Oman Strong Motion Network (OSMN) and Site-specific Characterization for the Entire Strong Motion Stations in Muscat Region

doi:10.21203/rs.3.rs-1826346/v1

In 2001, the earthquake monitoring center (EMC) at Sultan Qaboos University (SQU) started monitoring earthquakes that occur in and around the sultanate of Oman through 10 short-period stations. The number of monitoring stations grew to 20 broadband seismic stations between 2004 and 2011 deployed in different parts of the sultanate. In 2014, EMC-SQU began an extensive project for updating and improving the monitoring network by adding 6 broadband stations and establishing a strong motion network consisting of 67 monitoring stations. This provides a good opportunity to investigate the influences of the seismic source, path, and site effects in the ground motion, particularly in the near-source area. Such data are essential for improving seismic hazard maps and consequently seismic building codes and earthquake-resistant design. A wide site selection survey was carried out for the entire proposed stations taking into account the standard site selection criteria. In order to use the strong motion recordings at their full potential, information on the site response at the recording site is needed. So, site investigation at 25 stations in Muscat region was performed in terms of noise level, resonance frequency (using horizontal-to-vertical spectral ratio (HVSR) method), P-wave velocity (using shallow seismic refraction tomography (SRT) technique), and S-wave velocity (using multichannel analysis of surface waves (MASW) tool), in addition to the available geotechnical boreholes. The results reflect the site classification in the light of NEHRP/IBC standards and consequently the seismic hazard level at the ground surface.

OSMN

Muscat Stations

Site Characterization

Noise Level

HVSR

SRT

MASW

PSHA

A network for observing the strong ground motion that can happen in a given territory records data that give a superb opportunity to investigate how seismic source, ray-path, and site effects impact the ground motion, particularly that in the near-source vicinity. Such data are basic for updating seismic hazard findings (curves and maps) and subsequently improving infrastructures codes and earthquake-resistant design. High quality, strong ground motion data are additonally facilitate expanding the viable planning against seismic catastrophes, and the response during seismic crises.

The Sultanate of Oman occupies the southeastern corner of the Arabian Plate, which is a solitary tectonic plate surrounded by relatively high active tectonic zones. The seismic activity is initiated by active tectonics of Oman Mountains in addition to the active tectonics dominated by the collision of the Arabian Plate with the Eurasian Plate along the Zagros and Bitlis thrust systems (Johnson, 1998), subduction of the Arabian Plate beneath Eurasian Plate along the Makran Subduction Zone, the transformation of Owen Fracture zone that separates the Arabian and the Indian plates, rifting and sea-floor spreading in the Red Sea and Gulf of Aden, and Strike-slip faulting occurring along the Gulf of Aqaba Dead Sea fault system (Fig. 1). These aforementioned tectonic activities make Oman prone to earthquake activities according to stresses acting at the boundaries of the Arabian Plate.

The Earthquake Monitoring Center (EMC) at Sultan Qaboos University (SQU) started monitoring seismic activities in and around Oman using well distributed 10 short-period seismographs since 2001. During the last 19 years these stations passed through three updating phases and become in 2014 twenty broadband stations. In 2014, EMC planned for adding 6 permanent broadband stations to the existing seismic network and for establishing the Oman strong motion network (OSMN). For OSMN first stage, it was planned to start with 67 accelerometers to cover the metropolitan areas inside the sultanate (Fig. 2).

Based on the standard site selection criteria, the authors carried out a detailed survey for more than 110 station sites, giving a priority for 67 stations in the first stage of OSMN.

Strong ground motion recordings cannot be interpreted appropriately except if we know the geotechnical, geophysical, and geological dynamic properties of the layers through which seismic waves are travelling as. The site characterization plays a significant role in earthquake engineering.

The current research presents an overview of the near-surface soil profile characteristics at selected twenty-five strong ground motion stations sites in Muscat City, Sultanate of Oman. Site characterization can be defined in terms of fundamental (resonance) frequency, shear modulus (shear-wave velocity), primary-wave velocity, density, N-value of standard penetration test (SPT), layer thickness and lithology. To evaluate these properties at low strain levels (shear strains < 0.001%), where the stress-strain curve of soil behavior is linear, we use the shallow SRT and MASW (Park et al., 1997; Park et al., 1999; Xia et al., 2002) techniques. To assess these properties at high strain levels, where the soil behavior become non-linear, we use borehole geotechnical parameters such as N-value of SPT.

The weighing average of shear-wave velocity down to 30 m depth, which is known as Vs₃₀ is a vital indicator of soil classification in several building codes such as NEHRP and IBC. Vs₃₀ is utilized for the implementation of ground motion prediction equations (GMPEs). The Vs₃₀ value is estimated at the twenty-five OSMN station sites at Muscat area.

The authors studied in detail the probabilistic seismic hazard assessment (PSHA) at the bedrock conditions in and around Oman (El-Hussain et al., 2012; Deif et al., 2017; El-Hussain et al., 2018; Al-Shijbi et al., 2019 and Deif et al., 2020). In the present study, the authors utilized the results of the latest PSHA study as input ground motion to evaluate the seismic hazard at the ground surface level and to construct the design response spectrum at each station site of Muscat area (25 stations).

The main objectives of OSMN are monitoring, interpreting and estimating the ground shaking generated by potentially strong earthquakes. OSMN is generally designed to: i) observing ground motion to determine the forces lead to damage, ii) providing basic data for earthquake hazard, zoning and risk maps, iii) contributing to health monitoring and emergency management for critical facilities (early warning system, dams, liquefied natural gas (LNG) power plant, nuclear power plant, …… etc.), iv) providing reliable information on earthquake ground shaking by shak map software, and v) improving building code and safe settlement working.

2.1. Station site selection

Seismic site selection in a network to detect earthquakes and to monitor event time-series is mainly controlled by its signal-to-noise (SNR) characteristics. If the SNR at the site is too low due to any transient or man-made seismic noise, high trigger thresholds will be needed, resulting in a network of poor detection capability. On the other hand, if a seismic station is located on soft ground, very broadband (VBB) and/or broadband (BB) recordings are useless and short-period (SP) signals might be unrepresentative due to local soil effects. In addition, the absence of well-distributed network stations result inaccurate location of the seismic events. Therefore, an appropriate site-selection is essential for the success of the new seismic station or network.

Seismic site selection for a strong motion network is chosen and designed to measure earthquake shaking of large amplitude with peak ground accelerations (PGA) higher than 0.01g. We considered the heavy populated centers, buildings, infrastructure, active fault locations, seismic source zones, geologic site conditions, and protection from vandalism when selecting sites to install strong-motion stations.

