Abstract In this article we propose a workflow for creating geologically realistic depth to basement maps and apply it to the undercover area of Cloncurry, located in the Mount Isa province, Queensland. A magnetotelluric (MT) survey consisting of 457 soundings was inverted using a 1D trans‐dimensional Bayesian algorithm designed to be robust to non‐1D effects present in the data. Automated change‐point analysis was then used to derive probability distributions on basement depth for each site independently. These were laterally combined, along with drill hole data and a structural model derived from aeromagnetic and geological data, using a Bayesian estimate fusion algorithm to create a region wide depth to basement probabilistic map. Combining many different constraints in this way lead to a significant reduction in posterior uncertainty. Individual MT site change‐point posteriors were highly multi‐modal in certain areas, with multiple resistivity transitions that could correspond to the cover‐basement interface. The estimate fusion process correlates these uncertainties and the combined posterior was thus much less multi‐modal. Our results show that the sedimentary cover gradually thickens toward the north, while toward the east its thickening is controlled by a two‐steps fault system. This workflow highlights the value gained from integrating different types of geoscientific data but also shows the capability of the MT method used within a probabilistic workflow to accurately image depth to basement even using limited constraints and assumptions.
Many suspected historical Aboriginal gravesites are through-out southwestern Western Australia, where individual burial locations remain unknown. Because of the cultural and historical importance of these heritage sites, there is an increasing effort to identify such locations to enable better site preservation and appropriate commemoration. Gravesite reconnaissance routinely requires the investigation of large areas, and it is inefficient and disrespectful to excavate large volumes in search of burials. Geophysical remote sensing offers a noninvasive alternative for identifying and delineating gravesites. To these ends, the University of Western Australia (UWA) Society of Exploration Geophysicists (SEG) Student Chapter has been working with the South West Aboriginal Land and Sea Council (SWALSC) in a pilot study to acquire several near-surface geophysical surveys at two historically documented Aboriginal gravesites in Quairading and Toodyay, Western Australia. The resulting ground-penetrating radar (GPR), magnetics, conductivity mapping, and resistivity profiling data sets identify subsurface anomalies that are consistent in location with historical documentation and elders’ recollections and with the expected geophysical signatures of historical burials and early development activities. The geophysical survey results have been relayed to the SWALSC and community decision makers to assist in upcoming preservation and commemoration efforts. The pilot study could be helpful in identifying other Aboriginal gravesites throughout Western Australia and would represent a positive step toward resolving key longstanding issues among different community interests surrounding the uncertainties of some sites of suspected Aboriginal heritage.
Enhancement of potential field datasets using operators based on one or more of the spatial derivatives is common practice. The performance of these methods in the presence of noise is poorly understood; other than a general acceptance that they can be significantly affected, especially when higher order derivatives are used. Most published descriptions which involve noise tests use random noise and a dense and uniform sampling of the test region. More realistic tests of the effects of noise should account for the incomplete and anisotropic sampling within most datasets and also correlated noise such as due to incorrect levelling. An understanding of the effects of noise on the different methods of enhancement is particularly important when working with lower quality (older) and lower resolution datasets.Interpretation of geophysical data from West Africa, as part of a major project on the prospectivity of the region, is being undertaken. Much of the data available is of relatively low quality and resolution. An important component of the work will involve determining how best to enhance the gravity and magnetic datasets. Initial results working on gridded data show that the "generalized derivative operator" is the most robust derivative based enhanced product for low resolution data.
The Beetaloo Sub-basin is known for its vast unconventional hydrocarbon resources even though it is relatively underexplored. There is reasonably good coverage of 2D seismic within the sub-basin which is used as the basis for most structural interpretations. However, seismic quality varies, and it is occasionally deteriorated by the presence of basalts from the Kalkarindji suite and the karstic nature of the Gum Ridge formation. Aeromagnetic data, constrained by petrophysical logs are used, to map faults in the basalts of the Kalkarindji suite and their lateral extent to the South and the East of the sub-basin. The same structural elements are identified in the full tensor gravity gradiometry data. The top of this unit is observed in the electrical conductivity profiles, derived from Tempest data, in the NW part of the eastern sub-basin.
