Waveform modeling is crucial to improving our understanding of observed seismograms. Forward simulation of wavefields provides quantitative methods of testing interactions between complicated source functions and the propagation medium. Here, we discuss three experiments designed to improve under standing of high frequency seismic wave propagation. First, we compare observed and predicted travel times of crustal phases for a set of real observed earthquakes with calculations and synthetic seismograms. Second, we estimate the frequency content of a series of nearly co-located earthquakes of varying magnitude for which we have a relatively well- known 1D velocity model. Third, we apply stochastic perturbations on top of a 3D tomographic model and qualitatively assess how those variations map to differences in the seismograms. While different in scope and aim, these three vignettes illustrate the current state of crustal scale waveform modeling and the potential for future studies to better constrain the structure of the crust.
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
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.
The Geophysical Monitoring System (GMS) State-of-Health User Interface (SOH UI) is a web-based application that allows a user to view and acknowledge the SOH status of stations in the GMS system. The SOH UI will primarily be used by the System Controller, who monitors and controls the system and external data connections. The System Controller uses the station SOH UIs to monitor, detect, and troubleshoot problems with station data availability and quality.
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.
The ability to accurately locate seismic events is necessary for treaty monitoring. When using techniques that rely on the comparison of observed and predicted travel times to obtain these locations, it is important that the estimated travel times and their estimated uncertainties are also accurate. The methodology of Ballard et al. (2016a) has been used in the past to generate an accurate 3D tomographic global model of compressional wave slowness (the SAndia LoS Alamos 3D tomography model, i.e. SALSA3D). To re-establish functionality and to broaden the capabilities of the method to local distances, we have applied the methodology of Ballard et al. (2016a) to local data in Utah. This report details the results of the initial model generated, including relocations performed using analyst picked mining events at West Ridge Mine and three ground-truth events at Bingham Mine. We were successfully able to generate a feasible tomography model that resulted in reasonable relocations of the mining events.