Boundary Identification and Surface Updates Using MWD

Silversides, Katherine L.; Melkumyan, Arman

doi:10.1007/s11004-020-09891-0

Cited by 11 publications

(21 citation statements)

References 23 publications

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“…A top down cut-off of -0.5 was then applied to a continuous wavelet transform output to remove data from the damaged area. The cut-off values were obtained from data observations as detailed in Silversides and Melkumyan (2020).…”

Section: Methodsmentioning

confidence: 99%

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BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Silversides

Ball

Melkumyan

2020

Applied Earth Science

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Measure while drilling (MWD) data collected from production holes can provide information on the location of stratigraphic units in banded iron formation-hosted iron ore deposits. Stratigraphic modelling in these deposits is typically based on data from exploration holes, and adding more densely spaced production data can potentially increase model detail at the bench scale. Previous MWD classification methods struggle to differentiate between neighbouring ore units. In this paper, multivariate Gaussian Processes (GPs) were applied to locate the contact between two iron ore units in the Dales Gorge Member in the Brockman Iron Ore Formation. Production MWD points were then labelled based on the GP output. By altering parameters of the labelling process, 24.4-49.4% of the test data were labelled, with accuracies from 81.4 to 86.8%. Classifications from the same hole were compared to ensure MWD label consistency. The results demonstrate that the proposed method can improve geological unit classification from MWD data.

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Section: Methodsmentioning

confidence: 99%

“…Due to the high levels of noise, the MWD data was cleaned using the method described in Silversides and Melkumyan (2017) and Silversides and Melkumyan (2020). To remove data when other drilling modes (e.g.…”

Section: Data Preprocessingmentioning

confidence: 99%

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Silversides

Ball

Melkumyan

2020

Applied Earth Science

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“…Several ML techniques were applied to characterise rock conditions using MWD data in the reports identified with the GS search parameters. There are generally two types of ML techniques used in those findings: Neural Networks (NN) [12,16,[18][19][20] and Gaussian Process (GP) [8,11,21]. Other approaches, including as Support Vector Machines (SVM), Random Forests (RF), Boosting, Self-Organising Maps (SOM), and Fuzzy Logic, have been compared to NN in various studies [19,20]).…”

Section: Literature Sources and Disseminationmentioning

confidence: 99%

“…For example, a Banded Iron Formation (BIF) deposit might have classification zones of shale and BIF. These broad categorisation of geological zones from MWD data are useful for identifying the contacts between highly contrasting material types to update a depositscale model and increase its accuracy within localised areas [8]. This increased accuracy leads to improved blasting outcomes, in terms of achieving fragmentation around geological boundaries.…”

Section: Geology Recognition From Mwdmentioning

confidence: 99%

“…To perform the above correlations, multiple MWD metrics are collected by researchers. Depending on the manufacturer system setup, MWD datapoints are generated as time-series or depth series in blast holes ranging from 10 meters deep in surface iron ore mines to over 60 meters in openpit coal mines [8]. The most common variables acquired in open-pit mining MWD systems are time, 2 depth, rate of penetration (rop), torque or rotational pressure (tor), bit air pressure (bap; also called flushing air pressure), weight on bit (wob; also called weight on rods, thrust or feed pressure) and rotary speed or revolutions per minute (rpm) [9].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

Goldstein,

Aldrich,

O'Connor

2024

Preprint

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Measure While Drilling (MWD) refers to a comprehensive array of sensors employed to gather performance data throughout the process of rock drilling in mining blast holes. Multiple MWD metrics are collected by researchers depending on the setup of MWD sensors. Trends in MWD data can be correlated with pre-excavation subsurface conditions. The low-cost, extensive MWD data acquisition resolution of blast holes compared to expensive and sparsely collected exploration drill holes make the former one a desirable method for characterizing pre-excavation subsurface conditions. Nevertheless, the application of MWD technology in open pit mining is constrained by a substantial amount of data that requiring manual analysis within a short time. To address this limitation, computerized data acquisition and interpretation of MWD variables has been used to broadly determine open pit mining lithological boundaries. The results of artificial intelligence algorithms, such as Neural Networks, and Gaussian Processes, to recognize subsurface conditions, such as rock type, from MWD data have consistently returned &gt;90% prediction accuracy. However, methods of evaluating the importance of each MWD feature, such as Principal Component Analysis, has not been optimally used. This review focuses on the progression of intelligent MWD analysis using Machine Learning methods in open-pit mining, to determine the geotechnical, geological, and geochemical subsurface characteristics before open-pit mining excavation.

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Automatic detection of rock boundaries using a hybrid recurrence quantification analysis and machine learning techniques

Anvari

Mousavi

Sayadi

et al. 2022

Bull Eng Geol Environ

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Boundary Identification and Surface Updates Using MWD

Cited by 11 publications

References 23 publications

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data

Automatic detection of rock boundaries using a hybrid recurrence quantification analysis and machine learning techniques

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