TA - 15112065

DEFORMATION STUDY OF KAMOJANG
GEOTHERMAL FIELD
UNDERGRADUATE THESIS
Submitted in partial fulfillment of requirements for the degree of
SARJANA TEKNIK
at Geodesy and Geomatics Engineering Study Program
By:
Bagoes Dwi Ramdhani
15112065
GEODESY AND GEOMATICS ENGINEERING STUDY PROGRAM
FACULTY OF EARTH SCIENCES AND TECHNOLOGY
INSTITUT TEKNOLOGI BANDUNG
2016
AUTHORIZATION
UNDERGRADUATE THESIS
I hereby declare that the work in this Undergraduate Thesis titled “Deformation Study
of Kamojang Geothermal Field” is originally made by author and has not been
submitted, either some parts or all part, either by author or other people, either in
Bandung Institute of Technology or other Institution
Bandung, May 27th 2016
Author,
Foto 3cm4cm
Bagoes Dwi Ramdhani
15112065
Examined and approved by,
Supervisor I
Pembimbing II
Dr. Irwan Meilano, S.T., M.Sc.
NIP. 19740518 199802 1 001
Dr. Ir. Dina A. Sarsito, MT.
NIP. 19700512 199512 2 001
Authorized by,
Head of Geodesy and Geomatics Engineering Study Program
Faculty of Earth Science and Technology
Institut Teknologi Bandung
Dr. Ir. Agustinus Bambang Setiyadji, M.Si.
NIP. 19650825 199103 1 003
Abstract
GPS has proven to be an indispensable tool in the effort to understand crust
deformation before, during, and after the big earthquake events through data analysis
and numerical simulation. The development of GPS technology has been able to prove
as a method for the detection of geothermal activity that related to deformation.
Furthermore, the correlation of deformation and geothermal activity are related to the
analysis of potential hazards in the geothermal field itself. But unfortunately only few
GPS observations established to see the relationship of tectonic and geothermal
activity around geothermal energy area in Indonesia. This research will observe the
interaction between deformation and geothermal sources around the geothermal field
Kamojang using geodetic GPS. There are 4 campaign observed points displacement
direction to north-east, and 2 others heading to south-east. The displacement of the
observed points may have not able proven cause by deformation of geothermal activity
due to duration of observation. Since our research considered as pioneer for such
investigation in Indonesia, we expect our methodology and our findings could become
a starter for other geothermal field cases in Indonesia.
Keywords: Geothermal, Deformation, GPS
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Acknowledgement
In the name of Allah, the Beneficent, the Merciful. Praise to Allah SWT mercy and
guidance so that the author can complete this undergraduate thesis entitle
“Deformation Study of Kamojang Geothermal Field”.
Appreciation and honouration convey for all people who given inspiration and spirit
to author in processing this undergraduate thesis, especially for:
1. Author’s beloved parents, Achmad Sudjudi and Haidar Indiana, also author’s
sisters, Nimash Miftahul Sakinah and Mutiara Ayu Shabrina. I really
appreciate every single thing that you have done to author, especially for their
blessing to the author.
2. Dr. Irwan Meilano, S.T., M.Sc. and Dr. Ir. Dina A. Sarsito MT. as the
supervisors of this undergraduate thesis. Thank you for all the guidance and
knowledge that help author accomplish this undergraduate thesis.
3. Teguh Purnama Sidiq, S.T., M.Sc. and Dr. Zamzam Akhmad Jamaluddin
Tanuwijaya, M.Si. for being examiners of this undergraduate thesis.
4. Dr. Ir. Agustinus Bambang Setiyadji, M.Si. as the Head of Geodesy and
Geomatics Engineering Study Program and author’s guardian angel, second
father in this campus. Thank you for your cirtism, advice, and support during
author’s study and in processing this undergraduate thesis.
5. All lecturers in Geodesy and Geomatics Engineering Study Program. Thank
you very much for all the knowledge that you have given to Author
6. Dr. Ir. Dwi Wisayantono, M.T. as my faculty trustee. Thank you for your
supervision during this undergraduate year.
7. All administration staff of Geodesy and Geomatics Engineering Study Program
especially Mr. Dudung Suhendar for valuable assistance and helpful for every
single lectures activity on this campus.
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8. Pusat Vulkanologi dan Mitigasi Bencana Geologi (Center of Geothermallogy
and Geological Hazard Mitigation – PVMBG), especially Mr. Ade which
provides CGPS data.
9. SEA I GO Johor delegates, Andika Virdian, S.T. and Jery Adrian as my friends
studying together in the UTM, Johor Bahru.
10. Bintang Rahmat Wananda, S.T. which always foster and provide insights about
the world, best pasrtner in IGEO team.
11. IMJ squad who always become learning partner author for every exam during
the study on this campus.
12. IMG 2012 who share same struggle with Author in last three years. My best
Family. “Kami Geodesi 2012, HA!!”.
13. The Kamojang GREAT Team, Akbar Putra Perdana, Sangkap Tua, Andika
Hadi, Heri Rasmanto, Pandito, Fabianto San, David Tumagor Ifa Hanifah,
Shafira Irmarini, Billy Nurkalista, Fuad Rahman, Derian Rachmanda, and Sena
Aditya, thank you all for any kind of help and time we shared that you've given
to get this valuable data.
14. CoFellow mates in Graduate Research on Earthquake and Active Tectonics
(GREAT) Firza, Bintang, Refi, Afi, Suchi, Gio, Yola, Uda Arief, Nafis, Teti,
Nana, Dyah, Sewu, who have given advice, spirit and idea each other. Thank
you for this valuable time.
15. Senior of GREAT, Putra, Alwidya, Aning, Satrio, who have taught and much
helped the author understand GAMIT, given guidance and sharing the
knowledge about geodynamics.
16. Staff of Great. Ajeng and Maharlika, for your help. Sorry if often late return
the SPPD and bill from the survey.
17. Mr. Umar, Mr. Engkus, Mr. Hendra which always loyal accompany and take
the Kamojang Team in to the site.
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18. In the last, for someone who always beside the author, who didn’t tire to
accompany and patiently encounter the author, always and forever to be place
for author share everything. Angel of my heart, Nur Azmina. Love you.
