Systematic Analysis and Proposed AI-based Technique for Attenuating Inductive and Capacitive Parasitics in Low and Very Low Frequency Antennas

Abstract

Non-destructive mapping of underground utilities is one of the fundamental concepts of subsurface imaging technology that has a great contribution to the improvement of many infrastructure concerns. It is incorporated with electrical resistivity measurement of various ground conditions through electrodes with functional geometric configuration. Due to the presence of an electrical field, electromagnetic noise and interference will likely occur and might cause inaccuracy of data. On that note, it is vital to understand the different factors affecting the system and its impact as the initial step in the development of an effective filtering and shielding mechanism. Thus, this paper discusses the possible impacts of parasitic inductance and capacitance affecting the performance of low and very low-frequency antennas, and the collection of various optimization methods as well as the tools and software used in the mitigation of parasitic elements in an electronics system found in different research publications and journals. Furthermore, an AI-based framework was also provided as an initial step in the development of a parasitic antenna filter that performs well for underground imaging single antenna array. Genetic algorithm is the AI technique proposed for the optimization of the antenna filter by providing the best combination of material by considering its conductivity and thickness.