Applying deep learning for identifying bioturbation from core photographs

Timmer, Eric R.; Knudson, Calla; Gingras, Murray K.

doi:10.1306/08192019051

Cited by 6 publications

(1 citation statement)

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“…This study uses two different CNN, WaveNet (Oord et al, 2016) and Deep TEN (Zhang et al, 2017), to perform sequence-tosequence learning in the CCL data and texture classification in the core image dataset, respectively. The CNN currently applied for different rock types and analyses: igneous rocks (Fan et al, 2020;Fu et al, 2022), rock quality designation (Alzubaidi et al, 2021), and trace fossils detection (Ayranci et al, 2021;Timmer et al, 2021). Furthermore, a recent work proposed the use of elemental data in addition to images to improve the accuracy of classification (Xu et al, 2021).…”

Section: Core-based Lithofacies Analysismentioning

confidence: 99%

FaciesViT: Vision transformer for an improved core lithofacies prediction

et al. 2022

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Lithofacies classification is a fundamental step to perform depositional and reservoir characterizations in the subsurface. However, such a classification is often hindered by limited data availability and biased and time-consuming analysis. Recent work has demonstrated the potential of image-based supervised deep learning analysis, specifically convolutional neural networks (CNN), to optimize lithofacies classification and interpretation using core images. While most works have used transfer learning to overcome limited datasets and simultaneously yield a high-accuracy prediction. This method raises some serious concerns regarding how the CNN model learns and makes a prediction as the model was originally trained with entirely different datasets. Here, we proposed an alternative approach by adopting a vision transformer model, known as FaciesViT, to mitigate this issue and provide improved lithofacies prediction. We also experimented with various CNN architectures as the baseline models and two different datasets to compare and evaluate the performance of our proposed model. The experimental results show that the proposed models significantly outperform the established CNN architecture models for both datasets and in all cases, achieving an f1 score and weighted average in all tested metrics of 95%. For the first time, this study highlights the application of the Vision Transformer model to a geological dataset. Our findings show that the FaciesViT model has several advantages over conventional CNN models, including (i) no hyperparameter fine-tuning and exhaustive data augmentation required to match the accuracy of CNN models; (ii) it can work with limited datasets; and (iii) it can better generalize the classification to a new, unseen dataset. Our study shows that the application of the Vision transformer could further optimize image recognition and classification in the geosciences and mitigate some of the issues related to the generalizability and the explainability of deep learning models. Furthermore, the implementation of our proposed FaciesViT model has been shown to improve the overall performance and reproducibility of image-based core lithofacies classification which is significant for subsurface reservoir characterization in different basins worldwide.

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