125 years of legacy data at the Geological Survey of Western Australia: Capture and delivery

Riganti, Angela; Farrell, Terence R.; Ellis, Margaret J.; Irimies, Felicia; Strickland, C.; Martin, Sarah K.; Wallace, Darren J.

doi:10.1016/j.grj.2015.02.015

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Python libraries are written in italics: dh2loop 2. Python functions are written in italics followed by an open and close parenthesis: token_set_ratio() , hard copy, and digital format (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and digital format (2000-present) (Riganti et al, 2015). The minimum contents of a drilling report comprise a collar file, which describes the geographic coordinates of the collar location (Fig.…”

Section: Conventions and Terminologiesmentioning

confidence: 99%

“…Additional files may be included, such as a survey file describing the depth, azimuth and inclination measurements for the drilling path; assays, downhole geology and property surveys (e.g. downhole geochemistry, petrophysics) may also be available depending on the company's submission (Riganti et al, 2015). The data in the drilling reports are extracted with spatial attribution and imported to a custom-designed relational database (also called the Mineral Drill hole Database) curated by the GSWA that allows easy retrieval and spatial querying.…”

Section: Conventions and Terminologiesmentioning

confidence: 99%

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dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

Joshi¹,

Madaiah²,

Jessell³

et al. 2021

Preprint

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Abstract. Exploration and mining companies rely on geological drill core logs to target and obtain initial information on geology of the area to build models for prospectivity mapping or mine planning. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as it is compiled and recorded in an unstructured textural form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. It is subjective and plagued with uncertainty as it is likely to have been conducted by tens to hundreds geologists, all of whom would have their own personal biases. However, this is valuable information that adds value to geoscientific data for research and exploration, specifically in efficiently targeting sustainable new discoveries and providing better shallow subsurface constraints for 3D geological models. dh2loop (https://github.com/Loop3D/dh2loop) is an open-source python library that provides the functionality to extract and standardize geologic drill hole data and export it into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia Mineral Exploration Reports Database in the Yalgoo-Singleton Greenstone Belt (YSGB) region. For this study case, the extraction rate for collar, survey and lithology data is respectively 93 %, 865 and 34 %. It also addresses the subjective nature and variability of nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. 86% of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted to a matching rate of 16 % (7,870 successfully matched records out of 47,823 records). The standardized lithological data is then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing python libraries (lasio, welly, striplog).

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Section: Conventions and Terminologiesmentioning

confidence: 99%

Section: Conventions and Terminologiesmentioning

confidence: 99%

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

Joshi¹,

Madaiah²,

Jessell³

et al. 2021

Preprint

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show abstract

“…Datasets utilised by the experts in the Three-part Assessment of the Sandstone Greenstone Belt. These data are available from their respective Geological Survey of Western Australia repositories; WAROX, WACHEM, WAMEX, MINDEX and MAGIX (Riganti et al (2015) and comprehensive references therein). Table 10.…”

Section: The Business Of Exploration: Implicationsmentioning

confidence: 99%

Predicting grade-tonnage characteristics of undiscovered mineralisation: application of the USGS Three-part Undiscovered Mineral Resource Assessment to the Sandstone Greenstone Belt of the Yilgarn Block, Western Australia

Davies

Schodde

Sykes

et al. 2020

Applied Earth Science

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The United States Geological Survey (USGS) Three-part Undiscovered Mineral Resource Assessment provides a framework for estimating undiscovered mineral endowment. Previous studies that applied the Three-part Assessment to estimate the undiscovered orogenic gold endowment of the Sandstone Greenstone Belt, Western Australia, have relied upon dated or expert-derived grade-tonnage models. Here, several assessments are conducted using local grade-tonnage models, comprising known orogenic gold deposits within the entire Yilgarn Block and several individual terranes with contrasting lithosphere-to terrane-scale characteristics. These models are generated through comprehensive review of historical exploration, resource and production data. Based on these models, the Sandstone Greenstone Belt is estimated to contain significant undiscovered gold mineralisation, with a median total endowment of between 166 and 298 t gold, and mean of 167-319 t gold. Although these updated grade-tonnage models provide an approximately 80 per cent variation in predicted gold endowment, it is still evident that the belt remains an underexplored region within the Yilgarn Block, Western Australia.

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“…Mineral explorers, for instance, use the data to reduce the technical risks related to the discovery of mineral deposits, hence informing investment decisions. GSWA data are extensively used and generally highly valued, in part because of their state-wide coverage, but more particularly because of their quality, having been systematically captured into a suite of rigorously structured, thoroughly validated and internally consistent databases (Riganti et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Mineral Systems Atlas — delivering greater value from precompetitive geoscience data

Morin-Ka

Beardsmore

Duuring

et al. 2019

ASEG Extended Abstracts

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125 years of legacy data at the Geological Survey of Western Australia: Capture and delivery

Cited by 9 publications

References 2 publications

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

Predicting grade-tonnage characteristics of undiscovered mineralisation: application of the USGS Three-part Undiscovered Mineral Resource Assessment to the Sandstone Greenstone Belt of the Yilgarn Block, Western Australia

The Mineral Systems Atlas — delivering greater value from precompetitive geoscience data

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