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DETECTING OF ULF GEOMAGNETIC ANOMALIES ASSOCIATED ON 2017 MENTAWAI EARTHQUAKE (Mw 6.2), WEST SUMATRA WITH THE POLARIZATION RATIO Z/H METHOD

THE FINAL PROJECT PROPOSAL Courses Undergraduate PHYSICS

By: JULI AFRIANTI MANIK F1C014031

DEPARTMENT OF PHYSICS FACULTY OF MATHEMATICS AND NATURAL SCIENCES UNIVERSITY OF BENGKULU 2017

DETECTING OF ULF GEOMAGNETIC ANOMALIES ASSOCIATED ON 2017 MENTAWAI EARTHQUAKE (Mw 6.2), WEST SUMATRA WITH THE POLARIZATION RATIO Z/H METHOD

THE FINAL PROJECT PROPOSAL Courses Undergraduate PHYSICS

By: JULI AFRIANTI MANIK F1C014031

Has been approved and enacted to executed In the preparation of the final project/Thesis

The Supervisor

Bengkulu, Februari 14, 2018 Know the: Chairman of Department

Ashar Muda Lubis, S.Si., M.Sc., Ph.D NIDN. 0012127701

Suhendra, S.Si., M.T NIP. 197109281999031002

CHAPTER I INTRODUCTION 1.1. Background On September 01, 2017 at 00:06:54 EST earthquake occurs with an apparent (Mw = 6.2) shook the Mentawai Islands, West Sumatra. Earthquake is based in the northeast of the Mentawai Islands, its epicenter was located at 1.300 LS – 99.660 BT with a depth of 10 km below the seabed (BMKG, 2017). This earthquake is not a shallow potential tsunami and not to cause damage (Riyadi, 2017).

Figure 1.1. The Mentawai earthquake epicenters 2017 (Mw = 6.2; a distance of 178 km from SCN station)

The high incidence of earthquake relic in West Sumatra that occur can also be seen from genesis earthquake damage an inflicting huge casualties in the last few years among

other earthquake with a magnitude 7.8 SR and Tsunami Mentawai 25 October 2010, Padang earthquake, September 30, 2009 magnitude 7.6 SR (USGS, 2017), up to the year 2016 are recorded during the period of earthquake in the region of West Sumatra as many as 195 times gen earthquake well sourced on ground or at sea (Ma’muri, 2016). One important issue is investigation of the relationship between the changes of anomaly geomagnetic ULF (Ultra Low Frequency) and earthquake activity (Fraser-Smith et al. 1990; Hattori, 2004). Earthquake precursors research results with the observed anomalies in the spectra of ULF emissions f < 0.1 Hz based data geomagnetic has been reported by previous researchers produced expected with seismogenic activity before earthquake can be monitored by the observation of the Earth’s surface in the magnetometer (Fraser-smith et al. 1990). Study of precursor earthquake with data geomagnetic is expected to approach the prediction earthquake (Kopytenko et al. 1999; Hattori et al. 2006). Data geomagnetic is selected because it has a continuous data such as seismic data. A recording produced by such data can record activity pre-seismic, co-seismic and post-seismic (Karakelian et al. 2000) and information from the anomaly signals emissions ULF activity seismogenic the earned less than 30 days or known by the short term (Uyeda and Meguro 2004). Research study analysis of precursor earthquake in Sumatra has been done by Ibrahim et al. (2012) related on the Padang Earthquake, 30 September 2009 (Mw=7.6) and Mentawai Earthquake, October 25th, 2010 (Mw = 7.8). The results of the analysis show that the Mentawai earthquake events in 2010, long ULF emissions recorded anomalies is shorter compared to the Padang earthquake 2009. The result shows that there is existence of a relationship between the magnitude of earthquake, distance earthquake against time

