Automatic event detection on noisy microseismogramsmcee.ou.edu/aaspi/publications/2014/Fangyu_SEG.pdf · 2014-09-02 · Accurate automatic seismic event identification is a fundamental

Automatic Event Detection on Noisy Microseismograms Fangyu Li*, Jamie Rich, and Kurt J. Marfurt, The University of Oklahoma;

Huailai Zhou, Chengdu University of Technology;

Summary

Accurate automatic seismic event identification is a

fundamental problem for passive microseismic monitoring.

Simply stated, more accurate picks provide more accurate

location of microseismic events. This paper introduces a

new approach carrying out precise seismic event

determination based on high-order statistics (HOS). Short

term kurtosis to long term kurtosis ratio (S/L-Kurt) is a

simple, accurate and fast event identification method. By

measuring kurtosis, we are able to identify the transition

from Gaussian to non-Gaussian behavior that coincides

with the onset of the microseismic event in the presence of

noise. We test the reliability and robustness of the proposed

algorithm on synthetic and real field data and find our

method provides accurate picks even in noisy

microseismograms. The simplicity of the proposed method

makes it an attractive candidate for the analysis of huge

seismic data sets.

Introduction

Microseismic tremors are low amplitude events that are key

to understanding slow-slip earthquakes, incipient volcanic

activity, and hydraulic fracturing. In all three cases, the

seismic records register different kinds of waves

originating from a certain unknown source locations within

the subsurface of the earth. The location and moments of

such events can be determined by measuring the of the

arrival time, polarization, and amplitude of compressional

(P-wave), transverse (S-wave), and surface waves (Raleigh,

Love waves).

In microseismic analysis of hydraulic fracturing, accurate

and reliable picking of the first (P-wave) arrival is of

paramount importance. The arrival of a compressional

wave or P-wave, which has the largest propagation

velocity, denotes the onset of a seismic event. This arrival

can provide important geophysical and seismological

information, and is predominantly used for acoustic/seismic

source location, different types of signal frequency

analysis, mechanism determination, structure description,

seismicity designation, and hazard assessment. etc.

Traditionaly, reliable and accurate signal onset

determination is done by visual human analyst inspection.

However, during applications of passive seismic

monitoring, microseismic datasets may consist of tens of

hundreds of thousands of seismic traces, even though the

vast majority may be discarded after found to contain no

useful information. (Maxwell and Urbancic, 2001). With

the increase in the number of monitoring wells, the number

of stages in a given well, and simultaneous, sequential, or

zipper stimulation of multiple wells from the same or near

by pads, the increase in the amount of continuous data has

increased dramatically, thereby motivating the development

of reliable, automated seismic event picking algorithms. In

addition to consistency and ability to process large datasets,

automated procedures are key to real-time event location

applications that may influence completion decisions made

for subsequent stages.

In the last three decades, several automatic data processing

algorithms for determining individual P-wave arrival time

have been developed, based on characteristic function (CF)

(Allen, 1978), crossing of the threshold level of singal

average energy (Akram, 2011), ratio of short-term average

to long-term average (STA/LTA) (Baer and Kradolfer,

1987; Munro, 2004; Chen and Stewart, 2005), modified

energy ratio (MER) (Han et al, 2010), seismic wave

polarity assumption (Jurkevic, 1988), neural network (Zhao

and Takano, 1999; Gentili and Michelini, 2006), wavelet

transform (WT) (Anant and Dowla, 1997), high-order

statistics (HOS) (Saragiotis et al, 2002), and hybrid HOS

and WT (Saragiotis et al, 1999). However, despite their

sophistication, these methods can be overly sensitive to

noise, perform better in some locations than others, and

often require human intervention.

In this paper, we developed a new method involving the

computation of kurtosis, the fourth zero-lag cumulant

parameter. The proposed short term kurtosis to long term

kurtosis ratio (S/L-Kurt) method takes into account the

non-Gaussian nature of the P-wave in order to overcome

the above mentioned drawbacks. Though our proposed

algorithm is based on HOS, which in general has high

computational complexity, it is straightforward, simple to

implement and computationally tractable given current

field computer capabilities. Experimental results of

synthetic and field data show a promising performance both

on accuracy and robustness in the presence of noise.

