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Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin
Chen, Zhuoheng1; Liu, Xiaojun1; Yang, Jijin2; Little, Edward1; Zhou, Yu
2020-05-01
Source PublicationCOMPUTERS & GEOSCIENCES
ISSN0098-3004
Volume138Pages:10
AbstractTexture-based feature extraction and object segmentation are challenging in image processing. In this study, the U-Net architecture developed for biomedical image analysis was used to evaluate geologic characteristics depicted within scanning electron microscope (SEM) images of shale samples. With a revised weight function, the U-Net architecture allowed for effective discrimination of clay aggregates mixed with matrix mineral particles and organic matter (OM). In training, a local variability weight based on spatial statistics was used to enhance the contrast between features across boundary in the loss function of U-Net system optimization, thereby improving the ability of U-Net to distinguish the geologic features specific to our research needs. The Tensorflow neural network library was used to create semantic segmentation and feature extraction models in mineral identification. In the application example of the Devonian Duvernay shale study, we prepared 8000 randomly sliced image cuts (256 x 256 pixels) from four masked image tiles (6144 x 6144 pixels) with tagged feature objects, among which 6400 are for training and the remaining 1600 held image slices for validation. In the validation, the average of intersection over union (IOU) reaches 91.7%. The trained model approved by validation was used for clay aggregate segmentation and mineral classification. Three hundred SEM image tiles of source rock samples from different maturities in the Duvernay Formation were processed using the proposed workflow. The results show that the clay aggregates are clearly separated from other matrix mineral particles with acceptable boundaries, although both exhibit indistinguishable grey-level pixels. This approach demonstrates that texture-based deep learning feature extraction is feasible, cost-effective and timely, and can help geoscientists gain new insights by quantitatively analyzing specific geological characteristics and features.
KeywordSEM image Deep learning Segmentation Mineral classification Nano-porosity
DOI10.1016/j.cageo.2020.104450
Funding OrganizationPERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project
Language英语
Funding ProjectPERD (Program of Energy Research and Development) ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project[2016ZX05034-0305]
Funding OrganizationPERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; PERD (Program of Energy Research and Development) ; PERD (Program of Energy Research and Development) ; Natural Resources Canada ; Natural Resources Canada ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project
WOS Research AreaComputer Science ; Geology
WOS SubjectComputer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS IDWOS:000526797800003
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/95546
Collection页岩气与地质工程院重点实验室
Corresponding AuthorChen, Zhuoheng
Affiliation1.Geol Survey Canada, 3303-33rd St NW, Calgary, AB T2L 2A7, Canada
2.Chinese Acad Sci, Inst Geol & Geophys, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Chen, Zhuoheng,Liu, Xiaojun,Yang, Jijin,et al. Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin[J]. COMPUTERS & GEOSCIENCES,2020,138:10.
APA Chen, Zhuoheng,Liu, Xiaojun,Yang, Jijin,Little, Edward,&Zhou, Yu.(2020).Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin.COMPUTERS & GEOSCIENCES,138,10.
MLA Chen, Zhuoheng,et al."Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin".COMPUTERS & GEOSCIENCES 138(2020):10.
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