Seismic structure of Kuwait

Transcription

1 Geophys. J. Int. (27) 17, doi: /j X x Seismic structure of Kuwait Michael E. Pasyanos 1, Hrvoje Tkalčić 1, Rengin Gök 1, Abdullah Al-Enezi 2 and Arthur J. Rodgers 1 1 Earth Science Division, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA. pasyanos1@llnl.gov 2 Environment and Urban Development, Kuwait Institute for Scientific Research, Safat 1319, Kuwait Accepted 27 February 9. Received 27 January 23; in original form 26 September 11 1 INTRODUCTION In recent years, Lawrence Livermore National Laboratory (LLNL) has collaborated with scientists from a number of countries and institutions in the Middle East, either through joint deployments or collaborative projects. In Kuwait, LLNL has a collaborative project with the Kuwait Institute for Seismic Research (KISR) to develop an improved velocity model for event location for the recently installed national network (Harris et al. 1999). In 1998, two broadband stations were deployed in Jordan as part of a cooperative project between the Jordan Seismological Observatory (JSO), the United States Geological Survey (USGS) and LLNL (Rodgers et al. 23a). In 22, LLNL and King Saud University (KSU) installed a broadband station on the Arabian Shield near the town of Halban in central Saudi Arabia (Rodgers et al. 22). Most recently, in a cooperative SUMMARY We have used data from the Kuwait National Seismic Network (KNSN) to estimate the seismic structure of Kuwait using a limited amount of seismic data. First, we made surface wave dispersion measurements and calculated receiver functions from the relatively small amount of data available from the broad-band station, KBD. Models were derived from the joint inversion of teleseismic receiver functions and Rayleigh and Love fundamental mode surface wave group velocity dispersion. While both surface waves and receiver functions by themselves can be used to estimate lithospheric structure, we have successfully combined the two to reduce non-uniqueness in estimates based on the individual data sets. The resulting KUW1 model features a thick (8 km) sedimentary cover and crustal thickness of 45 km. Crustal velocities below the sedimentary cover are consistent with global averages for stable platforms. We infer upper-mantle velocities (7.84 km s 1 P-wave velocity; 4.4 km s 1 S-wave velocity) that are slightly lower than expected for a stable platform. In comparison with other crustal structure estimates for the Arabian platform to the west, the crust is thicker and the mantle is slower in Kuwait. This is consistent with the overall tectonic trends of the region that find increasing crustal thickness between the divergent plate boundary at the Red Sea and the convergent plate boundary at the Zagros Mts, as well as slow mantle velocities beneath this nearby orogenic zone. The resulting model fits the traveltimes of regional phases (Pn, Pg, Sn and Lg). Independent inversion of local earthquake traveltimes recorded by KNSN (allowing for event hypocentre relocation) results in a remarkably similar velocity structure, providing confidence that the joint inversion of receiver functions and surface wave group velocities can impose accurate constraints on crustal structure for local event location and network operations. Relocation of events in Kuwait improves the clustering of events and results in shallower hypocentres. Key words: Arabian platform, event relocation, Kuwait, receiver functions, surface wave dispersion, velocity models. venture with United Arab Emirates University, two broad-band stations were deployed in the UAE (Rodgers et al. 23b). In this paper, we demonstrate that combining receiver function and surface wave group velocity dispersion data