Publications

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The International Data Centre of the Comprehensive Nuclear-Test-Ban Treaty Organization relies on automatic data processing as the first step in identifying seismic events from seismic waveform data. However, more than half of the automatically identified seismic events are eliminated by IDC analysts. Here, an IDC dataset is analyzed to determine if the number of automatically generated false positives could be reduced. Data that could be used to distinguish false positives from analyst-accepted seismic events includes the number of stations, the number of phases, the signal-to-noise ratio, and the pick error. An empirical method is devised to determine whether an automatically identified seismic event is acceptable, and the method is found to identify a significant number of the false positives in IDC data. This work could help reduce seismic analyst workload and could help improve the calibration of seismic monitoring stations. This work could also be extended to address identification of seismic events missed by automatic processing.

A Laboratory-Directed Research and Development project was initiated in 2005 to investigate Human Performance Modeling in a System of Systems analytic environment. SAND2006-6569 and SAND2006-7911 document interim results from this effort; this report documents the final results. The problem is difficult because of the number of humans involved in a System of Systems environment and the generally poorly defined nature of the tasks that each human must perform. A two-pronged strategy was followed: one prong was to develop human models using a probability-based method similar to that first developed for relatively well-understood probability based performance modeling; another prong was to investigate more state-of-art human cognition models. The probability-based modeling resulted in a comprehensive addition of human-modeling capability to the existing SoSAT computer program. The cognitive modeling resulted in an increased understanding of what is necessary to incorporate cognition-based models to a System of Systems analytic environment.

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Terrestrial climate records and historical observations of the Sun suggest that the Sun undergoes aperiodic oscillations in radiative output and size over time periods of centuries and millenia. Such behavior can be explained by the solar convective zone acting as a nonlinear oscillator, forced at the sunspot-cycle frequency by variations in heliomagnetic field strength. A forced variant of the Lorenz equations can generate a time series with the same characteristics as the solar and climate records. The timescales and magnitudes of oscillations that could be caused by this mechanism are consistent with what is known about the Sun and terrestrial climate.