Probabilistic Programming for Transportable Source Characterization and Uncertainty Quantification of the North Korean Nuclear Tests 2006–2017

Here, we introduce a transportable technique to determine the yield and depth of burial (DOB) from seismic source spectra of underground nuclear explosions. We demonstrate this technique on the six declared North Korean nuclear tests. This approach derives source spectra in absolute units from regional phase (Pg) amplitudes by correcting the observations for geometric spreading, attenuation, and site amplification. We couple the source spectra and explosion source models with a probabilistic programming framework that integrates deep learning techniques and Bayesian modeling. This approach permits the exchange of information across various data categories to quantify both the data and model uncertainty. This technique stands out as an innovative use of broad‐area propagation models, making it transportable across various geologic settings. This method proves to be effective in scenarios with diverse and/or limited observational data, even when the source depth is unknown. We present new independent estimates of absolute yield and DOB that are consistent with the prior assessments, underscoring the potential of this method in enhancing transportable nuclear explosion monitoring capabilities.. Authors: Alfaro-Diaz, Richard Alexander [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000167235996); Kintner, Jonas Andreas [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000207396349); Phillips, William Scott [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000287250592); Delbridge, Brent Gregory [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000328088772); Carmichael, Joshua Daniel [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000157525738). DOE Contract: 89233218CNA000001. Subjects: 58 GEOSCIENCES; 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; Bayesian; explosion; machine learning; monitoring; nuclear; probabilistic; seismology; yield