A wide site selection survey performed for the new OSMN stations taking into account the aforementioned conditions and the standard site selection criteria (Heaton et al., 1996; COSMOS, 2001). The OSMN stations include 67 stations (see Table 1 and Fig. 2) and can be classified as free-field and urban-reference stations. In the current stage, OSMN has no borehole or structural response stations.

Table 1

Oman Strong Motion Network (OSMN) stations.

REGION	SN	CODE	COORDINATES		LOCATION
REGION	SN	CODE	Latitude	Longitude	LOCATION
MUSCAT	1	MCT-01	23°38'18.7"	58°03'05.8"	Germany University
	2	MCT-02	23°41'48.1"	58°02'52.1"	El-Nasiem Park
	3	MCT-07	23°38'02.0"	58°05'51.9"	Oman Society for Charity Store
	4	MCT-08	23°35'09.2"	58°01'37.3"	Al-Ittqan Primary School-Halban
	5	MCT-09	23°41'43.3"	58°08'58.2"	El-Shadii Polyclinic Center
	6	MCT-11	23°39'20.3"	58°13'23.5"	El Khoudh Park
	7	MCT-12	23°39'37.7"	58°11'46.0"	In front of Al-Zolfa Mosque
	8	MCT-14	23°37'20.8"	58°12'56.9"	El-Masharq Primary School
	9	MCT-15	23°35'28.2"	58°14'46.6"	Borg El-Sahwa Park
	10	MCT-16	23°36'45.7"	58°18'21.9"	Al Golf Club
	11	MCT-17	23°36'02.8"	58°21'26.3"	Al Aziba Polyclinic
	12	MCT-19	23°35'11.9"	58°19'38.4"	Free land in front of MOHE
	13	MCT-22	23°33'44.1"	58°23'01.0"	Buchar Heights Park
	14	MCT-25	23°35'52.3"	58°28'16.6"	kids Garden
	15	MCT-27	23°37'04.4"	58°29'27.7"	Al-Qurum Park
	16	MCT-28	23°37'06.3"	58°32'21.0"	Darsit Park
	17	MCT-30	23°33'32.2"	58°37'42.9"	Qandab Primary School
	18	MCT-31	23°34'52.3"	58°36'06.3"	Sidab Park
	19	MCT-33	23°35'36.7"	58°35'28.1"	Kalbowa Park
	20	MCT-34	23°37'15.9"	58°34'38.4"	El-Reyam Park
	21	MCT-38	23°35'00.7"	58°33'34.2"	Park in Al Wadi Al Kabier
	22	MCT-39	23°34'25.2"	58°34'13.7"	Ibn El Nafies School
	23	MCT-40	23°35'46.4"	58°32'37.7"	Sultan Qaboos Mosque
	24	MCT-42	23°28'18.4"	58°29'27.8"	Al Ammerat Polyclinic
	25	MCT-43	23°35'19.5"	58°25'46.1"	Al-khwair Public Park
BARKA	26	BRK-01	23°41'06.6"	57°58'49.6"	Al Nasiem Health Center
	27	BRK-08	23°42'37.4"	57°52'08.1"	Barka Health Center
	28	BRK-14	23°43'32.1"	57°44'02.9"	Al Sawadi Health Center
MUSANAH	29	MSN-04	23°45'25.5"	57°38'53.1"	Al Musanah Health Center
AS SUWAIQ	30	SWQ-03	23°48'32.9"	57°32'55.7"	Al Khalil Al Farahidi School
AS SUWAIQ	31	SWQ-10	23°51'11.7"	57°25'00.4"	Al Fardous School
Al Khabourah	32	KHB-04	23°57'56.3"	57°07'22.7"	Badr Al Kobra School
Al Khabourah	33	KHB-09	23°58'31.8"	57°05'48.4"	Al Khabourah Park
SAHAM	34	SHM-01	24°03'13.5"	56°58'06.5"	Hafit Health Center
SAHAM	35	SHM-13	24°09'05.9"	56°52'02.2"	Al Khamail School
SOHAR	36	SOH-01	24°24'33.5"	56°42'39.9"	Aslaan Park
	37	SOH-10	24°26'23.0"	56°36'33.1"	Falj Al Qabail Health Center
	38	SOH-11	24°20'57.9"	56°43'43.3"	Sohar Health Complex
	39	SOH-16	24°25'08.5"	56°39'32.4"	Al Multaqah Health Center
	40	SOH-17	24°15'49.8"	56°48'11.5"	Al Ouienat Health Center

Table 1 Continued

REGION	SN	CODE	COORDINATES		LOCATION
REGION	SN	CODE	Latitude	Longitude	LOCATION
LIWA	41	LWA-04	24°33'50.8"	56°33'01.0"	Neber Health Center
SHINAS	42	SHN-05	24°44'04.0"	56°28'09.5"	Saad Ibn Abi Waqaas School
SHINAS	43	SHN-15	24°48'03.5"	56°25'42.6"	Technical College
AL BURAMI	44	BRM-11	24°15'29.1"	55°47'02.3"	Al Brumai Health Center
AL BURAMI	45	BRM-18	24°14'09.7"	55°53'48.1"	Al Bruami College
DIBA	46	DBA-01	25°36'56.6"	56°15'05.1"	Diba Park
DIBA	47	DBA-03	25°38'47.5"	56°15'11.6"	Amr Ibn El Aas School
MEDHA	48	MDH-02	25°17'47.5"	56°20'16.9"	Medha Park
MEDHA	49	MDH-06	25°16'55.9"	56°19'55.0"	Elnasser Primary School
MUSANDAM	50	KSB-02	26°11'54.3"	56°15'15.1"	Beach Park in Khasab
	51	KSB-03	26°10'44.6"	56°14'52.0"	Khasab Park
	52	KSB-14	26°08'32.0"	56°14'57.3"	Bukha Hospital
NIZWA	53	NZW-03	22°55'12.4"	57°39'32.4"	Berkit Al Mooz Health Center
	54	NZW-08	22°51'28.8"	57°31'08.0"	Nizwa Hospital
	55	NZW-10	23°02'11.8"	57°28'04.4"	Tenouf Health Center
	56	NZW-12	22°58'28.2"	57°33'04.3"	Abu Obeida School
SUR	57	SUR-01	22°34'54.5"	59°31'02.3"	Sur Diabetes Center
	58	SUR-10	22°32'11.0"	59°28'53.4"	Shiekh Al Arab Bin Manah School
	59	SUR-15	22°36'18.7"	59°28'06.2"	Al Bir School
DUQM	60	DQM-05	19°40'23.6"	57°43'31.3"	Duqm Sea Port
MASIRAH	61	MSR-02	20°35'18.1"	58°51'04.8"	New Masirah Hospital
MASIRAH	62	MSR-03	20°39'33.7"	58°52'33.0"	Al Mohalab Ibn Abi Sufra School
SALALAH	63	SAL-07	17°03'06.7"	54°09'27.4"	Al Sa’adah Health Center
	64	SAL-23	16°57'52.9"	53°59'53.4"	Sea Fishing Center (Rysout)
	65	SAL-24	17°00'33.6"	54°03'08.6"	Sultan Qaboos Hospital
	66	SAL-40	17°02'12.7"	54°24'02.0"	Heeten School (Taqaa)
	67	SAL-44	16°59'16.7"	54°42'24.6"	Ibn Khaldoun School (Murbat)

Free-field strong-motion stations are intended to measure the ground shaking without structural response or soil-structure interaction contamination. Data from these stations are appropriate for use as input ground motions to nearby structures. This type of stations is installed within the permanent stations of the Oman seismic network (20 stations).

