Neeraj Nainwal, PhD

Airborne gamma-ray spectrometry (AGRS) is a widely used method for mapping surface radiogenic isotopes. However, its effectiveness depends heavily on optimizing flight and sensor parameters before conducting field surveys. Suboptimal survey designs can lead to compromised data quality, increased operational costs, and inconsistent outputs. Despite its significance, AGRS lacks robust tools to evaluate survey configurations before data acquisition. Existing methods often rely on iterative manual trial-and-error approaches, introducing inefficiencies and increasing the risk of errors. This study introduces RadSIMU, a Python-based forward modeling and simulation tool developed to aid in survey design before field data acquisition.

RadSIMU consists of four main components. First, the Survey Planner integrates Digital Elevation Models (DEM), radiogenic source distributions, sensor parameters (e.g., detector type, size, volume), and UAV settings (e.g., altitude, speed, flight line spacing, sampling rate). This component analyzes terrain geometry to identify optimal survey directions. Second, the Data Simulator models draped flights over the terrain and calculate radiogenic counts based on the chosen sensor and platform settings. Third, the Terrain Analyzer corrects for errors induced by terrain that occur due to the source and detector geometry. Finally, the Spectra Analyzer processes radiogenic counts and estimates the distribution of key isotopes, including potassium (K), thorium (Th), and uranium (U).

To validate its performance, RadSIMU was tested in a field study conducted at a copper exploration mining site in Arizona, USA. The survey planner and data simulator successfully identified optimal survey directions and UAV parameters, which were then compared with the selections made by experienced operators. The terrain and spectra analyzer corrected the terrain-induced errors to provide spatial distributions of potassium (K), thorium (Th), and uranium (U) isotopic counts. This correction improved consistency, reducing variations in radiation counts by 5% to 15% in areas near valleys and hills. The resulting distribution of isotopic counts showed a strong spatial relationship with local geological features. RadSIMU offers actionable insights for survey design, empowering users to make informed, data-driven decisions. It helps reduce uncertainties and improves data reliability before field data collection begins. Future developments of RadSIMU could expand its use to areas such as environmental monitoring and disaster response planning, further increasing its versatility and usability.