<p>Bayesian posterior sampling is a flexible and general purpose method that can be used to quantify uncertainty in geophysical inversion results. It produces large ensembles of plausible subsurface models consistent with the data and some spatial prior. Unfortunately, it is computationally expensive and becomes impractical for high-dimensional models. This problem is exacerbated by the challenges of joint inversion using data from different geophysical methods, which may be sensitive to different petrophysical properties at different resolutions. To speed up and simplify both implementation and application, we introduce Bayesian spatial ensemble fusion.</p><p>The method is demonstrated here using airborne electromagnetic (both VTEM and Tempest) and magnetotelluric data from Cloncurry in the Mount Isa province of Queensland, Australia. 1D transdimensional inversion is applied to individual sites to quantify uncertainty locally, which produces ensembles of 1D layered resistivity models with variable numbers of layers. These local ensembles are then fused together to produce ensembles of more complex 2D models as an approximation to what laterally constrained probabilistic joint ensemble inversion would have produced.</p><p>There are several benefits to this approach: Different and existing software can be used by different specialists to create the input ensembles, which reduces the need for complex coordination and simplifies coding. Forward calculations are performed once and then stored to be recycled in many subsequent fusions. Many inversions of the same data, or different combinations thereof, can then be performed using different priors, constraints and geological interpretations, at very little additional cost. Thorough exploratory uncertainty analysis is thus made more practical as specialists can elicit and test different interpretations more quickly.</p>
SUMMARY Thickness of cover over crystalline basement is an important consideration for mineral exploration in covered regions. It can be estimated from a variety of geophysical data types using a variety of inference methods. A robust method for combining such estimates to map the cover–basement interface over a region of interest is needed. Due to the large uncertainties involved, these need to be probabilistic maps. Predominantly, interpolation methods are used for this purpose, but these are built on simplifying assumptions about the inputs which are often inappropriate. The Bayesian estimate fusion is an alternative capable of addressing that issue by enabling more extensive use of domain knowledge about all inputs. This study is intended as a first step towards making the Bayesian estimate fusion a practical tool for cover thickness uncertainty mapping. The main contribution is to identify the types of data assumptions that are important for this problem, to demonstrate their importance using synthetic tests and to design a method that enables their use without introducing excessive tedium. We argue that interpolation methods like kriging often cannot achieve this goal and demonstrate that Markov chain Monte Carlo sampling can. This paper focuses on the development of statistical methodology and presents synthetic data tests designed to reflect realistic exploration scenarios on an abstract level. Intended application is for the early stages of exploration where some geophysical data are available while drill hole coverage is poor.
<p>Cloncurry is located in the Mount Isa province in Queensland, NE Australia. The Mount Isa Province is a well-known metallogenic province in Australia which hosts many IOCG deposits. One of them is the Ernest Henry IOCG deposit, which was found below cover in the 90&#8217;s. The cover in this area comprises of regolith and the Jurassic-Cretaceous sediments of Eromanga and Carpentaria Basins. This deposit appears to belong to a complex mineral system which extend over the entire Cloncurry District.</p><p>A magnetotelluric (MT) survey was conducted in 2016 by Geoscience Australia and The Geological Survey of Queensland in the vicinity of the Ernest Henry IOCG deposit, in order to characterize the electrical properties of the mineral system beneath it. The derived 3D electrical conductivity model highlights the variable cover thickness over the area, and a correlation between conductors located in the upper crust and known mineral occurrences such as the Ernest Henry mine.</p><p>The use of 3D deterministic inversions of MT data is very powerful to image the electrical structure of the mineral system at the crustal scale but lacks resolution to image a realistic sharp cover-basement interface and precludes quantitative assessment of uncertainty around the results.</p><p>In this work we propose a workflow to image a geologically realistic cover-basement interface and bring insights on the reliability and robustness of different parts of the model using a probabilistic inversion approach.