Detection and identification of converted modes and source independent converted phase imaging : Groningen, The Netherlands

Detection and Identification of Converted

Modes and Source Independent Converted

Phase Imaging: Groningen, The Netherlands

by

Ali Fuad AiJishi

B.S., University of Tulsa (2011)

Submitted to the Department of Earth, Atmospheric and Planetary

Sciences

in partial fulfillment of the requirements for the degree of

MASSACHUSTS INSTITUTE

Master of Science

at the

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MASSACHUSETTS INSTITUTE OF TECHNOLOGY

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June 2017

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0 Massachusetts Institute of Technology 2017. All rights reserved

Signature of Author. .

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Department of Earth, Atmospheric and Planetary Sciences

May 31, 2017

Signature redacted

Michael C. Fehler

Senior Research Scientist

Accepted by ...

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Thesis Supervisor

Robert D. Van der Hilst

Detection and Identification of Converted Modes and

Source Independent Converted Phase Imaging:

Groningen, The Netherlands

by

Ali Fuad AiJishi

Submitted to the Department of Earth, Atmospheric and Planetary Sciences

on 31-May-2017, in partial fulfillment of the requirements for the degree of

Master of Science in Earth and Planetary Sciences

Abstract

Passive seismic monitoring waveform data collected at the Groningen gas field contain many interesting events besides direct P- and S-arrivals. We begin by summarizing the station distribution at Groningen over time and discuss the earthquake catalog. We examine the converted arrivals in order to understand their nature. A combination of move-out analysis, raytracing and finite-difference simulations has revealed that the data contain converted phases from two shallow interfaces. We discuss the possibility of using the Source Independent Converted Phase Imaging method to image the position in the subsurface where these phases have been generated. We examine the limitations of that method for imaging the position of shallow interfaces such as the ones at Groningen. The station spacing required to apply the imaging method to the data is shown to correlate well with the estimated Fresnel Zone dimension for the interface depth and data frequency. By using a kinematic version of the Source Independent Converted Phase Imaging Condition, we can map those interfaces. The positions of the interfaces are shown to correlate well with the positions inferred by other means.

Thesis Supervisor: Michael C. Fehler Title: Senior Research Scientist

Acknowledgments

I would like to start by expressing my sincere gratitude to my advisor, Dr. Michael Fehler.

This work could not have been done without his dedicated mentorship. I deeply appreciate and respect the time he devoted in guiding me along this journey. I acquired a vast amount of knowledge and development in research while being his student. In addition, he also embodies the true meaning of humanity and humbleness which is evident in the way he conducts himself while treating others. These traits have left their mark on me and I will always cherish and respect Dr. Fehler for them.

MIT fostered an encouraging environment in general and the EAPS/ERL department specifically, was very supportive and played a vital role in the success of this thesis. The professors

I got to know and the ones I worked with were diligent and generous with their time. Many thanks

to professors Bradford Hager, Nafi Toks6s, and Dr. William Rodi (Bill Rodi) for their assistance and for also being in my thesis committee, along with Dr. Michael Fehler. Also, I was inspired by Professor Thomas Herring and his informative lectures as well as Professor Roben Juanes class, which is one of the best I have ever attended. Other professors who had a direct influence on my graduate journey include Professor Dale Morgan who is very challenging and motivating and Dr. Germdn Prieto who equipped us with theoretical basis.

I would like to thank Dr. Oleg Poliannikov who contributed a lot to the work. Last but not

least, Dr. Yunyue Li, Dr. Hua Wang and Dr. Niels Grobbe who were present whenever I reached out for them. Special thanks to Dr. Andray Sheblansky for sharing his code, which without, this work would not have been possible.

I would like to thank my family: my mother Hana, my father Fuad, my brothers

Mohammed, Hussain, Eyad and Ayman for their continuous support. I was inspired by Mohammad to pursue this graduate degree. He is my role model in the path to success. My appreciation to my grandmother and aunts for their countless prayers.

I cannot show enough gratitude to my fiancd, Razan, who encouraged and motivated me

in every step on the way. Throughout the time I spent working on this thesis, she was always supportive and giving. I could not imagine what my experience would have been like without her. The friendships I acquired at MIT are everlasting. My office mates Alan, Lucas, Yuval and Jamie. The friends who I traveled to St. Lucia with. Saleh and Marwa have been a brother and a sister in this journey.

Thanks to the ALM friends, who filled this journey with much love, joy, and wonder. The times we spent are very memorable.

uction 19

History of the gas field and production . . . 19

Field Location . . . 22

Reservoir Geology and the Sedimentary Succession. . . . 23

1.4 RD-Coordinate System. . . . . 1.5 Risk Assessment Preview . . . . 1.6 Thesis Scope . . . . 2 Description and Analyses of the Dataset 2.0.0 Introduction about the Data . . . . 2.1.1 Seismic Network Functions . . . . 2.1.2 Detection Limit . . . . 2.1.3 The Groningen Seismic Network . . . . 2.1.4 Stations Over Groningen Gas Field . . . . 2.2.0 Analysis of Events Magnitudes and Occurrences . . . . 2.2.1 Catalog Magnitudes . . . . 2.3.0 Velocity Model . . . . 2.4.0 Seismogram Data Analysis . . . . 2.4.1 Cultural Noise . . . . 2.4.2 Time Series . . . . . . . 24 . . . 26 . . . 27 30 . . . 30 . . . 31 . . . 32 . . . 32 . . . 35 . . . 36 . . . 40 . . . 45 . . . 45 . . . 45 . . . 46

3 Identification of Converted Phase in Waveforms Recorded at Groningen 3.1.0 Introduction to Converted Modes . . . . 3.1.1 Converted Modes Behavior . . . . 3.2.0 Borehole Phase Analysis. . . . . 3.3.0 Common Receiver Gather . . . . . 3.4.0 Synthetic Modeling of the Field Data . . . . 3.4.1 The Model Space . . . .