Bandung, May 27th 2016
Bagoes Dwi Ramdhani
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Contents
Abstract ......................................................................................................................... i
Acknowledgement ...................................................................................................... iii
Contents ..................................................................................................................... vii
Figure List ................................................................................................................... ix
Table List .................................................................................................................... xi
Chapter 1 Introduction ..................................................................................................1
1.1 Research Background ................................................................................1
1.2 Research Objective ....................................................................................4
1.3 Research Scope ..........................................................................................4
1.4 Research Methodology ..............................................................................5
1.5 Writing Systematics ...................................................................................7
Chapter 2 Data and Method .........................................................................................9
2.1 Geothermal Deformation ...........................................................................9
2.2 Kamojang Geothermal Field ....................................................................10
2.3 Generating GPS Network ........................................................................11
2.4 GPS Observation for Geothermal Deformation .......................................13
2.5 GPS Observation Data of KGF ................................................................16
2.6 Deformation Analysis ..............................................................................18
2.7 Data Processing ........................................................................................21
2.7.1 Data Processing GAMIT 10.5 ....................................................... 21
2.7.2 Outlier Removal ............................................................................ 22
2.7.3 Referencing Campaign Observation Point to CGPS ..................... 23
2.7.4 Displacement Calculation .............................................................. 24
2.7.5 Statistical Test ............................................................................... 25
Chapter 3 Result and Discussion ...............................................................................27
3.1 Establishment of GPS Network ...............................................................27
3.2 GPS Data Processing Result Using GAMIT 10.5 ...................................28
3.3 Time Series Plotting .................................................................................29
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3.4 Referencing Campaign Observation Points to CGPS .............................. 31
3.5 Displacement Calculation Result ............................................................. 32
3.6 Statistical Test.......................................................................................... 35
3.7 Deformation Analysis .............................................................................. 37
Chapter 4 Conclusion and Recommendation ............................................................ 45
3.8 Conclusion ............................................................................................... 45
3.9 Recommendation ..................................................................................... 46
References .................................................................................................................. 47
Appendix A: Time Series Plotting ................................................................................I
Appendix B: MATLAB Script .................................................................................... V
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List of Figures
Figure 1.1 Location of Kamojang Power Plant next to Guntur Volcano. ............... 3
Figure 1.2 Flowchart of research methodology ....................................................... 6
Figure 2.1 Location of Kamojang Geothermal Field. ........................................... 10
Figure 2.2 Location of GPS baseline around Kamojang Geothermal Field. ......... 12
Figure 2.3 Design of Benchmark ........................................................................... 13
Figure 2.4 Process of distance determination from receiver to satellite by using
phase data (Abidin, 2007). ................................................................... 14
Figure 2.5 The Location of observation point around Kamojang Geothermal Field
and CGPS PVMBG ............................................................................. 17
Figure 2.6 IGS Stations .......................................................................................... 18
Figure 2.7 Pressure Change of Hydrostatic. .......................................................... 20
Figure 2.8 Location of earthquake epicenter affecting GPS stations .................... 23
Figure 3.1 Process generating benchmark ............................................................. 27
Figure 3.2 Time series file (.pos) of KMJ1 station. ............................................... 28
Figure 3.3 KMJ1 time series. ................................................................................. 30
Figure 3.4 KMJ2 time series .................................................................................. 30
Figure 3.5 KMJ3 time series. ................................................................................. 30
Figure 3.6 POST time series. ................................................................................. 31
Figure 3.7 Displacement plotting of North-South Component.............................. 33
Figure 3.8 Displacement plotting of East-West component. ................................. 33
Figure 3.9 The situation around KMJ5 site. .......................................................... 34
Figure 3.10 Displacement of observed pint. .......................................................... 35
Figure 3.11 Displacement vector of Campaign observed point. ............................ 36
Figure 3.12 MT model of Kamojang Geothermal Field. (PT. LAPI, 2012). ......... 37
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Figure 3.13 Location of reservoir. ......................................................................... 38
Figure 3.14 Change of pressure and residual variance chart east reservoir. ......... 39
Figure 3.15 Change of pressure and residual variance chart west reservoir. ........ 40
Figure 3.16 Displacement model of east reservoir. ............................................... 42
Figure 3.17 Displacement model of west reservoir. .............................................. 42
Figure 3.18 Displacement by model and observation. .......................................... 43
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List of Tables
Table 3.1 List of GPS observation points’ coordinate. .......................................... 28
Table 3.2 Standard deviation of topocentric coordinates. ..................................... 29
Table 3.3 Referencing result relative to POST. ..................................................... 32
Table 3.4 t-students statistical test result of the displacement values. ................... 36
Table 3.5 Displacement by the model Mogi of the observed points with change of
pressure (a) variance for east reservoir. ............................................... 39
Table 3.6 Displacement by the model Mogi of the observed points with change of
pressure (a) variance for west reservoir. .............................................. 40
Table 3.7 Residual of change pressure in east reservoir. ....................................... 41
Table 3.8 Residual of change pressure in west reservoir. ...................................... 41
Table 3.9 Displacement model resultant................................................................ 43
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Chapter 1
Introduction
1.1 Research Background
The developments in science and technology today will spur the need for energy
resources, especially electricity energy. Meanwhile, we all know that Indonesia as
developing the country with over 200 million of the population that development of
science and technology is quite significant would require electrical energy to run all
the needs that exist. Growing electricity needs require the development of alternative
technologies to producing a power source. There are alternative technologies that have
been developed in Indonesia such as Hydroelectric Power Plant (HEPP), Electric
Steam Power Plant (ESP), Gas Power Plant (GPP), Waste-to-Energy Power Plant
(WEPP), Nuclear Power Plant (NPP), Solar Power Generation Plant (SPP), and
Geothermal Power Plant (PLTP) (Pandu, 2014).
Geothermal site Kamojang managed by Pertamina since 1983. In 2006 PT Pertamina
(Persero) builds a subsidiary of Pertamina Geothermal Energy (PGE) for undertaking
15 work areas of mining (WAM) geothermal in Indonesia including geothermal site
Kamojang. Kamojang geothermal power plants have a total capacity of 235MW which
140 MW are supplied by PGE in the form of steam to Indonesia Power (a subsidiary
of PT PLN) which consists of units 1,2,3 and 95mW in the form of electricity directly
supplied by PGE to PT PLN which consists of unit 4 and 5. Electricity power
production process at geothermal power plant Kamojang by utilizing steam generated
by the production wells to drive the turbines. From the movement of the turbine will
be converted into electricity. This production process will cause fluid migration. Fluid
migration in the form of gas or liquid from the production process can affect the
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structure of the constituent rock geothermal site which cause deformation of the
surface in geothermal site. One technology that can be used to monitor deformation is
Global Positioning System (GPS).
The development of using GPS method is not only to be an indispensable tool to
attempt understanding crustal deformation prior, during, and after large earthquake
occurrences through data analysis and numerical simulation (Abidin et al., 2009), but
also to the analysis of crustal deformation and geothermal resources. The development
of GPS technology in some geothermal site in the world has been used to: (1) detect a
potentially geothermal resources area, such as in Great Basin, Nevada, (Blewitt et
al.,2005;Kreemer et al.,2006;Hammond et al.,2007);(2) detect and analyzed the
subsidence rate related to the geothermal activities, such as in California (Mossop and
Segall, 1997; Floyd and Funning, 2013); (3) investigate geothermal deformation
associated with the changes in the geothermal system, such as in Aeolian Islands, Italy
(Esposito et al., 2010); analyzed natural and man-made deformation around
geothermal field, such as in Iceland (Khodayar et al., 2006). Unfortunately, research
about correlation deformation and geothermal activity in Kamojang has never been
analyzed using GPS before.
Kamojang Geothermal site having a potential of producing electricity based on a
volumetric analysis of 140MW-260MW for 25 years of utilization from reservoir area
of 8.5 to 15 Km2 [Fauzi, 1999]. Recent reservoir modeling suggests that the Kamojang
reserves are able to support another 30MW-60MW for 25 years’ operation. The
process of long-term monitoring is needed to support the development of the existing
potential in geothermal site Kamojang.
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Figure 1.1 Location of Kamojang Power Plant next to Guntur Volcano.
Moreover, a Kamojang geothermal field is located 7 Km o the west on the slopes of
Guntur Volcano. Guntur Volcano is an active Volcano type A [Meilano, 1997] which
recorded the last eruption in 1847, future event and ongoing deformation activities
around Guntur Volcano should be analyzed. Hence, scientific research related to the
interaction between deformation and geothermal activities should be conduct for a
comprehensive analysis due to such condition. Due to that matter, this research
proposes the first initiation of deformation studies correlating to the geothermal
activities around Kamojang field using GPS and a beginning phase of an establishment
deformation monitoring in the area. Furthermore, this method can be used in the
another geothermal site in Indonesia to optimize the geothermal potential.
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1.2 Research Objective
The objective of this research is to conduct a study of deformation due to geothermal
activity, that includes of:
1. Determine the formation of GPS network to observe deformation in the
geothermal site Kamojang.
2. Estimate the displacement of GPS observation point for analysis deformation
in geothermal site Kamojang.
3. Determine the model correlation of the value of GPS observation point
displacement with geothermal activity at Kamojang.
1.3 Research Scope
To give boundary of research conduct in this undergraduate thesis, the research scopes
used are:
1. The main of data which used in this research are two type data, there are CGPS
data and Periodic GPS data.
2. CGPS data used in this research is located around Guntur Volcano, West Java,
which is managed by Pusat Vulkanologi dan Mitigasi Bencana Geologi
(Center of Geothermallogy and Geological Hazard Mitigation – PVMBG).
3. Periodic GPS data used in this research is the data from GPS survey in March
to April in 2016.
4. Determine the displacement caused by geothermal activity at Kamojang.
5. Data processing using software GAMIT version 10.5 and visualization using
software Generic Mapping Tools (GMT) to plotting the time series of the
result.
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1.4 Research Methodology
Research method used in this research are:
1.
Literature Study that has relation with this research from textbooks, scientific
journal, scientific presentation, other relevant websites.
2. Survey planning and development of GPS network point.
3. Collecting data GPS campaign of the network from March to April in 2016.
Data of GPS campaign is obtained from periodic observation by surveying
team.
4. Collecting CGPS data from March to April in 2016. The CGPS data is obtained
from PVMBG.
5. Collecting IGS data based on International Reference Frame (ITRF) 2008 used
as a reference site, data can obtain from www.igs.org.
6. Data processing using software GAMIT version 10.5, both of Campaign
observation and CGPS, to obtain the coordinate and the displacement of the
observation point in the time domain.
7. Time series Plotting to remove outlier data.
8. Statistical test
9.
Calculation displacement refers to deformation model.
10. Plotting the data processing result using software GMT for the visualization of
the observation point displacement.
