Application of dimensionality reduction technique to improve geophysical log data classification performance in crystalline rocks

Konaté, Ahmed Amara; Pan, Heping; Ma, Huolin; Cao, Xiurong; Ziggah, Yao Yevenyo; Oloo, Margaret; Khan, Nashir

doi:10.1016/j.petrol.2015.06.035

Cited by 33 publications

(7 citation statements)

References 29 publications

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“…Unsupervised learning is a sub-category of machine learning for which the algorithms receive only inputs but no labelled data. The aim of unsupervised ML is for the machine to build representations of the data [14] that in the end helps the operator to gather new information about the dataset. In the course of unsupervised ML, almost all steps can be viewed as learning a probabilistic model of the data [15] (Figure 3).…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…for monitoring works), clustering (e.g. to identify structure within data [16] or applying K-Means clustering to recognise rock mass types within TBM operational data) and dimensionality reduction to visualise high dimensional space in a more comprehensible way [14] (e.g. for improving the performance of geophysical log data classification).…”

Section: Unsupervised Learningmentioning

confidence: 99%

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Capabilities and Challenges Using Machine Learning in Tunnelling

Marcher¹,

Erharter²,

Unterlaß³

2022

Theory and Practice of Tunnel Engineering

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Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Capabilities and Challenges Using Machine Learning in Tunnelling

Marcher¹,

Erharter²,

Unterlaß³

2022

Theory and Practice of Tunnel Engineering

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“…PCA is commonly used in hydrological studies to reduce the number of variables, extract useful information and to eliminate the noise from data (Konaté et al 2015b). PCA extracts eigenvalues from the original dataset and forms new principal components (PC) that are linear combinations of the parameters (Pearson 1901).…”

Section: Principal Component Regressionmentioning

confidence: 99%

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

2021

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Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

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“…to identify structures within data [26] using K-Means clustering to identify rock mass types based on TBM operational data) or dimensionality reduction (e.g. to visualize high dimensional space [25] using dimension reduction to improve the performance of geophysical log data classification).…”

Section: Will Enablementioning

confidence: 99%

Machine Learning in tunnelling – Capabilities and challenges

Marcher

Erharter

Winkler

2020

Geomechanics and Tunnelling

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Digitalization will change the way of gathering geological data, methods of rock classification, application of design analyses in the field of tunnelling as well as tunnel construction and maintenance processes. In recent years, a rapid increase in the successful application of digital techniques (Building Information Modelling and Machine Learning (ML)) for a variety of challenging tasks has been observed. Driven by the increasing overall amount of data combined with the easy availability of more computing power, a sharp increase in the successful deployment of techniques of ML has been seen for different tasks. ML has been introduced in many sciences and technologies and it has finally arrived in the fields of geotechnical engineering, tunnelling and engineering geology, although still not as far developed as in other disciplines. This paper focuses on the potential of ML methods for geotechnical purposes in general and tunnelling in particular. Applications such as automatic rock mass behaviour classification using data from tunnel boring machines (TBM), updating of the geological prognosis ahead of the tunnel face, data driven interpretation of 3D displacement data or fully automatic tunnel inspection will be discussed.

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Application of dimensionality reduction technique to improve geophysical log data classification performance in crystalline rocks

Cited by 33 publications

References 29 publications

Capabilities and Challenges Using Machine Learning in Tunnelling

Capabilities and Challenges Using Machine Learning in Tunnelling

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

Machine Learning in tunnelling – Capabilities and challenges

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