Site-Directed Research and Development logo, green and blue with orange writing

Re-Purposing Old Seismic Data to Calibrate Nuclear Test Monitoring Sites in Sparse Seismic Regions

Site-Directed Research and Development logo, green and blue with orange writing

Project #: 22-017 | Year 2 of 2

Reagan Turley, Cleat Zeiler, Michelle Scalise

Nevada National Security Site (NNSS)

Executive Summary

Leveraging historic seismic data to build calibrated models of sparse seismic regions is a critical step in characterizing testbeds and establishing tools to rapidly develop models for new areas of interest. Accurately determining the yields and locations of underground explosions requires highly calibrated models and a reliable sensing network. Using seismic data readily available at the Nevada National Security Site (NNSS), we extracted characteristics from ambient noise profiles and improved current models. This work provides a method to repurpose old seismic data and develop new seismic capabilities at the NNSS that can offer benefits to Ground-based Nuclear Detonation Detection (GNDD) and Defense Nuclear Nonproliferation (DNN) programmatic portfolios. Additionally, the seismic prospecting data is typically not integrated into monitoring networks; integrating these two data sets provides an opportunity to define new methods in detection and characterization of events of interest.


This project leveraged archived seismic data to improve subsurface characterization and earth model development to support nuclear explosion monitoring capabilities. The first year of the project was heavily focused on assembling datasets and establishing an efficient data processing pipeline. Seismic data sets were aggregated and built into a database. MATLAB algorithms were developed to quality control and pick the seismic data. With the acquisition of the seismic survey capability, we found that there is a significant increase in data volume, on the order of two magnitude units. This SDRD approached the question of how to manage this significant increase, which is also happening to the U.S. National Data Center. Collecting and evaluating the available data is a tedious and time-consuming task due to the large volume of data associated with each deployment.

The second year of the project heavily focused on developing software to process and analyze the catalogued seismic data. Seismic signals are composed of various types of seismic waves with differing properties and seismic velocities. Identifying and characterizing the discrete seismic waves is necessary to resolve subsurface velocity structure. Ambient noise tomography software was also developed to analyze and process the ambient noise seismic data collected at the NNSS. The Python-based code produces seismic P- and S‐velocity models that can be integrated into earth models. A primary goal of this project was to develop a methodology and workflow to utilize the large volumes of seismic data collected at the NNSS. A workflow was developed to streamline the processing and analysis of the ambient noise data using the ambient noise tomography Python software.

Figure 1. Examples of data to be sorted: Seismograms with P-wave (blue) and S-wave (red) for an event in Rock Valley (left). Time-versus-distance plot for the Apollo experiment (right).
Figure 1. Examples of data to be sorted: Seismograms with P-wave (blue) and S-wave (red) for an event in Rock Valley (left). Time-versus-distance plot for the Apollo experiment (right).


This project has demonstrated our ability to manage and process large volumes of existing seismic data sets by developing efficient workflows and the necessary algorithms. We initiated the development of a novel approach to calibrating and characterizing the local geology of International Nuclear Test Monitoring Sites. This was done by improving the velocity modeling for the NNSS, which is necessary to conduct a capability assessment for empirically derived signal observations with existing datasets. The Python-based software packages improve our internal capabilities to process and analyze various seismic data collected at the NNSS. We developed a workflow to streamline and automate the ambient noise model development. Future work is necessary to expand this capability to higher resolution tomographic models that can model high frequency seismic signals.

Mission Benefit

By providing a tie to readily available seismic sources and the rarer active sources, we will be able to aid the monitoring community in characterizing events that occur in aseismic regions. One goal of using data from a large source is to be able to calibrate the International Monitoring System (IMS) stations as well as the Air Force Technical Applications Center’s (AFTAC) seismic stations. A methodology with empirical results will provide key site characteristics as well as define the overall array health for international monitoring. The calibration and velocity characteristics for key sites could be turned into a full program for the DNN community to improve test detection. GNDD and DNN will directly benefit from this work because it fits into their portfolios for improving the ability to detect underground nuclear explosions. Understanding the propagation of seismic energy is a fundamental aspect of seismic monitoring related to detection of underground nuclear explosions.

Publications, Technology Abstracts, Presentations/Posters

Zeiler C. P., R. S. Turley, M. E. Scalise, R. L. White, J. R. Caylor. 2022. “Ensuring Timing from Nodal and Network Seismic Systems is Synchronized.” Poster presented at the Seismological Society of America Annual Conference, Bellevue, Washington.

This work was done by Mission Support and Test Services, LLC, under Contract No. DE-NA0003624 with the U.S. Department of Energy. DOE/NV/03624–1648.

Return to User-Centered Remote Testing and Operations
Go to SDRD Annual Report Index
Return to SDRD Homepage