Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning
Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/8/2913 |
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| author | Yongzhu Xiong Mingyong Zhu Yongyi Li Kekun Huang Yankui Chen Jingqing Liao |
| author_facet | Yongzhu Xiong Mingyong Zhu Yongyi Li Kekun Huang Yankui Chen Jingqing Liao |
| author_sort | Yongzhu Xiong |
| collection | DOAJ |
| description | Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F<sub>1</sub> score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain. |
| first_indexed | 2024-03-09T10:38:03Z |
| format | Article |
| id | doaj.art-cdc674d2a6224942be92e88ad34483c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T10:38:03Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cdc674d2a6224942be92e88ad34483c52023-12-01T20:49:42ZengMDPI AGEnergies1996-10732022-04-01158291310.3390/en15082913Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep LearningYongzhu Xiong0Mingyong Zhu1Yongyi Li2Kekun Huang3Yankui Chen4Jingqing Liao5School of Geography and Tourism, Jiaying University, Meizhou 514015, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou 514015, ChinaInstitute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou 514015, ChinaThe Eighth Geologic Survey, Guangdong Geological Bureau, Meizhou 514089, ChinaGeothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F<sub>1</sub> score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.https://www.mdpi.com/1996-1073/15/8/2913geothermal manifestationgeothermal energyDeep Learning (DL)Support Vector Machine (SVM)Decision Tree (DT)K-Nearest Neighbor (KNN) |
| spellingShingle | Yongzhu Xiong Mingyong Zhu Yongyi Li Kekun Huang Yankui Chen Jingqing Liao Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning Energies geothermal manifestation geothermal energy Deep Learning (DL) Support Vector Machine (SVM) Decision Tree (DT) K-Nearest Neighbor (KNN) |
| title | Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning |
| title_full | Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning |
| title_fullStr | Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning |
| title_full_unstemmed | Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning |
| title_short | Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning |
| title_sort | recognition of geothermal surface manifestations a comparison of machine learning and deep learning |
| topic | geothermal manifestation geothermal energy Deep Learning (DL) Support Vector Machine (SVM) Decision Tree (DT) K-Nearest Neighbor (KNN) |
| url | https://www.mdpi.com/1996-1073/15/8/2913 |
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