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

Author

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  • Seyed Poorya Mirfallah Lialestani

    (Department of Mining, Industrial and ICT Engineering, Universitat Politècnica de Catalunya, Av. Bases de Manresa 61-73, 08242 Manresa, Spain)

  • David Parcerisa

    (Department of Mining, Industrial and ICT Engineering, Universitat Politècnica de Catalunya, Av. Bases de Manresa 61-73, 08242 Manresa, Spain)

  • Mahjoub Himi

    (Department of Mineralogy, Petrology and Applied Geology, University of Barcelona, 08007 Barcelona, Spain)

  • Abbas Abbaszadeh Shahri

    (Johan Lundberg AB, 754 50 Uppsala, Sweden)

Abstract

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.

Suggested Citation

  • Seyed Poorya Mirfallah Lialestani & David Parcerisa & Mahjoub Himi & Abbas Abbaszadeh Shahri, 2022. "Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure," Energies, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4602-:d:846192
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    References listed on IDEAS

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    1. Paolo Fulignati & Paola Marianelli & Alessandro Sbrana & Valentina Ciani, 2014. "3D Geothermal Modelling of the Mount Amiata Hydrothermal System in Italy," Energies, MDPI, vol. 7(11), pages 1-20, November.
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    Cited by:

    1. Agnieszka Operacz & Agnieszka Zachora-Buławska & Izabela Strzelecka & Mariusz Buda & Bogusław Bielec & Karolina Migdał & Tomasz Operacz, 2022. "The Standard Geothermal Plant as an Innovative Combined Renewable Energy Resources System: The Case from South Poland," Energies, MDPI, vol. 15(17), pages 1-23, September.
    2. R.V., Rohit & R., Vipin Raj & Kiplangat, Dennis C. & R., Veena & Jose, Rajan & Pradeepkumar, A.P. & Kumar, K. Satheesh, 2023. "Tracing the evolution and charting the future of geothermal energy research and development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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