Insights from summary ray calculations on the SALSA3D global dataset.
Abstract not provided.
Hariharan, Anant↗
Engineering topics
Conley, Andrea
Publications and source records attributed to Conley, Andrea.
SALSA3D: Updated Tomographic Velocity Models for Improved Travel-Time Prediction and Uncertainty .
Abstract not provided.
Conley, Andrea↗
Exploring Model Resolution for the SALSA3D Global P-wave Velocity Model ? Current Status and Predicted Improvement via SMART Subsea Cable Sensors.
Abstract not provided.
Rowe, Charlotte↗
LocOO3D User's Manual
LocOO3D is a software tool that computes geographical locations for seismic events at regional to global scales. This software has a rich set of features, including the ability to use custom 3D velocity models, correlated observations and master event locations. The LocOO3D software is especially useful for research related to seismic monitoring applications, since it allows users to easily explore a variety of location methods and scenarios and is compatible with the CSS3.0 data format used in monitoring applications. The LocOO3D software, User's Manual, and Examples are available on the web at: https://github.com/sandialabs/LocOO3D For additional information on GeoTess, SALSA3D, RSTT, and other related software, please see: https://github.com/sandialabs/GeoTessJava, www.sandia.gov/geotess, www.sandia.gov/salsa3d, and www.sandia.gov/rstt
58 GEOSCIENCES↗
PCalc User's Manual
PCalc is a software tool that computes travel-time predictions, ray path geometry and model queries. This software has a rich set of features, including the ability to use custom 3D velocity models to compute predictions using a variety of geometries. The PCalc software is especially useful for research related to seismic monitoring applications.
58 GEOSCIENCES↗
Local to regional scale simulation of small events with 2D finite differences.
Abstract not provided.
Porritt, Robert↗
Aftershock Identification Using a Paired Neural Network Applied to Constructed Data
This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