Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method

Kamal Abdelrahman; Saddam A. Hazaea; Sattam A. Almadani

doi:10.1515/geo-2022-0724

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Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method

Kamal Abdelrahman , Saddam A. Hazaea und Sattam A. Almadani

Veröffentlicht/Copyright: 24. Dezember 2024

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Abstract

Geotechnical site characterization is very important for construction purposes. This study has been conducted in Diriyah area northwest of Riyadh City, Saudi Arabia, using the Multichannel Analysis of Surface Waves (MASW) method for site characterization through shear wave velocity profiling to 30 m depth. Nineteen MASW lines were carried out in various directions and lengths through the area. The entire process was meticulously parameterized to extract shear wave velocity for subsurface characteristics. MASW results revealed four distinct velocity zones based on National Earthquake Hazard Reduction Program. Fill material was approximately half a meter thick and was classified as very dense soil. The second layer exhibited velocities ranging from 800 to 1,500 m/s, indicating weathered and highly fractured limestone. The third layer showed velocities varying from 1,500 to 1,800 m/s, representing slightly weathered limestone. The fourth layer displayed high velocities ranging from 1,800 to 3,600 m/s, indicating hard and compact limestone rocks. Geotechnical boreholes were drilled down to depths of 10–35 m. These boreholes exposed the geological model that consisted of fill material (silty sand with gravel), followed by highly to moderately weathered limestone with vugs and cracks, and finally, massive limestone rock. Analysis of shear wave velocities identified weak zones, particularly fractured and weathered limestone rocks extending to 12 m in depth. Sinkholes of circular, elongated, and/or conical shapes were observed within this depth range. Moreover, some sinkholes were detected at depths greater than 12 m in specific locations (sites 1, 6, 9, 11, and 17). These sinkholes agreed with the previous study. These results highlight the need for targeted ground improvement methods, such as grouting or underpinning, particularly for construction over weaker zones. Accurate site classification and effective risk management are crucial for addressing these geotechnical and seismic challenges.

Keywords: shear wave velocity; MASW; sinkholes; geotechnical boreholes; Riyadh; Saudi Arabia

1 Introduction

Geophysical techniques have become essential for subsurface site characterization and geotechnical properties. The last decade has witnessed a substantial increase in their significance and demand, primarily fueled by extensive construction projects and the rising requirements of the growing population in Saudi Arabia. This surge underscores the critical role these techniques play in meeting the demands of infrastructure development and addressing the expanding human needs in Saudi Arabia. Incorporating geophysical methods in site characterization studies is vital for ensuring the success and sustainability of construction projects. These methods help make informed decisions regarding infrastructure development, provide stability, and mitigate potential risks [1–16]. In the Riyadh area, recent instances of soil settlements have been attributed to the prevalence of cavernous limestone near the ground surface. This has prompted an immediate requirement for advanced geophysical methods capable of analyzing the soil, offering crucial insights into its composition and the characteristics of underlying layers. Therefore, conducting geophysical investigations has become vital for soil evaluation, providing consultants and site engineers with essential data regarding subsurface conditions to aid in their work. Investigation of the subsurface to ascertain site classification and assess potential hazards is a critical method for the safety of engineering structures and human lives against unforeseen catastrophic outcomes in the future [1,17].

The uncertainty surrounding the upper 30 m of the earth remains a focal point of investigation for numerous researchers. Their keen interest lies in unraveling this enigmatic yet crucial segment of near-surface geology. This pursuit aims to furnish comprehensive insights into soil properties, bedrock depth, potential risks like cavities and sinkholes, ground stiffness, and site classification. Geophysical methods conducted both on the earth’s surface and within boreholes stand as the foremost and expeditious means to address the uncertainties surrounding the uppermost layers of the earth. They serve the dual purpose of resolving ambiguities and aiding the lead design engineer in proposing suitable and viable solutions [18]. The Multichannel Analysis of Surface Waves (MASW) is essential for investigating shallow subsurface depths, particularly within the top 30 m. It provides detailed insights into the soil and rock characteristics beneath the surface and their influence on foundation design, offering crucial information for making informed decisions.

The study area is located northwest of Riyadh alongside the banks of Wadi Hanifa (Figure 1). It is a very attractive area where local mud bricks are often used for constructing traditional homes, commonly featuring one or two stories. Diriyah is a historic city hosting the UNESCO World Heritage Site “Turaif,” which displays magnificent mud-brick structures in the traditional Najdi architectural style. The primary aim of this study is to measure the shear wave velocity profiles in the Diriyah area for site characterization employing the MASW method.

Figure 1

Location of geotechnical boreholes and MASW profiles in the study area.

