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

2. 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.

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

4. 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-->

5. 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.

6. 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)

7. 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

8. 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

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

10. 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

11. 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

12. 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

13. 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.

14. 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)

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

16. Thank You!