Seismic Microzonation Studies in the Southern Part of Progo River, Special Region of Yogyakarta, Indonesia

doi:10.21203/rs.3.rs-4939527/v1

BackgroundThere were more than 700 earthquakes with a magnitude of more than 5 Mw over the past 100 years in the Special Region of Yogyakarta, Indonesia. Due to the high intensity of seismic activities, it is essential to perform seismic hazard analysis by considering local site effects. Therefore, this study aimed to analyze the peak ground acceleration (PGA) value based on the earthquake scenario of May 27, 2006, with a magnitude of 6.3 Mw, which occurred on the eastern side of the Opak Fault.

MethodsThe study was conducted in the southern part of the Progo River, the Special Region of Yogyakarta, using 31 boreholes and 18 microtremor measurement points. The analysis was carried out using four methods: Kanai’s (1966) equation using microtremor data, deterministic equations with Ground Motion Prediction Equations Next Generations Attenuation West 2 (GMPE NGA West 2), Kanno's (2006) attenuation equation, and probabilistic method referring to the Indonesian Seismic code.

ResultsResults indicated that the highest value of PGA was obtained using the deterministic GMPE NGA West 2 weighted attenuation equation, which varied from 0.475 g to 0.549 g. Meanwhile, Kanno's (2006) attenuation equation resulted in values ranging from 0.266 g to 0.394 g. In contrast, PGA values obtained through microtremor measurement resulted in a smaller value, in the range of 0.126 g to 0.214 g. Probabilistic analysis in the study area produces values ranging from 0.373 g to 0.450 g.

Conclusion The location on the central side of the Progo River shows a lower PGA value than the other sides. PGA values will tend to be higher at locations near the earthquake source. The low PGA value that resulted from microtremor analysis was due to the consideration of local site effects in determining earthquake parameters in the study area. Determining the seismic hazard analysis method in infrastructure planning requires a comprehensive analysis by considering various parameters, such as the planning and design objectives, the location proximity to earthquake sources, historical seismic conditions, and the presence of the local site effects.

Seismic Hazard Analysis

Microtremor

Attenuation Relationship

Shear Wave Velocity

Ground Acceleration

The Special Region of Yogyakarta, located in the southern part of Java Island, is known for its high seismic activity due to the subduction zones and active faults in the south. In areas with a high potential seismic risk, Seismic Hazard Analysis (SHA) is one of the stages that must be carried out while planning and designing structures. SHA is an analysis performed to evaluate the ground motion level in a certain area and predict the potential seismic hazard by considering various uncertainties in seismic parameters defined as earthquake location, magnitude, and time of earthquake occurrence (National Center for Earthquake Studies, 2017). One of the parameters that is suitable to predict ground motion is Peak Ground Acceleration (PGA) as a representation of the most significant acceleration at the ground surface due to cyclic ground motion (Day, 2012).

The Indonesian archipelago lies at the confluence of four major global plates and several minor plates, including the Burma, Sunda, Banda Sea, Maluku Sea, Timor, Bird's Head, Maoke, and Woodlark (National Center for Earthquake Studies, 2017). Each of these plates moves relatively in different directions of plate movement. The Indo-Australian plate is moving relatively north and subducting into the Eurasian plate. Meanwhile, the Pacific plate has a relative movement towards the west. The continuous movement of the plates resulted in the formation of mountains, volcanoes, earthquakes, and tsunamis. To overcome these potential earthquake disasters, Indonesia has developed a large-scale seismic code that applies to the entire country.

The development of the Indonesian seismic codes has been carried out since 1983, which continuously updated in 2002, 2010, 2017, and 2020. The seismic code uses a probabilistic seismic hazard analysis method by considering observational data on various earthquake sources. The large scale of the hazard map has encouraged the development of microzonation studies on seismic hazards in several regions in Indonesia, especially in locations near earthquake sources. The development of these studies is generally carried out through observations by considering site conditions, such as geological conditions, earthquake history at the observation location, and the nearest earthquake sources.

According to the United States Geological Survey (USGS, 2024), more than 700 earthquakes with a magnitude of more than 5 Mw have been recorded in Yogyakarta in a 500 km radius during the last 100 years as shown in Fig. 1. On May 27, 2006, an earthquake occurred due to movement along the

eastern Opak Fault, which caused quite a massive impact. The 6.3 Mw earthquake resulted in 5,716 fatalities and nearly 38,000 injuries, with economic losses estimated at more than $3,134 million (Ministry of National Development Planning of the Republic Indonesia, 2006). The significant impact triggered by the Yogyakarta earthquake in 2006 encouraged the development of research related to SHA through various approaches and methods (Elnashai et al., 2006; Fathani et al., 2006; Fathani and Wilopo, 2017; Palupi et al., 2020; Pawirodikromo, 2018, 2022; Perdhana and Nurcahya, 2019) resulting in various seismic microzonation maps at certain areas in the Special Region of Yogyakarta.

The improvement of seismic microzonation studies continues along with infrastructure development throughout the province. This indicates the importance of comprehensive research regarding seismic hazards to plan and build earthquake-resistant structures in locations with high seismic intensity. The seismic microzonation analysis results can be developed into observational data that supports the development of the Indonesian seismic hazard maps.

The Trans South-South Java Road Project across the southern part of the Special Region of Yogyakarta is a corridor developed as part of the Ministry of Public Works and Public Housing's project to enhance development of the southern coastal region of Java. This is part of the national development priority programs to encourage economic growth through a reliable, integrated, and sustainable road and bridge network system. The Trans South-South Java Road Project crosses the Progo River in the south, where it is planned to build a bridge structure to improve connectivity along the southern coastal area of Java.

Studies from previous researchers related to seismic microzonation were generally carried out at the center of regional activities, spread evenly in urban areas, especially in Kulon Progo Regency, Bantul Regency, Yogyakarta City, and Gunung Kidul Regency. However, seismic microzonation in the southern part of the Progo River has not been studied yet. To enhance the goal of developing the southern region of Java Island through a reliable, integrated, and sustainable road and bridge system, it is essential to do a study related to seismic microzonation by considering geological conditions and seismic activities in the specific area.

This study focused on exploring the southern part of the Progo River, which is the location of the Pandansimo Bridge construction, part of the Trans South-South Java Road Project. The proximity of the study site to the Java subduction zone and the Opak Fault indicates a high potential for intense earthquakes. The main objective of this study is to analyze the seismic hazard by determining the PGA value based on various methods. This study used the historical data of the 2006 Yogyakarta earthquake as a reference parameter to analyze potential earthquake hazards through microzonation maps derived from each method.

