The Dynamic Network Experiment 2018 (DNE18) was a virtual experiment designed to quantitatively assess current capabilities for multi-modal data ingestion and processing for nuclear explosion monitoring at the local/regional scale. This assessment will allow us to identify and prioritize remaining challenges that need to be met to achieve desired monitoring capabilities. The experiment was a collaborative effort between Los Alamos National Laboratory, Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory, and Sandia National Laboratories. We describe efforts to test various velocity models for any bias or other recognizable patterns using two-week, analyst-built event (ABE) bulletin. The data set includes over 6000 events manually-built by the analyst using the Utah Seismic Network which includes about 182 seismo-acoustic stations, 152 of which have analyst arrival picks. There are active mines in the state of Utah, many of which are associated with clusters of events. The ABEs include mostly Pg and Lg arrivals for events within Utah and some just outside the state. Global events were also picked that included teleseismic P and S as well as core phases, etc. although these are not included in this study. We test local, regional, and global P and S velocity models (1-D, 2-D, 3-D) for their effect on the event locations, paying attention to overall epicenter shifts, residual reduction, and error ellipses. Many of the event clusters are good candidates for application of relative relocation techniques.

Historically, location algorithms have relied on simple, one-dimensional (1D, with depth) velocity models for fast, seismic event locations. The speed of these 1D models made them the preferred type of velocity model for operational needs, mainly due to computational requirements. Higher-dimensional (2D-3D) seismic velocity models are becoming more readily available from the scientific community and can provide significantly more accurate event locations over 1D models. The computational requirements of these higher-dimensional models tend to make their operational use prohibitive. The benefit of a 1D model is that it is generally used as travel-time lookup tables, one for each seismic phase, with travel-time predictions pre-calculated for event distance and depth. This simple, lookup structure makes the travel-time computation extremely fast. Comparing location accuracy for 2D and 3D seismic velocity models tends to be problematic because each model is usually determined using different inversion parameters and ray-tracing algorithms. Attempting to use a different ray-tracing algorithm than used to develop a model almost always results in poor travel-time prediction compared to the algorithm used when developing the model. We will demonstrate that using an open-source framework (GeoTess, www.sandia.gov/geotess) that can easily store 3D travel-time data can overcome the ray-tracing algorithm hurdle because the lookup tables (one for each station and phase) can be generated using the exact ray-tracing algorithm that is preferred for a specified model. The lookup surfaces are generally applied as corrections to a simple 1D model and also include variations in event depth, as opposed to legacy source-specific station corrections (SSSCs), as well as estimates of path-specific travel-time uncertainty. Having a common travel-time framework used for a location algorithm allows individual 2D and 3D velocity models to be compared in a fair, consistent manner.