In the urbanized areas, it is difficult to find truly free-field sites due to the contamination of the ground motion by local interaction with the built environment. So, urban strong motion reference stations with careful consideration can be utilized to minimize such interaction effects. Urban reference-stations may install on open ground or within small buildings.

Selected sites are considered as open ground urban reference station (OG-URS), if they fulfill the following specifications: i) no anomalies by nearby sites of interest, ii) not sited in or on a localized topographic feature, iii) located at a distance of at least one major structural dimension from any large building, and iv) designed to minimize soil-structure interaction in the frequency range 0–50 Hz. Figure (3a) demonstrates some selected OSMN station sites classified as OG-URS.

Some selected OSMN station sites are called within small-building urban reference stations (SB-URS). These stations are characterized by: 1) it is located within a small building, 2) it is installed on grade, 3) it is located in a building without significant basement, 4) it is located in a building with a small foundation, 5) it is located in a building of relatively lightweight construction, and 6) it is located at a distance of at least one major structural dimension from any large building. Figure (3b) shows some selected OSMN station sites classified as SB-URS.

3.1 Passive Techniques

a. Noise level

Ground motion vibrations originate from either earthquake signals or natural and cultural noise sources (Bormann, 2002; Wielandt, 2002; Havskov and Alguacil, 2004). Evaluating the performance of a network seismographs by as can be sessing the ambient noise at each station can improve the efficient methodologies for earthquake seismic data processing over a network.

Many authors studied the nature, origin, and composition of the noise wave-field (Rodgers et al., 1987; Given, 1990; Gurrola et al., 1990; Given and Fels, 1993; Peterson, 1993; Zürn and Widmer, 1995; Beauduin et al., 1996; Seo et al., 1996; Withers et al., 1996; Young et al., 1996; Kamura, 1997; Seo, 1997; Chouet et al., 1998; Friedrich et al., 1998; Vila, 1998; Webb, 1998; Uhrhammer, 2000; and Zhang et al., 2009). They proved that, the noise wave-field is classified into micros and microtremors. Microseisms (noise wave-field) at long distance caused by the oceans and open sea waves at low frequencies (< 0.3 to 0.5 Hz) and composed mainly of surface (Love and Rayleigh) waves. Microseisms at intermediate frequencies (0.3 to 0.5 and 1.0 Hz), are mainly initiated by wind and closed sea waves. Microtremors (cultural noise) generated by the local environment and human activities such as traffic roads, railways, rivers, lifelines, etc. at high frequencies (> 1.0 Hz).

An appropriate noise model can demonstrate all the aforementioned characteristics of the background ambient noise at the station site for seismic station performance assessment. That model is an estimate of the power spectral density (PSD) and represents the noise baseline at each station. Peterson (1993) estimated a new low-noise model (NLNM) and a new high-noise model (NHNM) by computing the PSDs of the background ambient noise at 75 stations in the Global Seismographic Network (GSN). This is done by applying averaging over the squared magnitude of the fast Fourier transform (FFT) of the overlapping ambient seismic noise data segments.

In the present study, the background ambient seismic noise in the Muscat region (at 25 stations sites as shown in Fig. 4) is analyzed using continuous digital records for at least three hours recording length at each station (day time records). The digitized records are gained using a broadband sensor (trillium compact 20s by Nanometrics) with a bandwidth from 0.05 Hz to 108 Hz. The self-noise of the seismometer is closed to the Peterson’s New Low Noise Model (NLNM). The digitizer used in measurements is Taurus (by Nanometrics) with specifications: three-channels 24-bit A/D converter, noise level < 1 count RMS at 100 samples per second (sps), gain 1, and dynamic range > 141 dB. The background ambient noise data were digitized by sample rate of 100 sps for all stations in Muscat region. The recorded data retrieved in MiniSEED format and it can also be extracted in Seisan, SEGY, SAC and ASCII formats.

The extracted data have been processed by software ANTELOPE to estimate the baseline noise model at each station by computing PSDs from continuous, overlapping (75%) time series segments in a wide frequency range from 0.05 to 50 Hz (Niquist frequency) following the same algorithm of Peterson (1993). The instrument transfer function (response) is deconvolved from each time segment, which passing through the following steps: a) removing the mean, b) removing the long-period trend, c) applying 10% sine function tapering, d) converting from the time-domain to the amplitude spectrum via FFT, and e) squaring the amplitude spectrum to obtain the power spectrum.

In order to characterize the noise baseline and evaluate the performance of each station site, records are represented in the form of acceleration versus frequency, velocity versus frequency and displacement versus frequency (e.g. Figure 5). The whole results of Muscat 25 stations (Fig. 6) as velocity versus frequency can be summarized as follows:

1. Microseisms (intermediate periods in the frequency ranges of < 0.3 to 0.5 Hz) are demonstrated as stable and low to moderate noise level compared with Peterson noise levels. This result has one exception as site MCT-40, where the baseline noise model at a frequency less than 0.3 Hz exceeds Peterson NHNM.

2. Microseisms (intermediate periods in the frequency ranges of 0.5 to 1.0 Hz) are stable and relatively low noise level compared with Peterson noise levels.

3. Microtremor (short period in the frequency ranges of > 1.0 Hz are observed as moderate noise level compared with Peterson noise levels, despite collecting data during daytime.

4. There are two noise transients at frequencies 1.5 Hz and 30 Hz. The first transient, appeared strongly at sites MCT-16, MCT-17 and MCT-19 in the middle of the map (Fig. 4) and decreases gradually eastward and westward. The second transient prevailed at all sites, as shown in Fig. (5).

b. Horizontal –to-vertical spectral ratio (HVSR)

It is well known that the site effect (ground motion amplification) caused by seismic energy trapping in soft soil or due to topographic undulations. This phenomenon (resonance and amplification) of ground motion is not observed on exposed flat rocky sites. The ground shaking amplification can vary significantly within a small distance, increasing the probability of damage to infrastructures. Ground motion amplification at a site has a specific frequency called resonance (fundamental) frequency or period. The most important factor for a man-made structure is to avoid matching the resonance frequency of the structure and the site. So, to evaluate the site response of a particular site (station) the resonance frequency (period) should be estimated.

