Optimizing image-based deep learning for energy geoscience via an effortless end-to-end approach

Koeshidayatullah, Ardiansyah

doi:10.1016/j.petrol.2022.110681

Cited by 22 publications

(4 citation statements)

References 17 publications

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“…We decided not to use a separate test set due to the small database size. To mitigate potential issues caused by an imbalance of class distribution, we utilized the train-time oversampling technique to achieve a more balanced class distribution [ 51 , 52 ].…”

Section: Resultsmentioning

confidence: 99%

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

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Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

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Section: Resultsmentioning

confidence: 99%

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

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“…This raises a serious concern about the explainability and generalizability of CNN models and how much we can trust the model. While another study highlighted how probability averaging may improve the results of classification (Alzubaidi et al, 2021), the class imbalance problem, which is very typical in the geosciences dataset, could have a detrimental impact on the overall performance of the deep learning model (Koeshidayatullah et al, 2020;Koeshidayatullah, 2022). This is further compounded by the fact that most of these studies rely heavily on the transfer learning method and intensive data augmentation to perform training (Baraboshkin et al, 2020).…”

Section: Core-based Lithofacies Analysismentioning

confidence: 99%

“…Since then, most deep learning implementations in geosciences, particularly for image analysis tasks, have relied primarily on transfer learning methods (Li et al, 2017;de Lima et al, 2019;Baraboshkin et al, 2020;Wu et al, 2020). Although this method allows for some breakthroughs in geological image classification and recognition, the model was originally trained on a domain that inherently has different data features and distributions, but can still produce a high-performance result that could raise some concerns in the long run (Pires de Lima and Duarte, 2021;Koeshidayatullah, 2022). Furthermore, this is compounded by the relatively stagnant performance and low explainability of various CNN models, which created the urgency to develop a deeper and wider CNN model.…”

Section: Introductionmentioning

confidence: 99%

FaciesViT: Vision transformer for an improved core lithofacies prediction

et al. 2022

Self Cite

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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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“…De Oliveira Werneck et al 18 (2022) used LSTM and GRU to make oil production forecasts, and GRU could be used to capture time series and be more effective in forecasting oil production. Koeshidayatullah 19 (2022) proposed an improved CNN model (through three strategies to improve model performance) for application in earth science. Lou et al 20 (2022) used deep learning to estimate the inclination of the formation and proposed a supervised deep learning model, which verified the effectiveness of the model by applying it to real seismic data.…”

Section: Introductionmentioning

confidence: 99%

Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data

Sun

Zhang

2023

ACS Omega

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In oil exploration and development, many reservoir parameters are very essential for reservoir description, especially porosity. The porosity obtained by indoor experiments is reliable, but human and material resources will be greatly invested. Experts have introduced machine learning into the field of porosity prediction but with the shortcomings of traditional machine learning models, such as hyperparameter abuse and poor network structure. In this paper, a meta-heuristic algorithm (Gray Wolf Optimization algorithm) is introduced to optimize the ESN (echo state neural) network for logging porosity prediction. Tent mapping, a nonlinear control parameter strategy, and PSO (particle swarm optimization) thought are introduced to optimize the Gray Wolf Optimization algorithm to improve the global search accuracy and avoid local optimal solutions. The database is constructed by using logging data and porosity values measured in the laboratory. Five logging curves are used as model input parameters, and porosity is used as the model output parameter. At the same time, three other prediction models (BP neural network, least squares support vector machine, and linear regression) are introduced to compare with the optimized models. The research results show that the improved Gray Wolf Optimization algorithm has more advantages than the ordinary Gray Wolf Optimization algorithm in terms of super parameter adjustment. The IGWO-ESN neural network is better than all machine learning models mentioned in this paper (GWO-ESN, ESN, BP neural network, least squares support vector machine, and linear regression) in terms of porosity prediction accuracy.

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Optimizing image-based deep learning for energy geoscience via an effortless end-to-end approach

Cited by 22 publications

References 17 publications

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

FaciesViT: Vision transformer for an improved core lithofacies prediction

Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data

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