A Novel Metaheuristic Strategy for the Elucidation of Gravity Anomalies with Application to Geothermal Energy Investigations and Volcanic Activity Studies: Hunger Games Search (HGS)

Abstract

Recent metaheuristic approaches are extensively and intensively being introduced to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a novel metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After analyzing the modal features of the predetermined objective function and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average (SMA) strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty tests have been also implemented to understand the reliability of solutions achieved. The solutions obtained by HGS have been compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly utilized Particle Swarm Optimization (PSO) algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, more stably, more robustly and more coherently with the available geophysical, geological, and borehole verification, as HGS is able to explore the model space more comprehensively without compromising its capability to approach the global minimum efficiently. This novel metaheuristic thus can be considered as a promising tool in geothermal energy investigations and the study of volcanic activities and further recommended to reconnaissance studies aimed at effectively recognizing subsurface structures from various geophysical anomalies.