Temporal and spectral pattern recognition for detection and combined network and array waveform

263 Pages·2017·48.58 MB·English

by  Benjamin Sick

Temporal and spectral pattern recognition
for detection and combined network and
array waveform coherence analysis for
location of seismic events
Von der Fakultät für Bau- und Umweltingenieurwissenschaften
der Universität Stuttgart zur Erlangung der Würde eines
Doktor-Ingenieurs (Dr.-Ing.) genehmigte Abhandlung
Vorgelegt von
ick
Benjamin S
aus Heilbronn
oswig
Hauptberichter: Prof. Dr. Manfred J
okelmann
Mitberichter: Prof. Dr. Götz B
12 2017
Tag der mündlichen Prüfung: . April
Institut für Geophysik der Universität Stuttgart
2017

The story so far: In the
beginning the Universe was
created. This has made a lot of
people very angry and been
widely regarded as a bad move.
— Douglas Adams, The Restaurant at the
End of the Universe
Simplicity is prerequisite
for reliability
— Edsger W. Dijkstra

ACKNOWLEDGEMENTS
My special thanks goes to the following people, organizations
and everybody who I forgot but would deserve to be thanked:
My sincere gratitude goes to my advisor, Prof. Manfred
Joswig who gave me the opportunity for this PhD thesis. Thank
you for your trust and the freedom you gave me to be inde-
pendent during the whole time of the thesis. I am thankful
to Prof. Götz Bokelmann who was so kind to be the second
reviewer. Thanks to my colleague Rudolf Widmer-Schnidrig
for proof reading the thesis and giving me detailed feedback.
Special thanks also to the colleagues of the institute for a nice
atmosphere and open ears for any problems. Especially: Patrick
Blascheck (also for proof reading parts of the thesis), Marco
Walter, Sabrina Rothmund and Rolf Häfner.
I would like to thank the CTBTO for financing my research
position at the institute for the development of the OSI SAMS
software. This allowed me to have a very meaningful program-
ming project and I could apply outcomes of the thesis there.
The CTBTO also gave me the Young Scientist award and the
possibility to attend various very interesting UN training exer-
14
cises, especially the IFE . Peter Labak together with Gregor
Malich of the CTBTO were always good partners who trusted
me with the development of the software.
The thesis could not have been possible without the people
deploying and maintaining the seismic stations and creating the
ground truth data:
1
. Basel dataset: Martin Häge and Marco Walter deployed
the stations. Martin Häge and Patrick Blascheck did the
manual analysis with an extensive ground truth of mul-
tiple thousand events by Patrick Blascheck. The external
data with an additional ground truth was recorded and
made available by the SED.
5
2
. DGMKdataset: UlrichSchwadererwiththehelpofPatrick
Blascheck, GregorMokelke, MarcSchmid, AlexanderKich-
erer, Tom Rohloff and Juan-Carlos Santoyo deployed the
initial DGMK network. Later maintenance and exten-
sions were done by Zaneta Gurbisz and Gregor Mokelke
supported mainly by Marc Schmid, Alexander Kicherer,
Marco Walter and me. Uwe Niethammer generated the
syntheticseismogramsforthisdataset. Thefundingforthe
761
project came from DGMK, Project : ExxonMobil Pro-
duction Deutschland GmbH, DEA Deutsche Erdoel AG,
GDF SUEZ E&P Deutschland GmbH, Wintershall Holding
GmbH. External data was provided by BGR and WEG.
3 14
. IFE dataset: The CTBTO organized the training exercise
and made the data available. Network deployment, main-
tenance and data screening was done by the OSI SAMS
team. Special thanks goes to the team members Nicolai
Gestermann and Martin Häge.
4
. PISCO dataset: The GFZ made the data available publicly
2010
with an accompanying ground truth (GEOFON, ).
Günter Asch provided the dataset initially together with
additional information.
5
. The data of the nuclear test of North Korea is available
from IRIS.
Although I re-implemented most of the algorithms from
scratch during this thesis, I would like to thank all the soft-
ware developers whose open-source libraries I used. I used
Python to create most of the plots with mainly the libraries
NumPy, SciPy, matplotlib and seaborn. I used ObsPy (Megies
2011
et al., ) for MiniSEED reading, acquisition of external data
via SeedLink and FDSN webservices and to test some of my trig-
ger implementations. I used the Generic mapping tools (GMT,
1998
Wessel & Smith ) to create the PISCO map. Scikit-learn
2011
(Pedregosa et al., ) was used for the Random Forest and
2015
Naive Bayes classifiers. Keras (Chollet, ) was used for the
conventional, recurrent and convolutional neural networks. The
6
PCA plots were created with the JFreeChart Java plotting library.
The thesis was written in LATEX and the PGF and TikZ packages
were used to combine Figures and add explanations.
Last but certainly not least I would like to thank my wife
Alice for being such a wonderful partner during all these years.
7

