A machine learning approach for mapping the very shallow theoretical geothermal potential

Assouline, Dan; Mohajeri, Nahid; Guðmundsson, Ágúst; Scartezzini, Jean-Louis

doi:10.1186/s40517-019-0135-6

Cited by 38 publications

(13 citation statements)

References 61 publications

(48 reference statements)

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“…In that study, even though the authors provided a preliminary map to annotate most favorable locations in Greenland in terms of geothermal potential, however, wellbore bottom-hole temperature data were not utilized. In another effort, machine learning was used to map very shallow geothermal potential (Assouline et al 2019). In shallow depths, geothermal energy can be a very good source to provide thermal energy for residential areas (Vieira et.al.…”

Section: Exploration Stagementioning

confidence: 99%

“…There are few comprehensive surveys that focused on analyzing the associated risks to provide insights about the potential of developing geothermal sites (Jordan et al 2016;Young et al 2010). Machine learning has been an emerging technology that helped the geothermal energy field in the mentioned stages (Assouline et al 2019;Beardsmore 2014;Faulds et al 2020;Rezvanbehbahani et al 2017;Shi et al 2021;Tut Haklidir and Haklidir 2020). In the next section, we briefly review the studies which applied machine learning successfully in the fields of geothermal exploration and drilling.…”

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confidence: 99%

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Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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Geothermal scientists have used bottom-hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost and Random Forest result in the highest accuracy for subsurface temperature prediction. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for 58 wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost-predicted temperatures along the depth. Finally, through our analysis, the most favorable geological locations are suggested for potential future geothermal developments.

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Section: Exploration Stagementioning

confidence: 99%

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confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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“…Many GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], Geographic Information System (GIS) [20], statistical modeling [21] and conventional Machine Learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

Section: Introductionmentioning

confidence: 99%

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

Xiong

Zhu

et al. 2022

Preprint

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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.

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“…A lot of GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], geographic information system (GIS) [20], statistical modeling [21] and conventional machine learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

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.

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A machine learning approach for mapping the very shallow theoretical geothermal potential

Cited by 38 publications

References 61 publications

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

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

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