11. Deformation model correlation analysis due to geothermal activity in
Kamojang.
For more detail, the research method is shown on flowchart on Figure 1.2
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Figure 1.2 Flowchart of research methodology
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1.5 Writing Systematics
This undergraduate thesis writing systematics is explained below:
CHAPTER 1
INTRODUCTION
This chapter explains research background, research
question, research objective, research scope, research
methodology and undergraduate thesis writing systematics.
CHAPTER 2
METHOD AND DATA
This chapter explains base theory related to concept of
deformation around geothermal site, general overview of
Kamojang Geothermal Site, GPS data Characteristics used in
this research, and step of data processing including the
processing strategy by using GAMIT 10.5
CHAPTER 3
RESULT AND DISCUSSION
This chapter explains data processing results and analysis the
displacement of GPS observation point which refers to
Geothermal activity in Kamojang.
CHAPTER 4
CONCLUSION AND RECOMMENDATION
This chapter explains about the conclusion as the final result
based on the research. The suggestion also will be given for
the better future research.
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Chapter 2
Data and Method
2.1 Geothermal Deformation
Many natural and man-induced processes result in injection and withdrawal of fluids
in the Earth’s interior. Both natural and man-made processes cause fluid migration,
and the resulting deformation is often so large that it obscures the deformation due to
tectonic processes such as plate boundary deformation. Examples of processes
involving fluid migration are ground-water extraction, mining, geothermal or
hydrocarbon production, naturally occurring fluctuations in geothermal and magmatic
systems, or transient post-seismic processes (Keiding et al, 2009). Examples include
migration of magmatic fluids at depth, oil and gas recovery, liquid waste disposal, and
geothermal energy production. These processes are commonly accompanied by
deformation of the host rocks. When such deformation can be detected and monitored,
it may provide important insights about the extent, morphology, and dynamics of
subsurface fluid reservoirs (Fialko, 2000).
It is widely known that geothermal field exploitation can be accompanied by ground
deformation (e.g., Wairakei, New Zealand (Allis et al, 1998); Geysers, U.S.A.
(Mossop and Segall, 1997)). Since the energy per unit mass of geothermal water is
relatively small compared with that from oil or coal, geothermal energy production
involves extraction of large volumes of water. A drastic reduction of underground
water is partially compensated by a reduction of the volume of the reservoir. Also,
fluid extraction cools the reservoir, causing further volume reduction and deformation.
There is ample evidence that the effects of reservoir deformation propagate to the
ground surface, causing both vertical and horizontal ground displacement.
Additionally, the strains caused by the volume decrease of the reservoir may cause slip
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along faults, leading to activation of seismicity (Narasimhan and Goyal, 1984).
Seismicity induced by geothermal fluid extraction has been reported for Geysers,
U.S.A. (Eberhart-Phillips and Oppenheimer, 1984) and Coso, U.S.A. (Fialko and
Simons, 2000).
2.2 Kamojang Geothermal Field
The Kamojang Geothermal Field (KGF) is one of the only a few dry steam reservoirs
in the world which have been developed for energy production. It is located in high
geothermal terrain in Garut, West Java Indonesia, 1500 m above sea level and about
40 km southeast of Bandung. Figure 2.1 shows the location of Kamojang Geothermal
Field.
Figure 2.1 Location of Kamojang Geothermal Field.
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It is the first operational field in Indonesia and has been producing electricity since
1983. The current total capacity of the PLTP is 235MW consisting of PLTP Units
1,2,3. A total of 140 MW is owned & operated by PLN and PLTP Unit 4 of 60 MW
and Unit 5 of 35 MW is owned & operated by PT PGE (total project). The surface
manifestations, which consist of hot pools, fumaroles, mud pots and hot springs, are
located in the Kamojang creature area.
From 1983 to 2015, more than 165 million tons of steam have been extracted from the
KGF and more than 45 million ton of condensed and river water were injected into the
reservoir system. The make-up has been adding continuously to maintain the larger
production rate. KGF has enlarger rapidly the amount rate produced from 8 to 13
MT/year while injection and recharge rate to the reservoir has a limited rate between
2 and 2.7 MT/year (Suryadarma et al. 2010). The large production in more than a
quarter-century at KGF caused some variation of several deformation phenomena in
surface the ground and geothermal reservoir. The deformation phenomena are
controlled by production, injection, and natural recharge rate. The GPS monitoring
conducted to monitor the deformation phenomena in the geothermal field throughout
exploitation.
2.3 Generating GPS Network
To perform GPS observations in the Kamojang geothermal field be a frame in the form
of benchmark observations and patent sturdy enough to be a campaign GPS
observation stations. The development process is done with the initial survey
framework determining the location of the planned location of the point - the point
benchmark. The location was chosen in this study are the points that are in the public
facilities located on the territory Kamojang. Figure 2.2 show the location of GPS
network around Kamojang Geothermal Field.
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Figure 2.2 Location of GPS baseline around Kamojang Geothermal Field.
Of predetermined locations, followed by the process of making the monument
benchmark. The benchmark has special specifications that have been determined to
have a strong foundation and fairly represent the movement of the surface of the soil
in the benchmark point. Specifications of the benchmark point constructed as follows:
1. The composition of concrete mix with cement: sand: gravel = 1: 2: 3.
2. peg made of concrete with reinforcement iron P8.
3. Amid still mounted screws with a length of 10 cm and a diameter of 2 cm.
4. The overall height is 150 cm stakes (20 cm above the surface, buried 130 cm).
5. Preparation of excavation for this marker with a depth of 150 cm. because on
the basis of sand excavation by 20 cm.
From the predetermined specification, can make the design of benchmark that will be
generated. Figure 2.3 show the design of the benchmark
`
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Figure 2.3 Design of Benchmark
2.4 GPS Observation for Geothermal Deformation
The main objective of monitoring geothermal activity is to help to predict the
geothermal eruption. Besides that, geothermal monitoring can also be useful for hazard
mitigation. These objectives can be completed by collecting some data, which are
geological data, geophysical data, and geochemical data. There are many methods of
geothermal monitoring; one of them is deformation monitoring. Basically, this
method’s aim is to get pattern and velocity of surface deformation, either horizontally
or vertically. Later on, the data and information of surface deformation are used to
show the characteristic of magma activity and the predicted volume of magma that
will spread when the eruption happened. Geothermal deformation monitoring can be
done with many systems and sensors; one of them is GPS. Based on the
implementation method, geothermal deformation monitoring using GPS can be
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categorized into two types. The first one is the periodic method that used static GPS
survey for the certain period of time. This method is based on differential positioning
using phase data. The second one is the continuous method, which is used in this
research. The concept of continuous method is based on real-time differential
positioning using phase data. In this method, the characteristic of geothermal activity
can be recognized by understanding the GPS observation points’ coordinate alteration
from time to time. The GPS satellites send signals continuously to the receiver installed
on the observation point. These signals give information about the position of the
satellite and distance from the satellite to receiver alongside with time information,
satellite the condition, and other supporting information. These signals consist of three
main components. The first one is called code that gives information about the distance
from the satellite to the receiver. This component is categorized into two types, which
is the P(Y)-code and C/A code. The second one is navigation message that gives
information about the position of the satellite. Navigation message consists of satellite
clock correction coefficient, orbit parameters, satellite almanac, UTC, ionosphere
correction parameters and other information like constellation status and satellite
condition. The last one is the carrier wave that categorized into L1 and L2. L1 brings
P(Y)-code and C/A-code alongside with navigation message while L2 only brings
P(Y)-code alongside with navigation message. Phase from the L1 and L2 signal can
be used to determine the distance between the receiver and the satellite. Figure 2.4
shows the process of distance determination from receiver to satellite by using phase
data (Abidin, 2007).
Figure 2.4 Process of distance determination from receiver to satellite by using
phase data (Abidin, 2007).
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The distance from the receiver to the satellite can be calculated by using equation (1)
below.
𝐷𝑟𝑒𝑐𝑠𝑎𝑡 = 𝜆 × (𝜙 + 𝑁)
(1)
Where Drecsat = Distance from receiver to satellite; λ = Wave length; φ = Total of
observed phase; N = Cycle ambiguity. The value of cycle ambiguity needs to be
determined first in order to make the distance calculated become the precise distance
between the receiver and satellite. This precise distance can be used for high accuracy
positioning. Basic data obtained from GPS observations is travel time needed (∆t) from
P-code and C/A-code alongside with carrier phase (φ) from L1 and L2 carrier wave.
