Potential geothermal structure inferred from the electrical resistivity and seismic reflection models in the western Ilan Plain, NE Taiwan

Chiang, Cheng-Yen; Yang, Zhi Xian; Chen, Chien Chih; Yeh, En Chao; Chen, Chow Son; Wang, Chien Ying

doi:10.1016/j.geothermics.2021.102124

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…This is the reason why the geoengineering characteristics depend on the scale of the project are often approximated with different techniques and computer modelling approaches [14][15][16][17]. Producing 3D conceptual shallow geothermal potentials [18,19], utilizing GIS [20][21][22], spatial data analysis [23][24][25], applying numerical technique [26][27][28][29], integrated of different geophysical prospecting techniques such as magnetotelluric [30][31][32][33][34], gravity [33,35], seismic [31,33], and electrical resistivity [31,36,37], as well as evident geological characteristics [38,39] are some of the carried efforts in Spain, Chile, Pakistan, Iran, India, Nigeria, Indonesia, Denmark, China, Thailand, Italy, Taiwan, Finland and Japan. However, simulating the geothermal resources using numerical techniques due to complexity of the model preparation (natural state properties of the rocks and geothermal system), description of the realistic problem and evaluation of the results as well as inability in providing any insight into generalizations is a very time-consuming task that demands extensive experience.…”

Section: Introductionmentioning

confidence: 99%

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

et al. 2022

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Mapping the subsurface temperatures can efficiently lead to identifying the geothermal distribution heat flow and potential hot spots at different depths. In this paper, an advanced adaptive multitask deep learning procedure for 3D spatial mapping of the subsurface temperature was proposed. As a result, predictive 3D spatial subsurface temperatures at different depths were successfully generated using geolocation of 494 exploratory boreholes data in Catalonia (Spain). To increase the accuracy of the achieved results, hybridization with a new modified firefly algorithm was carried out. Subsequently, uncertainty analysis using a novel automated ensemble deep learning approach for the predicted temperatures and generated spatial 3D maps were executed. Comparing the accuracy performances in terms of correct classification rate (CCR) and the area under the precision–recall curves for validation and whole datasets with at least 4.93% and 2.76% improvement indicated for superiority of the hybridized model. According to the results, the efficiency of the proposed hybrid multitask deep learning in 3D geothermal characterization to enhance the understanding and predictability of subsurface spatial distribution of temperatures is inferred. This implies that the applicability and cost effectiveness of the adaptive procedure in producing 3D high resolution depth dependent temperatures can lead to locate prospective geothermally hotspot active regions.

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