Adaptive sampling applied to blast-hole drilling in surface mining

Ahsan, Nasir; Scheding, Steve; Monteiro, Sildomar T.; Leung, Raymond; McHugh, None C. M.; Robinson, Danielle

doi:10.1016/j.ijrmms.2015.01.009

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…The use of MWD data attempts to add local accuracies in between the exploration drillholes through blast-holes located at approximately 5-meters burden and spacing. Most of the research to recognise subsurface geology from drilling metrics has taken place in Canada [7,12,16] and Australia [8,11,18,[20][21][22]27]. These countries have had strong research collaboration between the mining industry and universities.…”

Section: Mined Commoditiesmentioning

confidence: 99%

“…Since these earlier Canadian-based studies, research on openpit classification of geology has been entirely within Australian coal and iron-ore deposits. The research in coal geology recognition aimed to accurately predict coal roof locations to prevent blast damage in open pit mining with several reports presenting the results of various ML classification methods on the same MWD dataset from 35 blast holes in the Australian Hunter Valley coal region [18,22,27]. A few years later, coal seams in 6 gas wells were identified with 96% accuracy from the Surat Basin using five categorisation algorithms [19].…”

Section: Mined Commoditiesmentioning

confidence: 99%

“…NN and GPs are the two primary categories of ML algorithms utilised with MWD data for rock categorization. While some artificial intelligence algorithms have been applied to the same datasets, different ML architectures produced distinct outcomes [18,22,27]. Thus, no model has been proposed as optimal for characterising geological, geotechnical, and geochemical features from MWD data, as model selection depends on underlying aims, scientific objectives, and model restrictions.…”

Section: Current Challengesmentioning

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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Section: Mined Commoditiesmentioning

confidence: 99%

Section: Mined Commoditiesmentioning

confidence: 99%

Section: Current Challengesmentioning

confidence: 99%

See 1 more Smart Citation

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

Goldstein,

Aldrich,

O'Connor

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The use of MWD data attempts to add local accuracies in between the exploration drillholes through blast holes located at approximately 5 m burden and spacing. Most of the research to recognize subsurface geology from drilling metrics has taken place in Canada [7,12,16] and Australia [8,11,18,[20][21][22]27]. These countries have had strong research collaboration between the mining industry and universities.…”

Section: Mined Commoditiesmentioning

confidence: 99%

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

Goldstein,

Aldrich,

O’Connor

2024

MAKE

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Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock.

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“…As a common geomaterial, rock materials, widely distributed in the earth's crust, are vulnerable to dynamic loadings, generated from earthquake, explosive volcanic activity, rock bursts or drilling and blasting (Ahsan et al 2015;Braunagel and Griffith 2019;Doan and Gary 2009;Zhou et al 2010), etc. Characterization of the dynamic characteristics of rock material is of great importance to understand the evolution of active fault and the stability evaluation of geotechnical engineering (Chen et al 2020;Ma et al 2020;Moosavi et al 2018;Roy and Singh 2016).…”

Section: Introductionmentioning

confidence: 99%

Influence of initial microcracks on the dynamic mechanical characteristics of sandstone

Griffiths

et al. 2022

Geomech. Geophys. Geo-energ. Geo-resour.

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with different initial microcrack densities were studied using Split Hopkinson Pressure Bar apparatus. Results reveal that the strength of sandstone decreases with the increase microcrack density at a given strain rate, while, the dynamic increase factor of sandstone increases with increasing microcrack density. The sensitivity of fragmentation fractal dimension and dissipated energy to strain rate depend on the initial microcracks density. Moreover, the fractal dimension of fragmentation becomes more sensitive to the energy absorption with the increase of initial microcrack density. The present results contribute to understand the evolution of active fault and safety evaluation of geotechnical engineering. Article highlights• The initial microcrack density was quantified based on SEM combining with an image process method. • The DIF of sandstone increases with increasing microcrack density. • The fractal dimension of fragmentation becomes more sensitive to the energy absorption with the increase of initial microcrack density.

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Adaptive sampling applied to blast-hole drilling in surface mining

Cited by 15 publications

References 14 publications

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

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

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

Influence of initial microcracks on the dynamic mechanical characteristics of sandstone

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