The EarthScope Automated Receiver Survey is a fully automated data product developed by the University of South Carolina that calculates bulk crustal properties of stations using receiver functions. Developed under NSF/EarthScope funding EARS was transferred in May of 2010 to the IRIS DMC where it joined a growing suite of data products.
EARS continuously monitors the IRIS station database and the IRIS USGS/NEIC weekly PDE catalog for new candidate data. Once a new station and/or event are found, EARS calculates the corresponding receiver functions and flags those that meet EARS quality control criteria for further processing.
To improve the signal level of the receiver functions, EARS first removes their ray-parameter dependency by transforming receiver functions from the amplitude-time domain to amplitude as a function of crustal thickness, H, and the Vp/Vs ratio, K, using the predicted travel times of the Ps, PpPs and PsPs/PpPs phases over a suite of single-layer models. Next an HK stack is formed for the corresponding station by weighting the calculated H and K values based on the instantaneous phase of the receiver functions. This stack is then used to estimate the crustal thickness and Vp/Vs values (Figure 1). Figure 2 shows a schematic diagram of the process flow for EARS.
Currently EARS has calculated over 260,000 receiver functions for over 3,000 stations from 112 networks. By constantly processing data from the Transportable Array (TA) component of the USArray, EARS coverage of the continental US is extending to the east as USArray is rolling eastward (Figure 3).
Comments or questions regarding the EARS product at the DMC can be sent to email@example.com. Please cite the Crotwell and Owens article in SRL http://www.seismosoc.org/publications/SRL/SRL_76/srl_76-6_es.html if results from EARS are used.
EARS development was supported by the EarthScope Program through NSF grants #EAR-0346113 and EAR-0642890 to the University of South Carolina. Continuing operation of EARS at the IRIS DMS is supported by the NSF grants #EAR-0552316 and EAR-0733069.