2 Geological setting

Riyadh is situated above Mesozoic to Cenozoic sedimentary rocks overlaid by Quaternary deposits comprising gravel, sand, silt, clay, and limestone. The rocks exhibit varying degrees of fracturing due to weathering, and subsequent dissolution within the rock gives rise to both small and larger voids [19]. The specified area falls within the Jubaila, Hanifa, Sheet Gravel, Alluvium, and Arab formations [20,21], as shown in Figure 2. Jubaila Formation (Upper Jurassic) consists of a substantial lower unit composed of limestone and mudstone, along with a smaller upper unit of granular rock [23]. The Jubaila limestone, characterized by its high hardness, is situated between two softer strata. This limestone is further subdivided into upper and lower units, with a thickness of 116 m on both the east and west sides of Wadi Hanifa [24].

Figure 2

The geological map of the study area (modified after [22]).

Hanifa Formation often exceeds 75 m in height [25]. Because of anhydrite depletion, the Upper Jurassic Arab Formation comprises four carbonate-evaporite members (Arab D, C, B, and A). This is possible because the carbonates and the anhydrite beneath them form four significant deposition cycles, closely corresponding to a time-stratigraphic unit [25].

The cross-section of the geotechnical boreholes (Figure 3), spanning in a linear sequence from the southeast to the northwest with 24 boreholes, reveals a thorough comprehension of sediment types and their respective depths. The detailed information captured by this cross-section plays a crucial role in interpreting the geological composition of the area. A specially notable aspect of this cross-section is the depiction of two branches originating from Wadi Hanifa (Figure 3). The study area encompasses diverse sediment types, including limestone, fill material, silty sand with gravel, clayey sand with gravel, silty sand, lean clay with sand, and silty gravel with sand. Limestone, in particular, stands out as a significant constituent. Its impact is on the formation processes of the Jubaila and Arab formations. The information gleaned from this geotechnical investigation not only aids in comprehending the region’s geological history but also carries implications for diverse applications, ranging from construction planning to environmental assessment.

Figure 3

Geological cross-section between boreholes through the area shows the lithological units along southeast to northwest in this study; vertical lines represent locations of the drilled geotechnical boreholes.

The subsurface geological conditions are encountered in the bore holes between 10.0 and 35.0 m depth. These boreholes revealed that the subsurface geology at the site consists basically of fill material and clayey sand with gravel followed by creamy, moderately weathered having vugs and cracks filled by clayey sand at a depth of about 7.5 m below the ground surface, very highly fractured limestone followed by slightly fractured and massive limestone rock at about 12 m depth and extends till the end of the borehole. The general subsurface lithology in the study area is illustrated in Table 1.

Table 1

General lithological setting in the study area

Depth (m)	Thickness (m)	Lithological description
0.0–7.0	0.2–7.0	Fill/rubbish materials
0.0–7.5	0.1–7.5	Silty sand/with gravel, silty clayey sand, clayey sand/with gravel, poorly graded sand/with gravel, poorly graded sand with silt and gravel (sand)
0.0–4.5	1.0–4.5	Sandy silt, silt with sand, silt, sandy silt with gravel (silt)
0.0–4.5	0.5–3.0	Sandy lean clay, lean clay, sandy lean clay with gravel, sandy silty clay, sandy silty clay with gravel (clay)
0.0–15.0	1.0–6.0	Sandy lean clay, lean clay, sandy lean clay with gravel, sandy silty clay, sandy silty clay with gravel, sandy fat clay with gravel (wadi deposits)
0.0–35.0	1.5–35.0	Silty sand/with gravel, silty clayey sand, clayey sand/with gravel, poorly graded sand/with gravel, poorly graded sand with silt/with gravel (wadi deposits)
0.0–35.0	1.0–19.0	Silty gravel, silty gravel with sand, poorly graded gravel, poorly graded gravel with silt and sand (wadi deposits)
0.0–18.0	3.0–7.5	Sandy silt, silt with sand, silt, sandy silt with gravel (wadi deposits)
0.0–7.5	0.2–1.5	Completely weathered limestone
0.0–35.0	8.0–33.5	Limestone rock

3 Methodology

The seismic shear wave velocity plays a crucial role in establishing the elastic properties of soil and rock in geotechnical investigations [1]. While surface wave analysis was initially aimed at studying the earth’s crust and upper mantle [26–38], various authors expanded the utilization of surface wave for diverse engineering objectives by developing methodologies [39–44].

The MASW method is introduced in geophysics [39]. This technique is one of the seismic survey methods utilized in geotechnical engineering to evaluate the elastic properties, particularly the stiffness, of the subsurface. We will estimate shear wave velocity for 30 m ( V s 30 ) using the MASW procedure by recording and analyzing using the dispersion parameters approach [39].