improves estimates of the seismic velocity profile for the crust and uppermost mantle. We consider a case study for the Kuwaiti Al-Kabd seismic station KBD, a Streckeisen STS-2 broad-band station which is part of the Kuwait National Seismic Network (KNSN). Using a small sample of seismic data, we estimate the regional crust and upper-mantle structure using well-established methods, resulting in a velocity model for Kuwait. We quantify the improvements in structural estimates derived from the new data. In a second part of the study, we test regional traveltimes and simultaneously relocate local earthquakes and estimate velocity structure. In both cases, we find crustal structure consistent with our velocity profile for the region. GJI Seismology C 27 The Authors 299

2 M. E. Pasyanos et al. Figure 1. (a) Geographic and tectonic map of the Middle East. Inset shows area around Kuwait with oil fields shown in magenta. The star denotes the location of station KBD. Circles indicate recent seismicity (events M 5. from 199 to present). Dashed lines indicate regions with sediment thickness in excess of 5 km, while solid lines indicate plate boundaries. The arrow shows the relative motion of the Arabian Plate. DSF = Dead Sea Fault. (b) Sediment thickness map (in km) of the Middle East from Laske and Masters (1997). (c) Path map of group velocity dispersion measurements for 15 s Rayleigh waves. Regional paths recorded at station KBD are shown in red. Stars indicate the location of profiles shown in Fig. 2. (d) Group velocities (in km s 1 ) of 15 s Rayleigh waves derived from tomographic inversion. 2 BACKGROUND Kuwait sits at the northwest corner of the Persian Gulf (Fig. 1a). It resides at the northeastern edge of the Arabian platform that, along with the Arabian Shield, constitutes the Arabian Plate. There is a divergent relative plate motion along the Gulf of Aden and Red Sea Rift, left-lateral motion along the Dead Sea Fault and convergence to the north and northeast, where the plate is being driven against Eurasia. The compressive motion, on the order of 4 mm yr 1 (DeMets et al. 199; McClusky et al. 23), is responsible for the elevation of the Turkish and Iranian Plateaus, as well as the active uplift of the Zagros Mts. The convergence zone and erosion of these mountains, in turn, are responsible for the deep sedimentary basins of the Arabian platform and Mesopotamian Foredeep. Sediments in this area range up to 1 km thick, and the thickness is on the order of 7 km under Kuwait (Laske & Masters 1997). A sediment thickness map of the region is shown in Fig. 1b. Regionally, thick sediments are found in the Persian Gulf, Eastern Mediterranean and Caspian Basins. Thinner deposits are found in the Red Sea and in eastern Iran. As expected, seismicity in the region is primarily concentrated along the plate boundaries of the Arabian Plate with adjacent plates (Fig. 1a). The Zagros Thrust system defining the convergent boundary with the Eurasian Plate is particularly active. While there is quite a bit of intraplate seismicity within the Eurasian Plate (i.e. Turkish and Iranian Plateau), there is little within the Arabian Plate. In Kuwait itself, most seismicity is limited to shallow, smallmagnitude events related to the Minagish (southern) and Sabariyah (northern) oil fields (Fig. 1a; Bou-Rabee 2; Al-Awadhi & Midzi 21). The KNSN began operating in March 1997 and consists of eight three-component stations (seven short-period stations and one broad-band station) (Bou-Rabee 1999). The stations operate in triggered mode and transmit data when short-/long-term average (STA/LTA) levels exceed 6. The broad-band station KBD (Al-Kabd) is located in the central part of the country, southwest of Kuwait City.