disturbance where Padang earthquake that generate anomaly signals as a precursor of ULF recorded for 23 days and Mentawai earthquake generate anomaly signals as precursor just 5 days of disruption. In addition, Daniarsyad et al. (2016) has reported the analysis of precursor Bengkulu earthquake, 02 April 2015 (Mw= 5.7) and May 15, 2015 (Mw= 6.0) using station LWA (Liwa, Lampung). He found that the existence to increase of anomalous ULF signals associated with both the earthquakes, can be seen 24 days and 23 days before the occurrence of the earthquake. Based on previous studies, it has been a lot of studies developed precursors of earthquake by using geomagnetic data. Observations of the Earth’s magnetic phenomena in stationary on the need to know the characteristics of the variation of geomagnetic Earth data from time to time. ULF geomagnetic phenomena that occur and can be proven in previous research. To that end, this study first to analyze the fact observations that presents the results of the latest geomagnetic anomalies in detecting the ULF with signal analyses associated with earthquake data september 1, 2017 at Mentawai using method of polarization ratio Z/H. So, in terms of reducing the impact of disasters can be seismicity as the first step to earthquake predictions. 1.2. The Formulation of the Problem In this study analysis of earthquake precursor using data geomagnetic. Analysis of polarization by applying the ratio Z/H to detect the occurrence of the signal emission anomaly patterns characteristic of the ULF (Ultra Low Frequency) of Mentawai

earthquake using SCN (Sicincin) the obsever station. so that later the anomaly that is obtained can be used in explaining the phenomenon of earthquake precursor that occurs in the territory of the West Sumatra furthermore it can be applied for the purposes of disaster mitigation. 1.3. Research Limitation These research is to detection and analyze the data geomanetic with associated Mentawai earthquake Mw 6.2, the period 2017 in the region of West Sumatra and the distance of the epicenter to geomagnetic observation station SCN (Sicincin) is 178 km . 1.4. Objectives The purpose of this study are to analyze the characteristics of ULF electromagnetic signals in the spectrum and detection anomalies that can be used to explain the phenomenon of earthquake precursors in signal in the region of West Sumatra. 1.5. The Benefits The expected benefit of this research is to obtain the characteristics of earthquake precursors with ULF signal anomaly, so that it can serve as early signs of earthquake precursor as an effort to increase earthquake predictions. As well as to disaster mitigation.

CHAPTER II A REVIEW OF THE LITERATURE 2.1. Mechanism for Changes in Emissions of ULF Related to Earthquake Physical mechanism for changes in emissions of ULF earthquake performed with regard to the approach by some researchers, including: 1.

Effect of elektrokinetic, Fenoglio et al. (1995) describes the flow of electromagnetic waves called is the change in pressure on the fracturing of rocks that produce a steady flow of particle mass flow caused fracturing of rocks which amount is proportional to the magnitude and frequency of signal interference geomagnetic before earthquake 1989 Loma Prieta occurred. Effect of induction, changes the conductivity of geoelectricity in the lithosphere due to the activity on the source earthquake (focal zone) which causes a change in the amplitude of electromagnetic waves in non-lithospheric (Mogi, 1985; Kovtun, 1980).

2.

Effect of microfracturing, Molchanov and Hayakawa, (1995) describes a model of ULF emissions associated with primary earthquake (main shock), in which case the rock then the emission of electromagnetic waves that are recorded on the rise significant. The process is assumed to occur at the source earthquake with the spectrum of Ultra Low Frequency, ULF (f ≤ 10 Hz). Molchanov and Hayakawa (1998) proposes a electrification small cracks (microfracturing) as one of the possibilities in the search for mechanisms of electromagnetic emissions of Ultra Low Frequency (ULF). Effect of microfracturing (Molchanov and Hayakawa, 1995), ULF emissions model results in the range of 0.001-10 Hz are observed before and after earthquake with a magnetometer. Macroscopic in electrification