Shear wave velocity (V _s) is one of the elastic constants closely related to Young’s modulus [45]. In most situations, V _s directly indicates ground strength (stiffness) and estimates load-bearing capacity. MASW uses the dispersive characteristics of surface waves to determine the variation in shear wave (S-wave) velocity with depth [46]. Seismic data are collected using geophones placed on the Earth’s surface, following the generation of a seismic source, such as striking the ground with a hammer. The data are used to generate a dispersion image and then pick dispersion curves, illustrating the relationship between the phase velocity of surface waves and their frequency. A shear wave profile (1D velocity profile as a function of depth) is then extracted from the dispersion curve. The resulting shear wave profiles from multiple locations along a survey line are combined and contoured into a 2D pseudo-shear wave velocity section of shear wave velocity.

Recent studies and practical applications indicate the efficacy of this method in determining the distribution of shear wave velocity ( V s 30 ) in both soil and rock [28]. Additionally, it has been utilized to estimate site classes for soil/rocks, offering valuable guidelines for proper practices in real projects [47]. The method’s applicability extends to inaccessible areas with alluvial deposits, demonstrated through sparse MASW profiles featuring fixed receivers and multi-source offset geometry [48]. Moreover, it has been utilized to classify the seismic site effect of Dikili-İzmir in western Anatolia by applying MASW and ReMi methods [49]. Furthermore, the technique estimates seismic site classification by correlating V _s and Standard Penetration Test (SPT-N) for deep soil sites in the Indo-Gangetic Basin [50].

The average shear wave velocity for the depth (h) of soil referred to as V H is computed as follows [51]:

(1) V H = ∑ h i / ∑ h i v i ,

where H = ∑ h i is the cumulative depth in m. For 30 m average depth, shear wave velocity is written as

(2) V s 30 = 30 ∑ i = 1 N ( h i / v i ) ,

where h i and v i denote the thickness (in meters) and shear wave velocity in m/s (at a shear strain level of 10−5 or less) of the formation or layer, in a total of N layers, existing in the top 30 m. V s 30 is accepted for site classification as per National Earthquake Hazard Reduction Program (NEHRP) classification [52].

4 Data acquisition and processing

MASW survey has been conducted along 19 profiles using vertical geophones of 4.5 Hz. The length of these profiles ranges from 92 to 144 m, with a 2 m geophone interval. A total of 24 geophones are used in a 1D receiver array, and the source is positioned 10 m from the first geophone (Figure 4), with a sample interval of 0.125 m, two stacks, and a recording length of 0.512 s. MASW data have been collected through the quiet night periods from 12 AM to 6 AM during the weekend (when there is less traffic).

Figure 4

Geometry and layout of MASW survey in the study area.

MASW data have been processed (Figure 5). The process starts with importing Seg-2 formatted recorded data. Once the dispersion curves are picked and an initial model is created, an inversion routine produces shear wave velocity profiles. For S-wave velocity data inversion, synthetic dispersion curves are generated using established numerical methods and compared with field data. Improving the fit to field data involves employing the Jacobian matrix and calculating partial derivatives of field velocity data concerning the S-wave data of the model. Notably, this data processing approach estimates uncertainty or error in the derived S-wave values.

$Figure 5 MASW data processing sequence of profile No. 12: (a) the raw data, (b) the dispersion curve showing the approximate phase velocity and reference frequency calculated from a specific trend of arrivals, and (c) the processed shear wave image shows fracture weathered limestone, weak zone, and compact limestone.$

Figure 5

MASW data processing sequence of profile No. 12: (a) the raw data, (b) the dispersion curve showing the approximate phase velocity and reference frequency calculated from a specific trend of arrivals, and (c) the processed shear wave image shows fracture weathered limestone, weak zone, and compact limestone.

The overall process of processing MASW data typically involves four steps [53]: obtaining multichannel records, extracting fundamental-mode dispersion curves, inverting these curves to derive 1D (depth) V _s models (one profile per shot gather), and interpolating the obtained 1D models to create a 2D V _s model for each profile. As an illustrative instance in this study, the initial 0.1s segment of a chosen shot gather along Profile-12 (Figure 5a) is presented. The seismic traces from 24 channels within this segment were primarily scrutinized for their frequency characteristics, original signals, noise presence, and other geometric attributes crucial for subsequent data processing and inversion. Dispersion curve was generated (Figure 5b) based on the arrival times of various frequencies, their energy or amplitudes, and their apparent phase [54]. The resulting dispersion curve provides insights into both modes of Rayleigh waves: the fundamental and higher (overtones), representing the shallow subsurface geology.