Determination of the PGA was carried out through direct microtremor measurement to obtain amplification factor (A₀) and predominant frequency (f₀) values. The application of microtremor recording datasets in analyzing site characterization and ground motion in certain areas has also been widely used (Elnashai et al., 2006; Fathani et al., 2006; Fathani and Wilopo, 2017; Mase et al., 2021; Ornthammarath et al., 2023; Pawirodikromo, 2020; Siadari et al., 2023; Siburian et al., 2024), indicating its suitability for representing site conditions in certain areas. The microtremor measurement data is processed to obtain the shear wave velocity (V̅s) and PGA values for each test point across the study area. PGA value determination was also conducted using a deterministic method through attenuation relationship with Ground Motion Prediction Equations Next Generations Attenuation West 2 (GMPE NGA West 2) and Kanno's (2006) equation. The PGA values obtained through these methods were compared with the PGA value from the probabilistic method by referring to the Indonesian seismic code. Subsequently, the comparison of PGA values was provided in the form of microzonation map, which was categorized based on each method. The comparative study of PGA values obtained by various methods through microzonation map was to be considered as a reference in planning and evaluating seismic hazards, specifically in designing earthquake-resilient bridge structures.

2.1 The Study Location

The SHA in this study was conducted at the Pandansimo Bridge construction site, Srandakan District, Bantul Regency, Special Region of Yogyakarta, Indonesia, precisely in the southern part of the Progo River at 7º58´38,92´´S and 110º13´11,49´´E. The data used in this study were geotechnical investigation reports at the site containing soil mechanical properties and penetration resistance values from 31 borehole points distributed along the study area. In addition, microtremor measurement was conducted at 18 points along the study location.

2.2 Geological Condition

The Progo River located on the border of the provinces of Yogyakarta and Central Java, covers approximately 2,380 km² and stretches for 140 km. Administratively, this river borders Sleman Regency, Kulon Progo Regency, and Bantul Regency. It originates from the slopes of Mount Sindoro, Mount Merapi, and Mount Sumbing flowing to the south, with the river mouth located on the border between Srandakan District in Bantul Regency and Galur District in Kulon Progo Regency. Several tributaries flow into the Progo River including the Krasak and the Bedog River originates from Mount Merapi, and the Tangsi River, which originates at Mount Sumbing (Ministry of Tourism and Creative Economy, 2024). Young volcanic debris from Mount Merapi is often transported by rivers toward the southern part of Yogyakarta (Fathani and Wilopo, 2017). This results in geological conditions dominated by young volcanic deposits.

The geological conditions in the southern part of the Progo River are significantly uniform. Bantul Regency and Kulon Progo Regency are dominated by sedimentary soil. The condition of sediment deposition in this location mainly consists of fine sand, silt, and clay derived from the weathering of tertiary sedimentary rocks (Nurwihastuti et al., 2014; Pawirodikromo, 2022). This area primarily comprises young volcanic deposits of Merapi Volcano (Qmi), including tuff, ash, breccia, agglomerates, and undifferentiated lava flows. Other include Alluvium (Qa) typically composed of gravel, sand, clay, and silt across the coastal plain. Geological conditions at the study site are presented in Fig. 2 (Modified from the Geological Map of the Special Region of Yogyakarta, Rahardjo et al., 2012). There is an active fault in the Bantul Regency known as the Opak Strike-Slip Fault (Soehaimi et al., 2019). This fault is part of the active fault zone located approximately less than 10 km from the mouth of the Progo River. Furthermore, the Opak fault is a potential source of earthquakes, with a probable maximum strength range between 6.5 Mw to 7 Mw and an annual slip rate of around 2 to 5 mm/year. Movement along this fault caused the 2006 earthquake in the Special Region of Yogyakarta, leading to liquefaction across several areas in the provinces (Buana et al., 2019).

2.3 Shear Wave Velocity (V_s)

The average shear wave velocity (V̅_s) is essential data to obtain for conducting ground motion modeling to predict vibrations, design infrastructure, and determine the application of seismic characteristics in an area (Heath et al., 2020). In this study, the V̅s value was determined using two methods, based on the correlation of N-SPT values and the recorded microtremor data processing. The value of V̅_s represents the calculated average shear wave velocity at the first 30m soil layer. The V̅_s value was used to determine the site class and calculate PGA values.

An overview of the site conditions and the distribution of borehole and microtremor test points is shown in Fig. 2. Borehole points located in the western part were notated as TB. The TS notation indicates the location of the borehole point along the riverside. Meanwhile, the TT notation indicates borehole points on the east side of the study area. The 31 borehole points can be categorized into 6 points on the west side (TB), 14 points on the riverside (TS), and 11 points on the east side (TT).

At 31 borehole points, the V̅_s value was determined using a correlation equation with the penetration resistance value. Brandenberg et al., (2010) presented a statistical regression value to calculate the V̅_s value in an area by considering the corrected penetration resistance value (N₆₀) and vertical effective pressure (σˊ_v) (Brandenburg et al., 2010). The correlation of the V̅_s value with the corrected penetration resistance is shown in Eq. (1).

$$\:\text{ln}{\left({V}_{s}\right)}_{ij}={\beta\:}_{0}+{\beta\:}_{1}\text{ln}{\left({N}_{60}\right)}_{ij}+{\beta\:}_{2}\text{ln}\left({\sigma\:}_{v}^{{\prime\:}}\right)ij$$

1

Table 1 shows the β values as the regression coefficient parameters for each soil type, such as sand, silt, and clay.

Table 1

Regression coefficient parameter (Brandenberg et al., 2010)
Soil Type	β₀	β₁	β₂
Sand	4.045	0.096	0.236
Silt	3.783	0.178	0.231
Clay	3.996	0.23	0.164

Microtremor measurements were conducted at 18 points to obtain a more comprehensive analysis, 6 points each on the west side, riverside, and east side, as presented in Fig. 2. Determination of test point notations was defined according to the order of the field tests which is adjusted to the availability of field conditions. There were 6 microtremor test points determined adjacent to the borehole test points as a comparison to validate the data and analysis: TA 05 near TB BH-01; TA 10 near TB BH-04; TA 07 near TS BH 11; TA 16 near TS BH-27; TA 14 near to TT BH-04; and TA 01 near to TT BH-08.

The V̅_s value was determined at microtremor measurement points by processing the microtremor data into a Horizontal to Vertical curve (H/V Curve). Microtremor recording data is one of the tools that is applicable to predict the surface area effect on seismic ground motion using the Horizontal to Vertical Spectral Ratio (HVSR) Method.