The ambient seismic noise (microtremors) data have been used to estimate the site response. It gives a reliable and precise estimate of the fundamental frequency of the near-surface cover (Lermo et al., 1988; Nakamura 1989; Field and Jacob, 1995; Bard, 2000; Parolai et al., 2001, 2002; Mohamed et al., 2008; Mohamed, 2009; El-Husain et al., 2014).

The technique is used in two approaches: the first one called reference site method, where, the ambient noise measurements carried out simultaneously at two or more stations (Kanai, 1957; Lachet et al., 1996; Steidl et al., 1996). One of the sites must be located on a flat, hard rock (free from ground motion amplification). Another condition is the spatial distribution of ambient noise sources and the paths of waves travel to both sites are nearly identical. If the two conditions are fulfilled, the site response at a site is given by the microtremor spectral ratio of that site with respect to the flat hard rock site. In Muscat stations, these conditions have not been fulfilled, therefore the authors did not use this approach in the present study.

The second approach introduced by Nakamura (1989). He proved that the horizontal-to-vertical spectral ratio (HVSR) shows a peak at the resonance frequency. Nakamura (1989) assumed that the HVSR reflects the ground motion amplification factor like the horizontal shear-wave transfer function in case of ambient seismic noise consists mainly of body waves. The microtremor measurements by 3-components single station reflect that at the fundamental frequency the Rayleigh-waves vanish in the Z-component tends to a peak in the HVSR at that frequency. This means that the HVSR technique is reliable to estimate the fundamental frequency and gives a lower bound level of ground motion amplification (Lachet and Bard, 1994; Mucciarelli, 1998; Bindi et al., 2000; Bard, 2000; Parolai et al., 2001). This approach is used in estimating the resonance frequency at the Muscat 25 station sites of OSMN.

The microtremor measurements at 25 stations (Fig. 4) at Muscat area were carried out using continuous digital records for at least three hours recording length during daytime. The data acquisition parameters and instruments are the same as done in section a (Noise Level).

The microtremor data were handled utilizing the GEOPSY software (www.geopsy.org). For each site, the raw-data was corrected for the base-line effect to ensure the stationary assumption validity. Various numbers of windows, with non-overlapping 40 seconds (4000 samples) duration, were chosen among the quietest part of the recorded signals. This is done using the STA/LTA anti-trigger algorithm. The microtremor waveforms were tapered with a 10% cosine taper to avoid leakage and an amplitude spectrum is estimated using FFT for all the three components (Fig. 7a). The spectra were smoothed using Konno and Ohmachi (1998) smoothing function with a bandwidth coefficient value of 40. For each window, the two horizontal components were merged together utilizing a geometrical mean option to obtain the H-component. Dividing the H-components by the vertical ones to obtain a number of HVSR curves for each site (Fig. 7b). These HVSR curves are then averaged and the standard deviations at each frequency are calculated. The resonance frequency and the corresponding amplitude at each site could then be determined. Finally, each peak is checked, if it is of natural or industrial origin. The presence of strong sources acting during the recordings might be revealed also through a directional analysis of the HVSR curves. To perform such analysis, the horizontal components of motions are rotated in the range of 0° to 180° and are combined for the HVSR calculation at regular intervals. This is extremely helpful to check whether a site is a 1-D structural layering (Fig. 7c). Like the rotated HVSR, the rotated spectrum was performed, which is valuable to check the direction of energy release (Fig. 7d). The damping test (Dunand et al., 2002) for the reliable peak amplitude at certain frequency was performed (Fig. 7e), which determine its origin (industrial origin if z-value less than 1.0 or natural origin if z-value higher than 1.0).

Figure 8 and Table 2 demonstrates the HVSR curves and the fundamental frequency at the 25 station sites of the OSMN Muscat area. The results can be summarized as follows:

1. HVSR flat curves at sites MCT-02, MCT-07,MCT-12, MCT-14, MCT-15, MCT-17, MCT-22, MCT-25, MCT-30, MCT-31, MCT-33, MCT-34, MCT-38, MCT-39, MCT-40, and MCT-42, indicating a rocky (no soil effects) sites.

2. HVSR curves at sites MCT-01, MCT-19, and MCT-28 have peaks at a fundamental frequency ≥ 18.5 Hz, which indicate sites with thin soil layer.

3. HVSR curves at sites MCT-09, MCT-16, and MCT-17 demonstrate considerable thick, soft soil layer causing ground motion amplification.

4. At frequency 1.5 Hz, there is an artificial peak with higher amplitude at stations MCT-16, MCT-17, and MCT-19 (Fig. 4) and gradually decreases eastward and westward.

3.2 Active Source Techniques

i. P-wave shallow seismic refraction tomography

Seismic refraction tomography is a geophysical tool that utilizes measured travel times from a shot-point (seismic energy source as sledgehammer or power assisted weight drop) to a series of geophones (receivers) in order to construct a 2-D cross-sectional P-wave velocity model of the subsurface. Travel times as a function of seismic velocity are affected primarily by the dynamical properties of the subsurface layers (Gaines, 2011). The seismic wave propagation in the subsurface follows Snell’s law of refraction at rheological interfaces. The geophones are used to record the amplitude response of emitted energy from the source passing through the subsurface and capture the time that energy takes to arrive from the shot. The first-arrival time of seismic energy from the shot-point at each geophone is manually picked and consequently, this information is used to construct an inverted 2-D velocity model of the subsurface. To perform these inversion models, we use the Waveform Eikonal Travel-time (WET) inversion technique (Schuster and Quintus-Bosz, 1993). The WET tool is numerically more suited to construct an accurate velocity gradient (horizontally as well as vertically) rather than to delineate discrete horizontal velocity interfaces.

Data Acquisition

The purpose of the present work is to develop the required 2-D cross-sectional model in order to evaluate the bedrock and to outline any local subsurface structure. The data were carried out along 25 major lines at the network stations in Muscat region, using the 48-channel signal enhancement seismograph model StrataVisor-NZ manufactured by Geometrics company, with vertical geophones of 40 Hz natural frequency. The spread length was set to 94 m and the geophone spacing was 2 m. The procedure is to shoot the profile 15 times (Fig. 9 upper panel) at different surface distances along the spread line as listed in Table 3. The seismograph is connected to the geophones cable with 48 take outs and also is connected to a weight drop via trigger cable and trigger switch such that the recording begins within fractions of a millisecond of the impact of the weight drop. The seismograph is powered in the field by 12-V battery. While collecting data in the field, the record length for each shot was 0.3 sec, and the sampling interval for each record was set at 0.250 msec. The data at each shot point was stacked to improve the signal-to-noise ratio (S/N) on the final waveform, and the number of stacks varied from 4 to 10 times, depending on the surrounding ambient noise levels.