Contents

3.4.2 Forward Modeling Results . . . . 3.4.3 Waveform Matching . . . .

3.5.0 Summary of the Conversion modes observed at Groningen . . . .

4 Parameter Analysis of Source Independent Converted Phases Wave Equation

Imaging

. 76 . 86 . 87

90

4.0.0 Imaging Condition to Field Data . . . 90

4.0.1 Fresnel Zone . . . 91

4.0.2 Scattering and Intrinsic Absorption . . . 93

4.1.0 Imaging Conditions Described . . . . 94

4.1.1 Summary of the SICP-IC Method . . . 97

4.2.0 Application to Field Data . . . 97

4.2.1 Illumination the base of the North Sea Supergroup . . . 98

4.2.2 Assessment of the Images Illuminating the Base of the North Sea Supergroup 103 4.2.3 Imaging the base of the Rijnland Group. . . . 103

4.2.4 Assessment of the Images Illuminating the Base of the Rijnland Group . . .107

4.3.0 Summary of the Chapter. . . . 108

5 Groningen: Observed Phases and Source Independent Converted Phase Wave Equation Imaging 110 5.0.0 SICP Results From Groningen Dataset . . . . 5.2.0 Numerical Alternative. . . . . 5.2.1 Numerical Solution Described . . . . 5.3.1 Mapping the Base of the North Sea Supergroup . . 5.3.2 Mapping the Rijnland Group . . . . 5.4.0 Discussion of the Numerical Solution . . . . 5.4.1 Velocity Assessment Using the Numerical Solution . 6 Conclusion Discussion . . . . Future W ork . . . . References . . . 110 . . . 113 . . . 113 . . . 115 . . . 119 . . . 122 . . . 124 126 . . . .126 . . . .127 130

List of Figures

1-1 Gas production of Groningen gas filed in Billion Cubic Meters since the start of production in 1963 and until 2015 (de Waal et al, 2015). . . . 20 1-2 Netherlands gas production volume in billion cubic meters. Since the start of production in Groningen gas field to 2003 (EZ, 2004) . . . 21

1-3 The location of the gas field with respect to the Netherlands. Shaded area is the Groningen gas field . . . 22 1-4 The 25 x 40 km 1:50,000 scale sheets covering the Netherlands in the RD coordinate system (Officers of the General Staff, and Engraved at the Topographic Bureau, of the Ministry of War, 1850-1864). . . . 25

1-5 PGA contours. In green is Risk Assessment Scope Area. In purple KNMI hazard

assessment 2015 (Taig et al, 2016) . . . 26

2-1 Left: an overview of the KNMI network, triangles indicate locations of the borehole station; Squares the location of the accelerometers. Right: Dictions limits of seismic event magnitude (Dost et. Al., 2012). . . . 33 2-2 The old network with the addition of the 59 geophones in boreholes located on the field of Groningen . . . 34

2-3 A close view of stations in the vicinity of Groningen gas field. Shaded area is the gas

field. . . . 35 2-4 Number of events per year from the induced earthquake catalog from 1986 to 2016 of all magnitudes in black. In purple, the annual production of the Groningen Gas field. 37

2-5 Number of events per year by magnitude from the induced earthquake catalog from 1986 to 2016. Color of lines show above

1

2-6 Monthly volume of production from the year 2008 to 2015 (Bierman et al, 2015) with induced earthquakes for three-month discrete intervals. . . . 39

2-7 The maximum event magnitude per year and the annual volume of production from 1986 to end of2016. . . . 39

2-8 Maximum and minimum event magnitude per month from 1986 to end of 2016. . . 40

2-9 Cumulative sum of events less than or equal to a given magnitude and the magnitude. a) years 1990-1995 b) years 1995-2000 c) years 2000-2005 d) years 2005-20 10 e) years

2010-2015 f) full catalog 1986-2016. . . . 41

2-10 Location of all events in the catalog from 1986 to 2016 of al magnitudes in the area

plotted . . . ... . . . 42

2-11 Events of magnitude one and above of the catalog from 1986 to 2016 in the area plotted

. . . 4 3

2-12 Production facility over Groningen gas field (NAM and TNO). . . . 44

2-13 Location of Midlaren Gas field (EZ and TNO, 2009) . . . 44

2-14 A line fit between station G16 and station G580 that has the event of magnitude 2.4 from February 2016. . . . 47

2-15 Vertical component of raw seismograms recorded at the surface sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on

the 2 5th of February 2016 . . . 48

2-16 Vertical component of filtered data recorded at the surface sensors of stations: G 16, G2 1, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on the 2 5th

of February 2016. Filtered using band pass 2-16 Hz . . . 48

2-17 Spectrograms of Vertical component of the raw seismograms recorded at the surface

sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on the 2 5th of February 2016 . . . 49

2-18 East-west component of raw seismograms recorded at the surface sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake

2-19 East-west component of filtered data recorded at the surface sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on

the 2 5th of February 2016. Filtered using band pass 2-16 Hz . . . 50

2-20 Spectrograms of East-west component of the raw seismograms recorded at the surface sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on the 2 5th of February 2016 . . . 51

2-21 North-south component of raw seismograms recorded at the surface sensors of stations:

G16, G2 1, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake

on the 2 5th of February 2016 . . . 52 2-22 North-south component of filtered data recorded at the surface sensors of stations: G 16,

G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on

the 2 5th of February 2016. Filtered using band pass 2-16 Hz . . . 52 2-23 Spectrograms of North-south component of the raw seismograms recorded at the surface

sensors of stations: G16, G21, G27, WDB, G45, BFB, G50, G55 and G58 for the 2.4 magnitude earthquake on the 25t of February 2016 . . . 53

3-1 Compressional wave PPI hits the medium interface at Normal incident (6 = 90).

Reflected waves PPR and waves transmitted PP' through the interface are generated . .

. . . 5 6 3-2 Phases generated after an incident compressional wave PPI hits an interface at an angle

(O#90). Reflected waves ppR and transmitted wave ppT are generated through the interface. Reflected waves pSR and transmitted wave PST are generated at the interface.

