Heat flow estimation and quantification of geothermal reservoir of a basement terrain using geophysical and numerical techniques

Sedara, Samuel O.; Alabi, O.O.

doi:10.1007/s12665-022-10201-6

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…F 1 = 2*P*R/(P+R), (7) where, F 1 denotes the F 1 score; P and R denotes the precision and recall, respectively; OF means the overall F 1 score; F 1i denotes the F 1 score of the i type of the GSMs; n is the total classi cation number which equals eight in the present study.…”

Section: F 1 Score and Overall F 1 Scorementioning

confidence: 96%

“…The surface manifestations of a geothermal system in a volcanic-geothermal area are generally the features that rst stimulate mapping and exploration [1]. These manifestations, such as fumaroles, warm and hot springs, and mud pods, can give important hints to the availability and abundance of geothermal resources [7] and reveal the exploitation potential of geothermal resources [8]. Geothermal Surface Manifestations (GSMs) are crucial for preliminary surveys of geothermal potential.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

View full text Add to dashboard Cite

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 F1 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 and the biggest OF for both validation and test, and the fastest CT for both validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. It 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.

show abstract

Section: F 1 Score and Overall F 1 Scorementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The F 1 score is the harmonic mean of precision and recall taking both metrics into account in Equation (7). The overall F 1 score (OF) is used for the GSM recognition task to evaluate the accuracy performance of the different classifier models in general, which equals the average F 1 scores mathematically, as shown in Equation ( 8).…”

Section: F 1 Score and Overall F 1 Scorementioning

confidence: 99%

“…The surface manifestations of a geothermal system in a volcanic-geothermal area are generally the features that first stimulate mapping and exploration [1]. These manifestations, such as fumaroles, warm and hot springs, and mud pods, can give important hints to the availability and abundance of geothermal resources [7] and reveal the exploitation potential of them [8]. Geothermal surface manifestations (GSMs) are crucial for preliminary surveys of geothermal potential.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Energies

View full text Add to dashboard Cite

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 F1 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. On the contrary, 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.

show abstract

Numerical modeling of the Nevados de Chillán fractured geothermal reservoir

Oyarzo-Céspedes,

Arancibia,

Browning

et al. 2025

Geothermics

View full text Add to dashboard Cite

Heat flow estimation and quantification of geothermal reservoir of a basement terrain using geophysical and numerical techniques

Cited by 3 publications

References 19 publications

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Numerical modeling of the Nevados de Chillán fractured geothermal reservoir

Contact Info

Product

Resources

About