Post on 13-Feb-2016
description
Automatic detection and Automatic detection and location of microseismic location of microseismic events events Tomas Fischer
OutlineOutlineWhy automaticHow automaticErrors West Bohemia swarm 2000Hydraulic stimulation in gas field
in Texas
Why automatic Why automatic processing?processing?Huge datasetsImprove productivityImprove data homogeneityReal time processing – alarms
Utilization of automatic Utilization of automatic processingprocessingMeasurement of arrival times Measurement of amplitudesPhase-waveform extraction
Hypocentre locationSource parameters, focal mechanismsSeismic tomographyAttenuation studies…
ApproachesApproachesClassical - stepwise:
(single station / network)1. Phase detection & picking2. Hypocentre location
Simultaneous (seismic network)– source scanning / back-propagation(Kao & Shan, 2003; Drew 2005)
Classical approach – stepsClassical approach – stepsPhase detection – increased
signal energy, single station
Phase association – consistency betw. stations
Phase picking – identify phase onset
Location of hypocenters
Phase detectionPhase detectionTransform 3C seismogram to a
scalar > 0, characteristic function CF (Allen, 1978)
Find maxima of CF S-wave energy detector
ENZ
–maximum eigenvalue of signal
covariance matrix
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Σnini Σniei
Σniei Σeiei
⎛ ⎝ ⎜
⎞ ⎠ ⎟
in a running window
Distinguishing P and S-Distinguishing P and S-waveswavesHierarchic approach First find S-waves (higher amplitude, horiz.
polarization) Then find P-waves (perpendicular
polarization)
Distinguishing P and S-Distinguishing P and S-waveswavesEqual 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)
Phase associationPhase associationSimple 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
1 2
Source
Phase pickingPhase pickingFind onset – abrupt
amplitude increaseSTA/LTA
(non-overlapping)
Higher statistic momentsKurtosis
Waveform cross-correlationSTA/LTA
Kurtosis
Horiz. Polarization
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S4 =x i − X ( )
4
i=1
N
∑Nσ 4
Automatic locationAutomatic locationNo 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
Event locationEvent location2D array (Earth surface)
– P-waves sufficient (S-waves beneficial)
1
2
3
4 t1-t2
t3-t4
t2-t3
Event locationEvent location
depth1
2
3
4
5
t1>t2>t3=t4<t5
1
Map viewDepth
view
1D array (borehole)both P and S-waves needed
GoodnessGoodnessPicking success
◦Amplitude ratio @ pick◦Location residual
Location success◦Location residual◦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
Location residual calibration Location residual calibration (remove gross errors)(remove gross errors)Training dataset – if manual
processing availableLoc. error:
difference between manual and automaticlocations
6 samples
Location residual calibration Location residual calibration (remove gross errors)(remove gross errors)Dataset to be processed
Limit for choice of good locations
Swarm 2000 in West Swarm 2000 in West BohemiaBohemia
4 SP stations
0-20 km epicentral distance
synchronous triggered recording
Swarm 2000 Automatic Swarm 2000 Automatic processingprocessingCharacteristic functionS: maximum eigenvalue of the covariance matrix in
horizontal plane (Magotra et al., 1987)
P: sum of the Z-comp. and its derivative (Allen, 1978)
Method1. S-waves, minimum interval>maximum expected tS-tP2. P-waves in a fixed time window prior to S3. Only complete P and S pairs processed
=> homogeneous dataset
)()( tzKtzCFP
2
4 22neeenneenn
neSCF
Swarm 2000 in West Swarm 2000 in West BohemiaBohemiaResulting automatic picks
Swarm 2000 in West Swarm 2000 in West BohemiaBohemia>7000 detected events, 4500 well located
Homogeneous catalog downto ML=0.4Location error: ±100 m horiz. and ±200 m vert. 24.4 .2001
psonset9.pas - N KC m aster(psons22.txt)
- 1 0 1 2 3M l
1
10
100
1000
10000
NAll events
R M S<8 sm pl
0 10 20 30 40 50residuum lokace, sm pl.
0
1000
2000
3000
4000
5000
6000
7000
N
-600 -400 -200 0 200 400 600m eters
0
40
80
120
160ev
ents
-600 -400 -200 0 200 400 600m eters
Difference between manually and automately
obtained hypocentre locations
E-W coo., stdev 77 m
N-S coo., stdev 127 m
depth, stdev 127 m
-0.10 0.00 0.10 0.20 0.30 0.40secs
origin tim e, stdev 38 m s
Swarm Nový Kostel 2000
Automatic locations with RMS<8 smpl.Automatic locations with RMS<8 smpl.compared with 405 manually located eventscompared with 405 manually located events
P1 a P2
1 2 3 4 5 6+7 8 9
Automatic locations of the 2000 swarm
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
8 3C geophonescontinuous recording
Hydraulic stimulation in gas Hydraulic stimulation in gas fieldfieldS-wave picker Get the maximum eigenvalue tof the signal
covariance matrix
Find maxima of polarized energy
arriving at consistent delays j 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
j
jj ttL
Hydraulic stimulation in gas Hydraulic stimulation in gas fieldfieldP-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
ssps ...Pc
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tS − tP = tS α − β( ) /β
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas field
Hydraulic stimulation in Hydraulic stimulation in gas fieldgas fieldComparison 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.
PS
Comparison of manual and auto Comparison of manual and auto locationslocations
ConclusionsConclusionsautomatic 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
OutlinesOutlinesuse waveform cross-correlation
for picking