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In this paper we give an example from the Fortitude North prospect in the Kurnalpi Terrane of the eastern Yilgarn Craton, where the integration of geochemistry with borehole petrophysics can be used to identify lithological units, infer geological processes (e.g., magmatic differentiation, alteration and weathering) and constrain geological and geophysical models of the prospect. We use major and immobile trace element concentrations to discriminate between primary lithologies and identify differentiation trends in the mafic magmatic units. Density and seismic velocity measurements show broad correlation with primary rock types, with unaltered dolerite tending to have higher density and velocity compared to basalts and altered rocks. Magnetic susceptibility measurements are used to corroborate geochemical differentiation trends, specifically magnetite fractionation in dolerite units. A range of gold pathfinder and chalcophile elements are used to identify hydrothermal alteration related to gold mineralisation. Integrating geochemistry and petrophysics at drillhole- and prospect-scales helps to constrain important geological processes and demonstrates how those processes can be interpreted from geophysical data sets., Open-Access Online Publication: May 29, 2023

One of the main tasks in 3D geological modelling 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 examples 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 is 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.

We present two Python libraries (map2loop and map2model) which combine the observations 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 reasonable 3D geological model. At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. This remains true even after recognising that a map is also a model, with all the potential for hidden biases that this model status implies. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. 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 adjacency relationships. This work is being conducted within the Loop Consortium, in which algorithms are being developed that allow automatic deconstruction of a geological map to recover the necessary positional, gradient and topological data as inputs to different 3D geological modelling codes. This automation provides significant advantages: it reduces the time to first prototype models; it clearly separates the primary data from subsets produced from filtering via data reduction and conceptual constraints; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification and Value of Information studies. We use the example of the re-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.

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&ndash, 1.5 s by calculating cross-correlation functions, which are inverted for 3D shear-wave structure at the top-most (0&ndash, 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&ndash, 0.5 km depth and northwest of the Y2 ore-hosting intrusion (hereafter called Y2) at 0&ndash, 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&ndash, 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&ndash, 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.

The cratonisation of Western Australia during the Proterozoic overlapped with several key events in the evolution of Earth. These include global oxidation events and glaciations, as well as the assembly, accretionary growth, and breakup of the supercontinents Columbia and Rodinia, culminating in the assembly of Gondwana. Globally, Proterozoic mineral systems evolved in response to the coupled evolution of the atmosphere, hydrosphere, biosphere and lithosphere. Consequently, mineral deposits form preferentially in certain times, but they also require a favourable tectonic setting. For Western Australia a distinct plate-margin mineralisation trend is associated with Columbia, whereas an intraplate mineralisation trend is associated with Rodinia and Gondwana, each with associated deposit types. We compare the current Proterozoic record of ore deposits in Western Australia to the estimated likelihood of ore-deposit formation. Overall likelihood is estimated with a simple matrix-based approach that considers two components: The “global secular likelihood” and the “tectonic setting likelihood”. This comparative study shows that at least for the studied ore-deposit types, deposits within Western Australia developed at times, and in tectonic settings compatible with global databases. Nevertheless, several deposit types are either absent or poorly-represented relative to the overall likelihood models. Insufficient exploration may partly explain this, but a genuine lack of deposits is also suggested for some deposit types. This may relate either to systemic inadequacies that inhibited ore-deposit formation, or to poor preservation. The systematic understanding on the record of Western Australia helps to understand mineralisation processes within Western Australia and its past connections in Columbia, Rodinia and Gondwana and aids to identify regions of high exploration potential.

International audience; Geodynamic models, geological-geophysical interpretations, and isotope analysis illustrate that there are links between the nickel and gold mineral systems in the Halls Creek Orogen, Western Australia. Whole-rock Nd and 40Ar/39Ar analysis of rocks throughout the region, when compared with the geodynamic models suggest that nickel and gold mineralization in the Halls Creek Orogen can be related to basin development and subsequent basin closure during the convergence of North Australian Craton and Kimberley Craton, respectively. Whole-rock Nd analysis confirmed the input of juvenile melts in the centre of the orogen before the 1835–1805 Ma Halls Creek Orogeny, supporting the upwelling of decompression mantle melts during the basin development. These analyses also revealed the spatial links between nickel and gold mineralization and lithological units with positive εNd values. Spatially the link between these mineral systems appears to be related to the presence of deep-seated shear zones that formed early in the history of the orogen and were later reactivated. The results of geodynamic models, geophysical interpretation, and isotopic analysis are used to understand the critical processes in the gold and nickel mineralization, which are presented by predictor maps. The GIS-based knowledge-driven fuzzy logic method used to integrate the predictor maps and create the prospectivity maps. Herein we show that mafic-ultramafic units prospective for nickel mineralization formed by upwelling of decompression mantle melt during crustal thinning and extension during basin development, and typically consist of the most juvenile magmas in the region. Whereas, gold deposits formed during the compressional regime and basin closure, and are located along a major shear zone separating two terranes. This deep crustal-scale shear zone is implied to be the site of multiple stages of deformation that acted as fluid migration pathways during basin closure and subsequent collision. Another critical element that appears to be related to gold prospectivity is the presence of lithologies with a juvenile signature. In contrast to nickel analyses which are closely related to mafic-ultramafic units, the source component seems less influential when attempting to target orogenic gold deposits.