The HVSR method was developed through correlation from geotechnical investigations with the analysis of strong ground motion records at various geological site conditions. The use of microtremor measurement data has been confirmed as a suitable method of analysis to estimate the value of the amplification factor (A₀) and predominant frequency (f₀) in certain areas (Bonnefoy-Claudet et al., 2006; Mokhberi et al., 2017; Nakamura, 1989, 2008). This method is applicable to estimate the dynamic characteristics of soil layers as an additional analysis that can complement the geotechnical analysis, especially in areas with a limited number of borehole test points.

The measurement, processing, and interpretation of microtremor recording data were performed regarding the guidelines for the implementation of the HVSR method published in the SESAME European Research Project (Catello Acerra et al., 2004). Microtremor measurement is highly sensitive to vibration hence to obtain a reliable H/V curve, it is necessary to filter out vibration disturbances recorded during the test. In this study, microtremor testing at each point was carried out with a minimum duration of 30 minutes. The noise filtering process was carried out using Geopsy software version 2.9.1 to generate the H/V curves. The time window length used was set at 20 seconds. The H/V curve that has been obtained is then processed using Dinver plugin as Rayleigh wave inversion, resulting in an ellipticity and ground profile curve that shows V_s value for each soil layer. The Poisson's ratio parameter was set in the range between 0.2 to 0.5 with the soil density value used in the process adjusted to the soil density value obtained through geotechnical investigation.

2.4 Site Classification

Site classification referring to the Indonesian seismic code is categorized into 6 classes as presented in Table 2.

Table 2

Site classification based on the Indonesian seismic code (National Standardization Agency, 2016)
Site Class	V̅_s	N̅	S̅_u
	m/s		kPa
SA (hard rock)	> 1500	N/A	N/A
SB (rock)	750–1500	N/A	N/A
SC (hard soil, very dense soil, soft rock)	350–750	> 50	≥ 100
SD (medium soil)	175–350	15–50	50–100
SE (soft soil)	< 175	< 15	< 50
SF	SS^(a)
*SS^(a): Sites where specific geotechnical investigations and site-specific response analysis are required

Site classification is recommended using at least two of three parameters: the average shear wave velocity (V̅_s), the average standard penetration resistance (N̅), and the average undrained shear strength (S̅_u) (National Standardization Agency, 2016). This classification is essential for earthquake-resistant structure design, providing an amplification factor for determining the peak acceleration measured from bedrock to the soil surface.

The average shear wave velocity (V̅_s), the average standard penetration resistance (N̅), and the average undrained shear strength (S̅_u) can be determined by the following equations:

$$\:\stackrel{-}{N}=\frac{\sum\:_{i=1}^{m}{t}_{i}}{\sum\:_{i=1}^{m}\left(\frac{{t}_{i}}{{N}_{i}}\right)}$$

2

$$\:\stackrel{-}{{V}_{s}}=\frac{\sum\:_{i=1}^{m}{t}_{i}}{\sum\:_{i=1}^{m}\left(\frac{{t}_{i}}{{V}_{si}}\right)}$$

3

$$\:{\stackrel{-}{S}}_{u}=\frac{\sum\:_{i=1}^{m}{t}_{i}}{\sum\:_{i=1}^{m}\left(\frac{{t}_{i}}{{S}_{ui}}\right)}$$

4

Variable t_i represents the thickness of the i^th soil layer (m), N_i represents the standard penetration resistance value of the i^th soil layer, V_si represents the shear wave velocity value of the i^th soil layer (m/s), and S_ui represents the undrained shear strength value of the i^th soil layer (kPa).

2.5 PGA Determination Based on Microtremor Measurement

The H/V curves obtained from the noise filtering process show the recorded microtremor vibration frequency on the x-axis and the amplification factor on the y-axis. The value of the predominant frequency (f₀) and amplification factor (A₀) were obtained by referring to the centerline of the detected frequency range. The predominant frequency (f₀) can be processed to obtain the dominant period (T₀) using Eq. (5). The H/V curves formed can be interpreted as values that indicate the basin geometry and other site information, such as the estimated bedrock type, average shear velocity of deposits, and Long-Term Seismic Tremor (LTST) site depth to bedrock. Analysis of the H/V curves formed related to ambient vibration which depend on the vibration source and underground structures at the site.

$$\:{f}_{0}=\frac{1}{{T}_{0}}$$

5

Kanai (1966) presented an equation for PGA (gal) on the ground, considering three main parameters: earthquake magnitude (M), hypocenter distance (R),s and predominant period (T₀) obtained through microtremor measurement, as shown in Eq. (6).

$$\:a=\frac{5}{\sqrt{{T}_{0}}}{10}^{0.61M-\left(1.66+\frac{3.6}{R}\right)logR+1.67\frac{1.83}{R}}$$

6

PGA values determination using microtremor data around the Special Region of Yogyakarta has been performed in previous studies by Fathani et al. (2006), Fathani and Wilopo (2017), Palupi et al. (2020), Pawirodikromo (2020), Perdhana and Nurcahya (2019), Siadari et al. (2023) through observations at different specific locations.

2.6 PGA Determination Based on Attenuation Relationships

Deterministic Seismic Hazard Analysis (DSHA) has been used to estimate the potential ground motion in the worst-case scenario. Mase (2020) stated that the purpose of using deterministic methods in the seismic hazard analysis is to identify the controlling earthquake that triggers the most severe damage intensity. A key step of the DSHA method is to define the earthquake model applied in attenuation relationship equations.

The attenuation relationship is an empirical model representing ground motion at certain locations considering earthquake parameters. These include acceleration, shear wave velocity, earthquake intensity, and proximity from the earthquake source to the study site (National Center for Earthquake Studies, 2017). Several parameters are crucial to establish a Ground Motion Prediction Equation (GMPE), including period range, earthquake magnitude, epicenter distance, focal depth, earthquake source mechanism, and site conditions where earthquake data is recorded (Mase, 2020; National Center for Earthquake Studies, 2017). The suitability of selecting the earthquake model used in a particular area will depend on the identification of these seismic parameters.

This study considers the Opak fault as one of the earthquake sources for determining the ground motion prediction equation. Based on previous studies, (Elnashai et al., 2006; Murjaya et al., 2021; Pawirodikromo, 2018; Ulinnuha et al., 2022), movement of the Opak Fault was assumed to be the cause of the 2006 Yogyakarta earthquake. Therefore, the deterministic analysis in this study was conducted by considering the Opak fault as one of the most potential earthquake sources in the Special Region of Yogyakarta.