can be characterized with the permitivity 𝜀𝑔 and electrical conductivity 𝜎𝑔 some of the fluctuations of the electromagnetic field or a charge will stop after a time 𝜏𝑑 ~ 𝜖𝑔 /𝜎𝑔 ~ 105 − 108. Figure 2.1. Hattori et al. (2006) illustrates the three models. Two models to explain emission ULF induced microfracturing that is the effect of elektrokinetic and effect of microfracturing and one model explains the changes the amplitude of electromagnetic waves that are seen from the power ratio (SZ/SH). The components of H and Z components are very influential to changes in terrain geomagnetic. If the conductivity changes on H components significant and Z component is small then it is believed to be derived from the atmosphere or ionosphere, but if there is a large component of conductivity Z and H components is small then it is believed to be the result of activity the lithosphere. Subsequently this model used by Yumoto et al. (2008) to determine the precursors of earthquake.

Figure 2.1. LAI (Lithosphere, Atmosphere, Ionosphere) Coupling and its relationship with seismogenic. ULF emissions anomaly model related to earthquake (Hattori et al. 2006)

Yumoto et al. (2008) describes the physical image of 2.2. as follows: 1.

ULF emissions induced microfracturing and electrokinetic effect in source earthquake.

2.

ULF polarization (power) which change caused by changes at the source of earthquake conductive.

ULF waves amplitude observed at the surface depends on the season, local time and the position of latitude (Yumoto, 1986) which is a function of the parameters to observe the solar wind, magnetosphere, the ionosphere and the lithosphere that is physically described in by Chi et al. (1996) in the formula: A = B f (LT)𝜎

(2.1)

A is the amplitude of the observed ULF surface, B (magnetic field) is the source of the wave parameters ULF examples that come from the solar wind, the magnetosphere or the source earthquake (focal zone), f(LT) is the local time and 𝜎 is a factor of amplification in the lithosphere. The physical mechanisms are emission ULF depicted on Figure 2.2. that explains the charged particle emissions from the source both of ULF waves external influences such as the solar wind as well as the influence of the internal activity of the lithosphere that is described as follows: 1. On a layer of plasmasfer (MHD, magnetic hydro dynamic) is the layer of the Earth’s magnetosphere which is a sheath which has a magnetic field (B0) to protect the dynamic pressure of the solar wind. On a layer of plasmasphere the direction of the magnetic field strength (B0) counterclockwise and if there is a dynamic pressure

which have a certain speed (δV) from the solar wind that carry electrical and magnetic fields in it then it can be explained in physical mechanism in equation δE = −δV × 𝐵0 which could affect the magnetic field strength in the ionosphere, atmosphere and lithosphere so conversely, if due to the activity of the lithosphere (seismogenic), this mechanism is called LAI Coupling.

Figure 2.2. Physical mechanism of ULF emissions about physical process in plasmasphere, ionosphere, atmosphere and lithosphere (Yumoto et al. 2008)

2. On a layer of the ionosphere conductivity value is the amount (𝜎𝐼 = /0) so it appears a strong electric field (δE) that generates strong electric current described in the equation δJI = σI δE. 3. In the atmosphere of nature physical water is neutral so that the value of the conductivity being zero (𝜎 = 0) the magnetic field in the atmosphere appear due to the motion of the Earth’s rotation the direction West – East in line with the direction of the clock which can be explained by the laws of Faraday becomes

𝜕 𝜕𝑡

δB =

−∇ x ∂E, geomagnet field strength, so it’s in the atmosphere is a representation of the magnetic field strength in the lithosphere and the ionosphere physical relations become so that the following equation. δBG = δ𝐵𝐼 + δ𝐵𝐿

(2.2)

Then to explain the factors of amplification on the equation 2.2 into the equation as follows: δBG δB ⁄δ𝐵 = 1.0 + L⁄δ𝐵 𝐼 𝐼