Table 3

Locations of the 15 shot-points along profile MCT-01.
PROFILE	P-WAVE SHOOTING
CODE	File N^o	Source location	File N^o	Source location
MCT-01	1	Offset Normal (-10.0 m)	9	Inline G28-G29 (55.0 m)
	2	Normal (-1.0 m)	10	Inline G32-G33 (63.0 m)
	3	Inline G4-G5 (7.0 m)	11	Inline G36-G37 (71.0 m)
	4	Inline G8-G9 (15.0 m)	12	Inline G40-G41 (79.0 m)
	5	Inline G12-G13 (23.0 m)	13	Inline G44-G45 (87.0 m)
	6	Inline G16-G17 (31.0 m)	14	Reverse G48 + 1 m (95.0 m)
	7	Inline G20-G21 (39.0 m)	15	Offset Reverse G48 + 10 m (104 m)
	8	Inline G24-G25 (47.0 m)

Tomographic Data Processing

The first step in the processing workflow is to manually pick the first arrival times of the energy that is propagated outward from the shot point. The time of the first-break is the critical parameter, not the amplitude of the arrival. The arrival time is chosen for each trace along the waveform. This is repeated for each shot point in the survey to generate a pick file (Fig. 9 lower panel).

Once the picking of first arrivals has all been determined, we use the Rayfract™ software package (Ver. 3.16, 2009) in the inversion process. This package was selected over other available tomographic inversion packages because it utilized the WET algorithm that is ideally suited to sensitivity in both the vertical and horizontal directions.

To generate the tomogram model first, fill the required acquisition parameters in the header file of the software and import the data. This header includes survey geometry, equipment used to collect the data, dates and times of data collection, and the name of the group or individual that conducted the survey. Once the header file has been created, the pick file from each shot point along the survey line is individually imported. Figure (10a) shows the time-distance curves for the 15 shots at station site MCT-01.

Once the data have been imported, an initial velocity gradient model must be generated. This model (Fig. 10b) is constructed using the XTV inversion technique (Gawlas, 2001). Three separate methods are utilized for the velocity calculations: Modified Dix inversion, Intercept Time inversion, or Delta-t-V inversion. The inversion method used for the creation of the initial velocity model varies based on conditional factors in the data. All the three methods are utilized to compute the velocity, and then the most appropriate solution is selected based on the strengths of each method. If there are reflections in the data the Modified Dix method is used, while the Intercept Time technique is used if there are dramatic increases in velocity, and the Delta-t-V method is used when the data have velocity inversions. Each of these methods will produce a gradient velocity model (Fig. 10b) that is then compared to the measured travel times in order to begin optimizing the tomographic model (inversion technique).

After generating the initial model, the Eikonal equation (Schuster and Quintus-Bosz, 1993) is solved iteratively using the least squares method over a series of adjacent nodes (representing the subsurface over the area of the survey) in order to determine the minimum travel time possible between any given source-receiver pair. This process is repeated for all source-receiver pairs for which real data were collected.

These travel times, once calculated from a given shot point and receiver point (observed data), are recalculated with the source-receiver information utilizing the initial model. The two calculated travel times yielding a minimum travel time and pathway from the source to the receiver (ray-path). This process is repeated for all source-receiver pairs, and the result is a ray-path coverage diagram (Fig. 10c) that shows the ray-paths going from all shot points to all receivers in the survey. This ray-path coverage diagram, along with the initial velocity model, is used for the creation of the final inverted 2-D P-wave velocity model (Fig. 10d). Figure (11) demonstrates the P-wave velocity tomograms at the 25 station sites of the Muscat governorate region (OSMN).

ii. Multi-channel Analysis of Surface Waves (MASW)

The stiffness of the near-surface soil layers as one of the geotechnical properties plays an essential role in civil and earthquake engineering projects. The shear-wave velocity (Vs) of individual soil layers is directly proportional to the shear modulus (in other words the stiffness). Several tools can be applied to estimate the stiffness of soils. One of the techniques of low cost, non-invasive, and provide consistently reliable results is the surface wave method. (Park et al., 1997; Park et al., 1999; Xia et al., 2002). The dispersion property of Rayleigh-type surface waves in layered medium provides key information regarding the properties of near-surface materials. The basis of the technique is to determine the frequency-dependent phase velocity of the fundamental mode of Rayleigh waves, invert the dispersive phase velocity of recorded Rayleigh waves, and the Vs profile for the test site can be obtained. In earthquake design, the Vs is a vital parameter in both liquefaction potential and soil amplification evaluations. However, observation of multi-modal dispersion characteristics and generation of 2-D or 3-D dispersion images becomes possible (Park et al., 2001; Park et al., 2007; Xia et al., 2003; Donohue et al., 2013; Lin et al., 2004; Park and Carnevale, 2010; Mohamed et al., 2013; Mohamed et al., 2016)

Field Data Acquisition

For 1-D linear-array MASW data acquisition, low-frequency geophones (4.5 Hz) are placed vertically on the ground with geophone interval 1.0 m. The number of geophones is 24. The geophones were connected to a data logger (Seismograph model StrataVisorNZ, 48-channels by Geometrics) via geophone cable with 48 takeout. The spread length was 23 m. As an active MASW survey, an 8 kg sledgehammer was used to generate surface wave seismic energy by impact an aluminum plate. The data acquisition parameters are listed in Fig. (12a), while Fig. (12b) demonstrates one of the 13 recorded seismogram.

To obtain more accurate shear-wave velocity for a given site as a function of both depth and surface location a 2-D survey was performed for multiple field deployments as the source and geophones roll along the survey line (both the source and geophone-array shift 4.0 m). Each shift represents the 1-D model. Repeat the process thirteen times and by interpolation, we can establish the 2-D Vs section (Fig. 12c).

Data Manipulation

The quality of the acquired surface wave records is related to the resolution of the phase velocity spectrum (includes the sharpness of the amplitude peaks observed at each frequency, the extractable frequency range and the continuity of the fundamental mode high-amplitude band) and the natural geologic conditions. To get shear-wave velocity profile (image) from reliable phase velocity spectrum (dispersion image or overtone), the recorded data were passing through many steps as follows: i) converting the recorded seismic data format (SEG-2 to KGS or SEG-Y) and combining all shot gathers into a single file; ii) assigning field-geometry (layout) and the acquired data were recompiled into the roll along mode data set; iii) identifying and eliminating seismic noises such as body waves and higher mode through filtering and muting (Fig. 13a and b); iv) assessing the optimum ranges of frequency and phase velocity after constructing the dispersion image/overtone (Fig. 13c); v) extracting the dispersion curve (Fig. 13c), vi) then, the curve is inverted using a least–square approach and the initial model (obtained from the available geotechnical borehole parameters and the P-wave shallow seismic tomography) to generate a 1-D vertical shear-wave velocity model (Fig. 13d); and vii) the yielded sets of 1-Ds (13-models) have been interpolated to produce the 2-D shear wave velocity section of each station site (Fig. 13e). Figure )14( shows the 2-D section (horizontal and vertical shear-wave velocity variation) at the 25 station sites at the Muscat region.