A previous study (National Center for Earthquake Studies, 2017) has shown that three equations can be used for shallow crustal earthquake sources such as faults, with a strike-slip mechanism. These equations included Boore and Atkinson NGA (2014), Campbell and Bozognia NGA (2014), and Chiou and Youngs NGA (2014). Table 3 presents the seismic parameters of the attenuation model used in this study. The M variable on the table represents the range of earthquake strength on each fault mechanism, the V̅_s represents the average shear wave velocity of top 30m, R_JB indicates the value of Joyner-Boore distance to the rupture plane, R_RUP represents the closest distances to the fault rupture plane, and Z_TOR represents depth to the top of the fault rupture plane.

For strike-slip seismic mechanisms, Boore et al. (2014) developed an attenuation equation with earthquake magnitudes in the range between 3.0 to 8.5 Mw and a distance ranging from 0 to 400 km, using V̅s values between 150 to 1500m/s. PGA is obtained by considering the values of F_E, F_p, and F_s, which depend on the mechanism of the earthquake source, distance to the earthquake source, and site conditions shown in Eq. (7).


$\:\text{ln}Y={F}_{E}\left(M,mech\right)+{F}_{p}\left({R}_{JB},\:M,\:region\right)$
$\:+{F}_{s}\left({V}_{s30},\:{R}_{JB},\:{z}_{1}\right)+{\epsilon\:}_{n}\sigma\:(M,\:{R}_{JB},\:{\stackrel{-}{V}}_{s})$	(7)
$\:+{\epsilon\:}_{n}\sigma\:(M,\:{R}_{JB},\:{\stackrel{-}{V}}_{s})$

Table 3 Seismic parameter of the attenuation equation model

Attenuation Models	Fault Mechanism	M	V̅_s	R_JB	R_RUP	Z_TOR
		Mw	m/s	km	km	km
Boore et al. (2014)	Strike-slip Reverse-slip Normal-slip	3.0-8.5 3.0-8.5 3.5-7.0	150–1500	0-400	-	-
Campbell and Bozorgnia (2014)	Strike-slip Reverse-slip Normal-slip	3.3–8.5 3.3-8.0 3.3–7.5	150–1500	0-500	0-300	0–20
Chiou and Youngs (2014	Strike-slip Reverse-slip Normal-slip	3.5–8.5 3.5-8.0 3.5-8.0	180–1500	-	0-300	0–20

The Campbell and Bozorgnia (2014) GMPE is applicable for earthquakes with a regression model from strong motion data worldwide with a magnitude ranging from 3.3 to 8.5 Mw for strike-slip fault mechanism. Other data include a maximum distance to rupture plane of 500 km, as shown in Eq. (8). The values of fmag, fdis, fflt, fhng, fsite, fsed, fhyp, fdip, and fattn are functions of magnitude, distance from the origin of the earthquake to the site, fault mechanism, hanging wall effect, site conditions, basin conditions, hypocentral distance, dip angle, and unelastic attenuation, respectively

$$\:\text{ln}Y=\left\{\begin{array}{c}\text{ln}PGA\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Y=PGA,T<0.25\\\:{f}_{mag}+{f}_{dis}+{f}_{flt}+{f}_{hng}+{f}_{site}+{f}_{sed}+{f}_{hyp}+{f}_{dip}+{f}_{attn}\end{array}\right.$$

8

Chiou and Youngs (2014) developed attenuation equation for earthquakes with magnitudes of 3.5 to 8.5 Mw for strike-slip fault mechanism. The presented PGA equation applies to spectra with 5% attenuation and periods extending from 0.01 to 10 seconds. PGA calculation with Chiou and Youngs (2014) model is shown in Eq. (9).

$\:\text{ln}\left({y}_{ij}\right)=\text{ln}\left({y}_{refij}\right)+\:{\varphi\:}_{1}min.\:\left(\text{ln}\left(\frac{{\stackrel{-}{V}}_{sj}}{1130}\right),\:0\right)$
$\:{+\:\varphi\:}_{2}\left\{{e}^{{\varphi\:}_{3}(\text{min}\left({\stackrel{-}{V}}_{sj},\:1130\right)-360)}-{e}^{{\varphi\:}_{3}\left(11360-360\right)}\right\}.\text{ln}\left(\frac{{y}_{re{f}_{ij}}{e}^{{n}_{i}}+{\varphi\:}_{4}}{{\varphi\:}_{4}}\right)$	(9)
$\:+\:{\varphi\:}_{5}\left\{1-{e}^{-{\Delta\:}{z}_{\text{1,0}j}/{\varphi\:}_{6}}\right\}+{\eta\:}_{i}$

y _i,j is dependent variable corresponding to the amplitude of ground motion for earthquake i observed at station j. y_refij function representing the median of the population for the V̅_s = 1.130 m/s reference condition. η_i (between-event residual or event term) and ε_ij (within-event residual) are random variables representing errors in modeling that may affect the coincidental variability of the estimated motion.

Determination of PGA through the three GMPE equations refers to Pacific Earthquake Engineering Research Center (PEER) data which can be accessed by inputting parameters on PEER’s website (https://ngawest2.berkeley.edu/). Subsequently, the PGA value obtained through the GMPE NGA West 2 attenuation equations is weighted to calculate the uncertainties in seismic hazard analysis.

Attenuation relationship equations used are Boore et al. (2014), Campbell and Bozorgnia (2014), and Chiou and Youngs (2014) with weights of 0.33, 0.34, and 0.33 as shown in Fig. 3 (National Center for Earthquake Studies, 2019), respectively. Given the site location’s proximity to an active fault, PGA value obtained from weighting the GMPE NGA West 2 attenuation equation needs to be multiplied by 1.5 (150% median) to represent the 84th percentile response.

Based on historical USGS earthquake data as presented in Fig. 1, the Special Region of Yogyakarta has a high seismic risk level. Given this level of earthquake intensity, it is essential to consider historical earthquake data around the Special Region of Yogyakarta to obtain an overview of the history of earthquakes that significantly influence ground motion at the site.

Kanno et al. (2006) developed an attenuation equation that provides ground motion prediction using historical earthquake data. The main criteria used in this attenuation model are earthquakes with magnitudes greater than 5.5 M_w using ground motion data recorded on the surface, with a minimum of five affected stations. The application of the Kanno et al. Eq. (2006) has been used in various previous studies in determining ground motion parameters in certain areas (Basu et al., 2023; Hung and Kiyomiya, 2012; Morikawa and Fujiwara, 2013; Siadari et al., 2023). This empirical model considers the earthquake magnitude and hypocenter for both shallow (D ≤ 30 km) and deep (D > 30 km) earthquakes, as shown in the following equations.