. . . 5 7 3-3 Stations recording the event of 2.4 magnitude on February 2 5th of 2016. The information

about the event is given in the right top corner . . . 60 3-4 Vertical component seismic traces recorded in borehole G45 for the event whose location is shown in Figure 3-3. Traces depth is located next to each trace. Dashed lines indicate the direct P and S phases and their reflection from the surface. Arrows point to phases that are discussed in the text . . . 61

3-5 East-West component seismic traces recorded in borehole G45 for the event whose location is shown in Figure 3-3. Traces depth is located next to each trace. Dashed lines

indicate the direct P and S phases and their reflection from the surface. Arrows point to phases that are discussed in the text . . . 61

3-6 North-South component seismic traces recorded in borehole G45 for the event whose location is shown in Figure 3-3. Traces depth is located next to each trace. Dashed lines indicate the direct P and S phases and their reflection from the surface. Arrows point to phases that are discussed in the text . . . 62

3-7 Filtered at 2-16 Hz seismograms of the event of magnitude 2.4 on 2 5th of February 2016,

recorded by the surface component of station G45, HGZ is the vertical component, HGI is the northern component and, HG2 is the east-west component . . . 63

3-8 Absolute value of the filtered (at 2-16 Hz) seismograms of the event of magnitude 2.4 on 2 5th of February 2016 (Figure 3-3), recorded by the surface component of station G45, HGZ is the vertical component, HGl is the north-south component and, HG2 is the east-west component. Traces are normalize by their maximum value . . . 64

3-9 Smoothed RMS traces generated of filtered (at 2-16 Hz) seismograms of the event of magnitude 2.4 on 2 5th of February 2016 (Figure 3-3), recorded by the surface component

of station G45, HGZ is the vertical component, HG

1

HG2 is the East-West component . . . 64 3-10 Station G45 position and events of magnitude 1.0 and higher from November 2015 to

end of2016. . . . 66

3-11 Vertical component seismograms of events recorded by the surface sensors at station G45 of magnitude 1.0 and higher from November 2015 to the end of 2016 at distances up to 25 km from the station. The amplitudes are normalized by the maximum value in each trace. The zero time is event estimated onset time. Traces are numbered according to Table 3-1 . . . 68

3-12 North-south component seismograms of events recorded by the surface sensors at station

G45 of magnitude 1.0 and higher from November 2015 to the end of 2016 at distances up to 25 km from the station. The amplitudes are normalized by the maximum value in each trace. The zero time is event estimated onset time. Traces are numbered according to Table 3-1 . . . 69

3-13 East-west component seismograms of events recorded by the surface sensors at station

25 km from the station. The amplitudes are normalized by the maximum value in each

trace. The zero time is event estimated onset time. Traces are numbered according to Table 3-1. . . . 70 3-14 A line fit between station G16 and station G58 that includes the location of the event of

magnitude 2.4 from February 2016 in red octa-star. In penta-star turquoise, the source location of the forward finite difference modeling . . . 72

3-15 Ricker wavelet point source used in the simulated forward model . . . 73

3-16 Velocity Profiles: Compressional velocity in blue and shear velocity in red . . . . 74

3-17 The compressional velocity at the top, shear velocity in the middle and the density model

at the bottom. See appendices for Lam6 parameters . . . 75

3-18 Decomposed wavefield at 0.25 seconds after nucleation of the event: Top is

compressional wavefield at the top. Bottom is shear wavefield . . . 77

3-19 Decomposed wavefield at 0.325 seconds after nucleation of the event: Top is

compressional wavefield at the top. Bottom is shear wavefield . . . 78

3-20 Decomposed wavefield at 1.165 seconds after nucleation of the event: Top is

compressional wavefield at the top. Bottom is shear wavefield . . . 79

3-21 Decomposed wavefield at 1.831 seconds after nucleation of the event: Top is

compressional wavefield at the top. Bottom is shear wavefield . . . 80

3-22 Vertical surface time gather of the forward finite difference modeling with geometrical

spreading correction applied. See appendix for other spacing and different gain . . . 82 3-23 Horizontal surface time gather of the forward finite difference modeling with

geometrical spreading correction applied. See appendix for other spacing and different gain . . . 83 3-24 Vertical surface time gather of the forward finite difference modeling with each traces normalized by its maximum. See appendix for other spacing and different gain. . . 84

3-25 Horizontal surface time gather of the forward finite difference modeling with each traces

normalized by its maximum. See appendix for other spacing and different gain . . . 85 3-26 Vertical component seismic traces from the finite difference forward modeling

mimicking the recorded in borehole G45 for the event whose location is shown in Figure

3-3 which compares to the real borehole data in Figure 3-4. Traces depth is located next

3-27 Horizontal component seismic traces from the finite difference forward modeling mimicking the recorded in borehole G45 for the event whose location is shown in Figure

3-3 which compares to the real borehole data in Figure 3-5 and 3.6. Traces depth is located next to each trace . . . 87

4-1 Illustration of Fresnel zone radious . . . 92 4-2 Model used to image the base of the North Sea supergroup interface; the interface is not included. The compressional velocity at the top, shear velocity in the middle and the density model at the bottom. See appendices for Lame parameters . . . 100 4-3 Illumination of the base of the North Sea supergroup using SICP-IC with. Top: 8.8 m receiver recording station. Middle: 25 m receiver recording station. Bottom: 50 m receiver recording station. The lines in color are boundary interfaces described in chapter one . . . .10 1 4-4 Illumination of the base of the North Sea supergroup using SICP-IC with. Top: 100 m receiver recording station. Middle: 250 m receiver recording station. Bottom: 500 m receiver recording station. The lines in color are boundary interfaces described in chapter

one.. . . . 102

4-5 Model used to image the base of the Rjinland group interface; the interface is not included. The compressional velocity at the top, shear velocity in the middle and the density model at the bottom. See appendices for Lame parameters . . . 104 4-6 Illumination of the base of the Rijnland group using SICP-IC with. Top: 25 m receiver recording station. Middle: 50 m receiver recording station. Bottom: 100 m receiver recording station. The lines in color are boundary interfaces described in chapter one. .

. . . 10 5

4-7 Illumination of the base of the Rijnland group using SICP-IC with. Top: 250 m receiver recording station. Middle: 500 m receiver recording station. Bottom: 750 m receiver recording station. The lines in color are boundary interfaces described in chapter one. .

