Probabilistic Characterization of City Scale Subsurface Thermal Environment Heterogeneity.

Area of Science:

• Earth Science
• Geophysics
• Environmental Science

Background:

• Subsurface thermal environment (STE) characterization is essential for geothermal energy and urban heat management.
• City-scale STE is complex due to subsurface heterogeneity, challenging conventional deterministic models.
• Existing methods struggle to accurately represent STE's spatial distribution and heterogeneity.

Purpose of the Study:

• To develop an integrated modeling framework for reconstructing STE under steady-state conditions.
• To quantify the uncertainty associated with STE characterization.
• To assess the shallow geothermal potential for urban energy applications.

Main Methods:

• Synergistic integration of geostatistics, physics-based modeling, and Bayesian inference.
• Application of the framework to Changzhou city in the Yangtze River Delta.
• Sensitivity analysis to identify dominant sources of modeling uncertainty.

Main Results:

• Probabilistic reconstruction of STE factors including lithology, temperature, heat flow, and thermal conductivity.
• Identification of the upper temperature boundary as the primary source of uncertainty.
• Estimation of Changzhou's shallow geothermal potential at approximately 2.7 × 10^4 GWh.

Conclusions:

• The developed framework provides a scalable and uncertainty-aware approach for STE assessment.
• The findings highlight significant shallow geothermal potential in Changzhou, sufficient for winter heating.
• This methodology offers a pathway for global subsurface thermal resource management and sustainable energy planning.