(2.3)

ratio magnetic fields in the ionosphere (δ𝐵𝐼 ) that is reflected in the magnetic field in the lithosphere (δBL ) is a function of the electrical conductivity (𝜎𝐼 , 𝜎𝐿 ) in the ionosphere and the lithosphere to induction wave period (𝑇) ULF waves and this model is to describe the polarization of power ratio in Figure 2.2. if the electrical conductivity in the lithosphere is disturbed then changes the amplitude of field geomagnetic (Merzer and Klemperer 1997). 4. The layer of the lithosphere has a more solid physical so that the value of the conductivity in the lithosphere becomes finite or have a value of (𝜎𝐼 = /0) Thus, if any activity seismogenic then generates an electric current in the lithosphere becomes δJL = 𝜎𝐿 δE. 5. On a layer below the lithosphere that is the asthenosphere has the physical properties of the liquid and heat so that the value of conductivity into infinity (𝜎 = ∞) as a result of the electric field at these layers to zero (δE = 0) and of course on the layer also does not produce magnetic field (δE = 0). 6. Induction flow intensity below the surface depends on the period of the induction

wave in the ionosphere and the lithosphere that is called with the skin depth. Where the skin depth is a strong electric field of 𝐸⃗ sign in so far as the value of the conductivity 𝜎 the magnitude of the 𝐸⃗ = 37 % or

1 𝑒

𝐸⃗ where 𝑒 = 2.71 (the natural

numbers) so far δ resulting in an equation as follows (Yumoto et al. 2008): 𝑇

𝛿 (𝐾𝑚) = √(𝜋𝜇𝜎)

(2.4)

𝑇, 𝜇 and 𝜎 is the period of waves, magnetic permeability and electric conductivity in the lithosphere. 2.2. A Precursor of Earthquake Based on The Phenomenon of Electromagnetic Emission Signals in ULF (Ultra Low Frequency) Research studies about the phenomenon of the earthquake precursor electromagnetic data using geomagnetic method with the polarization ratio Z/H has been widely performed (e.g. Ta’uno et al. 2016; Yulyta, 2017; Armansyah et al. 2016; Ahyar and Sunardi, 2017; Kanata et al. 2014; Riani et al. 2016; Suadi et al. 2013; Prayogo and Sunardi 2014; Nuraeni et al. 2010). Then, Wahyuningsih (2016) by the same method of analyzing Lampung Earthquake 2016 by reporting based on ten earthquake who has observed, nine of whom have precursor anomaly signal ULF and found that the determination time began (onset time) increased signal ULF 2-4 occurred weeks before the occurrence of the earthquake. Though some researchers have reported that ULF emissions anomalies may be related to seismogenic. Ahadi (2014) had reported anomalies ULF frequency analysis associated with seismogenic f = 0.02-0.06 Hz. In view of the observational facts described in the previous section, the accumulation of convincing events that indicate the apparent presence of ULF emissions associated with

earthquakes have been considered highly desirable. For this reason, related projects have been initiated in several countries. In 1990, Fraser-Smith and Hayakawa introduced the form of polarization ratio (Z/H) at one station to determine the precursors earthquake and corrected with the Earth’s magnetic disruption index. Fraser-Smith et al. (1990) and prove the existence of electromagnetic activity relationships related to the 1989 Loma Prieta earthquake, followed by Molchanov et al. (1992) by comparing the activity of geomagnetic terrain close to a source of Spitak earthquake (Ms = 6.9) and Loma Prieta earthquake (Ms = 7.1). Figure 2.3. indicate electromagnetic waves associated with the 1989 Loma Prieta earthquake which is then considered as a precursor of the earthquake. Figure 2.3. it can be seen an increase in the emission of electromagnetic waves for 13 days (5 October 1989) before the genesis Loma Prieta earthquake (17 October 1989).