3.3 Vs₃₀ and site classification

The shear-wave velocity (Vs) plays an important role for evaluating the dynamic behavior of soil in near-surface layers. Therefore, site characterization in evaluating seismic hazards is based on the near-surface Vs values. The weighing average Vs for the uppermost 30 m of soil is referred to as Vs₃₀. For earthquake engineering design purposes, Vs₃₀ is accepted for site classification as per NEHRP (National Earthquake Hazards Reduction Program, 2001) classification, UBC (Uniform Building Code, 1997) classification (Dobry et al., 2000; Kanli et al., 2006), IBC (International Building Code, 2009) classification, and also in the new provisions of Euro code 8 (Sabetta and Bommer, 2002; Sẽco e Pinto, 2002). Both the Uniform Building Code (UBC) and Euro code 8 (EC8) codes use Vs₃₀ to classify sites based on the soil type.

The average shear-wave velocity (Vs) down to a depth “D” of soil is referred as “V_D” and is estimated as follows:

$${V}_{D= \frac{\sum hi}{\sum \left(\frac{hi}{vi}\right)} }$$

1

where: D = $\sum hi$ = cumulative depth in meters.

For the uppermost 30 m layers, the average shear-wave velocity is written as:

$${V}_{S30}= \frac{30}{{\sum }_{i=1}^{n}\left(\frac{hi}{vi}\right)}$$

2

where: hi denotes the thickness in meters and vi refer to velocity in m/sec (at a shear-strain level of 10^− 5 or less) of the i^th layer, in a total of n, existing in the uppermost 30 m.

The Vs₃₀ has been calculated using Eq. (2) at each station site of Muscat region (Table 4). Usually, for amplification and site response studies at the uppermost 30 m, Vs₃₀ is considered. However, if the rock is found within a depth of about 30 m, nearer surface Vs of the soil has to be considered.

Table 4

Site characterization (fo, Ao, Vs30, and site class) of the 25 stations-sites in Muscat area of OSMN.

SN	CODE	Reference	Latitude	longitude	f_o	A_o	Vs₃₀ (m/s)	Class
1	MCT-01	OG	23°38'18.73"	58°03'05.83"	22.5	3.4	530	C
2	MCT-02	OG	23°41'48.05"	58°02'52.11"	F	-	522	C
3	MCT-07	SB	23°38'02.03"	58°05'51.91"	F	-	645	C
4	MCT-08	SB	23°35'09.24"	58°01'37.28"	F	-	900	B
5	MCT-09	SB	23°41'43.33"	58°08'58.21"	5.8	2.7	350	D
6	MCT-11	OG	23°39'20.33"	58°13'23.51"	11.7	2.6	524	C
7	MCT-12	SB	23°39'37.73"	58°11'46.01"	F	-	566	C
8	MCT-14	SB	23°37'20.82"	58°12'56.89"	F	-	628	C
9	MCT-15	OG	23°35'28.18"	58°14'46.55"	F	-	480	C
10	MCT-16	OG	23°36'45.69"	58°18'21.94"	5.1	2.5	291	D
11	MCT-17	SB	23°36'02.80"	58°21'26.25"	F	-	648	C
12	MCT-19	OG	23°35'11.88"	58°19'38.38"	20.4	2.0	542	C
13	MCT-22	OG	23°33'44.05"	58°23'01.04"	F	-	1556	A
14	MCT-25	OG	23°35'52.30"	58°28'16.60"	F	-	1082	B
15	MCT-27	OG	23°37'04.35"	58°29'27.69"	4.7	2.5	350	D
16	MCT-28	OG	23°37'06.33"	58°32'21.02"	18.5	2.3	453	C
17	MCT-30	SB	23°33'32.16"	58°37'42.90"	F	-	610	C
18	MCT-31	SB	23°34'52.26"	58°36'06.33"	F	-	770	B
19	MCT-33	OG	23°35'36.67"	58°35'28.07"	F	-	1100	B
20	MCT-34	OG	23°37'15.94"	58°34'38.42"	F	-	1174	B
21	MCT-38	FF	23°35'00.70"	58°33'34.19"	F	-	601	C
22	MCT-39	SB	23°34'25.22"	58°34'13.74"	F	-	666	C
23	MCT-40	SB	23°35'46.35"	58°32'37.73"	F	-	519	C
24	MCT-42	SB	23°28'18.39"	58°29'27.79"	F	-	877	B
25	MCT-43	OG	23°35'19.50"	58°25'46.14"	20.4	2.4	367	C
OG: open ground station, SB: within small building station, f_o: fundamental frequency, F: flat HVSR curve, A_o: Amplification

According to the NEHRP and/or IBC standards, the station sites of Muscat area (OSMN) are classified into four categories (D, C, B and A) as listed in Table 4.

The probabilistic seismic hazard assessment (PSHA) at a site was introduced by Cornell (1968). In this method, the geological and seismological information aggregated and utilized as the input to a procedure for providing curves that provide the relation between ground motion parameters and the return periods at a specific site. For engineers, these curves considered as the basis for the earthquake-resistant design parameters of structures. Programs for making calculations based on Cornell’s method were published by McGuire (1976) and Bender and Perkins (1987). The procedure includes the following steps: i) identifying all earthquake source zones capable of producing damaging ground motions, ii) characterizing the distribution of earthquake magnitudes (the rates at which earthquakes of various magnitudes are expected to occur), iii) characterizing the distribution of source-to-site distances associated with potential earthquakes, iv) selecting a suitable ground motion prediction equations (GMPEs), and v) combine uncertainties in earthquake size, location and magnitude, utilizing a computation known as the total probability theorem (seismic hazard calculation).

In PSHA, we take into consideration all earthquake sources capable of producing damaging ground motions at a site. These sources could be faults or areal region. If individual faults are not recognizable (as in the regions of low seismicity), then source zones may be identified by areal regions in which earthquakes may occur anywhere. Once all possible source zones are bounded, we can identify the distribution of magnitudes and source-to-site distances associated with earthquakes from each source zone.

The authors studied in details the seismic activities (Fig. 15a) and seismic sources in and around the Sultanate of Oman (refer to Deif et al., 2020). They developed a preferable model consists of 28 seismic sources (Fig. 15b).