3 Seismic structure of Kuwait 1 We have data for a select subset of local, regional and teleseismic events from a 6-yr-time period. Dispersion for station KBD and Arabian Shield 3 DATA AND METHODS 4. Arabian Shield 3.1 Surface wave dispersion Seismologists have long used surface waves to characterize regional earth structure. We applied multiple-filter analysis technique (e.g. Herrmann 1973) to measure surface wave group velocities for tens of thousands of paths in Eurasia and Africa, including the Middle East (Pasyanos et al. 21; Pasyanos 25). As such, we have very good coverage of the region, even at shorter periods (<2 s) that are more difficult to reliably measure (Fig. 1c). Surface wave group velocities (SWGV) are very useful for determining crustal and upper-mantle structure since different periods are sensitive to structure over different depth ranges. While the average velocity structure is generally well determined, the dispersion values are relatively insensitive to discontinuity sharpness, and there is trade-off between discontinuity depths and velocities (Pasyanos & Walter 22). With data from KBD, we made dispersion measurements on paths from regional and teleseismic events into Kuwait. At regional distances, the majority of measurements were from events in the Zagros Mts in Iran (shown as the red paths in Fig. 1c). All of the paths were then tomographically inverted for lateral variations in group velocity using a conjugant gradient technique (Pasyanos et al. 21; Pasyanos 25). s were performed independently for both Love and Rayleigh waves from periods ranging from 7 to 1 s. An example of a group velocity map for 15 s Rayleigh waves is shown in Fig. 1d. At 15 s, Rayleigh waves are primarily sensitive to shallow crustal structure, particularly sediment thickness. In fact, there is a striking correspondence between the 15 s Rayleigh wave group velocity map shown in Fig. 1d and the sediment thickness map shown in Fig. 1b, giving us confidence in the accuracy of the dispersion maps. We find extremely slow group velocities for the deep sedimentary basins of the Persian Gulf, Mesopotamian Foredeep and South Caspian. The fastest velocities are found in regions having thin oceanic crust, like the Red Sea and the Arabian Sea. The fastest continental velocities overlie the Arabian Shield in the southwest Arabian Peninsula. The addition of a small set of measured paths to station KBD (red lines in Fig. 1c) has the effect of decreasing the short period group velocities in the Persian Gulf and Mesopotamian Foredeep by up to 5 per cent, but had little effect on longer periods. Having performed the series of tomographic inversions, we can simply access the model at any position to get the dispersion values at that point. At the location of station KBD, there are extremely slow group velocities at short periods (for both Love and Rayleigh waves), indicating thick sediments (Fig. 2). We also find the somewhat unusual overlap of Love and Rayleigh waves at intermediate periods (35 5 s). Love waves are usually faster than Rayleigh waves at all periods, but we sometimes find this situation in regions of thick sediments and/or thick crust. At long periods, we once again see separation of the group velocities between the Love and Rayleigh waves. Uncertainties (illustrated by the bars) are generally small until about 6 s where they increase with increasing period. Uncertainties of the 7 s group velocities are very high, owing to the poor path coverage. The uncertainties of Love wave group velocities are generally higher than Rayleigh waves at corresponding periods. In contrast, we also plot dispersion values from a location (latitude 23 N, longitude 42 E; indicated by a star in Fig. 1c) to the south- Group velocity (km/s) KBD Rayleigh Love Period (sec) Figure 2. Group velocities (in km s 1 ) at location of station KBD (black lines) and an Arabian Shield location (red lines). Rayleigh waves are indicated by triangles and solid lines; Love waves by circles and dashed lines. Uncertainties are shown by the bars. west in the Arabian Shield. The curves from the two locations differ greatly, most significantly at short periods, due to the differences in sediment thickness and crustal velocities between the shield and the platform. Using dispersion data only, we can invert for the crust and upper mantle. Recently, we employed a grid-search technique to determine lithospheric structure using simple several layer models (Pasyanos & Walter 22). Utilizing dispersion data is particularly useful in aseismic areas and areas without seismic stations, where surface wave data might provide the only sampling of a region. Here, rather than inverting for