Automatic detection and
location of microseismic events
Tomas Fischer
Outline

Why automatic

How automatic

Errors

West Bohemia swarm 2000

Hydraulic stimulation in gas field in Texas
Why automatic processing?

Huge datasets

Improve productivity

Improve data homogeneity

Real time processing – alarms
Utilization of automatic processing
Measurement of arrival times
 Measurement of amplitudes
 Phase-waveform extraction

Hypocentre location
 Source parameters, focal mechanisms
 Seismic tomography
 Attenuation studies
…

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)
Classical approach – steps

Phase detection – increased signal energy,
single station

Phase association – consistency betw.
stations

Phase picking – identify phase onset

Location of hypocenters
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
 n i n i

 n i e i
 n i e i 

 e i e i 
in a running window
Distinguishing P and S-waves
Hierarchic approach


First find S-waves (higher amplitude, horiz.
polarization)
Then find P-waves (perpendicular polarization)
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)
Phase association

Simple kinematic (geometric) criteria
e.g.
1
2
t2 < t1+t12
Source

A-priori information on source position
- plane wave consistency

Preliminary location
- test the phase consistency by location residual
Phase picking
Find onset – abrupt
amplitude increase

STA/LTA
(non-overlapping)
Horiz. Polarization

Higher statistic moments
Kurtosis
N
 x
S4 

 X
4
i
STA/LTA
i 1
Waveform cross
correlation
N
4
Kurtosis
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

Event location

2D array (Earth surface)
– P-waves sufficient (S-waves beneficial)
t3-t4
1
2
t2-t3
3
4
t1-t2
Event location

1D array (borehole)
both P and S-waves needed
1
depth
Depth
view
1
2
3
4
5
t1>t2>t3=t4<t5
Map
view
Goodness

Picking 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
(remove gross errors)

Training dataset – if manual processing available

Loc. error:
difference
between
manual and
automatic
locations
6 samples
Location residual calibration
(remove gross errors)

Dataset to be processed
Limit for choice of good locations
Swarm 2000 in West Bohemia
4 SP stations
 0-20 km
epicentral
distance
 synchronous
triggered
recording

Swarm 2000 Automatic processing
Characteristic function

S: maximum eigenvalue of the covariance matrix in
horizontal plane (Magotra et al., 1987)











4

CF

l

2
2
S

2.
3.
ne
P: sum of the Z-comp. and its derivative (Allen, 1978)
Method
1.
nn
ee nn
ee
ne
2

CF

z
(
t
)

K
z
(
t
)
P
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
Swarm 2000 in West Bohemia
Resulting automatic picks
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.
7000
10000
2 4 .4 .2 0 0 1
p so n s e t9 .p a s
- N K C m a ste r
(p so n s 2 2 .txt)
6000
1000
N
5000
4000
100
N
3000
A ll e v e n ts
2000
10
R M S<8 sm pl
1000
0
1
0
10
20
30
40
50
re sid u u m lo ka ce , sm p l.
-1
0
1
Ml
2
3
Automatic locations with RMS<8 smpl.
compared with 405 manually located events
S w a rm N o v ý K o s te l 2 0 0 0
160
D iffe re n c e b e tw e e n
m a n u a lly a n d a u to m a te ly
o b ta in e d h y p o c e n tre lo c a tio n s
120
e ve n ts
E -W c o o ., s td e v 7 7 m
N -S c o o ., s td e v 1 2 7 m
d e p th , s td e v 1 2 7 m
o rig in tim e , s td e v 3 8 m s
80
40
0
-6 0 0 -4 0 0 -2 0 0
0
200
m e te rs
400
600
-6 0 0 -4 0 0 -2 0 0
0
200
m e te rs
400
6 0 0 -0 .1 0
0 .0 0
0 .1 0
0 .2 0
se c s
0 .3 0
0 .4 0
12 3 4
5 6+7
8
Automatic
locations of the
2000 swarm
9
P1 a P2
Hydraulic stimulation in gas field
Hydraulic stimulation in gas field
8 3C geophones
continuous recording
Hydraulic stimulation in gas field
S-wave picker


Get the maximum eigenvalue ltof the signal
covariance matrix
Find maxima of polarized energy L t  
 l t  t 
j
j
j
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
Hydraulic stimulation in gas field
P-wave picker
•
Search for signal s polarized in S-ray direction p. We
use the characteristic functionc  s.p . s.s
P
•
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
t S  t P  t S     / 
•
Use Wadati’s relation
tP outliers
to remove
Hydraulic stimulation in gas field
Hydraulic stimulation in gas field
Hydraulic stimulation in gas field
Hydraulic stimulation in gas field
Hydraulic stimulation in gas field
Comparison of manual and auto picks for 296
manually picked events
P
S
Fig. 3. Distribution of
time differences between
automatically and manualy obtained arrival
times of test dataset.
Comparison of manual and auto locations
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

Outlines

use waveform cross-correlation for
picking