We propose a methodology for the recovery of lithologies from geological and geophysical modelling results and apply it field data. Our technique relies on classification using self-organizing maps (SOM) 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-regularisation. This prevents the three-dimensional 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 to 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 need 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 sampled by surface geological measurements and which was absent in previous geological models.

This paper investigates how scientific information and expertise was provided to decision-makers for consideration in situations involving risk and uncertainty. Seven case studies from the earth sciences were used as a medium for this exposition: (1) the 2010–2011 Canterbury earthquake sequence in New Zealand, (2) agricultural farming-system development in North West Queensland, (3) operational flood models, (4) natural disaster risk assessment for Tasmania, (5) deep sea mining in New Zealand, (6) 3-D modelling of geological resource deposits, and (7) land-based pollutant loads to Australia’s Great Barrier Reef. Case studies are lead-authored by a diverse range of scientists, based either in universities, industry, or government science agencies, with diverse roles, experiences, and perspectives on the events discussed. The context and mechanisms by which scientific information was obtained, presented to decision-makers, and utilised in decision-making is presented. Sources of scientific uncertainties and how they were communicated to and considered in decision-making processes are discussed. Decisions enacted in each case study are considered in terms of whether they were scientifically informed, aligned with prevailing scientific evidence, considered scientific uncertainty, were informed by models, and were (or were not) precautionary in nature. The roles of other relevant inputs (e.g. political, socioeconomic considerations) in decision-making are also described. Here we demonstrate that scientific evidence may enter decision-making processes through diverse pathways, ranging from direct solicitations by decision-makers to independent requests from stakeholders following media coverage of relevant research. If immediately relevant scientific data cannot be provided with sufficient expediency to meet the demands of decision-makers, decision-makers may (i) seek expert scientific advice and judgement (to assist with decision-making under conditions of high epistemic uncertainty), (ii) delay decision-making (until sufficient evidence is obtained), and/or (iii) provide opportunities for adjustment of decisions as additional information becomes available. If the likelihood of occurrence of potentially adverse future risks is perceived by decision-makers to exceed acceptable thresholds and/or be highly uncertain, precautionary decisions with adaptive capacity may be favoured, even if some scientific evidence suggests lower levels of risk. The efficacy with which relevant scientific data, models, and uncertainties contribute to decision-making may relate to factors including the expediency with which this information can be obtained, the perceived strength and relevance of the information presented, the extent to which relevant experts have participated and collaborated in scientific communications to decision-makers and stakeholders, and the perceived risks to decision-makers of favouring earth science information above other, potentially conflicting, scientific and non-scientific inputs. This paper provides detailed Australian and New Zealand case studies showcasing how science actions and provision pathways contribute to decision-making processes. We outline key learnings from these case studies and encourage more empirical evidence through documented examples to help guide decision-making practices in the future.

This paper synthesizes important elements from case studies presented in its companion paper (Quigley et al. in Environ Syst Decis, 2019, https://doi.org/10.1007/s10669-019-09728-0) to define mutual and distinct characteristics, and to develop a more holistic understanding of how earth science was used to support diverse examples of decision-making. We identify a suite of 28 different science actions used within the case studies that are classified as pertaining to (i) evidence acquisition and analysis, (ii) provision of science to target audience, or (iii) enhancing future science provision and utility. Sample action pathways provide empirically evidenced, albeit simplified, examples of how scientists may contribute to the progression of science through complex decision-making frameworks. Decision trees with multiple scientific and non-scientific inputs are presented based on empirical evidence and theory to provide scientists and decision-makers with simplified examples of complex multi-step decision-making processes under conditions of risk and uncertainty. Evidence for nonlinear engagement between decision-makers and science providers is presented, including non-traditional approaches such as provision of unsolicited science through the media and stakeholders. Examples of scientifically informed, precautionary decision-making with adaptive capacity, even where economically favourable decision alternatives exist, are provided. We undertake a self-elicitation exercise of case studies to derive values and uncertainties for % scientific agreement amongst utilized inputs and % uptake of potentially relevant and available science. We observe a tendency towards increased scientific uptake with increasing scientific agreement, but this is not ubiquitous; politically affected decisions and/or complex multi-decision scenarios under time pressure complicate this relationship. An increasing need for decision-making expediency that is not met by increased availability of relevant science evidence may rely on expert judgement, based on incomplete knowledge that is manifested as large uncertainties in defining a singular value for scientific agreement and uptake. We encourage scientists to further document their experiences using the science-action classification scheme provided herein to stimulate further comparative analyses of this nature.