$\:D\le\:30km$
$\:\text{log}pre={a}_{1}{M}_{w}+{b}_{1}X-\text{log}\left(X+{d}_{1}\:{10}^{{e}_{1}{M}_{w}}\right)$	(10)
$\:+{c}_{1}+{\epsilon\:}_{1}$	(10)
$\:D>30km$
$\:\text{log}pre={a}_{2}{M}_{w}+{b}_{2}X-\text{log}\left(X\right)+{c}_{2}+{\epsilon\:}_{2}\:$	(11)

The PGA calculation through the Kanno attenuation model in this study applies the scenario of the Yogyakarta 2006 earthquake with a strength of 6.3 Mw. The seismic parameters used in this study are based on United States Geological Survey (USGS) data which is accessible through the USGS’s website (https://earthquake.usgs.gov/earthquakes/map/). The Pre variable represents the PGA value in cm/s², the focal depth in km is indicated by the value d, and the values of a₁, b₁, c₁, d₁, a₂, b₂, and c₂, are the regression coefficient values, the e₁ value is the magnitude coefficient value which is a function from the distance e₁ = 0.5. The ε₁ and ε₂ values represent the errors between the predicted and observed values. The regression coefficient values used in the attenuation equation of Kanno et al. (2006) are given in Table 4.

Table 4

Regression coefficient for Kanno et al. (2006) attenuation
Attenuation model	Regression Coefficient
Attenuation model	a	b	c	d	e	ε
D ≤ 30 km	0.56	-0.0031	0.26	0.0055	0.5	0.37
D > 30 km	0.41	-0.0039	1.56	1.56	0.5	0.4

Kanno et al. used an amplification factor considering the average shear wave velocity as a correction value for local site effects as shown in Eq. (12).

$$\:G=p\text{log}{\stackrel{-}{V}}_{s}+q$$

12

$$\:\text{log}pr{e}_{G}=\text{log}pre+G$$

13

where G is the correction value of the shear wave velocity, with the coefficient values p=-0.55 and q = 1.35, and V̅_s is the average shear wave velocity (m/s). PGA value is calculated based on this correction factor as shown in Eq. (13).

2.7 PGA Determination according to the Indonesian Seismic Code

Indonesian seismic code provides PGA analysis using the probabilistic method, which determines soil parameters over a specific period at the study location. This method considers the possibility of fault movement and magnitude distribution probabilistically, including all historical earthquakes, maximum probable earthquakes, and local soil conditions (Fathani et al., 2006). Probabilistic analysis for bridge infrastructure design assumes a 75-year design life with a 7% exceedance probability and a 1000-year return period (National Standardization Agency, 2016, 2019). PGA value at the study location through the probabilistic method is obtained from https://lini.binamarga.pu.go.id/ issued by the Directorate General of Highways, Ministry of Public Works and Housing using coordinate points as data input.

The acceleration value derived through the probabilistic method represents the predicted acceleration at the bedrock. However, the PGA values at the ground surface need to consider the amplification factor due to the site class. PGA calculation by considering the local site effect (A_s) given in Eq. (14) with the site class coefficient value (F_PGA) refers to the value shown in Table 5 (National Standardization Agency, 2016).

$$\:{A}_{s}={F}_{PGA}\:.\:\:PGA$$

14

Table 5

Site class coefficient (F_PGA) (National Standardization Agency, 2016)
Site Class	Peak Ground Acceleration (PGA)
Site Class	0.1 g	0.2 g	0.3 g	0.4 g	0.5 g
SA	0.8	0.8	0.8	0.8	0.8
SB	1.0	1.0	1.0	1.0	1.0
SC	1.2	1.2	1.1	1.0	1.0
SD	1.6	1.4	1.2	1.1	1.0
SE	2.5	1.7	1.2	0.9	0.9

Geotechnical investigations were conducted at 31 borehole points along the study site by laboratory soil sample tests and standard penetration tests (SPT). The borehole test point distribution map is presented in Fig. 2. The results of geotechnical investigations show that the soil stratigraphy of the study site is dominated by sandy soil with a medium density level, with the dominant soil classifications described as silty sand, well-graded sand, and poorly graded sand. Figure 4 provides a cross-section of the soil stratigraphy at the study area, represented by 6 borehole points, namely TB BH-01, TB BH-04, TS BH-11, TS BH-27, TT BH-04, and TT BH-08. There are small portions of clay and silts in the form of sandy silty clay, at a depth between 24 to 38 m at TB BH-04.

Penetration resistance values at borehole points on the west side of the Progo River indicate the presence of hard soil layers beneath 30 m depth, characterized by N-SPT values of 60 at a depth of 30 m to 40 m. The soil that appears to be looser on the west side of the Progo River is at a depth of 0 to 8 m with an average N-SPT value below 30. In contrast to the west side, the penetration resistance values at the borehole points on the east side and riverside tend to be more variable. The soil layer at a depth of 40 m in four (4) borehole points on the east side (TT-BH 01, TT-BH02, TT-BH03, TT-BH04) and six (6) borehole points on the riverside (TS BH-17, TS BH-19, TS BH-21, TS BH-23, TS BH-25, TS BH-27) have N-SPT values in the range of 18 to 32. The soil layers on the east side and riverside tend to be slightly less dense than the west side with N-SPT values below 30 to a depth of 16m. Figure 5 shows the soil profile for each west side, riverside, and east side.

Figure 6 shows the H/V Curve as output from data processing through Geopsy version 2.9.1. Each line on the H/V curve represents the vibrations recorded by the seismograph during the test. The dashed lines indicate the range of all detected vibration frequencies.

In the noise filtering process, the number of windows (I_w) used is between 40 to 80. Microtremor recording data at 18 test points resulted in frequency values between 1.26 Hz to 3.66 Hz with an average frequency value recorded at 2.42 Hz. Frequency values above 1 Hz indicate that the vibration sources are generally due to human activities, wind, and water flow, and other vibration sources close to the ground surface.

Frequency values above 1 Hz also indicate that the test time was conducted during the day. Furthermore, considering the reliability of the curves formed, the f0 value obtained shows that the reliability criteria of the H/V curves produced are fulfilled (f₀ > 10/I_w).

Figure 6. shows that based on the 6 microtremor test sample points at the site, there is variability of the H/V curves resulting in different forms of curves at each test point. Broad peaks (multiple peaks) are shown at the point of TA 05 in Fig. 6 (a). This form of the H/V curve may occur due to the difference in slope between the soft soil layer and the hard soil layer in an elongated alluvial valley.

TA 10 and TA 14 in Fig. 6. (b) and (e) respectively show clear peaks. Clear peaks at the sedimentary deposits indicate that the upper part of the study site is soft soil (not a disturbance). This is consistent with the results obtained through SPT which shows that there is a layer of loose soil with N-SPT value below 15 with a thickness of 6 m on the ground surface at points TB BH-04 and TT BH-04. Ground motion amplification is very likely to occur at points that have clear peaks due to soil conditions.

