: 18-09-2013
DOI: 10.1093/GJI/GGT334
pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling
Publisher: Copernicus GmbH
Date: 10-03-2016
Abstract: Abstract. We present a novel methodology for performing experiments with subsurface structural models using a set of flexible and extensible Python modules. We utilize the ability of kinematic modelling techniques to describe major deformational, tectonic, and magmatic events at low computational cost to develop experiments testing the interactions between multiple kinematic events, effect of uncertainty regarding event timing, and kinematic properties. These tests are simple to implement and perform, as they are automated within the Python scripting language, allowing the encapsulation of entire kinematic experiments within high-level class definitions and fully reproducible results. In addition, we provide a link to geophysical potential-field simulations to evaluate the effect of parameter uncertainties on maps of gravity and magnetics. We provide relevant fundamental information on kinematic modelling and our implementation, and showcase the application of our novel methods to investigate the interaction of multiple tectonic events on a pre-defined stratigraphy, the effect of changing kinematic parameters on simulated geophysical potential fields, and the distribution of uncertain areas in a full 3-D kinematic model, based on estimated uncertainties in kinematic input parameters. Additional possibilities for linking kinematic modelling to subsequent process simulations are discussed, as well as additional aspects of future research. Our modules are freely available on github, including documentation and tutorial ex les, and we encourage the contribution to this project.
Reducing subjectivity in multi-commodity mineral prospectivity analyses: Modelling the west Kimberley, Australia
Publisher: Elsevier BV
Date: 07-2016
Multicommodity mineral systems analysis highlighting mineral prospectivity in the Halls Creek Orogen
Publisher: Elsevier BV
Date: 2016
Magma evolution in the Halls Creek Orogen; insight from geodynamic numerical modelling and geochemical analysis
Publisher: Informa UK Limited
Date: 12-2018
Mapping structural complexity using geophysics: A new geostatistical approach applied to greenstone belts of the southern Superior Province, Canada
Publisher: Elsevier BV
Date: 08-2021
Data fusion and porphyry copper prospectivity models, southeastern Arizona
Publisher: Elsevier BV
Date: 09-2014
Geodiversity: Exploration of 3D geological model space
Publisher: Elsevier BV
Date: 05-2013
The tectonics and mineral systems of Proterozoic Western Australia
Publisher: Elsevier BV
Date: 03-2018
Mapping undercover
Publisher: Copernicus GmbH
Date: 24-06-2020
Abstract: Abstract. Gravity and 3D modelling combined with geochemical analysis examine the subsurface within and below the poorly exposed Palaeoproterozoic Yerrida Basin in central Western Australia. Understanding the structure of a region is important as key features indicating past geodynamic processes and tectonic activity can be revealed. However, in stable, post-depositional tectonic settings only the younger sedimentary units tend to be widely exposed, rendering direct observation of basement and intrusive rocks impossible. Geophysical imaging and modelling can reveal the structure of a region undercover. High-magnitude density anomalies around the basin cannot be reconciled with current geological knowledge in the case presented here. The gravity anomalies infer an abundance of buried and high-density material not indicated by the surface geology. A hypothetical causative source for the high-magnitude gravity anomalies is mafic rocks that were intruded and extruded during basin rifting. The simplest and plausible stratigraphic attribution of these interpreted mafic rocks is to the Killara Formation within the Mooloogool Group. However, geochemistry reveals that the Killara Formation is not the only host to mafic rocks within the region. The mafic rocks present in the Juderina Formation are largely ignored in descriptions of Yerrida Basin magmatism, and results indicate that they may be far more substantial than once thought. Sulfur isotopic data indicate no Archean signature to these mafic rocks, a somewhat surprising result given the basement to the basin is the Archean Yilgarn Craton. We propose the source of mafic rocks is vents located to the north along the Goodin Fault or under the Bryah sub-basin and Padbury Basin. The conclusion is that the formation of the Yerrida Basin involves a geodynamic history more complex than previously thought. This result highlights the value in geophysics and geochemistry in revealing the complexity of the earlier geodynamic evolution of the basin that may be indiscernible from surface geology but may have high importance for the tectonic development of the region and its mineral resources.
Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion
Publisher: Society of Exploration Geophysicists
Date: 11-2017
Abstract: We have developed a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geologic data. In this framework, probabilistic geologic modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then uses petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-squares framework to capture and integrate information related to geophysical, petrophysical, and geologic data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geologic field data, we have generated a set of geologic models, which we used to derive a probabilistic geologic model. In this synthetic case study, we found that the integration of geologic information and petrophysical constraints in geophysical joint inversion could reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geologic modeling permits more accurate retrieval of model geometry, and it better constrains the solution while still satisfying the statistics derived from geologic data. The analysis of statistical indicators at each step of the workflow indicates that (1) the inversion methodology is effective when applied to complex geology and (2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Finally, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also indicates that, for this ex le, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geologic modeling.
Extensional episodes in the Paleoproterozoic Capricorn Orogen, Western Australia, revealed by petrogenesis and geochronology of mafic–ultramafic rocks
Publisher: Elsevier BV
Date: 03-2018
The evolution from plate margin to intraplate mineral systems in the Capricorn Orogen, links to prospectivity
Publisher: Elsevier BV
Date: 12-2020
Examining tectonic scenarios using geodynamic numerical modelling: Halls Creek Orogen, Australia
Publisher: Elsevier BV
Date: 06-2017
Visual saliency and potential field data enhancements:Where is your attention drawn?
Publisher: Society of Exploration Geophysicists
Date: 11-2014
Abstract: Interpretation of gravity and magnetic data for exploration applications may be based on pattern recognition in which geophysical signatures of geologic features associated with localized characteristics are sought within data. A crucial control on what comprises noticeable and comparable characteristics in a data set is how images displaying those data are enhanced. Interpreters are provided with various image enhancement and display tools to assist their interpretation, although the effectiveness of these tools to improve geologic feature detection is difficult to measure. We addressed this challenge by analyzing how image enhancement methods impact the interpreter’s visual attention when interpreting the data because features that are more salient to the human visual system are more likely to be noticed. We used geologic target-spotting exercises within images generated from magnetic data to assess commonly used magnetic data visualization methods for their visual saliency. Our aim was achieved in two stages. In the first stage, we identified a suitable saliency detection algorithm that can computationally predict visual attention of magnetic data interpreters. The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best matches the interpreter’s data observation patterns for magnetic target-spotting exercises. In the second stage, we applied this saliency detection algorithm to understand potential visual biases for commonly used magnetic data enhancement methods. We developed a guide to choosing image enhancement methods, based on saliency maps that minimize unintended visual biases in magnetic data interpretation, and some recommendations for identifying exploration targets in different types of magnetic data.
Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis
Publisher: Oxford University Press (OUP)
Date: 27-03-2019
DOI: 10.1093/GJI/GGZ152
Drillhole uncertainty propagation for three-dimensional geological modeling using Monte Carlo
Publisher: Elsevier BV
Date: 11-2018
Imaging Karatungk Cu-Ni Mine in Xinjiang, Western China with a Passive Seismic Array
Publisher: MDPI AG
Date: 07-2020
DOI: 10.3390/MIN10070601
Abstract: Karatungk Mine is the second-largest Cu-Ni sulfide mine in China. However, the detailed structure beneath the mine remains unclear. Using continuous waveforms recorded by a dense temporary seismic array, here we apply ambient noise tomography to study the shallow crustal structure of Karatungk Mine down to ~1.3 km depth. We obtain surface-wave dispersions at 0.1–1.5 s by calculating cross-correlation functions, which are inverted for 3D shear-wave structure at the top-most (0–1.3 km) crust by a joint inversion of group and phase dispersions. Our results show that low-velocity zones beneath Y1 ore-hosting intrusion (hereafter called Y1) at 0–0.5 km depth and northwest of the Y2 ore-hosting intrusion (hereafter called Y2) at 0–0.6 km depth are consistent with highly mineralized areas. A relatively high-velocity zone is connected with a weakly mineralized area located to the southeast of Y2 and Y3 (hereafter called Y3) ore-hosting intrusions. Two high-velocity zones, distributed at 0.7–1.3 km depth in the northernmost and southernmost parts of the study area respectively, are interpreted to be igneous rocks related to early magma intrusion. Furthermore, the low-velocity zone at 0.7–1.3 km depth in the middle of the study area may be related to: a possible channel related to initial magma transport mine strata or a potentially mineralized area. This study demonstrates a new application of dense-array ambient noise tomography to a mining area that may guide future studies of mineralized regions.
Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton
Publisher: Copernicus GmbH
Date: 22-10-2021
Abstract: Abstract. One of the main tasks in 3D geological modeling is the boundary parametrization of the subsurface from geological observations and geophysical inversions. Several approaches have been developed for geometric inversion and joint inversion of geophysical datasets. However, the robust, quantitative integration of models and datasets with different spatial coverage, resolution, and levels of sparsity remains challenging. One promising approach for recovering the boundary of the geological units is the utilization of a level set inversion method with potential field data. We focus on constraining 3D geometric gravity inversion with sparse lower-uncertainty information from a 2D seismic section. We use a level set approach to recover the geometry of geological bodies using two synthetic ex les and data from the geologically complex Yamarna Terrane (Yilgarn Craton, Western Australia). In this study, a 2D seismic section has been used for constraining the location of rock unit boundaries being solved during the 3D gravity geometric inversion. The proposed work is the first we know of that automates the process of adding spatially distributed constraints to the 3D level set inversion. In many hard-rock geoscientific investigations, seismic data are sparse, and our results indicate that unit boundaries from gravity inversion can be much better constrained with seismic information even though they are sparsely distributed within the model. Thus, we conclude that it has the potential to bring the state of the art a step further towards building a 3D geological model incorporating several sources of information in similar regions of investigation.
Structural controls on proterozoic nickel and gold mineral systems identified from geodynamic modelling and geophysical interpretation, east Kimberley, Western Australia
Publisher: Elsevier BV
Date: 04-2018
Integrated geophysical joint inversion using petrophysical constraints and geological modelling
Publisher: Society of Exploration Geophysicists
Date: 09-2016
Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China
Publisher: Elsevier BV
Date: 05-2020
Atmospheric sulfur is recycled to the crystalline continental crust during supercontinent formation
Publisher: Springer Science and Business Media LLC
Date: 22-10-2018
DOI: 10.1038/S41467-018-06691-3
Abstract: The sulfur cycle across the lithosphere and the role of this volatile element in the metasomatism of the mantle at ancient cratonic boundaries are poorly constrained. We address these knowledge gaps by tracking the journey of sulfur in the assembly of a Proterozoic supercontinent using mass independent isotope fractionation (MIF-S) as an indelible tracer. MIF-S is a signature that was imparted to supracrustal sulfur reservoirs before the ~2.4 Ga Great Oxidation Event. The spatial representation of multiple sulfur isotope data indicates that successive Proterozoic granitoid suites preserve Δ 33 S up to +0.8‰ in areas adjacent to Archean cratons. These results indicate that suturing of cratons began with devolatilisation of slab-derived sediments deep in the lithosphere. This process transferred atmospheric sulfur to a mantle source reservoir, which was tapped intermittently for over 300 million years of magmatism. Our work tracks pathways and storage of sulfur in the lithosphere at craton margins.
Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging
Publisher: Copernicus GmbH
Date: 31-03-2020
Abstract: Abstract. We propose a methodology for the recovery of lithologies from geological and geophysical modelling results and apply it to field data. Our technique relies on classification using self-organizing maps (SOMs) paired with geoscientific consistency checks and uncertainty analysis. In the procedure we develop, the SOM is trained using prior geological information in the form of geological uncertainty, the expected spatial distribution of petrophysical properties and constrained geophysical inversion results. We ensure local geological plausibility in the lithological model recovered from classification by enforcing basic topological rules through a process called “post-regularization”. This prevents the three-dimensional recovered lithological model from violating elementary geological principles while maintaining geophysical consistency. Interpretation of the resulting lithologies is complemented by the estimation of the uncertainty associated with the different nodes of the trained SOM. The application case we investigate uses data and models from the Yerrida Basin (Western Australia). Our results generally corroborate previous models of the region but they also suggest that the structural setting in some areas needs to be updated. In particular, our results suggest the thinning of one of the greenstone belts in the area may be related to a deep structure not s led by surface geological measurements and which was absent in previous geological models.