Subsequently, the data undergo transformation into the frequency–wave number domain through a 2D Fast Fourier Transform. This transformation includes appropriate spatial windowing to minimize processing artifacts and a subsequent step to transition to the phase velocity–frequency domain. The relationship between phase velocity and frequency aids in distinguishing the dispersion curve. Leveraging the dispersive characteristics of Rayleigh-type surface waves, the study utilizes this information to visualize shallow subsurface layers by estimating both 1D (depth) and 2D (depth and surface location) shear wave velocities. The dispersion function was employed to compute phase velocities within a specified frequency range. In Figure 5b, the dispersion curve for Profile-12 is depicted, obtained by selecting the fundamental mode of the Rayleigh surface wave, serving as input for the final inversion process. The resulting inverted MASW data are typically presented either as 1D profiles (V _s vs depth) of shear wave velocity vs depth (Figure 5c) or as 2D sections (V _s vs depth and distance) encompassing a series of adjacent 1D profiles (Figure 5d). To create a 2D (surface and depth) V _s map, each 1D V _s profile is positioned at a surface location corresponding to the midpoint of the receiver profile. Figure 5d illustrates a typical 2D MASW section of Profile-12.

5 Results and interpretation

The results of the MASW survey in the study area yielded final inversion results represented by a 2D pseudo shear wave velocity section (Figure 6), which include seismogram raw data, dispersion curve, 1D shear wave velocity (V _s), and 2D MASW cross sections. The resulting high-resolution 2D sections distinctly reveal subsurface details down to approximately 30 m in depth, including fractured weathered limestone, weakness zones, and compact limestone. Between 8 and 14 m depth in line 1, the derived V _s from seismic data decreased from 1,500 to 1,000 m/s. This weakening zone suggests cavities and sinkholes. In the depth range of 15–30 m, the V _s increases from 1,500 to 2,700 m/s.

Figure 6

Results of MASW line no. 1: (a) Seismogram raw data, (b) dispersion curve, (c) 1D shear wave velocity (V _s) profile obtained from MASW, and (d) 2D MASW section of historical Diriyah urban zone.

This higher velocity range indicates compact limestone. Down to 11 m in depth (in lines 2, 3, 4, 5, 10, 12, 13, 15, and 19), the shear wave velocity increases from 800 to 1,500 m/s. This decline signifies fractures and weak zones filled with loose materials. In the depth range of 12–30 m, the V _s increases from 1,500 to 3,400 m/s, pointing to compact limestone. Till 10 m depth (in lines 6, 7, 9, and 16), V _s increases from 500 to 1,500 m/s. This decay signifies the existence of fractures and weak zones filled with secondary materials. Within the depth range of 11–30 m, the shear wave velocities increase and range from 1,500 to 3,400 m/s, pointing to the presence of compact limestone. Between 5 and 11 m in lines 8 and 11, the shear wave velocity derived from seismic data exhibits an increased trend, with values spanning from 1,000 to 1,500 m/s. This abrupt change suggests the presence of weak zones characterized by cavities and sinkholes, which are filled with secondary materials. The shear wave velocities show an opposite pattern in the depth range of 12–30 m, where they increase and range from 1,500 to 3,400 m/s. Till a depth of 8 m in line 14, the shear wave velocity obtained from seismic analysis increases, with values ranging from 800 to 1,500 m/s. This variation implies the existence of fractures and weak zones filled with secondary materials. Within the 9–30 m depth range, the shear wave velocities increase from 1,500 to 3,400 m/s, indicating compact limestone presence. Below the ground surface to a depth of 15 m in line 17, the shear wave velocity increases, ranging from 600 to 1,500 m/s. This variation signifies fractures and weak zones filled with secondary materials.

In general, and within the depth range of 16–30 m, the shear wave velocities increase from 1,500 to 3,400 m/s, indicating compact limestone. Moreover, line 18 illustrates that the shear wave velocity rises to 7 m depth from 800 to 1,500 m/s. This weakening signifies the existence of fractures and weak zones filled with lesser materials. While from 8 to 30 m deep, the shear wave velocities increase from 1,500 to 3,400 m/s, pointing to compact limestone rocks. These weak zones have different depths, shapes, and extensions.

6 Discussion and conclusion

The results of MASW in the current work provide valuable insights into the subsurface geological conditions in the study area. A comprehensive approach was employed to verify and establish a correlation between soil properties obtained from geophysical investigations and geotechnical data from drilled boreholes. This correlation allowed for a thorough characterization of the site’s subsurface conditions. By sorting and averaging the shear wave velocities obtained at different depth slices and incorporating the ground truth information from the borehole data, a clear geological model was developed for site characterization to a depth of 30 m. The top layer of the model, spanning from 0 to 12 m, was derived from MASW and borehole data. In comparison, the second layer (12–30 m) relied exclusively on interpreting MASW data. The MASW results revealed distinct weakness zones, indicating potential geotechnical issues associated with subsurface weakness zones within the upper layer. These findings are depicted in Figure 7.