Carrier range can be calculated with some parameters obtained from GPS observations
that are combined to form equation (2) below.
𝐿𝑖 = 𝜌 + 𝑑𝜌 + 𝑑𝑡𝑟𝑜𝑝 + 𝑑𝑖𝑜𝑛𝑖 + (𝑑𝑡 − 𝑑𝑇) + 𝑀𝐶𝑖 − 𝜆𝑖 . . 𝑁𝑖 + 𝜗𝐶𝑖 (2)
Li = Carrier range; ρ = Geometric distance from receiver to satellite; dρ = Distance
error caused by orbit; dtrop = bias caused by troposphere refraction; dioni = bias
caused by ionosphere refraction on certain frequency (fi); dt, dT = Errors and offsets
from receiver clock and satellite clock; MCi = Multipath effect; λi = Wave length; Ni
= Cycle ambiguity; ϑCi = Noise of observations. Positioning method using GPS can
be categorized into two types which are the absolute method and differential method.
Both of these methods can be done in either static mode or kinematic mode. The
absolute method is the basic positioning method of GPS that is usually use
pseudorange data. This positioning method can be done at any point without depending
on the others. The accuracy of obtained position by using this method really depends
on data accuracy level and satellite geometry. This method is not for high accuracy
positioning use. On the other hand, the differential method is intended for high
accuracy positioning. The differential method determines the position of a point
relatively to other points with known coordinates. By reducing (differencing) data
observed by two receivers at the same time, some errors of the data can be eliminated
and reduced. This process will lead to a high accuracy positioning result. One of the
differential methods mainly used in GPS observations is Double Difference (DD)
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between receivers and satellites. Data obtained from this differential method involved
two receivers and two satellites. Equation (3) shows the phase data of Double
Difference (DD) between two receivers and two satellites.
𝑙
𝑘
Δ∇𝐿𝑘𝑙
𝑖𝑗 = ∆𝐿𝑖𝑗 − ∆𝐿𝑖𝑗
(3)
Where Δ∇𝐿𝑘𝑙
𝑖𝑗 = Phase data of Double Difference (DD) between two receivers and two
satellites; ∆𝐿𝑙𝑖𝑗 = Carrier range difference between two receivers and satellite 1; ∆𝐿𝑘𝑖𝑗
= Carrier range difference between two receivers and satellite 2. Carrier range of each
receiver according to the certain satellite is calculated based on equation (2) (Abidin,
2007). This research uses GPS observation from data campaign in 3 epochs (March 5,
March 25, and April 15) of observation points around Kamojang Geothermal Field
and CGPS POST of PVMBG. The processing method used in this research is the
differential method. This method is used to show the local deformation happening
around Kamojang Geothermal Field and eliminate the effects of plate tectonics and the
major earthquake.
2.5 GPS Observation Data of KGF
There are three main data used in this research. The first one is campaign GPS
observation data that is obtained from GPS observation in 3 epochs (March 5, 2016;
March 25, 2016; and April 15, 2016). The observation data consists of six observation
points that are located around Kamojang Geothermal Field. Those stations are KMJ1,
KMJ2, KMJ3, KMJ4, KMJ5, and KMJ6. The second one is continuous GPS
observation that is obtained from Pusat Vulkanologi dan Mitigasi Bencana Geologi
(Centre for Geothermallogy and Geological Hazard Mitigation - PVMBG). The station
is POST. POST is located in Observation Base of Guntur Volcano in Garut, West Java.
Figure 2.5 show the distribute of observation point at KGF and CGPS PVMBG.
16
Figure 2.5 The Location of observation point around Kamojang Geothermal Field
and CGPS PVMBG
The last one is IGS data that is used as a tie point. There are eight sites used in this
research. Those sites are XMIS (Christmas Island), PIMO (Philippines), KARR
(Australia), KAT1 (Australia), GUAM (Guam), PNGM (Papua New Guinea), PBRI
(India), and DGAV (Diego Garcia Island). These sites are distributed uniformly
around Indonesia. All of the data used in this research have a 30 seconds interval of
data acquisition. Later, the GPS observation points around Kamojang Geothermal
Field and the CGPS PVMBG will be tied to the IGS sites in order to determine their
coordinates. The distribution of IGS stations is shown in Figure 2.6
17
Figure 2.6 IGS Stations
GPS data that is processed using GAMIT 10.5 also need supporting data to solve bias
and error by modeling it. The supporting data are used for ocean tide loading
correction, atmospheric tide loading correction, ionosphere correction, orbit
correction, satellite clock correction, and information of satellite orbit. These data,
used for correction, are included in three main data. The first one is IONEX data that
includes ionosphere data in it. The second one is precise ephemeris data (SP3) that
includes orbit correction and satellite clock correction data. The last one is navigation
data. Navigation data includes information of satellite orbit.
2.6 Deformation Analysis
Deformation analysis is the study of the shape changes of an arbitrary body in time.
The approach must depend on the desired results and the available measuring devices.
The most common approach used is the geodetic method where observation data from
different epochs are compared. Based on the body type of observed object,
deformation analysis can be categorized into three types. Those three types are
(Agnarsson & Dubois, 1993):
18
1. Deformation of static bodies, where the movement vector between two or more
epochs is the result of interest.
2. Deformation of kinematic bodies, where the function of body movement is the
result of interest.
3. Deformation of dynamic bodies, where the process of deformation that changes
from time to time and then returns to its original state afterward is the result of
interest.
Deformation analysis of any type of deformable body includes two main types, which
are geometrical analysis and physical interpretation. The geometrical analysis
describes the change in shape and dimensions of the observed object, as well as the
rigid body movements of the object (translations and rotations). The main objective of
this type of analysis is to determine the displacement and estimating pressure source
of the observed object in space and time domains. On the other hand, physical
interpretation aims to establish the relationship between the causative factors (loads)
and the deformation happened (Chrzanowski et al., 1986). Furthermore, geometrical
analysis can be categorized into two types, which are (Chrzanowski & Chen, 1986):
1. Translation analysis, which shows the changes of position from an object. This
analysis uses the position difference of an object from time to time.
2. Strain analysis, which shows the changes of position, shape, and dimension
from an object. This analysis uses strain data that obtained directly from
observations or derived from the displacement of an object.
Strain analysis is important in order to analyze the deformation of the object observed
with relative geodetic networks that observed points are assumed located on the
deformable body. The approaches of strain analysis can be classified into two basic
types. Those types are (Chrzanowski et al., 1983):
1. Raw-observation approach, which based on the calculation of the strain
components or their rates directly from differences in the repeated
observations.
2. Displacement approach, which based on the calculation of the strain
components from differences in adjusted coordinates (displacements) of the
geodetic points.
19
This research will focus on the static body concepts because of the epoch used in this
research are suitable for it. The deformation analysis used in this research is the
geometrical analysis that focused on the displacement. The approach for displacement
analysis used in this research is the model Mogi, because two periods of time are
compared in this research.
Kiyoo Mogi (1958) was a Japanese national geothermallogist that said mathematical
models to determine the position of a source of pressure both from the magma chamber
using Lame constants λ = μ and v = 0.25. Kiyoo Mogi using this equation with the
pressure source conditions that are in the semi-elastic space. based models Mogi, the
source of deformation is formulated in the equation:
𝑈𝑟 =
3𝛼 3 ∆𝑃
4𝜇(𝑑 2 +𝑟 2 )1.5
(4)
where 𝛼 is the radius of the sphere which has a hydrostatic pressure, ∆𝑃 is the change
in pressure inside the ball, d is the depth from the surface to the centre of the ball, λ =
μ is Lame constants, r is the radial distance to the surface, R is the distance of the
centre of pressure to the point benchmark. Figure 2.7 show the illustration of the
Mogi’s model.
.
Figure 2.7 Pressure Change of Hydrostatic.
20
2.7 Data Processing
2.7.1 Data Processing GAMIT 10.5
GAMIT is a package of programs to process phase data to estimate threedimensional relative positions of ground stations and satellite orbits, atmospheric
zenith delays, and earth orientation parameters. GAMIT incorporates difference
operator algorithms that map the carrier beat phases into singly and doubly
differenced phases. These algorithms extract the maximum relative positioning
information from the phase data regardless of the number of data outages and
take into account the correlations that are introduced in the differencing process.