. . . 10 6

4-8 Illumination of the base of the Rijnland group using SICP-IC with. 1000 m receiver recording station. The lines in color are boundary interfaces described in chapter one. .

5-1 The suggested North Sea supergroup base depth velocity contrast . . . .

111

5-2 The illuminated area signifying the depth at which the real interface is. The shallow interface is illuminated because the velocity model we use has the sudden contrast, which leads to mode conversions satisfying the IC . . . .

111

5-3 Time gather of the event of 2 5th of February 2016 . . . 112

5-4 Time gather of the event of 2 5th of February 2016 . . . 112 5-5 Isochron map of the picked s-to-p converted phase arrival time of the event most neighboring to the station where a phase can be picked for the base of the North Sea surpergroup Arrival time is relative to the catalog event origin time . . . 116

5-6 Isochron map of the picked converted phase arrival time adjusted to zero offset for the base of the North Sea surpergroup . . . .. . . . 117

5-7 Isochron map of the difference between the picked s-to-p conversion and the direct shear wave arrival for the base of the North Sea surpergroup . . . 117

5-8 The depth calculated using the relationship in equation 5.3 for the base of the North Sea surpergroup . . . 118

5-9 The depth given form the legacy data for the base of the North Sea surpergroup . .118

5-10 Isochron map of the picked s-to-p converted phase arrival time of the event most

neighboring to the station where a phase can be picked for the base of the Rijnland group Arrival time is relative to the catalog event origin time . . . .119

5-11 Isochron map of the picked converted phase arrival time adjusted to zero offset for the

base of the Rijnland group . . . 120

5-12 Isochron map of the difference between the picked s-to-p conversion and the direct shear

wave arrival for the base of the Rijnland group . . . 120

5-13 The depth calculated using the relationship in equation 5.3 for the base of the Rijnland

group . . . 12 1 5-14 The depth given form the legacy data for the base of the Rijnland group. . . . 121

5-15 A cross plot of the suggested depth (legacy data) vs the depth calculated using equation

5.3 for the base of the North Sea surpergroup . . . 123 5-16 A cross plot of the suggested depth (legacy data) vs the depth calculated using equation 5.3 for the base of the Rijnland group . . . 123

List of Tables

1-1 Gas resource in the Netherlands as of

1

2-1 Seismic networks that KNMI provides data . . . 36

3-1 Magnitudes, distances, and bearing of events recorded by the surface sensors at station G45 from November 2015 to the end of 2016 . . . 67

Chapter 1

Introduction

1.1 History of the Gas Field and its Production

The Groningen gas field spans the provinces of Groningen, Drenthe and Noord Holland. The gas field was discovered on July 22, 1959 and put in production in 1963. The estimated volume of recoverable gas is 2700 to 2800 billion cubic meters; 1700 have been produced as of 2011 (about

1900 billion cubic meters as of the end of 2016). The production peaked in the 1970s as seen in

Figure

1-1.

Netherlands after its discovery and up until the oil crisis in 1970. After the oil crises, production from small fields was encouraged to be produced from (EZ, 2004; Weijermars et al, 2011; Mulder et al, 2006).

The Groningen gas field is operated by Nederlandse Aardolie Maatschappij BV (NAM), a joint venture between Shell and ExxonMobil. Each of company owns a 50% share of NAM.

100 B 80 60 .2 40 20 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year

Figure 1-1 Gas production of Groningen gas filed in Billion Cubic Meters since the start of production in 1963 and until 2015 (de

Waal et a/, 2015).

Groningen is one of the largest gas fields in the world, and the largest gas field in Europe. Fifty percent of the gas production in the Netherlands is reported from Groningen, while the rest are from 250-300 small gas fields (EZ, 2004). Reserves as of 2016 are shown in Table 1-1.

Table 1-1 Gas resource in the Netherlands as of 1 January 2016 in billions of Geq (Groningen Gas Equivalent (heating value of

35.17 MJ/Nm3) (Dutch Ministry of Infrastructure and the Environment and Netherlands Organization for Applied Scientific

Research, 2017).

Accumulations Reserves Contingent resources Total

Groningen 565 7 663

Other Onshore 83 37 120

Offshore 102 25 127

Total 841 69 910

Figure 1-2 shows a comparison over time of the produced volumes of Groningen with other small fields in Netherlands. The rise of production of small fields in the 1970s was after the oil crises and the Dutch "small field policy" in 1974 (EZ, 2004) (Campbell, 2005). Production of these small fields peaked in the year 2000 (Weijermars et al, 2011).

Netherlands gas production

Figure 1-2 Netherlands gas production volume in billion cubic meters, since the start of production in Groningen gas field to

2003 (EZ, 2004).

Since 1986, earthquakes have been recorded in the vicinity of the field. These so-called micro-earthquakes are relatively small, having Richter magnitude up to 3.6 (161 of August 2012,

Induced Catalog) (Dost, 2013).

Another consequence of gas production is land subsidence. The cumulative land subsidence near the center of the field due to gas production and natural soil compaction since the start of

production of the Groningen gas field is more than 250 mm. Since the deformation is spread over a large area, the average slope change caused by the deformation is negligible (3.5e-7 degree). The land subsidence occurs in a long timescale and for this reason, no human or building structure damage have been caused by it. Nonetheless, certain measures had been taken to eliminate environmental harm (e.g. water level and vegetation) (EZ, 2004).

1.2 Field Location

The Groningen gas field is located in northeastern-most Netherlands in Groningen province, Figure 1-3. Groningen province has the fourth least population of the 12 provinces in the Netherlands, and the fifth least in terms of population density.

Field Location 650000 _F 600000-550000 I- 5000001-450000 400000 -350000 F Groningen -Netheands Sordels -Provkcas of the Nethedandr. 300000'

0 50000 100000 150000 200000 250000 300000

X In m

1.3 Reservoir Geology and the Sedimentary Succession

The sequence stratigraphy of the formations in the field area from the reservoir to the surface are

from Permian to Neogene age. Several tectonic events took place during the deposition of these formations leading to a complex structure. The tectonic deformation can be seen in faults throughout the area (Geluk, 2005; Remmelts, 1996).