several layers over a half-space, we chose a more finely layered model having a layer thickness of 2 km in top 5 km (except for 1-km thick surface layers) and increasing to a thickness to 4 km and higher below 5 km. Poisson s ratio ranges between about.24 and.29 for most of the layers, except in the surface layer, where it is equal to.35. After the grid-search is performed for shear wave velocity values in each layer, compressional wave velocity is calculated using the Poisson s ratio constraint from the initial model. Poisson s ratio was not changed in the inversion. We used a modified version of the Western Eurasia and North Africa or WENA model (Pasyanos et al. 24) as an initial model, where adjustments were made to the crustal thickness. We use the group velocity of fundamental mode surface waves at periods between 7 and 1 s for Rayleigh waves and 7 7 s for Love waves to estimate lithospheric structure. Love waves data for periods greater than 7 s were not included because of their higher

4 2 M. E. Pasyanos et al. 1. Receiver Function Group Velocity (km/s) Observed a=2.5 a= time (s) Observed L Observed R period (s) Depth (km) Station KBD Backaz~53deg (Japan) slowness=.5 s/km p=.9 smoothness=1. Starting Model Vp (km/s) Figure 3. of surface wave group velocity dispersion. The figures to the left show the fit to the dispersion data (bottom) and the receiver function data (top). The model is shown to the right. uncertainties. Although the uncertainties were high, 7 s group velocities were included because of their importance for determining shallow sedimentary and crustal structure. Here, the surface wave inversion results are fit with a model having a crustal thickness of about 44 km (Fig. 3): 4 km thick sediments overlying a 4 km thick crust with a velocity gradient of.75 km s 1 km 1 (i.e. 3. km s 1 velocity contrast over 4 km). The surface waves could be fit almost equally well with the sediments overlying a single 4 km thick crustal layer of about 6.5 km s 1 (3.5 km s 1 S-wave velocity). The upper mantle has a P-wave velocity of 7.8 km s 1 (4. km s 1 S-wave velocity). While these simple models fit the surface waves quite well, they do a rather poor job of fitting the receiver functions (shown, but not used in the inversion). The amplitude and timing of the reverberations at 4 and 1 s, in particular, are poorly recovered. In addition, while this model fits the dispersion data well, it does not uniquely fit the data. Using only surface wave dispersion data, there are some fairly large tradeoffs between sediment thickness and average crustal velocity, between crustal velocity and thickness and between crustal thickness and upper-mantle velocity (Pasyanos & Walter 22). 3.2 Receiver functions Receiver functions (RF) are widely used for estimating the lithospheric velocity structure beneath a seismic station (Langston 1979; Owens et al. 1984; Ammon et al. 199). Teleseismic receiver functions isolate the P-to-S conversions and reverberations from an incident P wave. They are calculated by deconvolving the vertical component of the nearly vertically incident P wave and coda from the radial component. As such, they are laterally sensitive to the structure within about 1 km of the seismic station, ideal for estimating local earth structure. While major discontinuities, such as the basement and the Moho, are usually well constrained using this analysis, absolute velocities are often poorly determined (Ammon et al. 199). In addition, while this has become a fairly standard seismic technique, the results can be somewhat non-unique and subject to different interpretations. In one recent example from a deployment across the Apennines in Italy, three different groups performing receiver functions on one of the stations (station 9) made crustal thickness estimates ranging from 28 to 45 km (Agostinetti et al. 22; Levin et al. 22; Mele & Sandvol 23).

5 We have calculated receiver functions for available teleseismic events at station KBD. The epicentral distances of all events that we considered ranged between 6 and 85, which provide steep incidence angles of teleseismic P waves. Steep incident angles help satisfy the basic postulate upon which receiver function method is based, and makes the deconvolution procedure more efficient (e.g. Langston 1979). Although we have some waveform data for events as far back as 1997 September, we only have seismic data from teleseismic events from 2 May to 21 August and one event from 23 June. Due to the limited data availability, event selection was further hindered by a number of receiver functions with poor signalto-noise ratio and events from a fairly limited range of backazimuths (Fig. 4a). The teleseismic