ORCID Profile
0000-0003-2784-0686
Current Organisations
Monash University
,
University of Western Australia
,
University of Queensland
,
CSIRO
,
Corinda State High School
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Publisher: Springer Berlin Heidelberg
Date: 08-10-2013
Publisher: Elsevier BV
Date: 09-2009
Publisher: Informa UK Limited
Date: 27-09-2021
Publisher: Elsevier BV
Date: 10-2014
Publisher: CSIRO
Date: 2017
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 11-03-2020
DOI: 10.1007/S11004-020-09859-0
Abstract: Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and litude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type.
Publisher: CSIRO
Date: 2018
Publisher: CSIRO
Date: 2018
DOI: 10.25919/Y7HT-R568
Publisher: No publisher found
Date: 2018
DOI: 10.25919%2FY7HT-R568
Publisher: CSIRO
Date: 2018
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 06-2015
Publisher: MDPI AG
Date: 29-12-2022
DOI: 10.3390/MIN12010049
Abstract: Modelling of 3D domain boundaries using information from drill holes is a standard procedure in mineral exploration and mining. Manual logging of drill holes can be difficult to exploit as the results may not be comparable between holes due to the subjective nature of geological logging. Exploration and mining companies commonly collect geochemical or mineralogical data from diamond drill core or drill chips however, manual interpretation of multivariate data can be slow and challenging therefore, automation of any of the steps in the interpretation process would be valuable. Hyperspectral analysis of drill chips provides a relatively inexpensive method of collecting very detailed information rapidly and consistently. However, the challenge of such data is the high dimensionality of the data’s variables in comparison to the number of s les. Hyperspectral data is usually processed to produce mineral abundances generally involving a range of assumptions. This paper presents the results of testing a new fast and objective methodology to identify the lithological boundaries from high dimensional hyperspectral data. This method applies a quadrant scan analysis to recurrence plots. The results, applied to nickel laterite deposits from New Caledonia, demonstrate that this method can identify transitions in the downhole data. These are interpreted as reflecting mineralogical changes that can be used as an aid in geological logging to improve boundary detection.
Publisher: Springer Science and Business Media LLC
Date: 09-2004
DOI: 10.1038/NATURE02846
Publisher: MDPI AG
Date: 26-07-2021
DOI: 10.3390/MIN11080809
Abstract: The chemistry of hydrothermal monazite from the Carrapateena and Prominent Hill iron oxide-copper-gold (IOCG) deposits in the IOCG-rich Gawler Craton, South Australia, is used here to define geochemical criteria for IOCG exploration in the Gawler Craton as follows: Monazite associated with IOCG mineralisation: La + Ce 63 wt% (where La 22.5 wt% and Ce 37 wt%), Y and/or Th 1 wt% and Nd 12.5 wt% Intermediate composition monazite (between background and ore-related compositions): 45 wt% La + Ce 63 wt%, Y and/or Th 1 wt%. Intermediate monazite compositions preserving Nd 12.5 wt% are considered indicative of Carrapateena-style mineralisation Background compositions: La + Ce 45 wt% or Y or Th 1 wt%. Mineralisation-related monazite compositions are recognised within monazite hosted within cover sequence materials that directly overly IOCG mineralisation at Carrapateena. Similar observations have been made at Prominent Hill. Recognition of these signatures within cover sequence materials demonstrates that the geochemical signatures can survive processes of weathering, erosion, transport and redeposition into younger cover sequence materials that overlie older, mineralised basement rocks. The monazite geochemical signatures therefore have the potential to be dispersed within the cover sequence, effectively increasing the geochemical footprint of mineralisation.
Publisher: CSIRO
Date: 2022
DOI: 10.25919/1FDE-CY33
Publisher: CSIRO
Date: 2018
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 02-2020
Publisher: Informa UK Limited
Date: 21-12-2018
Publisher: CSIRO
Date: 2022
DOI: 10.25919/6QWZ-NH60
Publisher: CSIRO
Date: 2017
Publisher: Geological Society of London
Date: 12-09-2023
Abstract: Core analysts principally study the storage, flow, and saturation properties of porous rocks and sediments. Some parameters are specific to hydrocarbon production, but many have commonality with other subsurface disciplines such as hydrology and soil science. Traditional core analysis involves direct physical experimentation on core plugs to derive a range of parameters used as calibration for conventional well logs, and to predict hydrocarbon reserves and recovery. The mechanisms and processes for obtaining such data have evolved significantly during the last century, from the manual instruments of the mid-twentieth century to the accredited digital data collection and recording of the 1990s onwards.
Location: Australia
No related grants have been discovered for Evelyn Hill.