The sedimentary formations present are as follows: North Sea Supergourp (NS): It consists of Tertiary and Quaternary deposits. It is divided to lower, middle and upper North Sea groups. It is composed of sandstones, marl and clay. Chalk Group (CK): It is a limestone deposited during the upper Cretaceous period. Rjinland Group (KN): It is a lower cretaceous sandstone, claystone, and marl. Zechstein Group (ZE): This formation is composed of six different units. It is an upper (late) Permian deposit. It is composed of Cyclic evaporates consisting of carbonate, anhydrite and salt with anhydrite claystone on top. Upper Rotliegend Group (RO): The gas field reservoir, which is the lower early division of the Permian (Cisuralian to Guadalupian) late Paleozoic era. It is siltstones, claystones and evaporates. Between the Zechstein Group and the Rjinland Group, in some arias, The Altena Group exists. (Stauble, 1970; De Gans, 2007; Geluk, 2007a; Geluk,

2007b; Herngreen et al, 2003; Herngreen et al, 2007; Cohen et al, 2013).

The average elastic properties of each of these layers are given in Table 1-2.

Table 1-2 Average seismic velocities and density of the lithostratigraphic unit and an average thickness (Van Dalfsen, 2006;

YOcel, 2010).

Lithostratigraphic unit Vp V pAz

North Sea Supergourp 1800 m/s 480 m/s 2.0 g/cm3 600 m

Chalk Group 3800 m/s 1800 m/s 2.4 g/cm3 800 m

Rjinland Group 3000 m/s 1300 m/s 2.3 g/cm3

150 m

Altena Group 3000 m/s 1600 m/s 2.1 g/cm3 0-300 m

Zechstein Group 4200 m/s 2500 m/s 1.95 g/cm3 300-2000 m

1.4 RD-Coordinate System

Different coordinate systems are used in the European Netherlands such as International Terrestrial Reference System (ITRS), European Terrestrial Reference System 1989 (ETRS89), International Terrestrial Reference System (ITRS) and World Geodetic System 1984 (WGS 84).The flat projection however, is Universal Transversal Mercator projection (UTM) based on

WGS85 and the Rijksdriehoeksco6rdinaten system (Van der Marel, 2014).

The Rijksdriehoeksco6rdinaten (Dutch Triangulation System), simply RD-coordinate, is the Dutch national geographic coordinate system used in the Netherlands. It is maintained by the Land registry (Kadaster). We use the RD-coordinate system throughout this work unless otherwise noted. Units for this system are in meter (m).

The center point of the RD-coordinate projection is located in central Netherlands. However, it is not assigned the zero coordinate. Instead, the coordinate zero location is in France, southeast of Paris. This means that all values of the coordinates system in the Netherlands are positive. One advantage of this is that the Y-coordinates are always greater than the X-coordinate. The transition from the central Netherlands to the current zero is observed after 1945 to 1960. The coordinate system is based on 5600 points surveyed throughout the Netherlands, in addition to active satellite stations. Elevation is calibrated for a few hundred of the points.

(http://studenten.tudelft.nl/en/students/faculty-specific/architecture/facilities/tu-delfts-map-room/manuals/projections)

It is a conformal projection based on Bessel ellipsoid 1841. This system of projection has a minimal distortion. Figure 1-4 shows the distribution of 25 x 40 km sheets that span the Netherlands in RD-coordinates.

ETRS89 is based on a static reference frame that detaches the Eurasian Plate from continental drift making the angular moment of the reference frame zero. It is a three-dimensional space Cartesian system. It is a non-projected coordinate system. There is a future plan to transition to ETRS89 coordinate system from RD-coordinate and to standardize the RD-coordinate to ETRS89 conversion. However, the RD- will be kept for visual purposes, as well as analyzing geo-information or any geo-dataset (Broekman et al, 2014).

650 r- Map of Netherlands 600 1-550 k 500 450 I-400 350 Netherlarids Borders

- Provinces of the Nethedlands

0 50 100 150 200 250 300

X (km)

Figure 1-4 The 25 x 40 km 1:50,000 scale sheets covering the Netherlands in the RD coordinate system (Officers of the General

1.5 Risk Assessment Preview

KNM has studied the seismicity of the field (Dost et al, 2013; Bommer, 2013). One topic they have addressed is Peak Ground Acceleration (PGA) because of its importance for assessing damage to structures. The risk assessment (Taig et al, 2016) shows that the risk of ground shaking has dramatically increased from 2013 to 2015. Figure 5 shows contours of the PGA values estimated in 2013 and later in 2015.

600- 550-.2 400- 350-

300--Provine the Nethedand

0 so 100 150 200 250

X (km)

1.6 Thesis Scope

In this thesis, we examine the use of the seismicity of the Groningen region to image the shallow subsurface layers. Because of the sharp velocity contrast between the layers, a seismic wave traveling towards the subsurface is converted to different modes in several locations. We utilize these modes to illuminate the interfaces where converted phases are generated and produce a structural map of them.

In chapter 2, we discuss the available data and give a brief history of the seismic network in the Netherlands and within the area of study; the Groningen gas field. We discuss and analyze the catalog of induced earthquakes detected by KNMI. We discuss the recording station setup and its evolution over time. We look at waveforms and examine the signal to noise ratio and appropriate filtering of the waveforms.

In chapter 3, we give a brief introduction about seismic conversion modes and the Zoppeirtz equation. We then use different visual illustration practices to identify existing mode conversions. We use a finite difference forward modeling scheme to assess the interfaces at which these modes are likely to have occurred at Groningen. We present a set of waveform for a borehole array calculated using a finite difference forward modeling to emulate the observed data from an earthquake using the catalog location and source-receiver geometry.

In chapter 4, we give an introduction to the Fresnel zone length and relate it to seismic converted modes. After that, we summarize the method of Source Independent Converted Phase imaging condition of Shabelansky (2015). We do a parameter analysis to evaluate the network geometry that would best work for the method and tie it to the Fresnel zone size.

