Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Modern nuclear facilities, such as reprocessing plants, present inspectors with significant challenges due in part to the sheer amount of equipment that must be safeguarded. The Sandia-developed and patented Knowledge Generation system was designed to automatically analyze large amounts of safeguards data to identify anomalous events of interest by comparing sensor readings with those expected from a process of interest and operator declarations. This paper describes a demonstration of the Knowledge Generation system using simulated accountability tank sensor data to represent part of a reprocessing plant. The demonstration indicated that Knowledge Generation has the potential to address several problems critical to the future of safeguards. It could be extended to facilitate remote inspections and trigger random inspections. Knowledge Generation could analyze data to establish trust hierarchies, to facilitate safeguards use of operator-owned sensors.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

New technologies have been, and are continuing to be, developed for Safeguards, Arms Control, and Physical Protection. Application spaces and technical requirements are evolving - Overlaps are developing. Lessons learned from IAEA's extensive experience could benefit other communities. Technologies developed for other applications may benefit Safeguards - Inherent cost benefits and improvements in procurement security processes.

In this report, we systematically evaluate the ability of current-generation, satellite-based spectroscopic sensors to distinguish uranium mines and mills from other mineral mining and milling operations. We perform this systematic evaluation by (1) outlining the remote, spectroscopic signal generation process, (2) documenting the capabilities of current commercial satellite systems, (3) systematically comparing the uranium mining and milling process to other mineral mining and milling operations, and (4) identifying the most promising observables associated with uranium mining and milling that can be identified using satellite remote sensing. The Ranger uranium mine and mill in Australia serves as a case study where we apply and test the techniques developed in this systematic analysis. Based on literature research of mineral mining and milling practices, we develop a decision tree which utilizes the information contained in one or more observables to determine whether uranium is possibly being mined and/or milled at a given site. Promising observables associated with uranium mining and milling at the Ranger site included in the decision tree are uranium ore, sulfur, the uranium pregnant leach liquor, ammonia, and uranyl compounds and sulfate ion disposed of in the tailings pond. Based on the size, concentration, and spectral characteristics of these promising observables, we then determine whether these observables can be identified using current commercial satellite systems, namely Hyperion, ASTER, and Quickbird. We conclude that the only promising observables at Ranger that can be uniquely identified using a current commercial satellite system (notably Hyperion) are magnesium chlorite in the open pit mine and the sulfur stockpile. Based on the identified magnesium chlorite and sulfur observables, the decision tree narrows the possible mineral candidates at Ranger to uranium, copper, zinc, manganese, vanadium, the rare earths, and phosphorus, all of which are milled using sulfuric acid leaching.