ORCID Profile
0000-0003-3740-1214
Current Organisation
CSIRO
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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.
Applied Statistics | Natural Resource Management | Astronomy And Astrophysics | Astronomical and Space Sciences | Statistics | Knowledge Representation and Machine Learning
Environmental Management Systems | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Physical sciences |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
Publisher: EDP Sciences
Date: 13-05-2009
Publisher: American Astronomical Society
Date: 20-02-1999
DOI: 10.1086/306782
Publisher: Cambridge University Press (CUP)
Date: 2018
DOI: 10.1017/PASA.2018.5
Abstract: We present the first data release of the SkyMapper Southern Survey, a hemispheric survey carried out with the SkyMapper Telescope at Siding Spring Observatory in Australia. Here, we present the survey strategy, data processing, catalogue construction, and database schema. The first data release dataset includes over 66 000 images from the Shallow Survey component, covering an area of 17 200 deg 2 in all six SkyMapper passbands uvgriz , while the full area covered by any passband exceeds 20 000 deg 2 . The catalogues contain over 285 million unique astrophysical objects, complete to roughly 18 mag in all bands. We compare our griz point-source photometry with Pan-STARRS1 first data release and note an RMS scatter of 2%. The internal reproducibility of SkyMapper photometry is on the order of 1%. Astrometric precision is better than 0.2 arcsec based on comparison with Gaia first data release. We describe the end-user database, through which data are presented to the world community, and provide some illustrative science queries.
Publisher: American Astronomical Society
Date: 21-12-2006
DOI: 10.1086/510780
Publisher: Oxford University Press (OUP)
Date: 23-10-2015
Publisher: American Astronomical Society
Date: 23-04-2009
Publisher: Copernicus GmbH
Date: 15-07-2019
Abstract: Abstract. The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal properties of any single mode become inadequate uncertainty measures, and s ling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo s ling to assess uncertainty using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has a complex local covariance structure, hindering the efficiency of adaptive s ling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank–Nicolson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics and on 3-D geological structure, affect the shape and separation of posterior modes, influencing s ling performance as well as the inversion results. The use of uninformative priors on sensor noise enables optimal weighting among multiple sensors even if noise levels are uncertain.
Publisher: Copernicus GmbH
Date: 15-01-2019
DOI: 10.5194/SE-2019-4
Abstract: Abstract. Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques, which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data. Here, we demonstrate a Bayesian methodology to fuse geological field observations with aeromagnetic and gravity data to build robust 3D models in a 13.5 × 13.5 km region of the Gascoyne Province, Western Australia. Our approach is validated by comparing model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia. By fusing geological field data with magnetics and gravity surveys, we show that at 89 % of the modelled region has 95 % certainty. The boundaries between geological units are characterized by narrow regions with
Publisher: Oxford University Press (OUP)
Date: 13-11-2013
Publisher: American Astronomical Society
Date: 11-2002
DOI: 10.1086/344815
Publisher: American Astronomical Society
Date: 20-02-1999
DOI: 10.1086/306814
Publisher: Elsevier BV
Date: 11-2007
Publisher: Copernicus GmbH
Date: 06-03-2019
DOI: 10.5194/GMD-2018-306
Abstract: Abstract. The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multimodal properties of any single mode become inadequate uncertainty measures, and s ling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo s ling to assess uncertainty, using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has complex local covariance structure, hindering the efficiency of adaptive s ling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank-Nicholson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics or on 3-D geological structure, affect the shape and separation of posterior modes, influencing s ling performance as well as the inversion results. Use of uninformative priors on sensor noise can improve inversion results by enabling optimal weighting among multiple sensors even if noise levels are uncertain. Efficiency could be further increased by using posterior gradient information within proposals, which Obsidian does not currently support, but which could be emulated using posterior surrogates.
Publisher: Copernicus GmbH
Date: 28-09-2021
Abstract: Abstract. Unlike some other well-known challenges such as facial recognition, where Machine Learning and Inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled datasets that can be used to validate or train robust Machine Learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test datasets are often not particularly geological, nor geologically erse. To overcome these limitations, we have used the Noddy modelling platform to generate one million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic datasets. This model suite can be used to train Machine Learning systems, and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite, and discuss the opportunities such a model suit affords, as well as its limitations, and how we can grow and access this resource.
Publisher: American Astronomical Society
Date: 27-04-2011
Publisher: Cambridge University Press (CUP)
Date: 2016
DOI: 10.1017/PASA.2016.47
Abstract: This paper presents the first major data release and survey description for the ANU WiFeS SuperNovA Programme. ANU WiFeS SuperNovA Programme is an ongoing supernova spectroscopy c aign utilising the Wide Field Spectrograph on the Australian National University 2.3-m telescope. The first and primary data release of this programme (AWSNAP-DR1) releases 357 spectra of 175 unique objects collected over 82 equivalent full nights of observing from 2012 July to 2015 August. These spectra have been made publicly available via the WISEREP supernova spectroscopy repository. We analyse the ANU WiFeS SuperNovA Programme s le of Type Ia supernova spectra, including measurements of narrow sodium absorption features afforded by the high spectral resolution of the Wide Field Spectrograph instrument. In some cases, we were able to use the integral-field nature of the Wide Field Spectrograph instrument to measure the rotation velocity of the SN host galaxy near the SN location in order to obtain precision sodium absorption velocities. We also present an extensive time series of SN 2012dn, including a near-nebular spectrum which both confirms its ‘super-Chandrasekhar’ status and enables measurement of the sub-solar host metallicity at the SN site.
Publisher: Cambridge University Press (CUP)
Date: 2021
DOI: 10.1017/PASA.2021.17
Abstract: We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\\sim200\\,\\mathrm{Mpc}$ distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at $\\delta +10\\deg $ to a depth of $i_\\mathrm{AB}\\approx 20\\,\\mathrm{mag}$ . We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness ( $\\sim98\\%$ ) and purity ( $\\sim91\\%$ ) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing c aign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to $i_\\mathrm{AB}\\approx 21\\,\\mathrm{mag}$ .
Publisher: American Astronomical Society
Date: 06-2004
DOI: 10.1086/383534
Publisher: Copernicus GmbH
Date: 02-2022
Abstract: Abstract. Unlike some other well-known challenges such as facial recognition, where machine learning and inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled data sets that can be used to validate or train robust machine learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test data sets are often not particularly geological or geologically erse. To overcome these limitations, we have used the Noddy modelling platform to generate 1 million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic data sets (0.5281/zenodo.4589883, Jessell, 2021). This model suite can be used to train machine learning systems and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite and discuss the opportunities such a model suite affords, as well as its limitations, and how we can grow and access this resource.
Publisher: Wiley
Date: 03-2008
Publisher: Elsevier BV
Date: 04-2004
Publisher: American Astronomical Society
Date: 02-2001
DOI: 10.1086/318415
Publisher: American Astronomical Society
Date: 30-05-2013
Publisher: American Astronomical Society
Date: 30-05-2013
Publisher: EDP Sciences
Date: 12-2013
Publisher: American Astronomical Society
Date: 25-06-2009
Publisher: American Astronomical Society
Date: 18-11-2011
Publisher: American Astronomical Society
Date: 09-02-2015
Publisher: EDP Sciences
Date: 08-04-2011
Publisher: Oxford University Press (OUP)
Date: 29-07-2015
Publisher: Elsevier BV
Date: 11-2022
Publisher: Oxford University Press (OUP)
Date: 20-11-2012
Publisher: Copernicus GmbH
Date: 17-08-2021
DOI: 10.5194/GMD-2021-187
Abstract: Abstract. Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides an opportunity to directly quantify uncertainty in geological interpretations. For best results, the projection of the geological parameter space onto the finite-resolution discrete basis of the geophysical calculation must be faithful within the power of the data to discriminate. We show that naively exporting voxelised geology as done in commonly used geological modeling tools can easily produce a poor approximation to the true geophysical likelihood, degrading posterior inference for structural parameters. We then demonstrate a numerical forward-modeling scheme for calculating anti-aliased rock properties on regular meshes for use with gravity and magnetic sensors. Finally, we explore anti-aliasing in the context of a kinematic forward model for simple tectonic histories, showing its impact on the structure of the geophysical likelihood for gravity anomaly.