In chapter 5, we show imaging results for a field event using the imaging condition from chapter 4. We discuss the results. Then we present an alternative approach to benefit from the existing

conversion modes while benefiting from the attributes of the imaging condition. We show a good comparison between the estimated depths to the interfaced that caused the conversions and the depths given in the velocity model.

In chapter 6, we summarize the work done in this thesis and give some future work directions to proceed with in utilizing the method. In addition, we discuss possible investigations of the modes present in the data that we did not address thoroughly.

Chapter 2

Description and Analyses of the Dataset

2.0.0 Introduction about the Data

The extraction of natural gas from reservoirs is known to inadvertently cause seismic activity (Segall, 1989; Feignier et al 1990; Grasso et al 1990; Segall et al, 1994; Segall et al, 1998; Zoback et at 2002; Van Eijs et at, 2006). The early detection of this seismic activity is important to protect the local population and infrastructure that may be affected by the extraction. By

knowing the mechanisms that trigger the events and their locations, the approach for extraction can be modified to strategically ensure the safety of the people and the infrastructure, while sustaining good production.

All data for this study were acquired from a collection of seismic measurements made over the

Groningen gas field in northwest Netherlands (by KNMI).

The reason for acquiring the data was the increase of seismicity over the gas field beginning in the year 1986. The increase of seismicity led the operator, NAM, to reduce the gas production. Consequently, the revenue of the field has fallen and the supply of gas to Europe is not fulfilled (Norlen, 2015; Beckman, 2016).

The data set used were obtained from The Royal Netherlands Meteorological Institute (KNMI), the Dutch national weather service. KNMI manages, stores and keeps track of seismic activities in the Netherlands. In addition, it calculates an approximate location and a depth of each event it includes in the catalog. KNMI also calculates the magnitudes of these events.

2.1.1 Seismic Network Functions

In general, the primary function of a seismic network is to acquire data that can be used to locate the focal center of the events, and determine their magnitudes (Lee ,198 1). When planning to

deploy a seismic network, issues with low amplitude or saturated impulses can arise if the network is not designed to measure the right type of seismicity (e.g. tectonic movement, vs. human induced). Therefore, an understanding of the historical reasons for the placement of existing sensors in a seismic network is important when assessing the reliability and utility of the network for novel aims. Hence, a robust analysis requires characterization of both the seismicity of the area, and the instrumentation deployed there to record it.

The background of distribution and placement also ease to assess the competences the

information that recorded data contain. For certain purposes, the arrangement of the stations can be advantageous to amplify the signal to background noise.

The sensor type is likewise important. For example, using a broadband system is not the ideal approach available for some cases since high frequency cultural noise will mask lower frequency micro earthquake response. On the other hand, naturally occurring low frequency noise will mask higher frequency earthquake singles

2.1.2 Detection Limit

The detection limit of a network is the minimum magnitude of an event that can be detected in an area monitored by the geophones. The detection limit is linked to the area that is least surrounded

by geophones in the monitored region. Network with large stations separation cannot reliably

detect small events within the network. This does not imply that the minimum magnitude that can be attained is the detection limit. In reality, the closer the event is to a station, and the more stations that exist within one zone of the network the event happens in, the smaller the magnitude detection can be attained.

2.1.3 The Groningen Seismic Networks

The stations in the Groningen gas field area were installed at different times and the sensitivity of the network to detect lower magnitude events across the gas field has improved over time (Van

Elk et al, 2012; Van Elk et al, 2014). The number of stations in the network has increased with

time since seismic activates were first detected.

From 1986 when the first event (magnitude 2.7) was detected up to the year 1988, the detection limit was magnitude 2.5. During that period, the average station spacing was 20 km. Adding stations south of the field lowered the detection limit of the network to magnitude 2.3. Additional stations deployed southwest of the field further lowered the detection limit to magnitude 1.5. (De Crook et.al, 1998). Eight borehole recording stations were installed in 1995. Figure 2-1 shows station locations as of 1995 and the estimated detection limit across the Groningen region. In 2006, an additional eight stations were added to the network. In 2010, six more stations were installed, bringing the detection limit down to magnitude 1.0 (Van Elk et al, 2012).

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Figure 2-1 Left:KNMI network in 2010, triangles indicate locations of the borehole station; Squares the location of the accelerometers. Right: Dictions limits of seismic event magnitude (Dost et. Al., 2012).

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An additional 70 borehole stations were added in 2015. Of those 70 stations, four are installed close to other old geophone stations for the purpose of tying in the new instrument response with the old geophone responses. The layout up to date (February 2017) of the locations of stations over Groningen field is shown in Figure 2-2.

The majority of stations within the area of interest are borehole stations. There are also surface stations; Broadband conventional or accelerometers.

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650 600 550 Soo .4 450 400 3501 Station Locations W. 1 43" W*3 63 41 411 4044 - *2 40, 0? -0 0 E0' Stationd Locations

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Figure 2-2 The network in 2010 with the addition of the 59 borehole geophone stations located on thefield of Groningen.

The borehole stations have sensors at five different depth levels, each of three components; one vertical and two horizontals; all orthonormal. Prior to 2012, the network borehole stations had the depths spaced by 75m; surface station, 75m, 150m, 225m and 300m. The new borehole

stations that were added after 2012 are distributed as surface station, 50m, 100m, 150m and 200m. The surface sensor at each of the recording stations is a three component accelerometer. 2.1.4 Stations Over Groningen Gas Field

The stations are located over the field area along lines going roughly northwest to southeast. The coverage by the newly deployed borehole network is limited to the field area, Figure 2-3. A closer look at the layout shows the location of events that are in the southern and northeastern part of the field will be poorly constrained the estimated hypocenter is more unreliable as the azimuthal gap from the station distribution is high.

615 605 Station Locations .003 ~ *am 'GO1 espy 0 OG16 OG17 .G21 ,G26 .07 .061 *C4 0606 13OWSE OA44 413~ 68WIR 01G;w SG*TD 6A23 OR"5 .G24 m G9 GM 337 as ,GM .670 GP *7 g4PO OW 8 .040 .041 OG425 24 OGW '05 OM *0P5 OG5 *qss06OM 07 230 235 240 245 250 255 260 265 270 X In km

Figure 2-3 A close view of stations in the vicinity of Groningen gas field. Shaded area is the gas field.