Three points are showing H/V curves with two peaks, namely points TA 07, TA 16, and TA 01 shown in Fig. 6. (c), (d), and (f). The two peaks in an H/V curve can occur due to impedance contrast in adjacent soil layers that can be indicated by the different values of shear wave velocity. Generally, points with two peaks have relatively low velocities at the surface and higher velocities in deeper soil layers. Impedance contrast in this case is supported by the results of the geotechnical investigation at point TS BH-11 (TA 07) which showed that the top soil contained loose soil with a thickness of 2 m. The soil layer below the topsoil showed an N-SPT value of 60. In addition, the V̅_s values obtained for TA 07, TA 16, and TA 01 were 180. 11 m/s, 161.35 m/s, and 150.15 m/s at the top of the soil layer and V̅_s values of 580.29 m/s, 579.36 m/s, and 349.05 m/s at depths below 15 m. The reliability of using curves with two different peaks is indicated by the considerable difference in values between the values of f₀ and f₁.

The ground profiles generated from processing the H/V curves show the values of compression wave velocity (Vp) and shear wave velocity (Vs) in m/s for each soil layer as shown in Fig. 7. Ground profiles obtained at 6 sample points show a similar curve pattern. The curve shows that the Vs value in the soil layer up to a depth of 20 m is below 400 m/s. At deeper soil layers the Vs value increases to above 500 m/s. These values indicate that the soil layers at the site at depths below 20 m are significantly denser than the upper layers.

The analysis of soil density levels in deeper soil layers was supported by geotechnical investigation data including standard penetration test data to a depth of 40m. Figure 8. shows the comparison of N-SPT and V_s at 6 sample points. The V_s values at borehole points TB BH-01, TB BH-04, TS BH-11, TS BH-27, TT BH-04, and TT BH-08 were obtained through N-SPT correlation using the Brandenberg et al. (2010) as shown in Eq. (1). V_s values at points TA 05, TA 10, TA 07, TA 16, TA 14, and TA 01 were obtained by referring to ground profile curves through microtremor.

The V_s value derived from the N-SPT correlation is relatively smaller than the V_s value obtained through microtremor data. At a total of 31 borehole points, the V_s values were consistently below 400 m/s with V̅_s values between 210.98m/s and 271.04 m/s.

The highest V̅_s value is at the TS BH 11 point with a value of 271.04 m/s. On the other hand, the V̅_s value obtained through microtremor data at 18 microtremor test points has significantly varied values ranging from 227.99 m/s to 418.39 m/s. A total of three microtremor test points have values below 300 m/s, namely test points TA 04, TA 05, and TA 12. The highest V̅_s value is at TA 08, which is 418.39 m/s. The ground profile representation shown in Fig. 8. indicates the Vs values calculation through both N-SPT correlation and microtremor testing adequately describes the density at the site.

The determination of the site classification in this study was performed by referring to Table 2 using the average shear wave velocity (V̅_s) and average standard penetration resistance (N̅) parameters through Eq. (2) and Eq. (3). The site class at the site is dominated by medium soil (SD) at 27 borehole test points and 14 microtremor measurement points. Based on the N-SPT correlation, two test points are considered hard soil (SC), namely TS BH-05 and TS BH-11, while TS BH-13 and TS BH-15 are soft soil (SE). A total of four microtremor measurement points are considered as hard soil (SC), namely TA 06, TA07, TA 08, and TA 16.

3.1 PGA Determination based on microtremor measurement

Microtremor measurements recorded frequencies in the interval of 1.26 Hz to 3.66 Hz. PGA calculation was carried out using a 2006 earthquake scenario with 6.3 M_w, which obtained values varying from 0.126 g to 0.214 g.

Figure 9 (a) presents the distribution of PGA values derived through microtremor analysis. The PGA values are quite uniform on the west and east sides of the Progo River. However, the PGA value on the side of the river has a lower value. The microtremor recordings at the point show that at the point located in the center of the river, the predominant period value is higher compared to the points on the west and east sides.

Based on the analysis of the geotechnical investigation results and the determination of the site class, the soil conditions in the central part of the Progo River are relatively loose. Several points in this area are classified in the SE (soft soil) site class. Soils with lower density tend to have large period values so wave amplification at this location is very likely to occur. This large period value indicates a slower wave propagation time, resulting in a lower acceleration value.

Predictions of PGA values that occurred due to the Yogyakarta earthquake in 2006 have been performed by Elnashai et al. (2006) using the data available at YOGI and BJI stations located approximately 10 km and 90 km from the earthquake epicenter. The results of the reconstructive analysis carried out using vibration recordings at YOGI station gave the results of horizontal peak ground acceleration at around 0.197 g to 0.336 g while vertical peak ground acceleration is in the range of 0.183 g to 0.303 g with a mean value of 0.262 g in the East-West direction, 0.270 g in the North-South direction, and 0.243 g in the vertical direction. Meanwhile, PGA values at the BJI station 90 km away from the earthquake epicenter tend to be smaller around 0.028 g in the North-South direction and 0.020 g in the vertical direction.

The determination of PGA values based on microtremor recording data has been carried out by Fathani et al. (2006) on two scenarios of the Yogyakarta earthquake in 2006 with variations in epicenter distance. The first scenario was conducted using epicenter data referring to Indonesia Meteorological and Geophysical Agency (BMG) resulting in values of 0.140 g to 0.480 g. The second scenario was carried out using epicenter data issued by the United States Geological Survey (USGS) resulting in a map of the distribution of PGA values with a value range of 0.146 g to 0.534 g.

The use of microtremor recording data in determining PGA was also carried out by Pawirodikromo (2020) on 9 microtremor test points in the Special Region of Yogyakarta resulting in a predominant frequency range of 0.5 Hz to 12 Hz and resulting PGA values ranging between 0.05g to 0.45g. In Srandakan District, the PGA value obtained based on microtremor testing data at point 1 Argodadi, Sedayu is about 0.20g.

Fathani and Wilopo (2017) conducted research in Yogyakarta City, which is in the north direction of Bantul Regency, resulting in values varying from 0.05g to 0.30g. While Siadari et al., (2023) conducted the microzonation study in Magelang District, Central Java Province, located on the northwest side of the Special Region of Yogyakarta, directly adjacent to Sleman District resulting in values of 0.036g to 0.088g.

Considering these previous studies, the range of values generated based on microtremor measurement at 18 test points in the study area is close to the range of PGA values recorded at the YOGI station. This shows that the determination of PGA values using microtremor recording data in certain areas is suitable. However, to increase the accuracy of the calculation, data support from geotechnical investigation is necessary to represent local site conditions.