An alternative, mathematically equivalent approach to processing GPS phase
data is to use formally the carrier beat phases. By doing this way, the phase offset
due to the station and satellite at each epoch must be estimated. GAMIT
(program solve) incorporates a weighted least squares algorithm to estimate the
relative positions of a set of stations, orbital and Earth rotation parameters, zenith
delays, and phase ambiguities by fitting to doubly-differenced phase
observations. Because of the non-linear functional model relating the
observations and parameters, GAMIT produces two solutions. The first one is
used to obtain coordinates within a few decimeters while the second one is used
to obtain final estimates. (Herring et al, 2010a).
In current practice, the solution resulted from GAMIT is not usually used directly
to obtain the final estimates of station positions. GAMIT is used to produce
estimates and associated covariance matrix (“quasi observations”) of station
positions and (optionally) orbital and Earth rotation parameters which later
becomes an input to GLOBK other similar programs to combine the data with
those from other networks and times to estimate positions and velocities (Feigl
et al., 1993; Dong et al., 1998).
GAMIT can process GPS signals automatically by using sh_gamit program. This
automatic processing is controlled by some files. Those files are (Herring et al.,
2010b):
21
1. Automatic clean command files (autcln.cmd), which is used for cleaning raw
Rinex data.
2. Apriori file (.apr), which needs to be good in order to generate a good
solution from GAMIT.
3. Ionex files, which is global ionospheric maps of VTEC in the IONEX format
(Schaeret al., 1998).
4. Stations coordinate file (L-File), which contains a list of the best available
coordinates of the sites occupied during a particular experiment that changed
by GAMIT during processing.
5. Navigation file, which includes orbit parameters from satellite’s coordinates
in a geocentric system (X, Y, Z).
6. Processing control file (process.defaults), which contains the list of defaults
for the GPS analysis includes directory names and some processing control.
7. Session control table (sestbl.), which contains the appropriate option of
controlling GPS data analysis and the apriori measurement errors and
satellite constraint.
8. Site processing control file (sites.defaults), which contains the list of IGS
stations used in processing.
9. Site control table (sittbl.), which is specifying for each site apriori
coordinates constraints and atmospheric models.
10. Station information file (station.info), which contains sites hardware
information such as the receiver, antenna, and occupation time for each
session.
2.7.2 Outlier Removal
Observations are normally distributed which means that occasionally, the large
random error will occur. The Poor or invalid result will be produced when there
are outliers in a dataset although a least squares adjustment has been applied. To
get better adjustment calculation, blunder or outlier must be removed (Ghilani
& Wolf, 2006). The result from the GPS data processing using GAMIT 10.5 still
22
contains outlier, although it is already free from bias and error. So, it must be
removed in order to get a better result. Outliers can be detected manually by
combining all-time series result from the observed year into one file. This can
be done using sh_glred program. In this research, outliers are manually removed
from the tsview program in MATLAB by applying the statistical test. By using
the 95% confidence level (2σ), the data which value are outside the minimum
and maximum boundary of the statistical test will be considered as outliers and
will be removed. Figure 2.9 below shows the probability density curve with 95%
confidence level.
Figure 2.8 Location of earthquake epicenter affecting GPS stations
2.7.3 Referencing Campaign Observation Point to CGPS
Topocentric coordinates of GPS observation points around Geothermal Field are
plotted in the form of time series plotting to see the displacement of those points.
Unfortunately, topocentric coordinates resulted from GPS data processing using
GAMIT 10.5 still affected by block motion, specifically the Sundaland block
motion. The Sundaland block covers a large part of present-day Southeast Asia
that includes Indochina (Cambodia, Laos, Vietnam), Thailand, Peninsular
Malaysia, Sumatra, Borneo, Java, and the shallow seas located in between
(Sunda shelf). It is mostly surrounded by highly active subduction zones, in
which (clockwise from east to the west) the adjacent Philippine, Australian, and
Indian plates submerge. To the north, Sundaland is bounded by the south-eastern
23
part of 20 the India-Eurasia collision zone and the South China (Yantze) block
(Simons et al., 2007). Thus, in order to get the displacement values affected by
only the activity of Geothermal Field or local displacement, the topocentric
coordinates of GPS observation points around Geothermal Field are subtracted
with the topocentric coordinates of CGPS PVMBG (POST). POST are located
relatively far from Kamojang Geothermal Field, which means that these two
points are not affected by Guntur Volcano’s activity. In other words, these two
points only affected by block motion and the 2012 Sumatra earthquake effect.
So, by subtracting the topocentric coordinates of GPS observation points around
geothermal field with the topocentric coordinates of CGPS BIG, the effect of
block motion and the 2012 Sumatra earthquake can be removed. The reason
behind this process is the importance of local displacement that can be helpful
in understanding Geothermal Field’s activity to the displacement of GPS
observation points around it. Equation (5) and (6) below show the process of
referencing observation points to CGPS BIG.
𝑁𝑓𝑖𝑥 = 𝑁𝑜𝑏𝑠 − 𝑁𝐶𝐺𝑃𝑆
(5)
2
2
𝑆𝑡𝐷𝑒𝑣𝑁𝑓𝑖𝑥 = √𝑆𝑡𝐷𝑒𝑣𝑁𝑜𝑏𝑠
+ 𝑆𝑡𝐷𝑒𝑣𝑁𝐶𝐺𝑃𝑆
(6)
Where 𝑁𝑓𝑖𝑥 = Northing component of observation point relative to CGPS BIG;
𝑁𝑜𝑏𝑠 = Northing component of observation point; 𝑁𝐶𝐺𝑃𝑆
= Northing
component of CGPS BIG; 𝑆𝑡𝐷𝑒𝑣𝑁𝑓𝑖𝑥 = Northing component’s standard
deviation of observation point relative to CGPS BIG; 𝑆𝑡𝐷𝑒𝑣𝑁𝑜𝑏𝑠 = Northing
component’s standard deviation of observation point; 𝑆𝑡𝐷𝑒𝑣𝑁𝐶𝐺𝑃𝑆 = Northing
component’s standard deviation of CGPS BIG. Equation (4) and (5) are also
applied to the calculation of Easting and Up component.
2.7.4 Displacement Calculation
Displacement calculation can be done after observation points are fixed to
POST. First, calculate the mean value of Northing, Easting, and Up component
alongside with their standard deviation. According to the least-square principle,
24
the mean value is the most probable value. After that, displacement calculation
can be done by using equation (6) and (7).
𝑁𝑑𝑖𝑠𝑝 = 𝑁𝑙𝑎𝑠𝑡 − 𝑁𝑓𝑖𝑟𝑠𝑡
(7)
2
2
𝑆𝑡𝐷𝑒𝑣𝑁𝑑𝑖𝑠𝑝 = √𝑆𝑡𝐷𝑒𝑣𝑁𝑙𝑎𝑠𝑡
+ 𝑆𝑡𝐷𝑒𝑣𝑁𝑓𝑖𝑟𝑠𝑡
(8)
Where 𝑁𝑑𝑖𝑠𝑝 = Northing component displacement of observation point; 𝑁𝑙𝑎𝑠𝑡 = Mean
of observation point’s Northing component on last 10 days; 𝑁𝑓𝑖𝑟𝑠𝑡 = Mean of
observation point’s Northing component on first 10 days; 𝑆𝑡𝐷𝑒𝑣𝑁𝑑𝑖𝑠𝑝 = Northing
component displacement’s standard deviation of observation point; 𝑆𝑡𝐷𝑒𝑣𝑁𝑙𝑎𝑠𝑡 =
Mean of Northing component’s standard deviation on last 10 days; 𝑆𝑡𝐷𝑒𝑣𝑁𝑓𝑖𝑟𝑠𝑡 =
Mean of Northing component’s standard deviation on first 10 days. Equation (7) and
(8) are applied to the calculation of Easting and Northing component.
2.7.5 Statistical Test
The statistical test is done to show the significant displacement that later will be
used to comparison the displacement by observation with the displacement by
model. This significant movement can be helpful in understanding deformation
happening in Geothermal Field. The statistical test used in this research is the tstudent test. This test shows the relation between mean values of the population
with mean values of the sample based on the number of redundancies in the
sample set. This test used to degrade the confidence level of mean values of the
population that has relatively small sample set (Wolf & Ghilani, 1997). The
vector resultant of the displacement and its standard deviation become the input
for the t-student test. The confidence level used is 95% with α = 0.05 while the
t-value used in this test is 12.71. Equation (9) and (10) below show the vector
resultant of the displacement calculation and the resultant of displacement’s
standard deviation.