Publisher: EDP Sciences
Date: 29-05-2015
Publisher: Cambridge University Press (CUP)
Date: 2017
DOI: 10.1017/PASA.2017.24
Abstract: The SkyMapper 1.3 m telescope at Siding Spring Observatory has now begun regular operations. Alongside the Southern Sky Survey, a comprehensive digital survey of the entire southern sky, SkyMapper will carry out a search for supernovae and other transients. The search strategy, covering a total footprint area of ~2 000 deg 2 with a cadence of ⩽5 d, is optimised for discovery and follow-up of low-redshift type Ia supernovae to constrain cosmic expansion and peculiar velocities. We describe the search operations and infrastructure, including a parallelised software pipeline to discover variable objects in difference imaging simulations of the performance of the survey over its lifetime public access to discovered transients and some first results from the Science Verification data.
Publisher: American Astronomical Society
Date: 10-08-2005
DOI: 10.1086/431294
Publisher: American Astronomical Society
Date: 30-03-2010
Publisher: Oxford University Press (OUP)
Date: 22-03-2014
DOI: 10.1093/MNRAS/STU350
Publisher: Elsevier BV
Date: 09-2002
Publisher: American Astronomical Society
Date: 04-05-2009
Publisher: Elsevier BV
Date: 04-2011
Publisher: Oxford University Press (OUP)
Date: 16-05-2013
DOI: 10.1093/MNRAS/STT668
Publisher: American Astronomical Society
Date: 05-2014
Publisher: American Astronomical Society
Date: 18-04-2007
DOI: 10.1086/516818
Publisher: Elsevier BV
Date: 06-2006
Publisher: American Astronomical Society
Date: 12-03-2013
Publisher: American Astronomical Society
Date: 15-04-2011
Publisher: Oxford University Press (OUP)
Date: 05-2013
DOI: 10.1093/MNRAS/STT591
Publisher: Elsevier BV
Date: 04-2011
Publisher: American Astronomical Society
Date: 30-03-2012
Publisher: EDP Sciences
Date: 12-2013
Publisher: EDP Sciences
Date: 07-12-2012
Publisher: Oxford University Press (OUP)
Date: 24-12-2014
Publisher: American Astronomical Society
Date: 10-10-2006
DOI: 10.1086/507020
Publisher: American Astronomical Society
Date: 29-08-2012
Publisher: Oxford University Press (OUP)
Date: 18-10-2014
Publisher: American Astronomical Society
Date: 22-03-2012
Publisher: American Astronomical Society
Date: 09-12-2015
Publisher: Copernicus GmbH
Date: 06-03-2019
Publisher: Copernicus GmbH
Date: 09-05-2022
Abstract: Abstract. Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides an opportunity to directly quantify uncertainty in geological interpretations. For best results, care must be taken with the intermediate step of rendering parametric geology in a finite-resolution discrete basis for the geophysical calculation. Calculating geophysics from naively voxelized geology, as exported from commonly used geological modeling tools, can produce a poor approximation to the true likelihood, degrading posterior inference for structural parameters. We develop a simple integrated Bayesian inversion code, called Blockworlds, showcasing a numerical scheme to calculate anti-aliased rock properties over regular meshes for use with gravity and magnetic sensors. We use Blockworlds to demonstrate anti-aliasing in the context of an implicit model with kinematic action for simple tectonic histories, showing its impact on the structure of the likelihood for gravity anomaly.
Publisher: Oxford University Press (OUP)
Date: 29-10-2013
Publisher: American Astronomical Society
Date: 04-03-2014
Publisher: American Astronomical Society
Date: 07-10-2009
Location: Australia
Start Date: 12-2009
End Date: 04-2017
Amount: $3,097,098.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2020
End Date: 08-2025
Amount: $3,973,202.00
Funder: Australian Research Council
View Funded Activity