Parameters

Earthquake occurrence time Magnitude Depth The distance of the epicenter The components of the magnetic field used Frequency range (ULF) The duration of the time appearance of anomalies ULF before earthquake

Spitak Earthquake (Kopytenko et al. 1993) Desember 8, 1988

Loma Prieta Earthquake (Fraser-smith et al. 1990) October 17, 1989

Guam Earthquake (Hayakawa et al. 1999) August 8, 1993

6.9 6 Km 129 Km

7.1 15 Km 7 Km

8.0 60 Km 65 Km

3 Components

The components of H

3 Components

0.0005-5 Hz

0.01-10 Hz

-0.5 Hz

Intensity anomaly 3-4 the day before earthquake

Intensity anomaly 12 days before earthquake

Polarization (SZ/SH) anomaly appearing 1 month before earthquake

ULF emissions characteristics before earthquake (pre-seismic) ULF emissions characteristics after earthquake (postseismic)

An increase in emissions of ULF 4 hours before earthquake Does not appear to occur 1 month after earthquake

An increase in emissions of ULF suddenly 3 hours before earthquake It does not appear a few months after going on earthquake

The maximum Level of polarization SZ/SH 1 month after earthquake, the polarization is returning to normal level

Table 2.1. History of earthquake related to electromagnetic phenomena (Hattori, 2004)

Figure 2.3. Emission of electromagnetic waves associated with precursor Loma Prieta earthquake 1989 (Fraser-Smith et al. 1990)

2.3. The Spectrum of The Signal Emission of Ultra Low Frequency (ULF) According to Yumoto (2006) of the observations have been conducted frequency ULF (f < 10 Hz) capable is believed to be the most promising of the monitoring of the liveliness of the Earth’s crust due to the translucent power from an electromagnetic can be

considered with the depth where the activity of the Earth’s crust takes place and the fluctuation of electrical conductivity on the inside of the earth so that it can be detected directly. Electromagnetic waves have a frequency spectrum from high frequency to low frequency. The frequency spectrum is a natural low-frequency wavelengths such as frequency spectrum of earthquake (ground motion) ranged between f = 0-100 Hz (Newmark and Hall, 1982). Figure 2.4. Electromagnetic emission spectrum shows natural range between f = 0-100 Hz, known as Very Low Frequency (VLF) and Ultra Low Frequency (ULF).

Figure 2.4. Frequency spectrum of electromagnetic waves. Spectrum frequency and length waves which in this study using the spectrum of ULF ( < 0.1 Hz) with a wavelength of > 109 m. (Source: www.electronicdesign.com (in Ahadi, 2014)

The spectrum contains a variety of collection frequency (frequency content) presented in the frequency domain. Figure 2.5. Witte (1993) shows the signal in the form of timedomain and frequency domain. Present time domain amplitude and time while the frequency domain representation of magnitude and frequency.

Figure 2.5. Time domain and frequency domain (Witte, 1993)

To change the signal from the time domain to the frequency domain it takes the Fourier transform defined in the formula: ∞

𝑋 (𝜔) = ∫−∞ 𝑥 (𝑡) 𝑒 −𝑗𝜔𝑡 𝑑𝑡

(2.5)

The required frequency of DFT algorithm sampling 2N2 to avoid aliasing (frequencies outside the range) which will look like the sinc function (sine cardinal) is a function that often arise in the processing of the signal is also called the sinc function ∞

where it has been normalized into ∫−∞ sin 𝑐 (𝑥)𝑑𝑥 = 𝜋. In order to normalize all the fourier components of the required frequency limit using the sampling rate (v) of the instrument is called the Nyquist frequency with the formula: 1

𝑓𝑁𝑦𝑞𝑢𝑖𝑠𝑡 = 2 𝑣

(2.6)

2.4. The Determination of the Emission Signal ULF for Precursor Earthquake Yumoto et al. (2008) introduces the technique to study the polarization of earthquake precursor and comparation for component signal 𝐻 and 𝑍. Analysis of polarization power ratio using spectral analysis on spectrum of ULF.

