Simulation of an INS soil analysis system

Global climate change in either the form of global warming or global cooling is occurring relatively rapidly today. Studies have shown that increased levels of greenhouse gases, especially atmospheric carbon dioxide (CO₂) are the dominate component contributing to the change. A reduction in CO₂ may be influenced by making larger efforts to sequester carbon in soil and therefore to not only keep soil organic carbon (SOC) levels steady but by possibly increasing them through human assistance. Soil sequestration of carbon has been estimated to have one of the largest potentials to sequester carbon in the world. By some estimation up to 2 billion tons of carbon can be sequestered terrestrially. Therefore the efficient and repetitive monitoring of SOC on a local and global scale is a critical issue. The current soil measurement technique utilized around the world is chemical analysis of one form or another. Chemical analysis of soil is a well studied technique that returns relatively accurate results of density, moisture content, and elemental breakdown of a soil. However, chemical analysis is costly, time consuming, and destructive. As a result of the destructive nature of soil chemical analysis, repeated measurements of the same soil site is impossible. Also, due to time constraints, it would be difficult to analyze a large area utilizing chemical analysis. To surmount the inherent issues with chemical analysis a system based on inelastic neutron scattering (INS) is under development for non-destructive monitoring of carbon in soil. It is based on spectroscopy of gamma rays induced by fast (14 MeV) neutrons emanating isotropically from a D-T neutron generator (NG). The calibration of the INS system is a remains a challenge. Calibration of the system is necessary for relating the carbon gamma ray counts from the detectors to a carbon concentration in the soil volume measured. Utilizing a benchmarked Monte Carlo model of the INS system it is possible to create many calibration curves. The advantages of the model are that the calculations require a relatively short amount of time, and that all the soil variables are defined by the user.