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Detailed information about the stations and waveform data are available at http://www.knmi.nl. The site contains a list of available recording stations including information about the recording instrument, location, altitude, along with the date of deployment. The networks that are available from KNMI are listed in Table 2-1:

Table 2-1 Seismic networks that KNMI provides data.

Network name Ecuador Seismic FDSN code Deployment region Ecuador

Network EC

Start date Jan. 1, 2002 Operated by Instituto Geofisico Escuela Politecnica Nacional (IG-EPN Ecuador)

Network name Netherlands Antilles FDSN code Deployment region Netherlands Caribbean

Seismic Network NA

Start date Jan. 1, 2006 Operated by ORFEUS (KNMI) Data Center, Royal Netherlands

Meteorological Institute

Network name NARS Array FDSN code Deployment region Mexico

NR

Start date Jan. 1, 1983 Operated by Utrecht University (UU Netherlands)

Network name Netherlands Seismic FDSN code Deployment region Netherlands

Network (KNMI/ORFEUS ) NL

Start date Jan. 1, 1993 Operated by ORFEUS (KNMI) Data Center, Royal Netherlands

Meteorological Institute

Further Information about each of the networks can be found in the International Federation of Digital Seismograph Networks and Observatories & Research Facilities for European

Seismology (ORFEUS).

2.2.0 Analysis of Events Magnitudes and Occurrences

KNMI keeps a catalog of events that have been detected and located occurred. The catalog includes the estimated location and epicenter of each recorded induced seismic event. The

accuracy of the location is based on the onset times; i.e. first compressional wave phase detected. Locations are determined using P-wave arrival time information with an auto-picking algorithm. The micro-earthquake catalog starts from the first detected event which occurred in 1986 and is updated as new events happen. The magnitude of the earthquakes ranges from -0.8 up to a maximum magnitude 3.6.

The catalog consists only induced seismicity events. Another seismic catalog is available for tectonic events. These events usually occur in the southern part of the Netherlands.

Number of Events Per Year 130 120 -110 100 -90 -100 so 90 0 - 80 60 .. 70 C 600 so - 60 50

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Figure 2-4 Number of events per year from the induced earthquake catalog from 1986 to 2016 of all magnitudes in black. In purple, the annual production of the Groningen Gasfield.

Figure 2-4 shows the number of events per year in the catalog and the annual gas prouducion volume. The figure shows that there has been an increase of induced seismic events in and around the field area.

A correlation between the number of events and the production of the whole field is not direct

from the data shown in Figure 2-4. Because of the complexity of the Geology of the field and fault structures, a high reservoir compartmentalization of pressure takes place (Jupe et al, 2003;

Van Hulten, 2010; Jolley et al, 2010). A more detailed analysis of different zones of the field would yield a more precise correlation between the production and events happening in the vicinity of each zone.

Figure 2-5 shows the distribution of events of Magnitude above 1. The general correlation between production and number of high magnitude events is good. After or within a year of high production, the number of higher magnitude events is larger.

Number of Events of Magniutdes Per Year

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Figure 2-5 Number of events per year by magnitudefrom the induced earthquake catalog from 1986 to 2016. Color of lines show above 1 and 0.5-magnitude increments.

The correlation becomes obvious when comparing monthly production data to 3 months

cumulative events occurring in these months as shown in Figure 2-6. Figure 2-7 shows a general trend of increasing maximum events magnitude with time.

Number of Events Der

Pro :ution f Gas in the vic ity of Grnoningen

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Figure 2-6 Monthly volume of production from the year 2008 to 2015 (Bierman et al, 2015) with induced earthquakes for three-month discrete intervals.

Maximum Event Magnitude per Year

2 0 S - -- / lhiillikn111hl|||| 80 60!T 0 40 -4 20 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Figure 2-7 The maximum event magnitude per year and the annual volume of production from 1986 to end of 2016.

In the winter of 2016, the government and operators came to a decision to avoid or reduce the seasonal variation of the gas production from Groningen (GasTerra, 2016). However, the number of events in the year 2016 appears to have increased. This is probably an artificial increase attributed to the recording stations deployed in late 2015, which increased the sensitivity to smaller events. 660 40 Uo 1

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2.2.1 Catalog Magnitudes

An analysis of the Catalog provided magnitudes is deliberated. This is to insure that the manner of carrying out the magnitude calculation is dependable and consistently reliable along the whole Catalog.

The maximum and minimum event magnitudes occurring each month are shown in Figure 2-8. The first period shows no detection of low magnitude events because of the lack of sensitivity in the network at the beginning of the induced seismic catalog.

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39191 194 3980 3999 199 =99 393192 19113 3;4 995 3999 1999 1999 29"9 209 2001 20M 2993 2004 2005 200 2901 20092009 2010 2011 2032 2013 2014 2035 290 2017 2018

Y-ar Figure 2-8 Maximum and minimum event magnitude by month.

To examine the completeness of the catalog events, we look at the cumulative number of events as a function of magnitude. This analysis can be done for a large range of years; or for all events in the catalog of an area. The cumulative sum of magnitudes is usually viewed using a log scale. This tool is valuable to indicate the reoccurrences of a certain magnitude, or can be extrapolated to estimate the period in which a high magnitude event can occur.