earthquakes that we used for this purpose are located mainly along the Asian side of the Pacific Rim, defining mostly eastern backazimuths with KBD. One exception is a group of earthquakes located near the Prince Edward Islands region south of Africa, thus forming southern backazimuths with KBD. After applying and carefully comparing the results from both frequency and time-domain approaches, we decided to use the iterative time-domain deconvolution method of Ligorría & Ammon (1999) to deconvolve vertical from horizontal components. We grouped earthquakes into three groups based on their locations and corresponding back azimuths (Japan, Indonesia and South of Africa) and calculated corresponding radial and transverse receiver functions. The frequency content of the receiver function can be controlled by the Gaussian filter parameter, referred to as either alpha or simply a. We considered two frequency bands, with corresponding Gaussian factors alpha: 2.5 (f < 1.25 Hz) and 5. (f < 2.5 Hz). In the following inversions, the lower frequency RF was effectively given twice the weight of the higher frequency RF, since it is contained in both frequency bands used here. The higher frequencies help to better constrain the impedance contrast between two layers, while lower frequencies are in general more sensitive to the velocity gradients. However, receiver functions are very non-unique in describing the absolute velocity information. We again used the modified WENA model as the initial model, with the same layer thicknesses. As before, Poisson s ratio was fixed for each layer. We have three main groups of events coming from south of Africa to the south, the southwest Pacific to the southeast and the Japanese Islands to the northeast (Fig. 4a). There was only a single event to the west, coming from the mid-atlantic Ridge. Fig. 4b shows variations in the observed receiver functions (Gaussian filter a = 5.) with respect to the backazimuth. The top four events are from Japan and vicinity, the next eight from the southwest Pacific, and the last two from south of Africa. Events within each grouping tended to have similar characteristics, so they were stacked. Because of the narrow range of epicentral distances (and hence in the corresponding ray parameter), there is very little moveout, and the traces can be simply stacked. All events had strong signals at 2 and 4 s after the incident P wave. Compared to the other azimuths, signals coming from the southwest Pacific Ocean have an additional large reverberation arriving at 7 s. When the three sets of receiver functions were then inverted alone for layered velocity structure, the resulting velocity models were incompatible with the surface wave dispersion. Fig. 5 shows a receiver function inversion for the Japanese paths and compares the observed and predicted dispersion curves. While the fits to the receiver functions (upper left) were excellent, the model seriously misfits the dispersion data (lower left). A second problem is that the receiver function inversion results varied significantly among stacks from different backazimuths. Fig. 6 shows large variations between the models estimated from three data sets from different backazimuths, Seismic structure of Kuwait 3 each of which fit the receiver function data very well. The inferred velocities in the lower crust and shallow upper mantle varies more than the upper-crustal velocities. Finally, the profiles reveal unlikely velocity values, most notably the extreme velocities for the profile estimated from events south of Africa (Fig. 6). In most cases, it is difficult to identify the Moho discontinuity. of the complete stack (combining events from all three backazimuthal groups) resulted in a profile very similar to the Indonesian profile. For these reasons, we hoped to improve the profiles by combining the receiver function and surface wave data sets following Julià et al. (2, 23). 3.3 Joint inversions As mentioned earlier, both receiver functions and surface wave group velocity inversions for plane-layered structure suffer from non-uniqueness, particularly RF in describing absolute velocity information and SWGV in identifying discontinuities. For this reason, we merge the two datasets into one inversion scheme, performing a joint inversion for velocity structure. We combined the group velocities of fundamental-mode surface waves with the radial receiver functions obtained in the previous section. We adopted the method of Julià et al. (2), based upon an iterative damped least-squares scheme. In this formulation, a joint prediction error is defined as follows: E y z = p N y y i M 2 Y ij x j j=1 + 1 p N z z i M 2 Z ij x j j=1, N y σ yi N z σ zi i=1 where y, for instance, is dispersion data and Y the corresponding partial derivatives, z is the receiver function data and Z the corresponding partial derivatives, and x the shear velocities for a given set of M layers. N y and N z are the number of data points for each data set and σ 2 y i and σ 2 zi the corresponding variances. The factor p in eq. (1) is the value that trades off between the relative influence of each data set. This method, then, provides a simple way to control trade-offs between fitting the receiver functions and the dispersion curves (normalized by the data uncertainty and number of points), and also the trade-off between fitting the data and model smoothness. Both smoothness and weighting parameters are estimated empirically, after running a suite of inversions. The method is described in more detail in Julià et al. (2, 23). When we ran the joint inversion, we were able to fit the surface waves without significantly degrading the fit to the receiver functions. Fig. 7 shows the joint inversion using receiver functions from the Japanese backazimuth illustrating this. A plot of the profiles inferred from the joint inversion of SWGV and RFs from the three backazimuths is shown in Fig. 8. In contrast to the profiles determined using only receiver function data, the resulting models from the joint inversions were more consistent for the different backazimuths, although the profile for the African RF backazimuth still has some fluctuations with depth. The estimated velocity profiles are consistent with the tectonic setting of the region. The low velocities near the surface are consistent with the thick sedimentary cover in the Arabian platform. The crustal thickness ( 45 km, but perhaps even slightly thicker for the Indonesian RF stack) is similar to recent RF results for westernmost Iran (Paul et al. 26) and eastern Saudi Arabia (Al-Damegh et al. 24; Tkalčić et al. 26). It appears, i=1 (1)

6 4 M. E. Pasyanos et al. a) Event distribution for station KBD 6 6 KBD b) - baz=26.9 AUG 2 21 baz=47.2 AUG 19 2 baz=49.2 NOV 13 2 baz=59. AUG 18 2 baz=92.7 JAN baz=94. SEP 26 2 baz=94.5 FEB baz=97.1 APR 4 21 baz=98.2 MAR baz=98.3 MAR baz=98.7 AUG 28 2 baz=98.8 AUG 28 2 baz=187.1 SEP 25 2 baz=184.9 SEP 8 2 P P -6 P P*s P MC s P MC s P*s P MC s Ps Ps time (s) PpPms PpPms -6 - Figure 4. (a) Event distribution (stars) and plot of paths from teleseismic events to station KBD. Events used in the receiver functions are indicated by presence of a circle. (b) Variations of individual observed receiver functions at KBD with respect to backazimuth. Major phases are indicated on the waveforms. then, that the surface waves significantly reduce the non-uniqueness of the receiver function data, producing models that are consistent with both data types and are more realistic. Of course, we could as correctly say that the receiver functions have the same effect on the surface wave data. We developed an average velocity model for Kuwait (which we call the KUW1 model) by taking the mean of the S-wave velocity profiles from the three azimuths and simplifying the results into a few layers. For purposes of computing the average, we have reduced the weight of the southern African profile by 2. We then use values of

7 Seismic structure of Kuwait 5 1. Receiver Function Group Velocity (km/s) Observed a=2.5 a= time (s) Observed L Observed R period (s) Figure 5. Receiver function only inversion for events from Japanese backazimuths. Panels are the same as in Fig. 3. Poisson s ratio appropriate for each layer to derive a P-wave profile. The uncertainties of velocities in the model, based on the variance of the depth profiles, are between.5 and.1 km s 1 in the crust and slightly higher in the upper mantle. The KUW1 model is shown in green in Fig. 9 and listed in Table 1. We found a thick sedimentary column which, based on the ages of the main sedimentary deposits, we have grouped into two layers. In the crystalline crust, we have a 17-km thick upper crust (P-wave velocity = 5.89 km s 1, S-wave velocity = 3.4 km s 1 ), a 9-km thick middle crust (P-wave velocity = 6.41 km s 1, S-wave velocity = 3.7 km s 1 ) and an 11-km thick lower crust (P-wave velocity = 6.95 km s 1, S-wave velocity = 3.9 km s 1 ). We find a total crustal thickness of 45 km, with an uncertainty of about 2 4 km. Directly under the Moho discontinuity, the model has upper-mantle velocities of 7.84 and 4.4 km s 1. We have tested assumptions about Poisson s ratio by using crustal multiples to estimate the local Vp/Vs ratio in the crust (Zhu & Kanamori 2). A limitation of this method is that it assumes a single value for this parameter throughout the crust. While this may be a good approximation in some regions, it is probably not appropriate in areas where a significant portion of the crust is composed of sediments, which tend to have higher Vp/Vs ratios than crystalline crust. In any case, using this method results in an estimated Vp/Vs Depth (km) Station KBD Backaz~53deg (Japan) slowness=.5 s/km p=.1 smoothness=1. Starting Model Vp (km/s) ratio of about 1.82, indicating a Poisson s ratio higher than we assigned for the crust and lower than we assigned for the sediments, but approximately equal to the average Poisson s ratio of the total crustal column. 