Optimizing Non-Invasive Remote Sensing for Geothermal Exploration With T-Spherical Dual Hesitant Fuzzy Decision Model

Traditional geothermal detection methods, such as extensive ground-based surveys and drillings, are often costly, time-consuming, and environmentally intrusive. To address these challenges, this study presents a novel hybrid fuzzy multi-criteria decision-making model to evaluate and prioritize non-invasive, cost-effective remote sensing (RS) techniques. This model uses T-spherical dual-hesitant fuzzy set to manage the inherent ambiguities in the evaluation of multiple criteria. The logarithmic percentage change-driven objective weighting technique assigns the relative importance of criteria, and the multiple triangle scenarios-II methodology helps in comprehensive evaluation and ranking. By incorporating expert judgment and addressing inherent uncertainties, this model provides a systematic framework for optimizing RS technique selection. Findings indicate that thermal infrared imaging, with a significance score of 0.7187, holds transformative potential for geothermal energy development. Sensitivity and comparative analyses further confirm the robustness of this approach. This research offers a valuable resource for energy developers and policymakers aiming to leverage RS technologies for efficient geothermal resource management and development.