Maximum and Minimum Event Magnitude per Month

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Figure 2-9 shows the relationship between the magnitude and frequency of occurrence. Over time, the completeness of magnitude shows improvements. Some small magnitude events, between 0.5 and 1.0, were detected during the first five-year period (1990 -1995) when there were few stations within the field. More of these events have been detected as the network has improved.

a. Occurrence of Events >- Magnitude vs Magnlutde b. Occurrence of Events >= Magnitude vs Magnlutde 101 lo 1[ t 1(1990 -19951 V 1995 - 20001 101 E 10

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c. Occurrence of Events >- Magnitude vs Magnlutde

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10 104 12000 -2005) [2005 - 20101 E E 102 -2 102 EU 10 101 E ... _\-0--4 -1 -0.5 0 0 5 1.5 2 2.3 3 1.5 -1 -45 O .S 1 1.5 2 2.5 3 3.5 Magnitude Magnitude

e. Occurrence of Events >- Magnitude vs Magniutde - Occurrence of Events >- Magnitude vs Magniutde

21- 104 [2010 - 20151 1 [1986 - 20161 103 I2 ' 102 ₁₀₂ 101 101 E E 100 ____________________________________ 100 -1 -0.5 0 0.5 1 1.3 2 2.5 3 3.5 4 - -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Magnitude Magnitude

Figure 2-10 is a map showing the epicenters of all events in the catalog. The earthquakes are clustered in the field area. They appear to cluster along lines going roughly northwest southeast.

Earthquake Locations Magnitude above '-0.8' [1986 - 2016] 615 610 605 S) 0 0 0 0 0 C 0 O0 0e

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Figure 2-10 Location of all events in the catalog from 1986 to 2016 of al magnitudes in the area plotted.

Figure 2-11 shows location of events having magnitude greater then one. The higher event magnitudes form tighter clusters than is seen in the entire catalog.

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Earthquake Locations Magnitude above '1.0' [1986 -2016] 615F-610 605 6001[-E 585 F 580 575 570 0 0 0 .0 ** 0. 0. 000 0 *0 0

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Figure 2-11 Events of magnitude one and above of the catalog from 1986 to 2016 in the area plotted.

When compared to the layout of the prodcution facility, Figure 2-12, the association becomes clear (the production faculties are not certain to indicate where wells are concentrated, but may likely lie within the region). Locations of events of higher magnitude are better determined since the detection of the first compressional wave arrival can be picked for a larger number of stations.

Figure 2-12 Production facility over Groningien gas field(NAMndTNO

There are small fields around the Groningen gas field and earthquakes can be attributed to the gas production from them. One example is the Midlaren field located south of the Groningen gas

field on the border of Groningen and Drenthe Provinces highlighted in Figure 2-13. These fields

produce from the same reservoir as Groningen, the upper Rotliegend formation, as well as from other targeted formations.

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2.3.0 Velocity Model

Compressional and shear wave velocity and density models of the Groningen gas reservoir area were obtained from Royal Dutch Shell. The volume is given as a set of twelve horizons in X, Y and Z. The layers are situated between the horizons, with compressional-wave velocity and a shear-wave velocity given by empirical formulas for each layer. In addition, a density model is available for each layer.

Five layers are defined using a velocity gradient. Six horizons have constant velocity. Below the gas reservoir, the velocity increases with depth at a constant gradient. The velocity bellow the reservoir is poorly constrained because wells do not penetrate deeper than the reservoir.

2.4.0 Seismogram Data Analysis

The seismic data (time series) for each station in the network are available as a continuous record or as user specified segments windowed around each event (located and analyzed by KNMI). The segmented data have different start and end times from one station to another, and can also differ for the same station location with depth and/or component. In addition, different networks have different recording sampling rate.

2.4.1 Cultural Noise

Cultural noise of different characteristics is present in the data. A high frequency cultural noise matching the frequency of the power line used in Netherlands (220v@5OHz) (Salzer, 1987) (electric noise) is present with varying amplitude from station to station and within one borehole set. For some stations, the amplitude of the power line frequency masks the whole time series.

See Figures 2-15, 2-18 and 2-21 for example of raw seismograms. Other types of noise exists

such as domestic noise, bad stations ... etc.

To eliminate cultural noise, the use of the deeper positioned sensors is recommended since cultural noise is mostly in the from of surface waves whose amplitude decreases with depth. The deeper the station is placed, the less surface wave noise will be observed. However, in some cases deeper sensors are not preferred as will be discussed in chapter three. When using only the deeper set of stations, a reduction of station count in use will occur. Additional spacing gaps occur because some stations are instrumented with only surface accelerometers.

2.4.2 Time Series

To give a general idea to what the data look like, traces from a set of stations along a line are presented. The line is shown in Figure 2-14. Stations are sorted in to common midpoint using the catalog (estimated) event location and time. We show traces of a recent event with a magnitude energy sufficient for the signal to noise to be high.

As an example, traces from an event having magnitude 2.4 that occurred on February 2016 are shown in Figure 2-15, 2-18 and 2-21. The event had been recorded by 10 of

11

Sorting in common midpoint/source-point is a quick means of looking at the energy of events and the focal mechanism based on first compressional wave arrival and the direct shear wave arrival.

Stations Recorded the Event and Event Location (RD coord)

25th Feb 2016 _{Magnitude: 2.4 Year:}

2016 615 ~ _{Month: Feb} Day: 25"' Hour: 22 Minute: 26 Second: 30 89 Location: Froomnbosch Lat 53.184000 Lon: 6.781000 Depth: 3.0 0609 030 OEAM1 0G070 0GO80 G9 G2 600 - OG610 OG100 OBZN1 110 OG140 *BWSE 595 - 160 OG170 *G1 *WIROG670 G210 GftiITD 0G230 6G310 6OO . -90 - *G240 OG260 70 OG290 0G300 0G370 OG360 585 -0G320 30 *G3 0G680 G430 HAR 0G400 0G410 0G640 580 * 0440 Sao - sG4o *G666, 0G490 06500 0G656G520 'G530 575 sG560 5G570 5G84 OGS90 N030570 ~65w 00580 .. 590 Event location 230 235 240 245 250 255 260 265 X In km

Figure 2-14 A linefit between station G16 and station G580 that has the event of magnitude 2.4 from February 2016.

The spectrogram of these traces are shown in Figure 2-17, 2-20 and 2-23. The spectra of the data shows the central frequency ranging from about 10 Hz to 20 Hz. Different filtering approaches have been analyzed with different parameters (see appendix for the designated filter choice). The central frequency of the data is around 12 Hz. The low signal to noise ratio for the high

frequencies is low and would compromise the signal at the low spectrum if comprised in the time series making many traces look unusable until they are removed by filtering. We thus, use a filtered data for our subsequent analyses