𝑉𝑟 = √𝑑𝑒 2 + 𝑑𝑛2
(9)
25
𝑆𝑡𝑑𝑉𝑟 = √𝜎𝑒 2 + 𝜎𝑛 2
(10)
Where 𝑉𝑟 = Vector resultant of displacement; 𝑑𝑒 = Displacement in East-West
direction; 𝑑𝑛 = Displacement in North-South direction; 𝑆𝑡𝑑𝑉𝑟 = Resultant of
displacement’s standard deviation; 𝜎𝑒 = Standard deviation of East-West
component; 𝜎𝑛 = Standard deviation of North-South component. Null hypothesis
(𝑉𝑟 = 0) in this test means that the displacement was not significant while the
alternative hypothesis (𝑉𝑟 ≠ 0) means that the displacement was significant.
(Arman, 2015). The test used in this research shown in Equation (11).
𝑡=
𝑉𝑟
𝑆𝑡𝑑𝑉𝑟
(11)
The null hypothesis will be rejected if the t values are bigger than the t-condition
value that will be explained by equation (12) below.
𝑡 > 𝑡𝑣,𝑎⁄2
(12)
In the equation above, v is the degree of freedom that can be obtained by the
equation (13) below.
𝑣 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛 − 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠
26
(13)
Chapter 3
Result and Discussion
3.1 Establishment of GPS Network
Generating GPS network held on March 4th, 2016. There are six sites location of GPS
Network, the location of each campaign observation in the public place around
Kamojang Geothermal Field. There is some restricted area from the geothermal
company in Kamojang limited the process of establishing GPS Network. From the six
points that have been constructed, necessary for the development of distribution point
locations to represent the dynamics of the surface of Kamojang Geothermal Field.
Process generating GPS baseline Kamojang show in Figure 3.1.
Figure 3.1 Process generating benchmark
27
GPS network that have been generated will be used for GPS observation in 3
epochs (March 5th, March 25th, and April 15th).
3.2 GPS Data Processing Result Using GAMIT 10.5
The result of GPS data processing using GAMIT 10.5 is a list of coordinates of GPS
observation points including its standard deviation value and displacement of the GPS
observation points referred to ITRF 2008. The coordinates generated are geocentric
and geodetic. Meanwhile, the displacement referred to ITRF 2008 expressed in the
form of topocentric coordinates. The example of time series file (.pos) can be seen in
Figure 3.2 below.
Figure 3.2 Time series file (.pos) of KMJ1 station.
The list of coordinates of GPS observation points around Geothermal Field and CGPS
BIG derived from the data processing using GAMIT 10.5 can be seen in Table 3.1.
Meanwhile, the standard deviation of topocentric coordinates resulted from the data
processing are listed in Table 3.2.
Table 3.1 List of GPS observation points’ coordinate.
STATIONS
POST
KMJ1
KMJ2
KMJ3
KMJ4
KMJ5
KMJ6
28
X(m)
-1941159.271
-1934100.042
-1934250.493
-1934114.199
-1933999.425
-1933690.278
-1933863.376
Y(m)
6024021.303
6027617.733
6027609.255
6027740.451
6027670.000
6028072.769
6027753.261
Z(m)
-794045.766
-789084.831
-788834.691
-788231.518
-788934.159
-786807.839
-788624.838
ɸ(˚)
-7.19867
-7.15274
-7.15046
-7.14495
-7.15137
-7.13197
-7.14855
λ(˚)
107.86083
107.79003
107.79135
107.78981
107.78902
107.78524
107.78762
h(m)
866.792
1500.03
1506.501
1514.081
1500.161
1522.642
1499.084
Table 3.2 Standard deviation of topocentric coordinates.
STATIONS
Std Dn(mm)
Std De(mm)
Std Du(mm)
POST
KMJ1
KMJ2
KMJ3
KMJ4
KMJ5
KMJ6
5.57
4.47
4.36
3.92
4.34
7.14
3.65
6.91
5.44
5.62
4.87
5.54
8.50
4.68
27.27
19.30
19.97
15.97
18.44
47.09
14.50
The coordinates listed on Table 3.1 are the mean value of daily observations in each
GPS observation points from epoch 1 to 3 (March 5, March 25, and April 15). This is
done according to the least square principle that state mean value is the most probable
value. As we can see in Table 3.2, the mean standard deviation of each point is less
than or equal to 50 mm. The minimum standard deviation value is around 3.65 mm
while the maximum standard deviation value is around 47.09 mm. This means that the
results have accuracy level for about five centimeters, which is important to deliver
better results of displacement.
3.3 Time Series Plotting
Time series were generated by GAMIT software by combining each epoch data. The
time series that generated in this research were combined result from March 5th, March
25 th, and April 15 th. The time series of topocentric coordinates can be plotted after the
precise coordinates of observation points and CGPS PVMBG obtained. The coordinate
was referred to ITRF 2008 and was still affected by Sundaland Block Movement. The
movement of KMJ1 station was moving to south-east direction. The movement of
KMJ2 station was moving to north-east direction. The movement of KMJ3 station was
moving to north-east direction. The Time series of campaign observation point KMJ1,
KMJ2, and KMJ3 will show Figure 3.3, Figure 3.4, and Figure 3.5. For CGPS
PVMBG POST show in Figure 3.6 by blue dot.
29
Figure 3.3 KMJ1 time series.
Figure 3.4 KMJ2 time series
.
Figure 3.5 KMJ3 time series.
30
Figure 3.6 POST time series.
POST was continuous GPS station placed at observation base of Guntur Volcano. The
movement of POST station was the north-east direction. Another time series of each
station were enclosed in appendix A.
3.4 Referencing Campaign Observation Points to CGPS
As mentioned before, the displacement resulted from the calculation must be
unaffected by any block motion or earthquake effect. The block motion that mainly
affects the GPS observation points’ movement is the Sundaland block motion because
the GPS observation points are located in Sundaland block boundaries. As for the
earthquake, the most possible earthquake that can affect the movement of GPS
observation points is the Indian ocean on 6th April 2016. The effect from block motion
and earthquake can be removed by assigning CGPS PVMBG as the reference. By
subtracting the GPS observation points’ displacement with CGPS PVMBG’s
displacement, it will be resulting in the local displacement of GPS observation points
which is unaffected by any block motion and earthquake effect. CGPS PVMBG
stations used in this research is POST. Table 3.3 shows the referencing result relative
to POST station.
31
Table 3.3 Referencing result relative to POST.
Stations Northing(m) Easting(m) Std Northing(m) Std Easting(m)
KMJ1
-0.00133
-0.00012
0.00624
0.00863
KMJ2
-0.00558
0.00768
0.00622
0.0088
KMJ3
0.00580
-0.01390
0.00574
0.00807
KMJ4
0.00021
-0.00485
0.00643
0.00914
KMJ5
0.00681
0.00089
0.00921
0.00688
KMJ6
0.00178
-0.00022
0.00553
0.00791
Based on the displacement refers to POST, it is clearly seen that the magnitude of
displacement that refers to the CGPS BIG, also known as local displacement, is smaller
than the global displacement plotting that refers to ITRF 2008. Besides that, the
displacement patterns in local displacement plotting are more obvious than in global
displacement plotting. This local displacement is representing the displacement that
occurs around the geothermal field whether it is because the geothermal activity or
tectonic activity around the area.
3.5 Displacement Calculation Result
The trend of displacement can be determined by doing the analysis of each component
from the observation point. Each component of the displacement of five observation
points is plotted together in order to help analyzing trend of the displacement. Figure
3.7 and Figure 3.8 respectively show the displacement plotting of six observed points
as sequence’s in North-South component and East-West component.
32
Figure 3.7 Displacement plotting of North-South Component.
Figure 3.8 Displacement plotting of East-West component.
33
Based on time series plotting, there is some trend from each observation point. The
displacement of KMJ1 and KMJ5 is moving to south-east direction, and KMJ2, KMJ3,
KMJ4, and KMJ6 displacement direction is moving to north-east direction. From
Table 3.3 above have shown displacement calculation results. We could see the quality
of the data from the observation has a big value of RMS. The quality of data from an
observation can be seen from its RMS value. The smaller the RMS value at an
observation that shows better quality than observations that have large RMS value.
RMS value most in getting from point KMJ5. If we see from KMJ5 location which is
right alongside a highway of Kamojang, while the area frequently traveled by vehicles
from that have small to large size. This can affect the quality factor data from
observations on that point. In addition to the effects of multipath, the location of KMJ5
is not large enough, the situation of KMJ5 surrounded by tall pine tree bark, it could
distract the signal of the satellite to the receiver as we known as multipath effect.
Figure 3.9 will show the situation around KMJ5 site.