Mathematical Background

Statistics describe the patterns of a collection of data. There

are four basic statistic moments: the mean value (first

statistical moment), the standard deviation (second

statistical moment), skewness (third statistical moment) and

kurtosis (fourth statistical moment). Theoretically, any

order statistic moment can be used, but the computational

complexity increases with the order.

Page 2363SEG Denver 2014 Annual MeetingDOI http://dx.doi.org/10.1190/segam2014-1605.1© 2014 SEG

Dow

nloa

ded

09/0

1/14

to 1

29.1

5.12

7.24

5. R

edis

trib

utio

n su

bjec

t to

SEG

lice

nse

or c

opyr

ight

; see

Ter

ms

of U

se a

t http

://lib

rary

.seg

.org

/

Automatic Seismic Event Detection on Microseimograms

For dataset1 2, , , NX X X , the mean value is defined as

1

N

iiX

mean XN

, (1)

The standard deviation as

2

2 1( )

1

N

iiX X

stdN

, (2)

And the skewness (a measure of the lack of symmetry) as

3

1

3

( )

( 1)

N

iiX X

skewnessN

. (3)

The skewness for a normal distribution is zero; any

symmetric data should have skewness near zero.

Kurtosis is the measure of “peakedness” or heaviness of the

tails in the distribution of the sequence, and is defined as

4

1

4

( )3

( 1)

N

iiX X

kurtosisN

.

(4)

The kurtosis value will be high for anomalous outliers

indicating non-Gaussian signals (such as seismic events)

and can be viewed as the measure of Gaussianity.

Figure 1: A field microseismogram (Top) and normalized

distribution densities (Bottom). Before the seismic event, the data in the red box is noisy and exhibits an almost Gaussian distribution

(red dotted curve). In contrast the data in the black box containing

the P-wave event exhibits a distribution that is non-Gaussian, and more peaked than the reference Gaussian distribution given by the

red dotted curve.

Figure 1 shows different distribution densities of the

microseismograms with and without a seismic event.

Before the seismic event (within the red window), the

microseismogram exhibits an almost Gaussian distribution

with the value of kurtosis=0.7056. In the presence of the

seismic event (within the black window) the distribution is

non-Gaussian with the value of kurtosis=14.1312 giving a

more peaked distribution in Figure 1. In this example

kurtosis establishes an effective statistical test in

identifying signals having a non-Gaussian distribution. In

contrast, the skewness value doesn’t change as much as the

kurtosis, with both distributions appearing quite

symmetrical in Figure 1.

The S/L-Kurt Algorithm

Kurtosis is a measure of the signal’s Gaussianity. In order

to estimate the kurtosis value at each time sample, we

employ a sliding window. The choice of the length of the

window function is a fundamental problem. If the window

is not long enough, the background noise may show many

windows to be non-Gaussian characteristic. If the window

is too long, the non-Gaussian statics will be swamped in the

Gaussian statistics of the preceding and following noise and

not differentiate the seismic event.

Inspired by the STA/LTA method, we propose to utilize the

ratio of short term kurtosis (STK) and long term kurtosis

(LTK) to determine the onset of non- Gaussianity of the

seismograms. Let ls and ll be the length of short and long-

term window, respectively. Then the kurtosis of the short

and long-term windows preceding the time index j are

4

4

( )

( 1)

j

i ji j ls

j

j

X XSTK

ls

,

(5)

where,

2

2( )

,1

j j

i j ii j ls i j ls

j j

X X XX

ls ls

,

and 4

4

( )

( 1)

j

i ji j ll

j

j

X XLTK

ll

,

(6)

where,

2

2( )

,1

j j

i j ii j ll i j ll

j j

X X XX

ll ll

.

Finally, we define:

/j

j

j

STKS L Kurt ratio

LTK

,

where, is a small value added to the denominator to

avoid division by zero.