</p><p>We selected a subset of the MT survey and for each site we ran a probabilistic 1D trans-dimensional Markov chain Monte Carlo sampler for estimating subsurface conductivity and its associated uncertainty. These inversions are designed to be robust to non-1D effects present in the data. Next, we performed a petrophysical analysis using available down hole measurements to derive constraints on the electrical conductivities of the different lithologies found in the area. Then these petrophysical constraints, coupled to spatial lateral constraints, are used to fuse the 1D probabilistic ensembles into a 3D posterior ensemble.</p><p>The pseudo 3D model obtained is compared to a 3D model derived from a conventional 3D deterministic inversion using the same data to assess the value and validity of the workflow. Preliminary interpretation of the results is performed using petrophysical data and established local geology knowledge. Conclusions around the benefits of this workflow to give a different perspective on the characterization of a mineral system located under cover and to provide basis for future survey planning are presented.</p>
The ferricrete units (Fe oxide cemented colluvial-alluvial sediment) of the Yilgarn Craton in Western Australia formed during the humid tropical and sub-tropical climates of the Cenozoic. Ferricretes are generally developed on long-lived paleodrainage systems and are products of the ferruginisation of detritus provided by the continuous erosion of upslopes. These iron-rich accumulations can become Au-enriched, as is the case in several locations previously discovered in the Yilgarn Craton; many of these host economic secondary gold deposits (e.g., Moolart Well, Mt Gibson, and Bulchina), typically occurring downslope of low saprolite hills and near paleovalleys (i.e., inset-valleys). Inset-valleys are a common paleotopographic feature buried under Quaternary alluvial and colluvial sedimentary cover. Maps of these ancient channel networks can be used as a proxy for targeting ferricrete gold deposits. These inset-valley systems generally form dendritic and noisy patterns in high-resolution aeromagnetic data due to the presence of maghemite-rich nodules and detrital magnetic pisoliths on their flanks. The main aim of this study was to use high-resolution aeromagnetic data to target ferricrete units related to inset-valleys systems across the Yilgarn Craton. A spatial predictive model was used to learn and predict the geological units of interest from pre-processed aeromagnetic data. The predicted inset-valleys systems were able to confine the exploration space and define a new exploration frontier for ferricrete gold deposits.
This study assesses potential geological connections between the unconventional petroleum plays in the Beetaloo Sub-basin, regional aquifers in overlying basins, and the near surface water assets in the Beetaloo Sub-basin Northern Territory, Australia. To do so, we built an innovative multi-disciplinary toolbox including multi-physics and multi-depth imaging of the geological formations, as well as the study of potentially active tectonic surface features, which we combined with measurement of the helium content in water sampled in the aquifer systems and a comparative analysis of the surface drainage network and fault lineaments orientation. Structures, as well as potential natural active and paleo-fluid or gas leakage pathways, were imaged with a reprocessing and interpretation of existing and newly acquired Beetaloo seismic reflection 2D profiles and magnetic datasets to determine potential connections and paleo-leakages. North to north-northwest trending strike slip faults, which have been reactivated in recent geological history, are controlling the deposition at the edges of the Beetaloo Sub-basin. There are two spring complexes associated with this system, the Hot Spring Valley at the northern edge of the eastern Beetaloo Sub-basin and the Mataranka Springs 10 km north of the western sub-basin. Significant rectangular stream diversions in the Hot Spring Valley also indicates current or recently active tectonics. This suggests that those deep-rooted fault systems are likely to locally connect the shallow unconfined aquifer with a deeper gas or fluid source component, possibly without connection with the Beetaloo unconventional prospective plays. However, the origin and flux of this deeper source is unknown and needs to be further investigated to assess if deep circulation is happening through the identified stratigraphic connections. Few north-west trending post-Cambrian fault segments have been interpreted in prospective zones for dry gas plays of the Velkerri Formation. The segments located in the northern part of the eastern Beetaloo Sub-basin do not show any evidence of modern leakages. The segments located around Elliot, in the south of the eastern Beetaloo Sub-basin, as well as low-quality seismic imaging of potential faults in the central part of the western sub-basin, could have been recently reactivated. They could act as open pathways of fluid and gas leakage, sourced from the unconventional plays, deeper formations of the Beetaloo Sub-basin or even much deeper origin, excluding the mantle on the basis of low 3He/4He ratios. In those areas, the data are sparse and of poor quality; further field work is necessary to assess whether such pathways are currently active.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)