Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning

Liu, Jiangfeng; Ma, Shijia; Shen, Wanqing; Zhou, Junping; Hong, Yang

doi:10.46690/ager.2022.04.06

Cited by 17 publications

(3 citation statements)

References 16 publications

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“…Jing et al 6 proposed a K-means dynamic clustering algorithm that combines rock mechanical properties to identify lithology. 7 Liu et al 8 used a multiresolution graph clustering (MRGC) method to optimize logging data that were more sensitive to electrical phase clustering analysis. Geophysical features obtained from the electrical phase were linked to the lithology to select the dominant lithology.…”

Section: Introductionmentioning

confidence: 99%

“…It predicts that the lithology is more in line with the lithological profile than traditional KNN clustering in identifying sand and can better fit the lithology model. Jing et al 6 proposed a K‐means dynamic clustering algorithm that combines rock mechanical properties to identify lithology 7 . Liu et al 8 used a multiresolution graph clustering (MRGC) method to optimize logging data that were more sensitive to electrical phase clustering analysis.…”

Section: Introductionmentioning

confidence: 99%

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Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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show abstract

“…For example, exploration and geological Mao (2020), engineering and design Domingues et al (2017), mining equipment and machinery, coal processing and preparation, and waste management and disposal services Yıldız (2020). These services are integral to the successful operation of open-pit coal mines, ensuring efficient extraction, processing, and responsible management of coal resources while prioritizing safety and environmental considerations Liu et al (2022). Illegal mining is frequently rapid and aggressive to elude oversight, and such intense open-pit coal mining may seriously damage the surrounding ecosystem.…”

mentioning

confidence: 99%

Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

Yu,

Yang,

Cheng

2023

Front. Energy Res.

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Early detection of cracks enables timely mitigation and maintenance actions, ensuring the safety of personnel and equipment within the open-pit coal mine. Monitoring open-pit coal mines and cracks is essential for the safety of workers and for saving national assets. Digital twins (DTs) can be crucial in open-pit coal mine crack detection. DTs enable continuous real-time monitoring of the open-pit mine, including its structures and surrounding environment. Various sensors and internet-of-things devices can be deployed to collect data on factors such as ground movement and strain. Integrating this data into the DT makes it possible to identify and analyze anomalous behavior or changes that may indicate crack formation or propagation. Deep learning-based networks are a crucial factor in detecting open-pit coal mine cracks. In this work, we propose a deep learning-based densely connected lightweight network incorporated into the DT-based framework for detecting cracks and taking predictive maintenance-based decisions by combining historical data, real-time sensor data, and predictive models. The proposed DT-based framework provides insights into the potential crack formation, allowing for proactive maintenance and mitigation measures. We compare the performance of our proposed network on different evaluation measures such as precision, recall, overall accuracy, mean average precision, F1-score, and kappa coefficient, where our proposed lightweight multiscale feature fusion-based network outperformed all other state-of-the-art deep neural networks. We also achieved the best performance on mean average precision by surpassing all other models. Additionally, we also compared the performance of our proposed network with U-Net and recurrent neural network on model training and prediction time benchmarks by outperforming those cutting-edge models.

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Feature Recognition of Rural Household Domestic Waste Based on ZigBee Wireless Sensor Network

Zhao,

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning

Cited by 17 publications

References 16 publications

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

Feature Recognition of Rural Household Domestic Waste Based on ZigBee Wireless Sensor Network

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