The S/L-Kurt detects an increase of STK compared to the

preceding LTK, derived by proper windowing of the

seismic trace. When the S/L-Kurt exceeds an empirically

set threshold, an event is detected.

Synthetic Examples

In this section, we use synthetic examples and compare our

new algorithm to the well known STA/LTA algorithm to


Dow

nloa

ded

09/0

1/14

to 1

29.1

5.12

7.24

5. R

edis

trib

utio

n su

bjec

t to

SEG

lice

nse

or c

opyr

ight

; see

Ter

ms

of U

se a

t http

://lib

rary

.seg

.org

/


show its effectiveness. Our synthetic microseismic events

consists of two exponetially decaying sine waves, denoting

P-wave and S-wave arrivals.

Figure 2 demonstrates noise-free and very noisy situations,

and indicates that S/L-Kurt is more robust than the

STA/LTA method. Figure 3 addresses the situation where

for reasons of attenuation or moment tensor that the P-

wave is weaker than S-wave; the proposed method still

works well.

Figure 2: Synthetic microseismic event picking example. (Left) noise-free microseismogram with STA/LTA and S/L-Kurt result; (Right) 0 dB

noisy microseismogram with STA/LTA and S/L-Kurt result. It is clear that S/L-Kurt method is more robust.

Figure 3: Synthetic example. (Left) microseismogram with stronger P-wave onset and S/L-Kurt, STK, LTK results; those with stronger S-wave onset are on the (Right). S/L-Kurt method recognizes both seismic events, but the other two statistical attributes only detect the stronger one. (In

order to distinguish them, STK and LTK are normalized into [0 0.5], while S/L-Kurt is normalized into [0 1].)

The methods based on average or standard deviation are

actually responsive to the change of energy. Therefore, they

are effective in the noise free situation, but perform poorly

when the signal-noise ratio (SNR) is low.

In contrast, the S/L-Kurt method is based on Gaussianity

detection. The background noise is often assumed to

Gaussian random noise. In principal, the strength of the

Gaussian noise does not change the non-Gaussianity of the

seismic event up to and perhaps beyond the 0 dB level. We

hypothesize that this is why the S/L-Kurt scheme is more

robust in the presence of noise than methods based on other

principles.

In addition, Figure 3 displays the difference between S/L-

Kurt and kurtosis in sliding windows. Both LTK and STK


Dow

nloa

ded

09/0

1/14

to 1

29.1

5.12

7.24

5. R

edis

trib

utio

n su

bjec

t to

SEG

lice

nse

or c

opyr

ight

; see

Ter

ms

of U

se a

t http

://lib

rary

.seg

.org

/


are biased towards identification of the stronger event, such

that in the right part of the Figure 3, they recognize the S-

wave event rather than P-wave event. In contrast, the S/L-

Kurt method recognizes both seismic events.

Field data Examples

Before hydraulic fracturing, the perforation shots are used

to determine the orientation of geophones. Since the

perforation data have a relatively high SNR, we first apply

the proposed method on a perforation shot record. Figure 4

shows that the proposed method can not only accurately

identify the seismic P-wave onset, but also the two

subsequent S-wave arrivals.

Figure 4: Perforation shot example. (Top) Perforation shot; (Bottom) Normalized S/L-Kurt, STK and LTK results. (STK and

LTK are normalized into [0 0.5], while S/L-Kurt into [0 1].)

Figure 5: Field data example with strong P-wave. (Top) Micro-seismic record; (Bottom) Normalized S/L-Kurt, STK and LTK

results. (STK and LTK are normalized into [0 0.5], while S/L-Kurt

into [0 1].)

Figure 6: Field data example with strong S-wave. (Top) Micro-

seismic record; (Bottom) Normalized S/L-Kurt, STK and LTK results. (STK and LTK are normalized into [0 0.5], while S/L-Kurt

into [0 1].)

Figures 5 and 6 demonstrate the field microseismic data

application results. Regardless of whethere the P-wave or

S-wave is stronger, S/L-Kurt can detect both arrivals. A

simple threshold segmentation step can help us pick an

accurate seismic event first break.

