Top-of-holes sensing techniques: developments within Deep Exploration Technologies Cooperative Research Centre
Bibliographic record
Abstract
In this paper, we summarise advancements in top-of-hole sensing achieved within the Deep Exploration Technologies Cooperative Research Centre (DET CRC). It was demonstrated that the drill fines, which were previously discarded, show high potential to act as a representative sample media of the lithologies intersected by the drill hole and can be successfully used for analysis in real time. The Lab-at-Rig® (LAR®) system was developed for prospecting rigs (diamond drilling in the first instance and coil tube drilling in the future) and encompasses sample capture, sample preparation and presentation to sensors. In the initial setup of the LAR platform, there are two sensors, a portable X-ray fluorescence (pXRF) and a portable X-ray diffraction, capable of delivering chemical and mineralogical data in near real time. Laser-induced breakdown spectroscopy was also explored as a potential additional sensor for future versions of the LAR system, as it can yield information on elemental composition including essential light elements not currently measured by air-based pXRF detectors (e.g. Li, Na and Mg at low levels) or other elements problematic by pXRF (e.g. Au). The LAR system implements X-ray diffraction (XRD) analysis from which mineralogical data (mineral identification and most importantly mineral quantification) must be obtained in near real time. The existing challenge with XRD is that any data processing and especially data interpretation with available software packages requires some expertise in the field and background in crystallography and is time-consuming. Hence, SwiftMin®, the world’s first algorithm for automated processing of XRD data, was developed. It provides mineral identification and quantification and performs all calculations and processing independent from a user. SwiftMin returns a result in seconds and is able to batch process hundreds of XRD patterns in a matter of minutes. The above means, that SwiftMin is a technology that allows processing of large amount of XRD data quickly, saving time, costs and labour. The overall concept and vision developed within the DET CRC in top-of-hole sensing by coupling chemical and mineralogical analyses of drilling materials is to provide an end-to-end solution that supports rapid decision making by a geologist, at the time-scale of drilling the hole.KEY POINTSLab-at-Rig® workflow results in geochemical and mineralogical analyses by the time the drill hole is completed, providing objective logging and an opportunity to make important decisions during the course of a drilling campaign.SwiftMin® capability to process the data quickly and with no user interaction will allow X-ray diffraction to become a routine and cost-effective technique for analysis of geological materials.Finally, while laser-induced breakdown spectroscopy results look promising, particularly for such elements as Na, Mg and Au, application of laser-induced breakdown spectroscopy for rapid, on-site analysis of geological materials requires some further research, above all on how to minimise the ‘matrix effects’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".