Ida et al. (2008) improve analysis of polarization ratio to assure the presence of ULF emissions (f = 0.01 Hz). This frequency is selected from the results of research of Hayakawa et al. (2007) which may be associated with the effects of the seismogenic by adding a statistical analysis using standardization and normalization of daily with the following formula: 𝐸𝑖 = ( 𝑋𝑖 - 𝜇𝑖 ) / 𝜎𝑖

(2.7)

𝑋𝑖 is averaged for 1 day each component (𝑖= components H, D and Z), for 𝜇𝑖 is average the component in a specific time period and 𝜎𝑖 is standard deviation. Prattes et al. (2011) fix the model calculation of Ida et al. (2008) and Masci et al. (2009) by analyzing the standardization and normalization of daily with the following formula. |𝑆𝐻 (𝜔)|2

𝑆𝐻 𝐷𝑎𝑦 (𝜔) =

2𝜋.∆𝑓

(2.8)

and |𝑆𝑍 (𝜔)|2

𝑆𝑍 𝐷𝑎𝑦 (𝜔) =

2𝜋.∆𝑓

(2.9)

Then to get a better statistical analysis used averaged daily. 1

𝑆∑ 𝐻 𝐷𝑎𝑦 = √𝑁 ∑[𝑆𝐻 (𝜔)]2 1

𝑆∑ 𝑍 𝐷𝑎𝑦 = √𝑁 ∑[𝑆𝑍 (𝜔)]2

(2.10)

(2.11)

So for the daily value of H and Z components are as follows. 𝐻𝐷𝑎𝑦 = 𝑍𝐷𝑎𝑦 =

𝑠 ∑ 𝐻 𝐷𝑎𝑦 − 𝜇 ∑ 𝐻 𝑀𝑜𝑛𝑡ℎ 𝜎 ∑ 𝐻 𝑀𝑜𝑛𝑡ℎ 𝑠 ∑ 𝑍 𝐷𝑎𝑦 − 𝜇 ∑ 𝑍 𝑀𝑜𝑛𝑡ℎ 𝜎 ∑ 𝑍 𝑀𝑜𝑛𝑡ℎ

(2.12) (2.13)

Analysis of polarization ratio prattes et al. (2011) is to assure the quality of the daily variation of the value from geomagnetic data. Thus the value of each component were analyzed (𝑍 and 𝐻) can be controlled if emissions is derived from geomagnetic or from the activity of disturbance instrument. 2.5. Index Dst Geomagnet Disorders Observation of the day of geomagnet disorder made in index value, that is to know the activity of solar storm caused by the activity of the sunspot. The sunspot activity is generating a storm of the sun or solar wind. In this study used geomagnet index is an index of the Dst (Disturbance storm time). Dst index is an index that represents the index of the geomagnet activity of geomagnet at low latitudes. Dst index is an index that is calculated from geomagnet disorder variation component H on low latitude geomagnet station – the Equator (Low latitude – equator). Analysis of disorders of the geomagnet later made the controls to see the geomagnet interference coming from the solar storm activity or of the lithosphere that is due to a fault in the Earth. In this study because the space in scope in the area of Sumatra with geographical position is located at the equator, used index Dst (Disturbance storm time) that can represent the activity of the geomagnet at low latitudes (Low latitude, ± 20o) and Equatorial Electro Jet (EEJ). Dst index published by WDC-Geomagnetic, University of Kyoto, Japan.

CHAPTER III RESEARCH METHODS 3.1. Data The data used in this research is the earthquake data from BMKG catalog to case studies of Mentawai Islands of the year 2017, West Sumatera (http://www.bmkg.go.id), geomagetic data obtained in the form of raw data that is daily variation of SCN (sicincin) station, and the geomagnet activity of the index data (Dst) are obtained from the World Data Center for Geomagnetism (http://wdc.kugi.kyoto-u.ac.jp/) 3.2. Time and Place The location of the case study of earthquake in the Mentawai, West Sumatra. With its geographical position 1.300 LS – 99.660 BT. Geomanetic data retrieval form of secondary data obtained from stations on earth magnetic Sicincin (SCN) in January 2018. 3.3. Data Processing Data collection geomagnetic (raw data get from the geomagnet station of the SCN in the form of daily variation of data i.e. data activation of the magnetosphere and lithosphere) and data Mentawai earthquake the year 2017 in the region of West Sumatra (Mw 6.2) and depth 10 km. After the raw data obtained are then done conversion of raw data (in extension biner data into ascii data), this is done to facilitate the reading of the data from the sensor. The Data used is the horizontal component (𝐻) and vertical (𝑍) per second and selected data per day. Then do the reduction of noise by way of doing an analysis of the trend of daily to