CONTENTS
1 introduction 31
2 challenges of automatic processing 37
3 outline of test datasets 41
31 42
. Basel . . . . . . . . . . . . . . . . . . . . . . . . . .
32 43
. DGMK . . . . . . . . . . . . . . . . . . . . . . . . .
33 14 45
. IFE . . . . . . . . . . . . . . . . . . . . . . . . . .
34 48
. PISCO . . . . . . . . . . . . . . . . . . . . . . . . . .
4 algorithmic building blocks 51
41 52
. Single stations . . . . . . . . . . . . . . . . . . . . .
411
. . Single station event detection and phase
52
picking . . . . . . . . . . . . . . . . . . . . .
412
. . Single station event classification by pat-
66
tern recognition . . . . . . . . . . . . . . . .
42 89
. Coincidence processing . . . . . . . . . . . . . . . .
43 91
. Coherence analysis . . . . . . . . . . . . . . . . . .
431 92
. . Beamforming . . . . . . . . . . . . . . . . .
432 105
. . Combined array spectrograms . . . . . . .
433
. . Comparison of array algorithms at one
105
mini-array . . . . . . . . . . . . . . . . . . .
5 clustering by unsupervised pattern recogni
-
tion 111
51 114
. Feature extraction . . . . . . . . . . . . . . . . . . .
511 115
. . Interpretation . . . . . . . . . . . . . . . . .
52 117
. Application of clustering algorithms . . . . . . . .
53 118
. PCA . . . . . . . . . . . . . . . . . . . . . . . . . . .
531 118
. . Cluster analysis with PCA . . . . . . . . . .
532 119
. . Feature reduction . . . . . . . . . . . . . . .
533 125
. . Visualization by back-transformation . . .
534 125
. . Variations along principal components . .
54 127
. SOM . . . . . . . . . . . . . . . . . . . . . . . . . . .
541 133
. . Visualization of SOM nodes . . . . . . . . .
542 133
. . Event locations of SOM nodes . . . . . . .
9
543 135
. . Amplitude invariance . . . . . . . . . . . .
544 136
. . Supervised classification with SOM . . . .
6 waveform coherence for seismic event loca
- -
tion 145
61 146
. Conventional processing . . . . . . . . . . . . . . .
62 148
. Source scanning . . . . . . . . . . . . . . . . . . . .
621 153
. . Characteristic function (CF) . . . . . . . . .
622 157
. . Weighting with Fisher ratio . . . . . . . . .
623 158
. . Normalization of characteristic functions .
624
. . Finalstackingoftraveltimecorrectedchar-
161
acteristic functions . . . . . . . . . . . . . .
63 164
. Application . . . . . . . . . . . . . . . . . . . . . . .
631 164
. . Basel Deep Heat Mining project monitoring
632 166
. . Gas field monitoring in northern Germany
633
. . CTBTO on-site inspection Integrated Field
2014 172
Exercise . . . . . . . . . . . . . . . . .
7 conclusions and outlook 181
71 185
. Outlook . . . . . . . . . . . . . . . . . . . . . . . . .
72 188
. Algorithm implementations . . . . . . . . . . . . .
189
References
207
Appendices
a combined real time detector 209
-
b noise removal by simplified source scanning211
c visual seismic event screening by super sono
- -
grams 215
c1 215
. Abstract . . . . . . . . . . . . . . . . . . . . . . . . .
c2 216
. Introduction . . . . . . . . . . . . . . . . . . . . . .
c21 217
. . Nanoseismic Monitoring . . . . . . . . . . .
c22 219
. . Heumoes slope example dataset . . . . . .
c3 219
. Super-sonogram event screening and classification
c31 221
. . Sonogram calculation steps . . . . . . . . .
c32 223
. . Super-sonograms . . . . . . . . . . . . . . .
c33 225
. . Signal classification . . . . . . . . . . . . . .
c4 227
. Software . . . . . . . . . . . . . . . . . . . . . . . .
c5 232
. Conclusions and discussions . . . . . . . . . . . .
c6 233
. Acknowledgements . . . . . . . . . . . . . . . . . .
10