Uncertainty Estimation for Multi-Phenomenology Explosion Monitoring

Summary We develop and demonstrate a new paradigm for modeling prompt forensics data from potential nuclear explosions of concern. Related scenarios include nuclear terrorism, which may involve low-yield detonations. Traditional modeling appropriate for higher-yield historical nuclear testing is generalized to capture uncertainties in yields (such as when using conversion to obtain “equivalent” nuclear yields for conventional explosives in low-yield experiments) and to capture variation among source-to-sensor path effects for which no calibration data are available. Special attention is paid to quantifying these sources of uncertainty and to their formal inclusion in comprehensive yield uncertainty — uncertainty that would otherwise be underestimated and potentially lead to mistaken conclusions. For the example scenario considered, two useful stand-alone monitoring phenomenologies are based on geophysical data (from surface effects characteristics and local seismic metrics). By fusing signatures from multiple phenomenologies, the Multi-Phenomenology Explosion Monitoring (MultiPEM) framework provides improved yield characterization relative to reliance on single-phenomenology analysis alone, especially when individual sensors have complementary sensitivities to emplacement/environmental conditions.