Array Response Functions with ArrayGUI Nawa Dahal Robert Martin-Short Sharmin Shamsalsadati Voon Hui Lai IRIS Short Course Aug, 2015

Array Seismology Applications: Numerous seismometers placed at discrete points in a well-defined configuration to record ground motions. Applications: Lower the detection threshold of global earthquakes Detect and identify nuclear explosion Detect phases that usually are not detected by single station High-resolution tomographic images on regional scale Detect small-scale structures in the Earth’s mantle Use in ambient noise interferometry studies Through the use of array data and the appropriate processing techniques the relative size of seismic signals, with respect to the ambient seismic noise within the Earth, is increased. This enables us to study phases that normally do not show up in seismograms of single sta- tions with amplitudes large enough to study travel times and/or waveforms. array seismology has the potential to refine the scale at which Earth’s interior is resolved, and we think that with the deployment of more temporary and permanent arrays more questions about how the Earth works will be answered, and perhaps many more will be raised.

New trend in seismology Bigger, wider, denser and more powerful

Challenges in Array Seismology Need a good array configuration to ensure wave coherency. Need an easy way to check station quality for large number of stations. Need to determine the direction of seismic sources (particularly for ambient noise interferometry) Beamforming studies requires wave to be coherent-->

Dataset: ALBACORE OBS Array Importance of choosing array configuration to study scattered tsunami waves All stations 9 selective stations Higher frequency Good arrays show a distinctive maximum of the ARF with small sidelobes and no azimuthal dependence of the ARF.

Introducing ArrayGUI One stop to access array and station quality PSD plots Input: Specify 1. Network / Area of interest 2. Time interval ARFs Interactive python GUI Beamforming analysis over time (future development)

Screen shots from the example GUI: Polygon drawing tool: select array geometry Look at station metadata; create PSD plots Create response function for stations of interest

User inputs network code, time window Note: Assure user already has the data, in future, call use GUI to download this too User inputs network code, time window User choose array geometry GUI displays network map User makes ARF Option to view station PFD/PDF plots, and metadata Draw a Bad ARF User creates beamforming plot Good ARF

Set array response function defaults: frequency range of interest, approximate phase velocity. Also set PSD and station metadata defaults. .

A Python GUI Tkinter module (Tk GUI toolkit) Contains functionality (ex: make buttons, menus, pop-up windows) Easy to use within code organized in classes: each method can describe a different GUI aspect. Buttons link to commands: Make new maps on the fly Allow selection of individual stations Link to Obspy modules and to IRIS PDF/PSD noise toolkit Download/analyse waveforms from the selected stations

Existing tools Problem with Existing Tools IRIS Tools: Open source codes but not integrated. (ref) Established codes can be optimized Improve workflow IRIS Tools: IRIS DMC PSD plot Plot ARFs using obspy function. Example of beamforming in backprojection tools. No physical product except personal Matlab scripts. Existing tools

Power Spectral Density plot Allows analysis of noise characteristics of a selected station over a selected time window User select station in ‘station options’ window; inputs time range. Program links to IRIS noise toolkit; produces enhanced PSD plot

Array Response Function (ARF) Purpose: Access quality of array configuration (geometry, interstation distance, wave frequencies) Input: station coordinates, frequencies, limits of wavenumber Main code: (obspy-array_transff_wavenumber) Output: plot of ARF, list of parameters, and map Array Response Function (ARF) Station list. Frequency. Parameters.

Future development: Beamforming Analysis Purpose: Determine signal source directivity over time Steps: -Fix slowness, frequencies, stations -Perform beamforming using “delay-and-sum” method across all back-azimuths -Plot relative power for each back-azimuth over time Rewrite existing MATLAB scripts into Python (consider using HPC)

Potential Applications/Users Simplify usage of large array data sets Detect direction of ambient noise sources Facilitate education and outreach

Thank You!