Figure 3.9 The situation around KMJ5 site.
we could plot the displacement of each campaign observation point with GMT
software. Figure 3.10 respectively show the displacement value direction of each
campaign observation point.
34
Figure 3.10 Displacement of observed pint.
3.6 Statistical Test
The statistical test used in this research is the t-students statistical test with 95%
confidence level and t-condition value of 12.71. The main objective of this test is to
see whether the displacement is significant enough to cause deformation on
Geothermal Field. This test was done by applying Equation (5) until Equation (9).
Based on the test results, the t-value of all displacements of GPS observation points
are lower than their t-condition value, which means that all of the displacement values
tested passed the test. It can be concluded that the displacement values are not
significant enough to cause deformation on Geothermal Field. The results of the tstudent test on the result of this research respectively shown in Table 3.4.
35
Table 3.4 t-students statistical test result of the displacement values.
Stations
Northing(m)
Easting(m)
KMJ1
-0.00133
-0.00012
Std
Northing(m)
0.00624
Std
Easting(m)
0.00863
KMJ2
-0.00558
0.00768
0.00622
0.0088
KMJ3
0.00580
-0.01390
0.00574
0.00807
KMJ4
0.00021
-0.00485
0.00643
0.00914
t-students
t-value
Status
0.125394
Ho qualify
0.8809240
12.71
12.71
1.5208828
12.71
Ho qualify
Ho qualify
0.4344043
12.71
Ho qualify
Ho qualify
Ho qualify
KMJ5
0.00681
0.00089
0.00921
0.00688
0.5974163
12.71
KMJ6
0.00178
-0.00022
0.00553
0.00791
0.1858330
12.71
Although the results are not significant, of the patterns that can be used in the attempted
viewed displacement. As the displacement is not significant for all observation, the
vector of displacement can be plotted. Figure 3.11 below respectively show the
displacement with its errors, which symbolized by size of the circle at the end of black
arrow.
Figure 3.11 Displacement vector of Campaign observed point.
36
3.7 Deformation Analysis
The direction of the shift observation points indicates a direction that is different, but
the direction is mostly form the trend direction is toward the east. Using Mogi model
calculations, determine the effects on the pressure changes magmatic resources of
Kamojang geothermal Field. The equation of horizontal displacement of Mogi model
show in equation (4).
𝑈𝑟 =
3𝛼 3 ∆𝑃
4𝜇(𝑑 2 +𝑟 2 )1.5
(4)
𝑈𝑟 = 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙
𝜇 = 𝑠ℎ𝑒𝑎𝑟 𝑚𝑜𝑑𝑢𝑙𝑢𝑠
∆𝑃 = 𝑝𝑟𝑒𝑎𝑠𝑠𝑢𝑟𝑒 𝑐ℎ𝑎𝑛𝑔𝑒 𝑜𝑓 𝑝𝑟𝑒𝑎𝑠𝑠𝑢𝑟𝑒 𝑠𝑜𝑢𝑟𝑐𝑒
𝛼 = 𝑟𝑎𝑑𝑖𝑢𝑠 𝑜𝑓 𝑝𝑟𝑒𝑎𝑠𝑠𝑢𝑟𝑒 𝑠𝑜𝑢𝑟𝑐𝑒 𝑠𝑝ℎ𝑒𝑟𝑒′𝑠
𝑑 = 𝑑𝑒𝑝𝑡ℎ 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑡𝑜 𝑐𝑒𝑛𝑡𝑒𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑝ℎ𝑒𝑟𝑒
𝑟 = 𝑟𝑎𝑑𝑖𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
The Subsurface model of Kamojang Geothermal Field is show in Figure 3.12 (PT.
LAPI, 2012).
Figure 3.12 MT model of Kamojang Geothermal Field. (PT. LAPI, 2012).
37
Based on Magnetotellurics (MT) model of Kamojang Geothermal Fields, the Location
of pressure source of Kamojang Geothermal field is divided into two sides (west and
east reservoir). For east reservoir is predicting in east of pangkalan complex and south
of Mt. Kamojang, 1.75 km from Surface with radius of pressure source sphere is 0.5
km and west reservoir is predicting in west pangkalan complex, 2 km from surface
with radius of preassure source sphere is 0.5 km (PT. LAPI, 2012). From the equation
we can find the change pressure of the heat source by fitting displacement observation
with displacement by model. We assume the shear modulus Kamojang Area is 𝜇 = 30
G. The location of the east and west reservoir is shown in Figure 3.13 by black Star
symbol.
Figure 3.13 Location of reservoir.
The displacement of each observation point is move on radial direction from the heat
source position. Correlation of displacement observation and model Mogi
displacement show in Figure 3.14 and Table 3.5 for the east reservoir and in Figure
3.15 and Table 3.6 for the west reservoir.
38
Table 3.5 Displacement by the model Mogi of the observed points with change of
pressure (a) variance for east reservoir.
Radial
Station Disp(obs) Distance(r)
km
KMJ3
KMJ2
KMJ6
KMJ4
KMJ1
KMJ5
0.01506
0.00949
0.00279
0.00485
0.00154
0.00687
1.47
1.55
1.78
1.83
1.87
2.26
delta P
(Mpa)
25
0.00962
0.00948
0.00894
0.00881
0.00870
0.00756
displacement (cal)
delta P
delta P
(Mpa)
(Mpa)
20
15
0.00770 0.00577
0.00758 0.00569
0.00715 0.00536
0.00705 0.00528
0.00696 0.00522
0.00605 0.00454
delta P
(Mpa)
10
0.00385
0.00379
0.00358
0.00352
0.00348
0.00302
25
0.01
25
0.016
KMJ3
0.014
Displacement (m)
0.012
0.010
KMJ2
20
0.00
19
0.008
15
0.006
0.00
87
KMJ5
KMJ4
0.004
0.01
94
10
KMJ6
0.002
KMJ1
0.000
1.4
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
Radial Distance of observation point to heat source (Km)
Disp(obs)
25
20
15
10
2.3
0.0000 0.0150 0.0300
Residual
Figure 3.14 Change of pressure and residual variance chart east reservoir.
39
Table 3.6 Displacement by the model Mogi of the observed points with change of
pressure (a) variance for west reservoir.
Radial
Station Disp(obs) Distance(r)
km
KMJ6
KMJ4
KMJ1
KMJ2
KMJ3
KMJ5
0.00279
0.00485
0.00154
0.00949
0.01506
0.00687
delta P
(Mpa)
25
0.00993
0.00999
0.00997
0.00984
0.00977
0.00813
1.27
1.35
1.52
1.65
1.7
2.42
displacement (cal)
delta P
delta P
(Mpa)
(Mpa)
20
15
0.00794 0.00596
0.00799 0.00599
0.00798 0.00598
0.00787 0.00591
0.00782 0.00586
0.00651 0.00488
delta P
(Mpa)
10
0.00397
0.00400
0.00399
0.00394
0.00391
0.00325
0.016
KMJ3
0.014
0.012
0.01
70
Displacement (m)
25
0.010
KMJ2
20
0.00
55
15
0.00
60
0.008
0.006
KMJ5
KMJ4
0.004
0.01
75
10
KMJ6
0.002
KMJ1
0.000
1.2
1.4
1.6
1.8
2
2.2
Radial Distance of observation point to heat source (Km)
Disp(obs)
25
20
15
10
2.4
0.0000 0.0150 0.0300
Residual
Figure 3.15 Change of pressure and residual variance chart west reservoir.
40
From those step, we could have best fit for change pressure of reservoir by listing the
residual of each correlation model in Table 3.7 for the east reservoir and in Table 3.8
for the west reservoir.
Table 3.7 Residual of change pressure in east reservoir.
residual
Station
delta P(Mpa) delta P(Mpa) delta P(Mpa) delta P(Mpa)
25
20
15
10
KMJ3
0.0054
0.0074
0.0093
0.0112
KMJ2
0.0000
0.0019
0.0038
0.0057
KMJ6
-0.0061
-0.0044
-0.0026
-0.0008
KMJ4
-0.0040
-0.0022
-0.0004
0.0013
KMJ1
-0.0072
-0.0054
-0.0037
-0.0019
KMJ5
-0.0007
0.0008
0.0023
0.0038
total residual
0.0125
0.0019
0.0087
0.0194
Table 3.8 Residual of change pressure in west reservoir.
