Enhanced self-potential inversion using a hybrid Second Horizontal Gradient and Bat Algorithm-SHGBA-framework for...

Area of Science:

• Geophysics
• Geothermal Energy Science

Background:

• Self-potential (SP) data inversion is crucial for geothermal reservoir characterization.
• Conventional methods often struggle with noise, regional effects, and require extensive prior assumptions.
• Accurate subsurface parameter estimation is vital for efficient geothermal exploration and management.

Purpose of the Study:

• To introduce the SHGBA framework, integrating the Second Horizontal Gradient (SHG) technique with a modified Bat Algorithm (BA).
• To enhance the accuracy and robustness of SP data inversion for geothermal reservoir characterization.
• To minimize reliance on prior assumptions in subsurface modeling.

Main Methods:

• The SHGBA framework combines SHG for regional effect suppression and BA for global optimization.
• It refines SHG-derived anomalies using BA's population-based optimization to estimate parameters like K, z, x_o, q, and Ɵ.
• The method was tested on synthetic models (noise-free, noisy, multi-source) and real SP data from Kilauea Volcano.

Main Results:

• SHGBA consistently and accurately recovered subsurface parameters under various synthetic conditions, demonstrating robustness.
• Application to Kilauea Volcano's Hi'iaka dike SP data successfully characterized basaltic dike intrusions.
• The framework effectively tracked temporal thermal changes, indicating its utility in monitoring.

Conclusions:

• The SHGBA framework offers a robust and accurate solution for nonlinear, multi-parameter SP data inversion.
• Its minimal reliance on prior constraints makes it highly suitable for geothermal exploration and volcanic monitoring.
• This approach can significantly enhance drilling efficiency, reservoir management, and sustainable geothermal energy production.