Work

Uncertainty estimation for multiphenomenology explosion monitoring

Year: 2026

Type: article

Abstract: SUMMARY We develop and demonstrate a new paradigm for modelling prompt forensics data from potential nuclear explosions of concern. Related scenarios include nuclear terrorism, which may involve low-yield detonations. Traditional modelling 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 multiphenomenology explosion monitoring framework provides improved yield characterization relative to reliance on single-phenomenology analysis alone, especially when individual sensors have complementary sensitivities to emplacement/environmental conditions.

Source: Geophysical Journal International

DOI: 10.1093/gji/ggag125

Authors: Brian J Williams, Rick Picard, Dale N Anderson

Institutions: Los Alamos National Laboratory, Desert Research Institute

Language: English

Topic: Seismology and Earthquake Studies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,416