Characterizing Rock Facies Using Machine Learning Algorithm Based on a Convolutional Neural Network and Data Padding Strategy

Wei, Zhili; Hu, Hao; Zhou, Hua‐wei; Lau, August

doi:10.1007/s00024-019-02152-0

Cited by 24 publications

(5 citation statements)

References 5 publications

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“…Kong and Jang showed that increasing the kernel increases the accuracy of neural networks [31]. Although strong evidence has not been obtained regarding this, however, this study and other available studies showed kernel size increased the accuracy [32]. In this study, filters are negatively correlated with accuracy, which suggests that when more filters are used, it can cause overfitting.…”

Section: Discussion and Findingsmentioning

confidence: 56%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

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Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.

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Section: Discussion and Findingsmentioning

confidence: 56%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…On the basis of mineral composition, igneous rocks are classified into silicic, intermediates, mafic, and ultramafic rocks [4]. Several methods to develop classification architectures have been suggested including Artificial Neural Networks (ANNs) [5][6][7], decision trees [8], statistical techniques [9][10], and decision-making rules [11][12]. Rough Set (RS) theory is one of the most motivating areas of computational intelligent research, having become increasingly popular with geologic applications.…”

Section: Imentioning

confidence: 99%

Classification of Volcanic Rocks based on Rough Set Theory

Shaaban

Tawfik

2020

Eng. Technol. Appl. Sci. Res.

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Classification of volcanic rocks is a fundamental task in the geologic studies. Volcanic rocks are igneous rocks that cooled rapidly above the surface of the Earth's crust. They are classified according to their oxide chemical content. Furthermore, volcanic rocks can also be classified numerically by statistical means. But these methods are mostly dependent on human expert decision making and have a high cost. In this paper, a novel approach in the classification of volcanic rocks is proposed. This method is based on the rough set mathematical theory. The continuous data of the information system are firstly discretized using the information loss method. Secondly, the discretized decision table is reduced and the decision rule sets are extracted. The results are consistent with previous methods and show that the proposed method reduces time and calculation costs.

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“…Logging data provide a cost-effective and time-efficient alternative to core analysis, as it encompasses various petrological parameters that can accurately characterize the physical characters of the subsurface rock formation. As a result, the utilization of well logs for lithofacies identification has gained considerable popularity [16][17][18][19]. Various mathematical techniques have been implemented to train lithofacies identification models using labeled logging data, and machine-learning algorithms perform well [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.

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Characterizing Rock Facies Using Machine Learning Algorithm Based on a Convolutional Neural Network and Data Padding Strategy

Cited by 24 publications

References 5 publications

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Classification of Volcanic Rocks based on Rough Set Theory

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

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