reduce the noise from the environment and influence the global geomagnet activity as activity of the lithosphere, atmosphere, ionosphere as well as solar wind so that the remaining data is derived from the dynamics of the magnetic field of the Earth. Analysis of the spectrum to change data geomagnet H and Z components of the domain time to frequency domain, by calculating the frequency of a signal using the analysis of the transformation Fast Fourier Transform (FFT). ∞

𝑋 (𝜔) = ∫−∞ 𝑥 (𝑡) 𝑒 −𝑗𝜔𝑡 𝑑𝑡 In this case the signal input to the specified window has a long 2𝑚 . Then choose the analysis window will be used. The output of the FFT syntax (x, n) is a complex vector, with complex amplitude in this study 0.1-0.001 Hz. Data is already in the frequency domain filter corresponding later in the span of ULF period (10-45 seconds) and (45-150 seconds) using the filter butterworth. The results of filtering averaged per hour. Next, conducted the analysis using MATLAB programming language. The methods used in the analysis is to use a running average of 10 daily. This method in in do for smoothing data in search of inclination pattern (trend). The Data used for the input in the analysis is the data file that contains the results of the processing date, the polarization (Z/H) maximum, median polarization (Z/H), polarization (Z/H) average per hour. Then the results of the data stored on the FIG. Do a comparison with the correct anomalies which appear to use the Dst index i.e. the year 2017. This data is retrieved from the http://wdc.kugi.kyoto-u.ac.jp/dst_realtime/201709/index.html. The geomagnetic anomaly data processing method in the outline shown in the flowchart below.

Start

Raw data

1. Binary data to ascii conversion 2. Fill the empty data with Trend data 3. Remove the spike signals with median filter (noise reduction) Data rectified (corr)

1. Ploting diff component data H and Z 2. Bandpass filter Pc3 and Pc4 3. Power spectrum (Pwelch method) 4. The Ratio of the Power Spectrum of Z/H

Anomaly < 1

1. Data polarization 2. DST data 3. Data Hourly Mean H and Z The END 3.3. Data processing flowchart

3.4. Data Analysis Based on the results of the data processing has been done, and then analyzed using the methods of the polarization ratio Z/H in the determination of the earthquake precursor anomalous emissions can know ULF activity in association with seismogenic way comparing the results of the data filter components Z data results filter component of H (Z/H) between components with H and Z (Z/H) < 1 comes from external factors and if the storm comes from internal factors (Z/H) > 1 (Hayakawa et al. 2007). Where the electromagnetic anomaly analysis indicators is limited by the moving average of the standard deviations (𝜎 ± 1) indicated by an increase in the value of the ratio of vertical and horizontal component (Z/H) which passes through the limits of standard deviation has been made. After there are anomalies, then some time later an earthquake will hapen, so that it can be said to be the first signs of the incident earthquake. 3.5. Flowchart Stages of research done in the achievement of the purpose described in the flowchart as follows.

Start

Earthquake Data Acquisition (BMKG) (Waktu, Posisi, Magnitude) Geomagnet Data (components H,D,Z,F,X,Y)

Analysis Of The Daily Variation Of Correction

Fast Fourier Transform (FFT)

Frequency ULF

Polarization Ratio Z/H

ULF Anomalies

Correction with DST Precursor Earthquake

Finish

Gambar 3.5. Research flowchart

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