Tectonic faults are capable of producing earthquakes of various magnitudes. Gutenberg and Richter (1944) studied records of earthquake magnitudes, and noted that the number of earthquakes in a region greater than a given size generally follows a particular distribution known as the Gutenberg-Richter recurrence law as follows:

$${log}_{10} m=a-bm$$

3

where $m$ is the rate of earthquakes with magnitudes greater than m, a-value indicates the overall rate of earthquakes in a region (the intercept of the above relationship at $m$ equal zero) and b-value indicates the relative ratio of small and large magnitudes (the slope of straight line of Eq. 3). Consequently, the maximum earthquake magnitude (M_max) for each seismic source can be produced. According to Kijko (2004), the authors studied in details the a-value, b-value and M_max, which are used in the current study (Deif et al., 2020).

To anticipate ground shaking at a site, it is essential to model the distribution of distances from earthquakes to the sites of interest. It is simple to identify the distribution of source-to-site separations utilizing only the geometry of the source. There are in fact a few definitions generally utilized in PSHA. One can use the distance to the epicenter or hypocenter, distance to the closest point on the rupture surface, or distance to the closest point on the surface projection of the rupture. Some distance definitions account for the depth of the rupture, while others consider the only distance from the surface projection of the rupture. The distances between the sources and station-sites of Muscat were taken into account according to the utilized ground motion prediction equations (GMPEs) in the logic tree.

The aforementioned parameters are quantified the distribution of potential earthquake magnitudes and locations. The main target is analyzing ground motions not earthquakes, therefore the next step deal with the ground motion prediction models (attenuation models). These models predict the probability distribution of ground motion intensity, as an element of numerous indicator factors, for example, the earthquake’s magnitude, distance, fault mechanism, the near-surface site conditions, the potential presence of directivity effects, etc. Selecting appropriate GMPEs considered as the major sources of epistemic uncertainty in PSHA (Toro, 2006). Due to the lack of ground motion acceleration time histories in the Sultanate of Oman, the GMPEs developed in other parts of the world, where its tectonic setting is matched to those around Oman were used. The utilized GMPEs in the current study are provided by the authors (El-Hussain et al., 2012; Deif et al., 2017; El-Hussain et al., 2018; Al-Shijbi et al., 2019 and Deif et al., 2020).

The essential result from a PSHA is a suite of hazard curves for response spectral ordinates for various spectral periods. Seismic hazard analysis was performed at the bedrock level (Vs₃₀ = 760 m/sec) within a logic tree framework, which permits alternatives for each decision-making process to be combined in the seismic hazard assessment. The results were obtained by following each branch on the logic tree, for more detail, refer to El-Hussain et al., 2012; Deif et al., 2017; El-Hussain et al., 2018; Al-Shijbi et al., 2019 and Deif et al., 2020. They conducted the PSHA for a return period of 475-years, which corresponds to 10% probability of exceedance in 50 years. The obtained hazard curves (at PGA and 5% damped PSA for 0.2, 1.0, and 2.0 sec) are evaluated as the weighted average of all yielded hazard curves of the applied logic tree.

4.1 Uniform hazard spectra (UHS)

The uniform hazard spectrum at the bedrock level is developed by first carrying out the PSHA calculations for spectral accelerations at a range of spectral periods. Then, a certain rate of exceedance is selected, and identifies the spectral acceleration amplitude corresponding to that rate for each period. These amplitudes spectral accelerations are then plotted versus their periods. This spectrum is called a UHS since each ordinate has an equal rate of being exceeded and UHS is considered as an envelope of separate spectral acceleration values at different periods, each of which may have come from an alternate earthquake event, and it is considered an appropriate probabilistic representation of the basic earthquake actions at a specific location. The 5% damping average horizontal component UHS at the bedrock level in the Muscat area for 475-years return period is shown in Fig. (15c).

4.2 Seismic Hazard Deaggrgation

The hazard curve (PSH) represents a combined effect of all earthquakes scenarios that contribute to exceedance of a specific hazard level, in terms of magnitude ($m$) and distance (R). From the engineering point of view, it is required to find a representative earthquake scenario that most contribute to the hazard level. The deaggregation process illustrates these relative contributions. The results of deaggregation may change with the return period and spectral ordinate. The deaggregation results can provide beneficial information in selecting typical time histories for seismic design.

To determine the earthquakes, which are contributing most to the total hazard at the 25 Muscat stations of OSMN, the hazard curves of PGA and Sa (spectral acceleration) at spectral periods of 0.2, 1.0, and 2.0 sec were deaggregated to estimate the contribution of each seismic source to a specific hazard level for 475-years return period. The results at the 25 stations-sites in the Muscat area indicate that the hazard is dominated by nearby small to moderate earthquakes generated in the Oman Mountains seismogenic zone for short periods and by distant larger events produced by Makran Subduction seismic zone for longer periods. Figure (15d) shows the deaggregation results at station-site MCT-16 for 475-years return period.

4.3 Amplification curve

Ground motion amplification caused by trapping the energy generated by earthquake action (which is called site response to earthquake force action) can be estimated by the transfer function of the near-surface layers and damping coefficient of soil (Kramer, 1996). Schnabel et al. (1972) introduced the code SHAKE91 for 1-D ground response analysis used to evaluate the site response on the ground-motion. The most important input data are subsurface model describing the Vs, density, thickness, and lithology. The geotechnical boreholes used in the present study (Fig. 4), in addition to the output of the geophysical survey (Vp, Vs, density and layer thickness) are used to evaluate the linear site response at the 25 stations in Muscat area. For non-linear behavior of soft soil, SHAKE 91 introduces shear-modulus reduction (G/Gmax) and damping curves (ξ), which related to the change in shear-strain.

The ground-motion time histories obtained by spectral matching (PSHA analysis) are used as input ground motion at the bottom of the soil layers to produce the site-response (amplification curve) as shown in Fig. (16a).

4.4 Seismic response spectra at the surface

The site-response curve (the effect of near-surface soft layers) at each station sites are convolved with The 5% damping average horizontal component UHS at the bedrock level in the Muscat area for 475-years return period to obtain the median 5% damped horizontal response spectrum for 475-years return period at each station site in Muscat area. Figure (16b) shows one of the seismic response spectra at the station (MCT-16).

4.5 Design response spectra (DRS)

Seismic design depends on representing the earthquake actions of an equivalent static force applied to the construction. These forces are resolved from the maximum acceleration response of the construction under the expected earthquake-induced ground shaking, which is represented by the acceleration response spectrum. The site coefficients (site effect parameters) for short-periods (Fa) and long-periods (Fv) of spectral accelerations for different site classes were introduced. Therefore, the output of PSHA are utilized as an input for the development of a set of design parameters according to one of the codes.

