Group 3: Team LVF
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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)
*xcorrCCt (t )
PPt(t*) xcorr
corr (t ) =
2 2
é
ùû SqrtC Ct ( t )ù
RMS
RMSéëPP(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?