Station
KMJ6
KMJ4
KMJ1
KMJ2
KMJ3
KMJ5
total residual
delta P(Mpa)
25
-0.0071
-0.0051
-0.0084
-0.0004
0.0053
-0.0013
0.0170
Residual
delta P(Mpa) delta P(Mpa)
20
15
-0.0052
-0.0032
-0.0031
-0.0011
-0.0064
-0.0044
0.0016
0.0036
0.0072
0.0092
0.0004
0.0020
0.0055
0.0060
delta P(Mpa)
10
-0.0012
0.0009
-0.0025
0.0056
0.0112
0.0036
0.0175
The best fit for value of pressure change in the reservoir is choose from the closest
residual value to the 0. From the table 4.3 is show 20 Mpa is the minimum value of
the residual, so the best fit value of pressure change in the east reservoir is 20 Mpa.
From the table 4.4 is show 20 Mpa is the minimum value of the residual, so the best
fit value of pressure change in the west reservoir is 20 Mpa. From the two reservoir
source give displacement to difference direction for each point observed. The
displacement model due to east reservoir is show in Figure 3.16 and the displacement
model due to west reservoir is show in Figure 3.17
41
Figure 3.16 Displacement model of east reservoir.
Figure 3.17 Displacement model of west reservoir.
42
The displacement by model of each observed campaign is the resultant displacement
from two reservoir source. Determine the resultant vector can decipher each vector
into its component e and n for each reservoir. By completing each component, then
the resultant is made by Pythagoras theorem. The resultant displacement model by two
reservoirs is show in Table 3.9
Table 3.9 Displacement model resultant.
Station
KMJ1
KMJ2
KMJ3
KMJ4
KMJ5
KMJ6
dn
-0.00489
-0.00433
0.00151
-0.00343
0.00761
-0.00029
de
0.00204
-0.00026
-0.00320
0.00179
-0.00273
0.00065
d resultant
0.00530
0.00433
0.00354
0.00387
0.00808
0.00071
The Resultant Displacement by model is plot by red arrow with the Displacement by
the observation by black arrow is show in Figure 3.16.
Figure 3.18 Displacement by model and observation.
43
The direction indicates the direction of the second displacement is relatively the same
at some point observed. But need to realize that the duration of the observations made
and the restrictions assumptions still to be developed in the future to obtain a more
optimal result to justify the phenomenon of deformation.
44
Chapter 4
Conclusion and Recommendation
4.1 Conclusion
It had been stated in Chapter 1 that the research objectives are determining the
formation of GPS baseline, displacement, and the deformation analysis.
According to the results and analysis from the previous chapter, these are the
conclusion of the research:
1. The Formation of GPS network was established for six station
observation point around Kamojang. The distribution of the GPS baseline
in the area of research has not been able to represent the deformation
characteristics of Kamojang area thoroughly. Additional GPS sites are
needed in the western part of Kamojang.
2. The Displacement of observed points is 0.1 mm ± 10 mm to 13 mm ± 12
mm based on GPS observation in 3 epochs (March 5th to April 15th). The
quality of observation data is still affected by an error that caused the
disruption of GPS signals.
3. The depth of east reservoir is at 1.75 Km from the surface with radius of
pressure is 0.5 Km and 20 Mpa of pressure change. The depth of west
reservoir is at 2 Km from the surface with radius of pressure is 0.5 Km
and 20 Mpa of pressure change. The east reservoir is more active than
west reservoir due to resultant displacement from both reservoirs.
Although the quality of displacement is still very low but in general the
displacement patterns indicate the outward direction from the pressure
source.
4. The duration of the observations made has not been able to determine to
capture the signal deformation caused by the activity of Kamojang
geothermal field. More GPS observation is needed to estimate the
displacement.
45
4.2 Recommendation
Due to the limited of the number of the station used and study, this research might not
as well represent the deformation phenomena in Kamojang Geothermal Field,
Indonesia. The geothermal area is well known has a unique geodynamics. Future
research could generate more station with even distribution in Kamojang geothermal
area to be observed so that it could get the more representative result of deformation
in Kamojang geothermal Field. It is also needed to improve by combining campaign
stations with CGPS that placed in Kamojang Area in order to monitor the dynamics in
the region continuously and can be a reference point in its processing of GPS data.
This research also still contained lack of software improvement. GAMIT 10.5 is very
useful software to process GPS data and getting its displacement. Studying more about
this GAMIT 10.5 software is considered for the future research.
46
References
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49
Appendix A: Time Series Plotting
A.1 Time Series Plotting
KMJ1
KMJ2
I
KMJ3
KMJ4
II
KMJ5
KMJ6
III
POST
IV
Appendix B: MATLAB Script
clc; close all;
clear all;
format long g;
filename = 'cpklpost.xlsx';
%reading pos file
pos = xlsread(filename);
for i=1:length(pos(:,1))
nX(i,1)=pos(i,5)
nY(i,1)=pos(i,6)
nZ(i,1)=pos(i,7)
end
geoc=[nX nY nZ nStd];
%transforming to geodetic coordinates
a=6378137.0;
b=6356752.314245;
e2=(a^2-b^2)/a^2;
ei2=(a^2-b^2)/b^2;
for i=1:length(nX)
P(i,1)=sqrt(nX(i)^2+nY(i)^2);
tt(i,1)=atan2(nZ(i)*a,P(i)*b);
phi(i,1)=rad2deg(atan2(nZ(i)+ei2*b*(sin(tt(i)))^3,P(i)-e2*a*(cos(tt(i))^3)));
lam(i,1)=rad2deg(atan2(nY(i),nX(i)));
No(i,1)=a/sqrt((1-e2*(sind(phi(i)))^2));
h(i,1)=(P(i)/cosd(phi(i)))-No(i);
end
geod=[phi lam h];
%transforming to topocentric coordinates
L=mean(phi);
B=mean(lam);
H=mean(h);
geodmean=[L B H];
for i=1:length(phi)
N(i,1)=-sind(L)*cosd(B)*(nX(i)-mean(nX))-sind(L)*sind(B)*(nY(i)-mean(nY))+cosd(L)*(nZ(i)mean(nZ));
E(i,1)=-sind(B)*(nX(i)-mean(nX))+cosd(B)*(nY(i)-mean(nY));
U(i,1)=cosd(L)*cosd(B)*(nX(i)-mean(nX))+cosd(L)*sind(B)*(nY(i)-mean(nY))-sind(L)*(nZ(i)mean(nZ));
stdN(i,1)=sqrt((sind(L)*cosd(B)*geoc(i,4))^2+(sind(L)*sind(B)*geoc(i,5))^2+(cosd(L)*geoc(i,6))^2);
stdE(i,1)=sqrt((sind(B)*geoc(i,4))^2+(cosd(B)*geoc(i,5))^2);
stdU(i,1)=sqrt((cosd(L)*cosd(B)*geoc(i,4))^2+(cosd(L)*sind(B)*geoc(i,5))^2+(sind(L)*geoc(i,6))^2)
;
end
topo=[N E U stdN stdE stdU];
V
coor=[pos(:,1) topo];
subplot(3,1,1)
plot (coor(:,1),coor(:,2),'.b')
hold on
axis([2010 2016 -0.1 0.1])
xlabel('Time (year)')
ylabel('North-South (m)')
subplot(3,1,2)
plot(coor(:,1),coor(:,3),'.b')
hold on
axis([2010 2016 -0.1 0.1])
xlabel('Time (year)')
ylabel('East-West (m)')
subplot(3,1,3)
plot(coor(:,1),coor(:,4),'.b')
hold on
axis([2010 2016 -0.3 0.3])
xlabel('Time (year)')
ylabel('Up-Down(m)')
%outlier removal
dN=detrend(N);dE=detrend(E);dU=detrend(U);
aveN=mean(dN);aveE=mean(dE);aveU=mean(dU);
stdNm=std(dN);stdEm=std(dE);stdUm=std(dU);
baN=aveN+2*stdNm;bbN=aveN-2*stdNm;
baE=aveE+2*stdEm;bbE=aveE-2*stdEm;
baU=aveU+2*stdUm;bbU=aveU-2*stdUm;
o=0;
for i=1:length(N)
if dE(i)>baE || dE(i)<bbE
dE(i,2)=1;
end
if dN(i)>baN || dN(i)<bbN
dN(i,2)=1;
end
if dU(i)>baU || dU(i)<bbU
dU(i,2)=1;
end
end
q=dE(:,2)+dN(:,2)+dU(:,2);
for i=1:length(dE)
if q(i-o,1)>0
q(i-o,:)=[];
coor(i-o,:)=[];
o=o+1;
end
end
save(['CLN',num2str(filename),'.txt'],'coor','-ascii','-double')
VI