4 MODEL COMPARISON AND VALIDATION 4.1 Model comparisons We compared our results to other profiles for Kuwait and the Arabian platform (Fig. 9 and Table 2). The first two simply consider the sedimentary profile. The Laske sediment model (magenta lines) has three layers for the region with thicknesses of.93, 4. and 2.35 km, and P-wave velocities of 2.53, 4.2 and 5. km s 1, respectively. Drilling has shown about 6 km of sedimentary rocks having ages ranging from Triassic to Pleistocene, including 1.5 km of Jurassic sediment, 3.3 km of Cretaceous sediment and 1.3 km of Tertiary sediment (Bou-Rabee 2). Other models consider the whole crustal profile. The first is that taken from the CRUST2. model (Laske et al. 21), shown in black. CRUST 2. classifies this region as DA, a platform model with 6-km thick sediments. The region has a crustal thickness of

8 6 M. E. Pasyanos et al. Receiver Functions Only smoothness=1. p= Depth (km) WENA - starting model Indonesia Japan South Africa Velocity (km/s) Figure 6. A comparison of P-wave velocity profiles from receiver function only inversions for events from Indonesia (orange), the Japanese Islands (magenta) and south of Africa (cyan). The starting model is shown in black. 41 km with average crustal velocities of 5.88 and 3.28 km s 1 for P and S, respectively. The mantle below the Moho has velocities of 8.2 and 4.7 km s 1. The next profile (red lines) is taken from Bou- Rabee (2) and is derived from CRUST5.1 (Mooney et al. 1998). Here, the sediment thickness is 7 km, the crustal thickness is 4 km and the upper mantle has velocities of 8. and 4.49 km s 1. The AP model (Rodgers et al. 1999) is an average model for Arabian platform (4-km sediment layer) and was derived from complete regional waveform modelling paths from the Zagros Mountains recorded in central Arabia. It is plotted on the figure in cyan. The final model (shown in blue) that we examine is the profile from the WENA model (Pasyanos et al. 24), which was also the starting model that we used in our inversions. This area falls in Region 7 (Eastern Arabian platform) of the WENA model, which has a base model of 11 km of sediments and a 42-km thick crust, but both the sediment thickness and crustal thickness of the final profile were thinned from the base model. The sediment profile from this model is similar to the Laske sediment model, as it was used in the construction of the model. Upper-mantle P- and S-wave velocities are 8.1 and 4.58 km s 1, respectively. In general, the upper-mantle velocities that we find in KUW1 are slower than these other studies and that derived from a Pn tomography study of the Middle East (Al-Lazki et al. 24). In this last study, they find upper-mantle P-wave velocities of about 8. km s 1 for this region, although it is in a region that is poorly resolved. In comparison to profiles of the Arabian Peninsula to the southwest, Kuwait has thicker sediments, comparable crustal velocities, thicker crust and slower upper-mantle velocities. Crustal velocities below the sedimentary cover are also consistent with global averages for stable platforms. The Arabian Shield is typically found to have a crustal thickness of 4 km with increasing thickness to the northeast heading into the Arabian platform (Johnson 1998; Al-Damegh et al. 25). Upper-mantle velocities increase moving to the northeast away from the Red Sea, but likely decrease near the Zagros Mts, as mantle velocities are generally slower under active tectonic zones (Mooney et al. 1998; Pasyanos & Walter 22). Of course, it is well established that sediment cover increases significantly over this profile (Kaplan et al. 1985; Seber et al. 1997). It appears, then, that our results from Kuwait are consistent with previous reports of crustal structure and the overall tectonic trends of the region. 4.2 Regional traveltimes While the SWGV and RF data are reasonably well fit by the KUW1 model, we would like to test the model with an independent data set. Regional phase traveltimes provide such a data set, as they are

