Application of Artificial Neural Networks for Identification of Lithofacies by Processing of Core Drilling Data

Yang, Mingsheng; Hu, Yuanbiao; Liu, Baolin; Wang, Lu; Zhou, Zheng; Jia, Mingrang

doi:10.3390/app132111934

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…These models are particularly well suited to deal with complex data, such as associated with panoramic borehole imaging, for example [73,75,76], or vibrational data [77]. In addition, they could also better support the processing of MWD metrics when considered as time series data [78].…”

Section: Future Developmentsmentioning

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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Section: Future Developmentsmentioning

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

View full text Add to dashboard Cite

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“…Deep learning methods, on the other hand, are based on neural networks and can automatically learn features and patterns through multilayered networks for classification. The core of deep learning methods is ANN [60], such as CNNs and RNNs. These methods optimize network weights using large-scale training data and backpropagation algorithms to automatically extract complex signal features and perform advanced classification.…”

Section: Considers Multiple Aspects Improves Classification Accuracymentioning

confidence: 99%

Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

Shu,

Dawod

2023

Applied Sciences

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Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.

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Application of Artificial Neural Networks for Identification of Lithofacies by Processing of Core Drilling Data

Cited by 2 publications

References 26 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

Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

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