3.2 PGA Determination Based on Attenuation Relationships

Deterministic Seismic Hazard Analysis (DSHA) in this study was conducted through two approaches, applying the attenuation equation based on considering the nearest earthquake source and controlling earthquakes that cause significant damage.

Based on previous studies (Soehaimi et al., 2019; Ulinnuha et al., 2022), the Opak faults are one of the potential sources of earthquakes that are estimated to cause earthquakes with a magnitude of 6.5 Mw to 7.0 Mw. The attenuation equation model used in the deterministic analysis in its consideration of the Opak Fault, namely the New Generation Attenuation Ground Motion Prediction Equation (GMPE NGAWest 2) through the equations of Boore et al. (2014), Campbell and Bozorgnia (2014), Chiou and Youngs (2014). The weighting in this deterministic analysis was carried out regarding the logic tree framework as shown in Fig. 3, which is 0.33; 0.34; and 0.33 respectively. The amplification factor value of 1.50 (150% median) was used to represent the 84th percentile. Thus, the PGA value obtained from these equations ranges from 0.475 g to 0.549 g, as shown in Fig. 9 (b).

Another approach was taken for the controlling earthquake, which was estimated to produce a significant damage on the study area. This analysis was performed using Kanno et al. (2006) equation, as shown in, as shown in Eq. (13). The 2006 earthquake in Yogyakarta with 6.3 Mw scenario was used, resulting in a PGA range of 0.266 g to 0.394 g. PGA distribution map using the attenuation relationship by Kanno et al. (2006) is shown in Fig. 9 (c).

Figure 9 (b) and (c) show that there is uniformity in the distribution of PGA values obtained by the deterministic method with the GMPE NGA West 2 attenuation equation and the attenuation equation of Kanno et al. (2006). The PGA values obtained by the deterministic calculation method through both attenuation equations tend to be higher on the east side. This study shows that the more proximity the site to the earthquake source, the more the PGA value is generated.

The development of deterministic analysis in seismic hazard analysis has resulted in the development of attenuation equations with various approaches. Elnashai et al. (2006) conducted deterministic analyses for several areas in the Special Region of Yogyakarta, using the attenuation equations of Ambraseys et al. (2005) and Campbell (2003). In Bantul Regency, the PGA values obtained are in the range of 0.121 g to 0.3591 g in soft soil and 0.1127 g to 0.2939 g in stiff soil through the attenuation equation of Ambraseys et al. (2005). While using Campbell (2003), PGA values produced in the range of 0.122 g to 0.492 g in soft soil and 0.122 g to 0.449 g in stiff soil.

Siadari et al. (2023) conducted a deterministic analysis of the Yogyakarta earthquake in 2006 using the attenuation equations of Kanno et al. (2006) and Fukushima and Tanaka (1990) in Magelang District, Central Java Province. The values obtained tend to be smaller, in the range of 0.115 g to 0.181 g using Kanno's et al. (2006) attenuation model. Whereas Fukushima's (1990) attenuation model resulted in a value of 0.082 g to 0.114 g. The difference in values may occur because the research location of Siadari et al. (2023) is in Central Java Province with a distance to the earthquake source estimated at 20 km to 35 km

The deterministic analysis uses an approach to the potential earthquake that generates the most damage. In this context, the identification of earthquake sources in this analysis is done by selecting earthquake sources that are considered to have the most potential earthquakes with large magnitudes and cause the most damage. Several parameters are considered in the deterministic analysis, namely magnitude, distance, and other parameters related to the earthquake source. This analysis tends not to consider site conditions and the probability of reoccurrence. Therefore, when compared to the values obtained through microtremor test data recording and probabilistic seismic hazard analysis, the PGA values obtained through deterministic analysis tend to be higher. This indicates that the deterministic analysis process is often considered more conservative because it produces the maximum possible value.

The application of deterministic seismic hazard analysis is more appropriate when used in locations near earthquake sources with the potential to trigger earthquakes with strong magnitudes. In addition, deterministic analysis is also suitable if used in the planning and designing of earthquake-resistant structures, especially in strategic or vital infrastructure where the impact of damage is highly avoided. It can be concluded that one of the important steps in deterministic seismic hazard analysis is to thoroughly identify the study area, the earthquake sources, and the design of the planned structure.

3.3 PGA Determination based on the Indonesian Seismic Code

The PGA value through probabilistic analysis in the study area resulted in a uniform value at all review points, which is 0.414g. This value is then multiplied by an amplification factor according to the site class for each test point. The overall site class at the research location is in the SD (medium soil) site class; however, there are several location points with SC (hard soil) and SE (soft soil) site classes.

The site class coefficients for each site class SC, SD, and SE were obtained through a linear interpolation calculation of the amplification factor (F_PGA) as shown in Table 2. Linear interpolation calculation was performed on the PGA value of 0.414 g for each site class SC, SD, and SE resulting values of 1.00, 1.086, and 0.90, respectively. As a result, the PGA values obtained for each site class are 0.414 g, 0.450 g, and 0.373 g, as shown in Fig. 9 (d). The lower PGA values in the SE (soft soil) site class indicate the conformity of the distribution pattern of PGA values with the PGA values obtained through microtremor analysis.

Pawirodikromo (2022) conducted a probabilistic analysis study in Pleret District, the northern part of Bantul Regency using a 3-D seismic source with a 10% probability exceeding 50 years of building lifetime. The resulting PGA value in bedrock is 0.254 g to 0.289 g. The amplification factor used is 1.401 and 1.426 resulting in a PGA value at the surface of 0.398 g to 0.412 g.

The PGA values obtained in the southern part of Bantul Regency tend to be higher. This can occur because this study considers a 75-year design life with a 7% exceedance probability and a 1000-year return period. The larger the use of design life and return period will result in a larger final PGA value because it indicates the broader use of data used. The probabilistic analysis in this case reviews all earthquake mechanism schemes and historical earthquakes that have occurred by considering site conditions. These considerations result in the PGA value through probabilistic analysis tend to be smaller when compared to the PGA value from deterministic calculations.

Moreover, probabilistic methods in seismic hazard analysis are appropriate for various types of infrastructure. However, in planning and designing infrastructure close to the earthquake source (less than 10 km), the use of deterministic analysis or Site-Specific Response Analysis will produce more appropriate ground motion predictions.

This study showed that the V̅s value based on N-SPT correlation was smaller compared to the V̅s value obtained from microtremor measurement. The V̅s value obtained from microtremor measurement ranges from 227.99 m/s to 418.39 m/s, while the N-SPT correlation showed 207.44m/s to 271.04 m/s. The highest V̅s value, 418.39 m/s, is at the TA 08 microtremor measurement point, classifying it as a hard soil (SC) site class. However, the N-SPT correlation method at this specific point indicates a value of 271.04 m/s, classifying it as medium soil (SD).