Figure 7
2D MASW models show the lateral and vertical variation in the shear wave velocities across lines (1–19) in historical Diriyah.

Figure 7

2D MASW models show the lateral and vertical variation in the shear wave velocities across lines (1–19) in historical Diriyah.

MASW results revealed four distinct velocity zones based on NEHRP. Fill material was approximately half a meter thick and was classified as very dense soil. The second layer exhibited velocities ranging from 800 to 1,500 m/s, indicating weathered and highly fractured limestone. The third layer showed velocities varying from 1,500 to 1,800 m/s, representing slightly weathered limestone. The fourth layer displayed high velocities ranging from 1,800 to 3,600 m/s, indicating hard and compact limestone rocks. Based on these results, the fractured and weathered limestone zones may extend to 12 m depth over the investigated area. Various sinkholes of circular, elongated, and conical shapes are observed within this depth range. Specifically, sinkholes at sites 1, 6, 9, 11, and 17 are depicted at depths exceeding 12 m.

Furthermore, the study conducted [18] observed Rock Quality Designation (RQD) values indicating a sequence of fractured, weathered, and compacted limestone rocks. Analyzing the 2D MASW data within the depth range between 12 and 30 m, the compact limestone has been encountered at different depths. These data revealed that compact limestone was encountered starting from around 5 m and extending to 12 m depth in the analyzed section. The correlated cross-sections of the 2D MASW data indicate the presence of potentially hard limestone rocks at depths ranging from approximately 12 m and extending beyond 30 m. It is recommended that civil engineers consider these findings for construction and development purposes in the study area.

By comparing and verifying the resulting shear wave section with geotechnical borehole data (Figures 8–10), it becomes possible to identify potential weakness zones and localized cavities straightforwardly. Indicators of a cavity include signs such as complete or partial loss of drilling water and direct tool drop. Additional observations, such as low RQD, low unconfined compressive strength, low solid core recovery, and low total core recovery, can suggest the presence of moderate to highly weathered or fractured rock sequences. However, the most effective identification of a localized cavity feature occurs when encountering low shear velocity values within and surrounded by higher velocity values.

Figure 8

MASW ground model of line no. 1 correlated with geotechnical data of BH-112. The BH-112 sits on the line itself.

Figure 9

MASW ground model of line 11 correlated against borehole BH-98. The BH-98 sits on the line itself.

Figure 10

MASW ground model of line 18 correlated against borehole BH-41. The BH-41 sits on the line itself.

Based on the calculation of the average shear wave velocity V s 30 , the average velocity increases in the study area’s western, southwestern, and central parts, ranging between 1,500 and 2,100 m/s. According to the NEHRP classification illustrated in Table 2 [52], these areas fall under category A. In contrast, the velocity decreases slightly in the southern and southeastern parts, ranging between 1,500 and 1,350 m/s, classified as category B (Figures 11 and 12).

Table 2

Seismic site characterization depending on shear wave velocity [52]

NEHRP site class	S-velocity (V _s) (m/s)	Description
A	>1,500	Hard rock
B	760–1,500	Rock
C	360–760	Very dense soil and soft rock
D	180–360	Stiff soil
E	<180	Soft soil

Figure 11

Contour map for V _s30 variation in the study.

Figure 12

Site characterization based on V _s30 variation of the rock in the study area.

Acknowledgements

Deep thanks and gratitude to the Researchers Supporting Project number (RSP2025R351), King Saud University, Riyadh, Saudi Arabia for funding this research article.

Author contributions: This work was made possible by the significant contributions of all authors. K.A., S.A.H., and S.A.A., conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, visualization, presentation of geophysical datasets and writing – review and editing the final MS. All authors reviewed the drafts critically and provided the final approval for publication. All authors have read and approved the published version of the manuscript.
Conflict of interest: Authors state no conflict of interest.

References

[1] Abdallatif T, Khozym A, Ghandour A. Determination of seismic site class and potential geologic hazards using multi-channel analysis of surface waves (MASW) at the Industrial City of Abu Dhabi, UAE. NRIAG J Astron Geophys. 2022;11(1):193–209.10.1080/20909977.2022.2055829Suche in Google Scholar

[2] Abdelrahman K, Al-Amri A, Al-Arifi N, Abdelmoneim E. Seismic risk assessment at the proposed site of Gemsa wind power station, southwestern coast of Gulf of Suez, Egypt. J Geol Soc India. 2017;89:192–6.10.1007/s12594-017-0583-1Suche in Google Scholar

[3] Abdelrahman K, Fnais M, Abdelmonem E, Magram K, Bin Saadoon A. Seismic vulnerability assessment in the new urban area of Diriyah Governorate, Riyadh, Saudi Arabia. Arab J Geosci. 2017;10:1–12.10.1007/s12517-017-3222-7Suche in Google Scholar

