Airborne gamma ray spectrometry improvement using autoregressive integrated moving average model

The precise and timely manner modeling of received photon counts from gamma-ray sources has an important role in providing afore information for Airborne Gamma Ray Spectrometry (AGRS). In this manuscript, the Auto-Regressive Integrated Moving Average (ARIMA) model has been used to model AGRS. The proposed method provides gamma source and environmental disturbances ARIMA model, using known radioactive sources, to arrange the afore information for AGRS process experts to analyze the spectrometry data. The model extraction process and training will be done offline using different sizes and types of radioactive sources. The extracted models then being validated by evaluation functions to determine the type and amount of radionuclides during online AGRS. In order to evaluate the implemented modeling, the proposed ARIMA method is compared with other process modeling methods, including the Auto Regressive Moving Average ARMA in three bias, median absolute deviation (MAD) and the mean square error (MSE) criteria. The results show that the proposed method models the received photon counts much more accurate than other common methods.