Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin

Abstract

Texture-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 × 256 pixels) from four masked image tiles (6144 × 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.