Author

Erin L. Taxon

Date of Award

Summer 2022

Document Type

Open Access Thesis

Department

Earth and Ocean Sciences

First Advisor

Thomas Owens

Abstract

An Assessment of the Performance of the EarthScope Automatic Receiver Survey: A User's Guide to EARS.

With the advent of digital seismic recording, the ability to process and interpret large quantities of seismic data has become increasingly vital. EarthScope Automated Receiver Survey, EARS, was launched in 2005 to estimate bulk crustal properties in real time, at all broadband seismograph stations, globally. EARS utilizes the receiver function HK stacking method to estimate a station’s crustal thickness (H) and ratio of P wave velocity (Vp) and S wave velocity (Vs), known as Vp/Vs (K). Receiver function analysis observes the arrival times and amplitudes of five phases (Ps, PpPs, PsPs, PpSs and PsSs) that are generated within the Earth; HK stacking converts amplitude into a function of thickness and Vp/Vs. EARS processes data automatically, updating a station’s H and K estimates as new events are converted to receiver functions and added to the HK stack, where the thickness and Vp/Vs estimate is shown as a global maxima in the HK plot. As of July 1st, 2021, the EARS database consisted of 7,032 stations globally, and has processed over 1.4 million radial receiver functions. Since its release, detailed analysis has not been performed on the robustness of the estimates that EARS is producing. The goal of this research is to assess the reliability of the results and how they have been affected as the database has grown. Establishing strengths and limitations is essential, not only for improving EARS but also when utilizing its results in other fields and studies across the scientific community. To assess reliability, comparisons were made to other existing crustal thickness models Crust1.0 and Crust2.0. Standard deviations of H and K estimates were calculated for all stations, with analysis being conducted on how the standard deviation is impacted by number of events and quality of events used, along with examining the metadata. EARS has been cited over 100 times, being utilized in geophysical research and crustal thickness analysis across the globe. Here, we attempt to compile recommendations on how to best implement EARS and identify various parameters to look for in automated seismic analysis to improve its utility.

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