Several points should be considered in the analysis of microtremor data, that is the possibility of the influence of wind and water flow at the study site that provides disturbance to microtremor vibration recordings, particularly at vibration frequencies below 1 Hz (low frequency). The presence of humans, vehicles, and construction activities at a certain distance is very likely to have a disturbance effect on microtremor vibration recordings. Therefore, to obtain more reliable results, microtremor analysis needs to be supported by additional data, such as geological, geotechnical, and other data related to conditions at the site.

PGA values were determined using four methods: (1) probabilistic methods as stated in the Indonesian seismic code; (2) deterministic through attenuation relationship equation based on distance and magnitude at the active fault nearby (GMPE NGA West 2); (3) deterministic methods by using Kanno's (2006) attenuation relationship equation based on historical earthquakes; and (4) predominant frequency (f₀) data from microtremor measurement. The highest PGA value, ranging from 0.475 g to 0.549 g, was obtained through a deterministic method by attenuation equation using parameters from the Opak fault. Indonesian seismic code obtained values ranging from 0.373 g to 0.450 g. PGA value based on the attenuation equation for the 2006 earthquake scenario range of 0.266 g to 0.394 g. The lowest value was obtained based on predominant frequency (f₀) data through microtremor testing, which varied between 0.126g and 0.214g. The points with the highest PGA values were at TT BH 10 and TT BH 1 which were classified as medium soil (SD).

In conclusion, this study showed that PGA values obtained from deterministic methods would generate the highest value because it is considered the closest earthquake source with the 84th percentile (150% median). PGA analysis using attenuation relationships might be more suitable for vital structures. Based on the Indonesian seismic code, the PGA value tended to be smaller due to considerations of probability and uncertainty in the calculations. The V̅s and PGA data obtained through microtremor measurement were considered to represent the conditions at the study location adequately. However, to increase the accuracy of determining the V̅s and PGA values, additional test points could be added around the borehole locations to represent the geotechnical conditions. PGA determination analysis using all the methods above can be applied in any location by having a comprehensive geotechnical investigation and identifying attenuation relationships that are representative enough for the area.

Author Contribution

GBA and TFF conceived of the presented idea. TFF and HS assisted GBA to evaluate geological aspect and develop the seismic hazard framework. GBA performed the measurements, analysis, and drafted the first manuscript. TFF and HS refine the analysis and supervised the manuscript. All authors provided critical feedback and helped establish the research, analysis, and final manuscript.

Acknowledgement

This research was supported by the Ministry of Public Works and Housing of Indonesia. We gratefully acknowledge the National Road Implementation Agency for Central Java, the Special Region of Yogyakarta, and the Meteorology, Climatology, and Geophysics Agency for providing data and technical support. We would like to thank Setiawan Wibowo, Putu Adi Wibawa, Vederieq Yahya Enderzon, Adhi-SWS, KSO and PT. Indec Internusa, KSO-PT. Adhy Duta Prima for the provision of site investigation data, site permissions, and assistance.

Data Availability

Data is provided within the manuscript.

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\(\:\text{ln}Y={F}_{E}\left(M,mech\right)+{F}_{p}\left({R}_{JB},\:M,\:region\right)\)
\(\:+{F}_{s}\left({V}_{s30},\:{R}_{JB},\:{z}_{1}\right)+{\epsilon\:}_{n}\sigma\:(M,\:{R}_{JB},\:{\stackrel{-}{V}}_{s})\)	(7)
\(\:+{\epsilon\:}_{n}\sigma\:(M,\:{R}_{JB},\:{\stackrel{-}{V}}_{s})\)

\(\:\text{ln}\left({y}_{ij}\right)=\text{ln}\left({y}_{refij}\right)+\:{\varphi\:}_{1}min.\:\left(\text{ln}\left(\frac{{\stackrel{-}{V}}_{sj}}{1130}\right),\:0\right)\)
\(\:{+\:\varphi\:}_{2}\left\{{e}^{{\varphi\:}_{3}(\text{min}\left({\stackrel{-}{V}}_{sj},\:1130\right)-360)}-{e}^{{\varphi\:}_{3}\left(11360-360\right)}\right\}.\text{ln}\left(\frac{{y}_{re{f}_{ij}}{e}^{{n}_{i}}+{\varphi\:}_{4}}{{\varphi\:}_{4}}\right)\)	(9)
\(\:+\:{\varphi\:}_{5}\left\{1-{e}^{-{\Delta\:}{z}_{\text{1,0}j}/{\varphi\:}_{6}}\right\}+{\eta\:}_{i}\)

\(\:D\le\:30km\)
\(\:\text{log}pre={a}_{1}{M}_{w}+{b}_{1}X-\text{log}\left(X+{d}_{1}\:{10}^{{e}_{1}{M}_{w}}\right)\)	(10)
\(\:+{c}_{1}+{\epsilon\:}_{1}\)	(10)
\(\:D>30km\)
\(\:\text{log}pre={a}_{2}{M}_{w}+{b}_{2}X-\text{log}\left(X\right)+{c}_{2}+{\epsilon\:}_{2}\:\)	(11)

Seismic Microzonation Studies in the Southern Part of Progo River, Special Region of Yogyakarta, Indonesia

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. METHODOLOGY

2.1 The Study Location

2.2 Geological Condition

2.3 Shear Wave Velocity (V_s)

2.4 Site Classification

2.5 PGA Determination Based on Microtremor Measurement

2.6 PGA Determination Based on Attenuation Relationships

2.7 PGA Determination according to the Indonesian Seismic Code

3. RESULTS

3.1 PGA Determination based on microtremor measurement

3.2 PGA Determination Based on Attenuation Relationships

3.3 PGA Determination based on the Indonesian Seismic Code

4. DISCUSSION

5. CONCLUSION

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1

Seismic Microzonation Studies in the Southern Part of Progo River, Special Region of Yogyakarta, Indonesia

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. METHODOLOGY

2.1 The Study Location

2.2 Geological Condition

2.3 Shear Wave Velocity (Vs)

2.4 Site Classification

2.5 PGA Determination Based on Microtremor Measurement

2.6 PGA Determination Based on Attenuation Relationships

2.7 PGA Determination according to the Indonesian Seismic Code

3. RESULTS

3.1 PGA Determination based on microtremor measurement

3.2 PGA Determination Based on Attenuation Relationships

3.3 PGA Determination based on the Indonesian Seismic Code

4. DISCUSSION

5. CONCLUSION

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1

2.3 Shear Wave Velocity (V_s)