[4] Abdelrahman K, Alamri AM, Al-Otaibi N, Fnais M. Geotechnical assessment for the ground conditions in Makah Al-Mukarramah city, Saudi Arabia. J King Saud Univ-Sci. 2020;32(3):2112–21.10.1016/j.jksus.2020.02.011Suche in Google Scholar

[5] Al-Heety AJ, Hassouneh M, Abdullah FM. Application of MASW and ERT methods for geotechnical site characterization: A case study for roads construction and infrastructure assessment in Abu Dhabi, UAE. J Appl Geophys. 2021;193:104408.10.1016/j.jappgeo.2021.104408Suche in Google Scholar

[6] Aldahri M, Mogren S, Abdelrahman K, Zahran H, El Hady S, El-Hadidy M. Surface soil assessment in the Ubhur area, north of Jeddah, western Saudi Arabia, using a multichannel analysis of surface waves method. J Geol Soc India. 2017;89:435–43.10.1007/s12594-017-0626-7Suche in Google Scholar

[7] Alhumimidi MS. Geotechnical assessment of near-surface sediments and their hazardous impact: case study of Jizan City, southwestern Saudi Arabia. J King Saud Univ-Sci. 2020;32(3):2195–201.10.1016/j.jksus.2020.02.031Suche in Google Scholar

[8] Almadani SA, Abdelrahman K, Qaysi S. Integrated geoinvestigation for evaluation of an engineering site-a case study from the western Riyadh city, central Saudi Arabia. Arab J Geosci. 2021;14:1–12.10.1007/s12517-021-06767-3Suche in Google Scholar

[9] Deere D, Hendron A, Patton F, Cording E. Design of surface and near-surface construction in rock. Proceedings of the 8th Symposium on Rock Mechanics; 1967.Suche in Google Scholar

[10] Fnais M, Al-Amri A, Abdelrahman K, Al-Yousef K, Loni O, Moneim E. Assessment of soil-structure resonance in southern Riyadh City, Saudi Arabia, Arab. J Geosc (published online). 2015;8:1017–27.10.1007/s12517-013-1247-0Suche in Google Scholar

[11] Keskinsezer A, Dağ E. Investigating of soil features and landslide risk in Western-Atakent (İstanbul) using resistivity, MASW, Microtremor and boreholes methods. Open Geosci. 2019;11(1):1112–28.10.1515/geo-2019-0086Suche in Google Scholar

[12] Al-Heety AJ, Shanshal ZM, Hassouneh M, Abdelrahman K, Ali SM, Dey BK. Borehole Seismic Test and Seismic Surface Waves Survey for Site-Specific Investigation at Al Layyah Power Generation Plant, Sharjah, United Arab EmiratesSuche in Google Scholar

[13] Al-Saigh NH, JaddoaAl-Heety A. Seismic refraction tomography and MASW survey for geotechnical evaluation of soil for the teaching hospital project at Mosul University. J Zankoy Sulaimani-Part A (JZS-A). 2014;16(1):1.10.17656/jzs.10279Suche in Google Scholar

[14] Karslı H, Babacan AE, Akın Ö. Subsurface characterization by active and passive source geophysical methods after the 06 February 2023 earthquakes in Turkey. Nat Hazards. 2024;120(6):5257–86.10.1007/s11069-024-06422-6Suche in Google Scholar

[15] Karslı H, Babacan AE, Sayıl N, Çoban KH, Akın Ö. An assessment of seismicity and near surface geophysical characteristics of potential solid waste landfill sites in the Eastern Black Sea Region of Türkiye. Environ Sci Pollut Res. 2024;31(9):14156–77.10.1007/s11356-024-31964-4Suche in Google Scholar PubMed PubMed Central

[16] Sharafeldin SM, Essa KS, Youssef MA, Karsli H, Diab ZE, Sayil N. Shallow geophysical techniques to investigate the groundwater table at the Great Pyramids of Giza, Egypt. Geosci Instrum Methods Data Syst. 2019;8(1):29–43.10.5194/gi-8-29-2019Suche in Google Scholar

[17] Senkaya M, Babacan AE, Karslı H, San BT. Origins of diverse present displacements in a paleo-landslide area (Isiklar, Trabzon, northeast Turkey). Environ Earth Sci. 2022;81(8):245.10.1007/s12665-022-10372-2Suche in Google Scholar

[18] Abdelrahman K, Hazaea SA, Almadani SA. Geological-geotechnical investigations of the historical Diriyah urban zone in Riyadh, Saudi Arabia: an integrated approach. Front Earth Sci. 2023;11:1202534.10.3389/feart.2023.1202534Suche in Google Scholar

