Transcript Group 3: Team LVF

Team LYF:
Applying Python to
Waveform Matching Detection
Xin Liu (USC), Dongdong Yao(GT), Lili Feng(CU-Boulder)
Motivation
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...)
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
Work Flow
select study region:
 choose available earthquakes(template)
 search nearby stations
 determine study period
sliding window cross-correlation:
 fetch template waveform
 predict arrivals, and compute SNR
 check corresponding continuous data(hourly/daily)
 operate the cross-correlation
stack and output:
 stack over all channels
 output positive detections(MAD)
Algorithm
Template (35 sec): P(t)
Continuous data (one day here): C(t)
Correlation with template (frequency domain)
*xcorrCCt (t )
PPt(t*) xcorr
corr (t ) =
2 2
é
ùû SqrtC Ct ( t )ù
RMS
RMSéëPP(t t)Sqrt
ë
û
 
Apply moving average filter F(t) (35 sec)
C t   C t .^ 2*conv F t 
2
all 1s!
35 s
Waveform
1 day
Correlation coefficient (CC) trace
6
courtesy Xiaofeng Meng
Stacked CC trace
7
courtesy Xiaofeng Meng
Classes Design
Simple Test
Event:
2015/04/12 02:23:05.15 Ohio M3.2
Stacking
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
Any question or comment?