Team LYF: Applying Python to Waveform Matching Detection Xin Liu (USC), Dongdong Yao(GT), Lili Feng(CU-Boulder)

Bottlenecks:  Large disk usage for saving event and continuous waveforms (up to TB or more)  Large potential computational cost (up to months, even years computation)  Quality control of the available data (Need to be done...) Proposed Solutions:  Dynamically fetcing the data and getting the result without saving tons of data  Parallel computing (mpi, gpu)  Monitoring the data quality while fetcing the data (Need to be done...) Motivation

Our Goal Write a simple, concise and reusable package for detecting earthquake events and future work Use object-oriented Python and divide a big job of multiple stages to multiple classes Use IRIS FDSN web service and get stacked detection trace with a single run

sliding window cross-correlation:  fetch template waveform  predict arrivals, and compute SNR  check corresponding continuous data(hourly/daily)  operate the cross-correlation select study region:  choose available earthquakes(template)  search nearby stations  determine study period stack and output:  stack over all channels  output positive detections(MAD) Work Flow

Algorithm Template (35 sec): P(t) Continuous data (one day here): C(t) Correlation with template (frequency domain) Apply moving average filter F(t) (35 sec) all 1s!

6 35 s 1 day Waveform Correlation coefficient (CC) trace courtesy Xiaofeng Meng

7 Stacked CC trace courtesy Xiaofeng Meng

Classes Design

Simple Test Event: 2015/04/12 02:23:05.15 Ohio M3.2

Stacking

Any question or comment? Future work  More interactive quality control of the waveforms  Utilize the parallel computing  Improve the current code (stacking, etc.)  Store detection information in SQL database