This paper proposes and demonstrates improvements for the Monte Carlo simulation for Uncertainty Estimation (MCUE) method. MCUE is a type of Bayesian Monte Carlo aimed at input data uncertainty propagation in implicit 3D geological modeling. In the Monte Carlo process, a series of statistically plausible models are built from the input data set which uncertainty is to be propagated to a final probabilistic geological model (PGM) or uncertainty index model (UIM). Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUE. 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 arise from the effect of the geometrical ruleset on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces geological incompatibility 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 MCUE is introduced. Boolean topological signatures recording lithological units’ adjacency are used as n-dimensional points to be considered individually or clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples 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 PGMs and UIMs.

International audience; Existing 3D geological systems are well adapted to high data-density environments, such as at the mine scale where abundant drill core exists, or in basins where 3D seismic provides stratigraphic constraints, but are poorly adapted to regional geological problems. There are three areas where improvements in the 3D workflow need to be made: in the handling of uncertainty; in the model building algorithms themselves; and in the interface with geophysical inversion. All 3D models are under-constrained, and at the regional scale this is especially critical for choosing modelling strategies. The practice of only producing a single model ignores the huge uncertainties that underlie model-building processes, and underpins the difficulty in providing meaningful information to end-users about the inh erent risk involved in applying the model to solve geological problems. Future studies need to recognize this and focus on the characterization of model uncertainty, spatially and in terms of geological features, and produce plausible model suites, rather than single models with unknown validity.

SEG Conference on Keystone - Building Exploration Capability for the 21st Century, Keystone, CO, SEP 27-30, 2014; International audience; Existing three-dimensional (3-D) geologic systems are well adapted to high data-density environments, such as at the mine scale where abundant drill core exists, or in basins where 3-D seismic provides stratigraphic constraints but are poorly adapted to regional geologic problems. There are three areas where improvements in the 3-D workflow need to be made: (1) the handling of uncertainty, (2) the model-building algorithms themselves, and (3) the interface with geophysical inversion. All 3-D models are underconstrained, and at the regional scale this is especially critical for choosing modeling strategies. The practice of only producing a single model ignores the huge uncertainties that underlie model-building processes, and underpins the difficulty in providing meaningful information to end-users about the inherent risk involved in applying the model to solve geologic problems. Future studies need to recognize this and focus on the characterization of model uncertainty, spatially and in terms of geologic features, and produce plausible model suites, rather than single models with unknown validity. The most promising systems for understanding uncertainty use implicit algorithms because they allow the inclusion of some geologic knowledge, for example, age relationships of faults and onlap-offlap relationships. Unfortunately, existing implicit algorithms belie their origins as basin or mine modeling systems because they lack inclusion of normal structural criteria, such as cleavages, lineations, and recognition of polydeformation, all of which are primary tools for the field geologist that is making geologic maps in structurally complex areas. One area of future research will be to establish generalized structural geologic rules that can be built into the modeling process. Finally, and this probably represents the biggest challenge, there is the need for geologic meaning to be maintained during the model-building processes. Current data flows consist of the construction of complex 3-D geologic models that incorporate geologic and geophysical data as well as the prior experience of the modeler, via their interpretation choices. These inputs are used to create a geometric model, which is then transformed into a petrophysical model prior to geophysical inversion. All of the underlying geologic rules are then ignored during the geophysical inversion process. Examples exist that demonstrate that the loss of geologic meaning between geologic and geophysical modeling can be at least partially overcome by increased use of uncertainty characteristics in the workflow.

International audience; We solve the 3-D gravity inverse problem using a massively parallel voxel (or finite element) implementation on a hybrid multi-CPU/multi-GPU (graphics processing units/GPUs) cluster. This allows us to obtain information on density distributions in heterogeneous media with an efficient computational time. In a new software package called TOMOFAST3D, the inversion is solved with an iterative least-square or a gradient technique, which minimizes a hybrid L1-/L2-norm-based misfit function. It is drastically accelerated using either Haar or fourth-order Daubechies wavelet compression operators, which are applied to the sensitivity matrix kernels involved in the misfit minimization. The compression process behaves like a pre-conditioning of the huge linear system to be solved and a reduction of two or three orders of magnitude of the computational time can be obtained for a given number of CPU processor cores. The memory storage required is also significantly reduced by a similar factor. Finally, we show how this CPU parallel inversion code can be accelerated further by a factor between 3.5 and 10 using GPU computing. Performance levels are given for an application to Ghana, and physical information obtained after 3-D inversion using a sensitivity matrix with around 5.37 trillion elements is discussed. Using compression the whole inversion process can last from a few minutes to less than an hour for a given number of processor cores instead of tens of hours for a similar number of processor cores when compression is not used.

Several stochastic approaches have recently been developed to evaluate uncertainties in 3-D geological models based on imprecise geological data. Multiple models are generated through random changes of input parameters, according to defined probability distributions. This leads to a new problem: randomly generated realizations might be topologically different or they might be in contrast to expected geological constraints or settings. We present here a method to enable automatic checks of models based on reliability filters encapsulating aspects of geological knowledge. We apply those filters to a model of a graben structure and obtain an ensemble of acceptable realizations, representing the range of uncertainty, but preserving expected geological features.