[19] Al-Othman AA. Environmental study of Wadi Hanifah, Riyadh, with special reference to wastewater disposal and the quality of the stream water, groundwater and sediments. Doctoral dissertation. Cardiff: Cardiff University; 2002.Suche in Google Scholar

[20] Steineke M, Bramkamp R. Mesozoic Rocks of Eastern Saudi-Arabia. AAPG Bulletin-American Association of Petroleum Geologists; 1952.Suche in Google Scholar

[21] Vaslet D, Al-Muallem M, Maddah S, Brosse J, Fourniguet J, Breton J, et al. Explanatory notes to the geologic map of the Ar Riyadh quadrangle, Kingdom of Saudi Arabia. Geoscience map GM-121, scale 1:250,000, sheet 24I. Deputy Ministry for Mineral Resources, Ministry of Petroleum and Mineral Resources, Kingdom of Saudi Arabia; 1991. p. 54.Suche in Google Scholar

[22] Alzahrani H, Abdelrahman K, Hazaea SA. Use of geoelectrical resistivity method for detecting near-surface groundwater potential zones at Riyadh city, Saudi Arabia. J King Saud Univ-Sci. 2022;34(7):102253.10.1016/j.jksus.2022.102253Suche in Google Scholar

[23] Basyoni MH, Khalil M. An overview of the diagenesis of the Upper Jurassic carbonates of Jubaila and Hanifa formations, central Saudi Arabia. Arab J Geosci. 2013;6:557–72.10.1007/s12517-011-0317-4Suche in Google Scholar

[24] Manivit J, Pellaton C, Vaslet D, Le Nindre Y, Brosse J, Breton J, et al. Geologic map of the Darma quadrangle, sheet 24H, Kingdom of Saudi Arabia. Saudi Arab Deputy Ministry Miner Resour Geosci Map, GM-101, scale. 1985;1(250,000):133.Suche in Google Scholar

[25] Powers R, Ramirez L, Redmond C, Elberg E. Geology of the Arabian Peninsula. United States Department of the Interior, Geological Survey; 1966.Suche in Google Scholar

[26] Almalki H, El-Werr A-K, Abdel-Rahman K. Estimation of near-surface geotechnical parameters using seismic measurements at the proposed KACST expansion site, Riyadh, KSA. Arab J Geosci. 2011;4(7):1131.10.1007/s12517-010-0124-3Suche in Google Scholar

[27] Alzahrani H, Abdelrahman K, Qaysi S, Al-Otaibi N. Shear-wave velocity profiling of Jizan city, southwestern Saudi Arabia, using controlled-source spectral analysis of surface-wave measurements. J King Saud Univ-Sci. 2021;33(7):101592.10.1016/j.jksus.2021.101592Suche in Google Scholar

[28] Ashraf MM, Kumar N, Yusoh R, Hazreek Z, Aziman M. Site classification using Multichannel Channel Analysis of Surface Wave (MASW) method on soft and hard ground. Journal of Physics: Conference Series; 2018.10.1088/1742-6596/995/1/012108Suche in Google Scholar

[29] Ayele A, Woldearegay K, Meten M. Multichannel analysis of surface waves (MASW) to estimate the shear wave velocity for engineering characterization of soils at Hawassa Town, Southern Ethiopia. Int J Geophys. 2022;2022:7588306.10.1155/2022/7588306Suche in Google Scholar

[30] Cardarelli E, Cercato M, De Donno G. Surface and borehole geophysics for the rehabilitation of a concrete dam (Penne, Central Italy). Eng Geol. 2018;241:1–10.10.1016/j.enggeo.2018.05.008Suche in Google Scholar

[31] Clinton JJ. Determining variable depth to bedrock using ERT and MASW: A geophysical investigation in St. Louis, MO. Masters Theses. Missouri University of Science and Technology; 2020.Suche in Google Scholar

[32] Dorman J, Ewing M, Oliver J. Study of shear-velocity distribution in the upper mantle by mantle Rayleigh waves. Bull Seismol Soc Am. 1960;50(1):87–115.Suche in Google Scholar

[33] Herrmann R, Al-Eqabi G. Surface wave inversion for shear wave velocity. Shear Waves Mar Sediment. 1991;545–56.10.1007/978-94-011-3568-9_63Suche in Google Scholar

[34] Knopoff L. Observation and inversion of surface-wave dispersion. Tectonophysics. 1972;13(1–4):497–519.10.1016/0040-1951(72)90035-2Suche in Google Scholar

[35] Mokhtar TA, Herrmann R, Russell D. Seismic velocity and Q model for the shallow structure of the Arabian shield from short-period Rayleigh waves. Geophysics. 1988;53(11):1379–87.10.1190/1.1442417Suche in Google Scholar