Conclusion

We have developed a seismic event indentification

algorithm based on HOS. The proposed S/L-Kurt method

provides reliable and accurate seismic event identification

on both synthetic and field data examples in the presence

of 0 dB Gaussian noise. The examples shown indicate that

the method can detect not only the P-wave arrival but also

both S-wave arrivals.

Acknowledgments

We express our gratitude to the industry sponsors of the

Attribute-Assisted Seismic Processing and Interpretation

(AASPI) Consortium for their financial support. The field

seismograms were provided by Devon Energy. Also thanks

to the National Natural Science Foundation of China

(seismic multi-wave fields characteristics analysis of the

thin interbedded reservoirs, Grant No. 41204091).


Dow

nloa

ded

09/0

1/14

to 1

29.1

5.12

7.24

5. R

edis

trib

utio

n su

bjec

t to

SEG

lice

nse

or c

opyr

ight

; see

Ter

ms

of U

se a

t http

://lib

rary

.seg

.org

/

http://dx.doi.org/10.1190/segam2014-1605.1 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2014 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES

Akram, J., 2011, Automatic P-wave arrival time picking method for seismic and microseismic data: Recovery: CSPG CSEG CWLS Convention.

Allen, R. V., 1978, Automatic earthquake recognition and timing from signal traces: Bulletin of the Seismological Society of America, 68, 1521–1532.

Anant, K. S., and F. U. Dowla, 1997, Wavelet transform methods for phase identification in three-component seismograms : Bulletin of the Seismological Society of America, 87, 1598–1612.

Baer, M., and U. Kradolfer, 1987, An automatic phase picker for local and teleseismic events: Bulletin of the Seismological Society of America, 77, 1437–1445.

Chen, Z., and R. Stewart, 2005, Multiwindow algorithm for detecting seismic first arrivals: CSEG National Convention, 355–358.

Gentili, S. , and A. Michelini, 2006, Automatic picking of P- and S-phases using a neural tree: Journal of Seismology, 10, no. 1, 39–63, http://dx.doi.org/10.1007/s10950-006-2296-6.

Han, L., J. Wong, and J. C. Bancroft, 2010, Time picking on noisy microseismograms: GeoCanada Working with the Earth.

Jurkevic , A., 1988, Polarization analysis of three-component array data: Bulletin of the Seismological Society of America, 78, 1725–1743.

Maxwell, S. C., and T. I. Urbancic , 2001, The role of passive microseismic monitoring in the instrumented oilfield: The Leading Edge, 20, 636–639, http://dx.doi.org/10.1190/1.1439012.

Munro, K.A., 2004. Automatic event detection and picking P-wave arrivals: CREWES Research Report 18, 12.1–12.10.

Saragiotis, C. D., L. J. Hadjileontiadis, and S. M. Panas, 1999, A higher-order statistics-based phase identification of three-component seismograms in a redundant wavelet transform domain: Proceedings of the IEEE Workshop High-Order Statistics, 396–399.

Saragiotis, C. D., L. J. Hadjileontiadis, and S. M. Panas, 2002, PAI-S/K: A robust automatic seismic P-phase arrival identification scheme: IEEE Transactions on Geoscience and Remote Sensing, 40, no. 6, 1395–1404, http://dx.doi.org/10.1109/TGRS.2002.800438.

Zhao, Y., and K. Takano, 1999, An artificial neural network-based seismic detector: Bulletin of the Seismological Society of America, 77, 670–680.


Dow

nloa

ded

09/0

1/14

to 1

29.1

5.12

7.24

5. R

edis

trib

utio

n su

bjec

t to

SEG

lice

nse

or c

opyr

ight

; see

Ter

ms

of U

se a

t http

://lib

rary

.seg

.org

/

Documents

Automatic event detection on noisy microseismogramsmcee.ou.edu/aaspi/publications/2014/Fangyu_SEG.pdf · 2014-09-02 · Accurate automatic seismic event identification is a fundamental