Detection of geological structure using gamma logs for autonomous mining

Silversides, Katherine L.; Melkumyan, Arman; Wyman, Derek A.; Hatherly, Peter; Nettleton, Eric

doi:10.1109/icra.2011.5980489

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The manual construction of a library in our first experiment followed a method similar to that used in Silversides et al (2011). This involved compiling an initial library that included typical AS1 and AS2 shale signatures and examples of the signatures of other rocks in the stratigraphic sequence.…”

Section: Manual Library Methodsmentioning

confidence: 99%

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Silversides

Melkumyan

Wyman

et al. 2015

Computers & Geosciences

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Section: Manual Library Methodsmentioning

confidence: 99%

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Silversides

Melkumyan

Wyman

et al. 2015

Computers & Geosciences

Self Cite

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“…Conventional methodologies, which include both labor-intensive manual techniques and automated approaches, have inherent limitations. The origin of automatic stratification can be traced back to mathematical statistical methods, encompassing intra-layer difference, optimal segmentation, change point analysis, and extreme variance clustering [11][12][13][14][15]. While these pioneering methods were transformative, they generally necessitated substantial computational resources and had difficulty with the acquisition of accurate prior probabilities [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Zhou,

Zhu,

Zhu

et al. 2023

Processes

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Ascertaining the positions of geological boundaries serves as a cornerstone in the characterization of shale reservoirs. Existing methods heavily rely on labor-intensive manual well-to-well correlation, while automated techniques often suffer from limited efficiency and consistency due to their reliance on single well log data. To overcome these limitations, an innovative approach, termed DRAG, is introduced, which uses deep belief forest (DBF), principal component analysis (PCA), and an enhanced generative adversarial network (GAN) for automatic layering recognition in logging curves. The approach employed in this study involves the use of PCA for dimensionality reduction across multiple well log datasets, coupled with a sophisticated GAN to generate representative samples. The DBF algorithm is then applied for stratification, incorporating a confidence screening mechanism to improve computational efficiency. In order to improve both accuracy and stability, a coordinate system is introduced that adjusts for stratification variations among neighboring wells around the target well. Experimental comparisons demonstrate the superior performance of the proposed algorithm in reducing stratification fluctuations and improving precision.

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“…The blast holes have a single assay for their entire length (∼10 m) but have a spacing of ∼5 m. Modified from Silversides et al. (2011). …”

Section: Introductionmentioning

confidence: 99%

Mineralogy identification through linear unmixing of blast hole geochemistry

Silversides

Melkumyan

2017

Applied Earth Science

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Validating and updating geological models is important in mining to reduce the errors between the predicted and mined values. In the Banded Iron Formation-hosted iron ore deposits in the Hamersley Ranges Western Australia, geological models are based on widely spaced exploration data, resulting in errors on smaller scales. Closely spaced chemical assays from production blast hole drilling become available for updating models, but do not routinely provide mineralogy. This study identifies mineralogy by applying linear unmixing methods to chemical assays. Linear spectral mixture analysis (LSMA), extended linear mixing model (ELMM) and spectral unmixing within a multi-task GP framework (SUGP) successfully identified the mineralogy in synthetic mixtures, with RMSEs of 13.75%, 11.78% and 11.97%. LSMA had consistently poorer results. SUGP was more accurate when using fewer endmember examples. ELMM was easier to train and required less processing power. Therefore either SUGP or ELMM should be chosen to identify the mineralogy depending on the application.

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Detection of geological structure using gamma logs for autonomous mining

Cited by 6 publications

References 17 publications

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Mineralogy identification through linear unmixing of blast hole geochemistry

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