1. Automatic detection and location of microseismic events
Tomas Fischer
Automatic detections and location of microseismic events
Approaches: (1) first detection, phase picking, then location, testing different combinations, (2) Simultaneous (source scanning algorithm, backward propagation
ad(1) blind picking

2. Outline Why automatic How automatic Errors West Bohemia swarm 2000
Hydraulic stimulation in gas field in Texas

3. Why automatic processing?
Huge datasets
Improve productivity
Improve data homogeneity
Real time processing – alarms

4. Utilization of automatic processing
Measurement of arrival times
Measurement of amplitudes
Phase-waveform extraction
Hypocentre location
Source parameters, focal mechanisms
Seismic tomography
Attenuation studies …

5. Approaches
Classical - stepwise: (single station / network) 1. Phase detection & picking 2. Hypocentre location
Simultaneous (seismic network) – source scanning / back-propagation (Kao & Shan, 2003; Drew 2005)

6. Classical approach – steps
Phase detection – increased signal energy, single station
Phase association – consistency betw. stations
Phase picking – identify phase onset
Location of hypocenters

7. Phase detection
Transform 3C seismogram to a scalar > 0, characteristic function CF (Allen, 1978)
Find maxima of CF
S-wave energy detector
E N Z l– maximum eigenvalue of signal covariance matrix
in a running window

8. Distinguishing P and S-waves
Hierarchic approach
First find S-waves (higher amplitude, horiz. polarization)
Then find P-waves (perpendicular polarization)

9. Distinguishing P and S-waves
Equal approach
evaluate horiz. & vert. polarization
find consecutive intervals of perpendicular polarization (ampl. ratio or hor/vert gives hint to which one is P and S)

10. Phase association
Simple kinematic (geometric) criteria e.g. t2 < t1+t12
A-priori information on source position - plane wave consistency
Preliminary location - test the phase consistency by location residual
Source

11. Phase picking Find onset – abrupt amplitude increase
STA/LTA (non-overlapping)
Higher statistic moments Kurtosis
Waveform cross- correlation
Horiz. Polarization
Kresleno pomoci
AutoPickN1…
STA/LTA
Kurtosis

12. Automatic location
No special needs (each location algorithm is automatic)
Hydrocarbon reservoir stimulations – linear array of receivers – besides arrival times also backazimuth (polarization) needed => modify the location algorithm to include also the fit to the polarization data

13. Event location
2D array (Earth surface) – P-waves sufficient (S-waves beneficial)
t3-t4 1
t1-t2
t2-t3 2 3 4

14. Event location 1D array (borehole) both P and S-waves needed 1
Map view
depth
Depth view 1 2 3 4 5
t1>t2>t3=t4<t5

15. Goodness Picking success Location success Amplitude ratio @ pick
Location residual
Location success
Sharpness of foci image ?
! Location residual – results from
Unknown structure
Timing errors
Picking errors (Gaussian & gross)
=> Residual is not a unique measure of picking success

16. Location residual calibration (remove gross errors)
Training dataset – if manual processing available
Loc. error: difference between manual and automatic locations
6 samples

17. Location residual calibration (remove gross errors)
Dataset to be processed
Limit for choice of good locations

18. Swarm 2000 in West Bohemia 4 SP stations 0-20 km epicentral distance
synchronous triggered recording

19. Swarm 2000 Automatic processing
Characteristic function
S: maximum eigenvalue of the covariance matrix in horizontal plane (Magotra et al., 1987)
P: sum of the Z-comp. and its derivative (Allen, 1978)
Method
S-waves, minimum interval>maximum expected tS-tP
P-waves in a fixed time window prior to S
Only complete P and S pairs processed => homogeneous dataset

20. Swarm 2000 in West Bohemia
Resulting automatic picks

21. Swarm 2000 in West Bohemia >7000 detected events, 4500 well located
Homogeneous catalog downto ML=0.4
Location error: ±100 m horiz. and ±200 m vert.

22. Automatic locations with RMS<8 smpl
Automatic locations with RMS<8 smpl. compared with 405 manually located events

23. Automatic locations of the 2000 swarm
P1 a P2

24. Hydraulic stimulation in gas field

25. Hydraulic stimulation in gas field
8 3C geophones
continuous recording

26. Hydraulic stimulation in gas field
S-wave picker
Get the maximum eigenvalue l(t) of the signal covariance matrix
Find maxima of polarized energy arriving at consistent delays tj to vertical array (derived from expected slowness)
Identify the S-wave onsets tS by STA/LTA detector in a short time window preceding the maxima of L(t)
Measure S-wave backazimuth
Array compatibility check by fitting hodochrone tS(z) by parabola, outliers repicked or removed

27. Hydraulic stimulation in gas field
P-wave picker
Search for signal s polarized in S-ray direction p. We use the characteristic function
Find maxima of P-wave polarized energy Cp(t) arriving at consistent slowness (similar as in S-wave detection)
Identify the P-wave onsets tP by STA/LTA detector in a short time window preceding the maxima of Cp(t)
Measure the P-wave backazimuth
Use Wadati’s relation to remove tP outliers

28. Hydraulic stimulation in gas field

29. Hydraulic stimulation in gas field

30. Hydraulic stimulation in gas field

31. Hydraulic stimulation in gas field

32. Hydraulic stimulation in gas field
Comparison of manual and auto picks for 296 manually picked events
Fig. 3. Distribution of time differences between automatically and manualy obtained arrival times of test dataset. P S

33. Comparison of manual and auto locations

35. Conclusions
automatic processing useful in case of huge datasets & provides homogeneous results
two approaches
classic – mimics human interpreter
modern – direct search for the hypocentre
classic – network consistency beneficial
two case studies show successful implementation of polarization based picker

36. Outlines
use waveform cross-correlation for picking