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
Publisher: Copernicus GmbH
Date: 16-08-2021
Abstract: Abstract. At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. We present two libraries (map2loop and map2model) that automatically combine the information available in digital geological maps with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a prototype 3D geological model. The information stored in a map falls into three categories of geometric data: positional data, such as the position of faults, intrusive, and stratigraphic contacts gradient data, such as the dips of contacts or faults and topological data, such as the age relationships of faults and stratigraphic units or their spatial adjacency relationships. This automation provides significant advantages: it reduces the time to first prototype models it clearly separates the data, concepts, and interpretations and provides a homogenous pathway to sensitivity analysis, uncertainty quantification, and value of information studies that require stochastic simulations, and thus the automation of the 3D modelling workflow from data extraction through to model construction. We use the ex le of the folded and faulted Hamersley Basin in Western Australia to demonstrate a complete workflow from data extraction to 3D modelling using two different open-source 3D modelling engines: GemPy and LoopStructural.
Topological analysis in Monte Carlo simulation for uncertainty propagation
Publisher: Copernicus GmbH
Date: 10-10-2019
Abstract: Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling. In the Monte Carlo process, a series of statistically plausible models is built from the input dataset of which uncertainty is to be propagated to a final probabilistic geological model or uncertainty index model. Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUP. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets' variability and plausible model's variability. Non-linearity is shown to mainly arise from the effect of the geometrical rule set on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces topological heterogeneity and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUP is introduced. Boolean topological signatures recording lithological unit adjacency are used as n-dimensional points to be considered in idually or clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic ex les with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the probabilistic geological models and uncertainty index models. Highlights. Monte Carlo uncertainty propagation (MCUP) methods often produce topologically distinct plausible models. Plausible models can be differentiated using topological signatures. Topologically similar probabilistic geological models may be obtained through topological signature clustering.
The provision and utility of science and uncertainty to decision-makers: earth science case studies
Publisher: Springer Science and Business Media LLC
Date: 05-04-2019
The topology of geology 1: Topological analysis
Publisher: Elsevier BV
Date: 10-2016
Validating 3-D Structural Models with Geological Knowledge for Improved Uncertainty Evaluations
Publisher: Elsevier BV
Date: 2014
A role for data richness mapping in exploration decision making
Publisher: Elsevier BV
Date: 08-2018
Sedimentary basins reduce stability of Antarctic ice streams through groundwater feedbacks
Publisher: Springer Science and Business Media LLC
Date: 28-07-2022
3D modeling of the Ashanti Belt, southwest Ghana: Evidence for a litho-stratigraphic control on gold occurrences within the Birimian Sefwi Group
Publisher: Elsevier BV
Date: 12-2014
Identifying mineral prospectivity using 3D magnetotelluric, potential field and geological data in the east Kimberley, Australia
Publisher: Geological Society of London
Date: 08-10-2018
DOI: 10.1144/SP453.8
A multi-disciplinary approach for defining nickel and gold mineral systems; Halls Creek Orogen, Western Australia
Publisher: Informa UK Limited
Date: 11-11-2019
Making the link between geological and geophysical uncertainty: geodiversity in the Ashanti Greenstone Belt
Publisher: Oxford University Press (OUP)
Date: 03-09-2013
DOI: 10.1093/GJI/GGT311
An irregular triangle mesh buffer analysis method for boundary representation geological object in three-dimension
Publisher: Springer Science and Business Media LLC
Date: 14-12-2016
Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization
Publisher: Copernicus GmbH
Date: 17-10-2017
DOI: 10.5194/SE-2017-115
Abstract: Abstract. Three-dimensional (3D) geological modeling aims to determine geological information in a 3D space using structural data (foliations and interfaces) and topological rules as inputs. They are necessary in any project where the properties of the subsurface matters, they express our understanding of geometries in depth. For that reason, 3D geological models have a wide range of practical applications including but not restrained to civil engineering, oil and gas industry, mining industry and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator’s parameterization) combined with input uncertainty (observational-, conceptual- and technical errors). Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide accurate estimates of uncertainty. This paper’s focus is set on the effect of structural input data uncertainty propagation in implicit 3D geological modeling using GeoModeller API. This aim is achieved using Monte Carlo simulation uncertainty estimation (MCUE), a heuristic stochastic method which s les from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, s le analysis and statistical consistency of the s led distribution. Pole vector s ling is proposed as a more rigorous alternative than dip vector s ling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of inappropriate disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e. scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.
Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling
Publisher: Elsevier BV
Date: 07-2019
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Commonwealth Scientific And Industrial Research Organisation
Location: Australia
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