The international building code (IBC, 2006) was utilized in the current study to provide the site-specific design response spectra (DRS) for a 475-year return period at the 25 station sites of Muscat area. In IBC, modified values of the ground-motions at the short period (0.2 sec) and 1.0 sec period are obtained and termed as S_Ms and S_M1, respectively. The short period and 1.0 sec design, elastic spectral accelerations S_SD (= 2/3 S_Ms) and S_1D (= 2/3 S_M1) can be calculated and used to construct the DRS.

In the current study, the author obtained the DRS at each station site in Muscat area Fig. (16c) represents the DRS at the station (MCT-16)

The establishment of networks for monitoring strong earthquakes and the outcomes which are acquired from them, has become an urgent need in the earthquake-resistant design and has significant contribution to a seismic disaster reduction in the metropolitan area.

In 2014, the earthquake monitoring center (EMC) at Sultan Qaboos university (SQU) planned to establish the Oman strong motion network (OSMN) consisting of 67 permanent monitoring stations. The station site selection was done (Fig. 1 and Table 1) to make it possible to obtain records on bedrock, on a surface soil and in structures (within small buildings).

In the present study, a complete site characterization of twenty-five strong motion monitoring stations at Muscat area is carried out, utilizing geotechnical boreholes (12-boreholes) in addition to passive-source techniques such as noise level evaluation and HVSR and active-source techniques such as p-wave shallow seismic refraction tomography and MASW to estimate the fundamental (resonance) frequency, p-wave and shear-wave velocity profiles.

The noise baseline was obtained at the Muscat 25 stations and utilized to evaluate the noise level at these stations (Fig. 6). The results demonstrate that the noise level is low to moderate compared to Peterson’s levels. The authors plan to update the evaluation of noise levels after installing the instruments taking into consideration estimating the probability of occurrence of a given power at a specified frequency (probability density function (PDF)) to evaluate the distribution of power spectral density (PSD) of the background noise in a wide frequency range.

The fundamental (resonance) frequency at Muscat station sites are evaluated using the HVSR technique. Seventeen stations out of twenty-five are characterized by flat HVSR curves reflecting the absence of near-surface soil effect (Fig. 8 and Tables 2 & 4). Four stations have peaks of fundamental frequency ≥ 18.5 Hz, which indicate very thin soil (Fig. 8 and Tables 2 & 4).

In the present study, the P-wave and shear-wave velocity models for 25 stations with varying soil thickness are carried out. Consequently, the Vs₃₀ and site classification (according to IBC, 2006) are deduced. Three station sites (MCT-09, MCT-16 and MCT-27) out of 25 are of class D with Vs₃₀ less than 360 m/sec (Table 4). These sites are characterized by low fundamental frequency and soft materials as described in the available geotechnical boreholes (Fig. 17). 15 stations are of class C, 6 stations are of class B, and only one station is of class A (Table 4). The station sites of class A and B are characterized by flat HVSR curves that demonstrate the absence of soil effect.

The ground motion amplification may obtain from the analysis of ambient noise using HVSR technique in case of ambient noise consists mainly of body waves, but in fact the ambient noise contains surface waves. Therefore, ground motion amplification factor is evaluated using the soil column deduced from the geotechnical boreholes, p-wave tomograms and the 2-D shear-wave velocity using SHAKE91 code.

The authors investigated in details the PSHA in and around Oman (El-Hussain et al., 2012; Deif et al., 2017; El-Hussain et al., 2018; Al-Shijbi et al., 2019 and Deif et al., 2020). We used the outputs of these studies and its update to evaluate the seismic hazard level in terms of UHS, seismic response spectrum by introducing the site effect, and the design response spectrum at each station site. This is done, after deaggregation process to determine the earthquakes, which are contributing most to the total hazard at each site of Muscat stations .

The final output of the current study is obtaining station-sheet includes: i) the noise baseline for the vertical component (panel 1), ii) the HVSR curves (panel 2), iii) the P-wave velocity tomogram in m/sec (panel 3), iv) the 2-D shear-wave velocity section in m/sec (panel 4), v) the uniform hazard spectrum (UHS) at the bedrock level (panel 5), vi) the ground motion amplification versus frequency curve (panel 6), vii) the seismic response spectra at the surface (panel 7), and viii) the design response spectrum (panel 8). Figures (18 and 19) show the aforementioned outputs for class D (soft soil) and class B (rocky site), respectively.

Funding

The authors declare that the Earthquakes Monitoring Center of Sultan Qaboos University, Sultanate of Oman supports us during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Adel M.E. Mohamed: Conceptualization; Methodology; Writing; Original draft preparation; Supervision. Issa El-Hussain: Research administration Supervision; Validation; and review. Ahmed Deif: Conceptualization; Methodology; Supervision; and review. Yousuf Al-Shijbi: Conceptualization; Formal analysis; and review. Mohamed Ezzelarab: Mehodology, Formal analysis and review.

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Table 2 is available in the Supplementary Files section.

No competing interests reported.

floatimage1.png
Table 2 Microtremor (ambient noise) analysis using HVSR technique at Muscat region station sites.

Oman Strong Motion Network (OSMN) and Site-specific Characterization for the Entire Strong Motion Stations in Muscat Region

Status:

Version 1

Abstract

Figures

1. Introduction

2. Oman Strong Motion Network (Osmn)

2.1. Station site selection

3. Site Investigation At Muscat Stations

3.1 Passive Techniques

3.2 Active Source Techniques

3.3 Vs₃₀ and site classification

4. Seismic Hazard Level At Muscat Stations

4.1 Uniform hazard spectra (UHS)

4.2 Seismic Hazard Deaggrgation

4.3 Amplification curve

4.4 Seismic response spectra at the surface

4.5 Design response spectra (DRS)

5. Discussion And Conclusion

Declarations

References

Table 2

Additional Declarations

Supplementary Files

Status:

Version 1

Oman Strong Motion Network (OSMN) and Site-specific Characterization for the Entire Strong Motion Stations in Muscat Region

Status:

Version 1

Abstract

Figures

1. Introduction

2. Oman Strong Motion Network (Osmn)

2.1. Station site selection

3. Site Investigation At Muscat Stations

3.1 Passive Techniques

3.2 Active Source Techniques

3.3 Vs30 and site classification

4. Seismic Hazard Level At Muscat Stations

4.1 Uniform hazard spectra (UHS)

4.2 Seismic Hazard Deaggrgation

4.3 Amplification curve

4.4 Seismic response spectra at the surface

4.5 Design response spectra (DRS)

5. Discussion And Conclusion

Declarations

References

Table 2

Additional Declarations

Supplementary Files

Status:

Version 1

3.3 Vs₃₀ and site classification