[36] Pegah E, Liu H. Application of near-surface seismic refraction tomography and multichannel analysis of surface waves for geotechnical site characterizations: A case study. Eng Geol. 2016;208:100–13.10.1016/j.enggeo.2016.04.021Suche in Google Scholar

[37] Rahimi S, Wood CM, Coker F, Moody T, Bernhardt-Barry M, Kouchaki BM. The combined use of MASW and resistivity surveys for levee assessment: A case study of the Melvin Price Reach of the Wood River Levee. Eng Geol. 2018;241:11–24.10.1016/j.enggeo.2018.05.009Suche in Google Scholar

[38] Taipodia J, Dey A. A review of active and passive MASW techniques. National Workshop Engineering Geophysics for Civil Engineering and Geo-Hazards (EGCEG); 2012.Suche in Google Scholar

[39] Park CB, Miller RD, Xia J. Multichannel analysis of surface waves. Geophysics. 1999;64(3):800–8.10.1190/1.1444590Suche in Google Scholar

[40] Brahma J. Seismic site characterization using shear wave velocities of Gandhinagar City, Gujarat, India. Sci Technol. 2011;1(1):17–23.10.5923/j.scit.20110101.03Suche in Google Scholar

[41] Foti S. Multistation methods for geotechnical characterization using surface waves; 2000. p. 229.Suche in Google Scholar

[42] McMechan GA, Yedlin MJ. Analysis of dispersive waves by wave field transformation. Geophysics. 1981;46(6):869–74.10.1190/1.1441225Suche in Google Scholar

[43] Valentina SL, Claudio S, Sebastiano F. Multimodal interpretation of surface wave data. 8th EEGS-ES Meeting. European Association of Geoscientists & Engineers; 2002.Suche in Google Scholar

[44] Xia J, Miller RD, Park CB. Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics. 1999;64(3):691–700.10.1190/1.1444578Suche in Google Scholar

[45] Kumar A, Jayakumar T, Raj B, Ray KK. Correlation between ultrasonic shear wave velocity and Poisson’s ratio for isotropic solid materials. Acta Mater. 2003;51(8):2417–26.10.1016/S1359-6454(03)00054-5Suche in Google Scholar

[46] Xia J. Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of surface-wave methods. J Appl Geophys. 2014;103:140–51.10.1016/j.jappgeo.2014.01.016Suche in Google Scholar

[47] Foti S, Hollender F, Garofalo F, Albarello D, Asten M, Bard P-Y, et al. Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project. Bull Earthq Eng. 2018;16:2367–420.10.1007/s10518-017-0206-7Suche in Google Scholar

[48] Rehman F, El-Hady SM, Faisal M, Harbi HM, Ullah MF, Rehman S, et al. MASW survey with fixed receiver geometry and CMP cross-correlation technique for data processing: a case study of Wadi Fatima, Western Saudi Arabia. Open J Geol. 2018;8(5):463–73.10.4236/ojg.2018.85027Suche in Google Scholar

[49] Karabulut S. Soil classification for seismic site effect using MASW and ReMi methods: a case study from western Anatolia (Dikili-İzmir). J Appl Geophys. 2018;150:254–66.10.1016/j.jappgeo.2018.01.011Suche in Google Scholar

[50] Aas A, Sinha SK. Seismic site characterization using MASW and correlation study between shear wave velocity and SPTN. J Appl Geophys. 2023;215:105131.10.1016/j.jappgeo.2023.105131Suche in Google Scholar

[51] Kanlı AI, Tildy P, Prónay Z, Pınar A, Hermann L. VS30 mapping and soil classification for seismic site effect evaluation in Dinar region, SW Turkey. Geophys J Int. 2006;165(1):223–35.10.1111/j.1365-246X.2006.02882.xSuche in Google Scholar

[52] Building Seismic Safety Council (BSSC). Shear wave velocity of soils and NEHRP site classification map of Chiang Rai City, Northern Thailand. Electron J Geotech Eng. 2012;17:2891–904.Suche in Google Scholar

[53] Rahimi S, Wood CM, Teague DP. Performance of different transformation techniques for MASW data processing considering various site conditions, near-field effects, and modal separation. Surv Geophys. 2021;5:1197–225.10.1007/s10712-021-09657-1Suche in Google Scholar

[54] Wathelet M. Array recordings of ambient vibrations: surface-wave inversion. PhD dissertation., Liége University; 2005, p. 161.Suche in Google Scholar

Received: 2024-04-06

Revised: 2024-10-09

Accepted: 2024-10-10

Published Online: 2024-12-24

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

https://doi.org/10.1515/geo-2022-0724

Schlagwörter für diesen Artikel

shear wave velocity; MASW; sinkholes; geotechnical boreholes; Riyadh; Saudi Arabia

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