ORCID Profile
0000-0001-5456-191X
Current Organisation
Deakin University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Informa UK Limited
Date: 02-10-2022
Publisher: Informa UK Limited
Date: 02-10-2022
Publisher: Elsevier BV
Date: 08-2005
Publisher: Elsevier BV
Date: 05-2020
Publisher: Informa UK Limited
Date: 14-03-2017
Publisher: Informa UK Limited
Date: 03-2009
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 08-2022
Publisher: Informa UK Limited
Date: 10-2020
Publisher: Informa UK Limited
Date: 1988
Publisher: Informa UK Limited
Date: 03-2013
Publisher: Informa UK Limited
Date: 12-1994
Publisher: Springer Science and Business Media LLC
Date: 31-07-2014
Publisher: Informa UK Limited
Date: 06-10-2015
Publisher: Springer Science and Business Media LLC
Date: 22-06-2012
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 26-05-2019
Publisher: Elsevier BV
Date: 07-2022
Publisher: Informa UK Limited
Date: 03-2010
Publisher: Society of Exploration Geophysicists
Date: 04-05-2022
Abstract: Magmatic rocks are frequently encountered during hydrocarbon exploration in rift-related sedimentary basins. As magmatic rocks may contribute both positively and negatively to the hydrocarbon systems, their spatio-temporal distribution and structural elements are crucial for exploration in frontier basins. With the proliferation and increased density of seismic reflection data, various subsurface magmatic features can be discriminated and illuminated via conventional interpretation approaches, such as attribute extraction, opacity rendering or geo-body extraction. However, these manual interpretation techniques are labor-intensive, subject to interpreter bias and often bottleneck with respect to time data delivery. A supervised machine learning approach could efficiently resolve these issues by amalgamating suitable seismic attributes, such as energy, reflection strength, texture, and similarity, and automatically delineating these magmatic features in 3D seismic reflection data. Our machine learning neural network classified igneous features from non-igneous features in two different seismic surveys within the natural laboratory of the offshore Otway Basin, SE Australia. This multi-layer perception neural network designed in this study resulted in an optimized igneous probability meta-attribute cube that could effectively reveal the extension and distribution of igneous features and several structural elements in the study area. We presented the detailed workflow of this artificial neural network and observed the efficiency of this approach in different seismic surveys. These results illustrate the potential of neural network in imaging other complex igneous features from 3D seismic data in the Otway Basin and worldwide.
Publisher: Elsevier BV
Date: 05-2016
Publisher: CRC Press
Date: 16-11-2022
Publisher: Society of Exploration Geophysicists
Date: 11-2020
Abstract: The Plio-Pleistocene Whalers Bluff Formation (WBF) of the offshore Otway Basin is composed of mixed siliciclastic-carbonate sediments. In seismic cross sections, this formation includes an interval that consists of higher litude seismic reflections that display alternating depressional ponds and raised ridges. This interval is shallowly buried and lies between 40 and 150 ms two-way traveltime below the present-day seafloor. In this study, we have used 2D and 3D seismic data sets in combination with the available shallow subsurface well logs to characterize the geomorphology and investigate the origin of these enigmatic features. The ponds are expressed as densely packed, circular to polygonal, and in some cases, hexagonal-shaped features in time-slice maps, and they closely resemble previously documented honeycomb structures. In our study area, the honeycomb-like structures (HS) are comprised of large (200–800 m diameter range) depressed ponds that are separated by narrow (approximately 20 m at the top) reticulate ridges. In total, these HS cover an area of 760 km 2 . Geospatial analysis shows that the ponds of HS, especially those in the northeast of the study area, are aligned along the northwest–southeast trend lines. There are several possible origins for the HS. The most probable mechanism is that the HS resulted from the bulk contraction of soft sediment, associated with shallow-burial diagenesis processes such as subaqueous dewatering of the fine-grained successions within the WBF. Interestingly, irregular furrows of various lengths on the seafloor correspond to the ridges of the HS, and we hypothesize that these furrows may have formed due to differential compaction of the underlying alternating ponds and ridges. Our results demonstrate the benefits of using seismic reflection data sets in combination with geospatial analysis to investigate the buried paleogeomorphologic features and their impact on the present-day seafloor physiography. Geological feature: Honeycomb-like, soft sediment deformation associated with shallow-burial diagenesis, Otway Basin, southeastern Australia Cross-section appearance: Alternating depressional ponds and raised ridges Map view appearance: Densely packed, oval to polygonal-shaped features Features with a similar appearance: Acquisition footprints, carbonate mounds/dissolution features, polygonal faults, pockmarks, opal-A to opal-CT transition Formation: Whalers Bluff Formation, offshore Otway Basin Age: Pliocene to recent Location: Continental shelf of the Otway Basin, southeastern Australia Data sets: 2D and 3D seismic reflection data, borehole data, from Geological Survey of Victoria, Australia Analysis tools: Interpretation and visualization (Petrel 2019 and DUG Insight, v.4.7, 2020), Geospatial analysis (ESRI‘s ArcMap 10.5)
Publisher: Copernicus GmbH
Date: 10-1996
DOI: 10.1144/JM.15.2.161
Abstract: Abstract. Ponticocythereis species are a discrete phylogenetic group within the Trachyleberididae that evolved in the SW Pacific and Australasian regions during the Tertiary. The presence of similar scale-like spines on the unrelated species Ponticocythereis manis Whatley & Titterton, 1981 and Trachyleberis floridus sp. nov. is presented as an ex le of convergent evolution.
Publisher: Magnolia Press
Date: 10-04-2018
DOI: 10.11646/ZOOTAXA.4407.2.12
Abstract: The concept of the thaerocytherid ostracod genus Neohornibrookella was established by Jellinek (1993) on valve and carapace specimens from Kenyan coastal waters, identified by him as belonging to the species Cythere lactea Brady, 1866. Thus, Jellinek (1993) nominated Cythere lactea as the type species for Neohornibrookella. However, he misidentified these Kenyan specimens, which are here interpreted to belong to the broadly distributed, thermophilic shallow marine species, Hermanites transoceanica Teeter, 1975. In accordance with Article 70.3 of the International Code of Zoological Nomenclature, 1999, either the species originally nominated as the type species (Cythere lactea Brady, 1866), or the species represented by the Kenyan specimens on which the genus was described (Hermanites transoceanica Teeter, 1975), may be accepted as the type species for Neohornibrookella Jellinek, 1993. In accordance with Article 70.3.2 of the International Code of Zoological Nomenclature, 1999 I choose Hermanites transoceanica Teeter, 1975 as the types species for Neohornibrookella Jellinek, 1993 because it conforms with the established concept of the carapace morphology for this genus, as per discussion below.
Start Date: 2006
End Date: 2006
Funder: Australian Institute of Nuclear Science and Engineering
View Funded ActivityStart Date: 2013
End Date: 2013
Funder: Australian Institute of Nuclear Science and Engineering
View Funded ActivityStart Date: 2010
End Date: 2012
Funder: Australian Institute of Nuclear Science and Engineering
View Funded ActivityStart Date: 2009
End Date: 2009
Funder: Australian Institute of Nuclear Science and Engineering
View Funded ActivityStart Date: 2007
End Date: 2007
Funder: Australian Institute of Nuclear Science and Engineering
View Funded ActivityStart Date: 2010
End Date: 2010
Funder: Australian Research Council
View Funded Activity