9 Seismic structure of Kuwait 7 1. Receiver Function Group Velocity (km/s) Observed a=2.5 a= time (s) period (s) Observed L Observed R Depth (km) Station KBD Backaz~53deg (Japan) slowness=.5 s/km p=.5 smoothness=1. Starting Model Vp (km/s) Figure 7. Joint receiver function and surface wave inversion for events from Japanese backazimuths. An influence parameter of p =.5 was used to equally weigh the receiver function and surface wave data sets. Panels are the same as in Figs 3 and 5. sensitive to the crustal thickness and the P- and S-wave velocities of the crust and uppermost mantle. We used the TauP toolkit (Crotwell et al. 1999) to calculate traveltime curves for the KUW1 model (Fig. 1). We then used traveltimes from events in the Zagros Mts recorded at KBD to compare traveltime data to the curves. In order to ensure accurate traveltimes, events were limited to those that have met the GT25 criteria (Bondár et al. 24), meaning the event locations are highly likely to be accurate within 25 km. This minimizes uncertainties in traveltimes due to event mislocation. Most Zagros events ranged in distance from to 97 km. Regional phases (Pn, Pg, Sn and Lg) propagate efficiently in the Arabian platform (Al-Damegh et al. 24) and all four phases were often picked on the available seismograms. We also had several local events (which did not meet the GT qualifications, but were within the footprint of the national network) within 1 from KBD, which recorded Pg and Sg. Local events are further discussed in the next section. It appears that the model is successful at predicting traveltimes for both P and S waves at local and near-regional distances, paths which sample the Arabian platform (Fig. 1). The traveltimes calculated for Sg also seem to do a reasonable job of fitting the crustal guided Lg phase. At the farthest regional distances (>5 or so), the model is unable to reliably match the traveltimes, especially the phases Pg and Sn. We do not view this as a problem, as these paths are mostly propagating within the Zagros Mts and should require a velocity model more appropriate for this region. It does appear, however, that a Zagros model should have faster S-wave velocities in the mantle (to match Sn) and even slower P-wave velocities in the crust (to match Pg). 4.3 Local seismicity We also test the validity of the model using local seismicity by comparing the crustal structure with the KUW1 model obtained using SWGV and RF. We used the arrival times from KNSN recordings of nearly 15 local events located by KISR using the Kuwait model of Bou-Rabee (2) discussed earlier. The quality of any seismic bulletin depends on the accuracy of the seismic velocity model used to determine the hypocentres. Neglecting the coupling between the velocity models and hypocentral locations causes systematic errors. In order to relocate the earthquakes and independently determine a velocity model for the region, we used a technique that employs a damped least squares method to invert for both event

10 8 M. E. Pasyanos et al. Joint RF+SW smoothness=1. p= Depth (km) WENA - starting model Indonesia Japan South Africa Velocity (km/s) Figure 8. A comparison of P-wave velocity profiles from the joint receiver function and surface wave inversions. Colours are the same as for Fig. 6. hypocentres and crustal velocity parameters (Kissling 1988). Factors that also impact the accuracy of the earthquake location process are the data quality and the accuracy of analyst arrival times (picks). Consequently, we first repicked the arrivals and compared them with the KNSN-reported picks. In general we found the KNSN arrival times were accurate, however, as a result of repicking we were able to include more P and S wave arrivals for emergent or poorer signal-to-noise ratio arrivals after trying different filters. In total, we have 634 P-wave picks and 435 S-wave picks from 137 events. We used the VELEST program (Kissling et al. 1995) to simultaneously solve for the hypocentres and velocity model. This algorithm finds the combination of event hypocentres and velocity model that is consistent with the P- and S-wave arrival times at the recording stations. Due to the smaller number, poorer signal-to-noise ratio and larger uncertainties of the S-wave picks, we obtain a seismic model by inverting for a P-wave model and assuming a Vp/Vs ratio of The search for a minimum 1-D model starts with a trial-and-error process using a wide range of initial velocity models. The goal of this procedure is to first define the range of possible solutions and second to find the most appropriate solution for the available data. The dashed lines in Fig. 11 show several models determined using various initial models. In some cases, unrealistically high or low starting velocity profiles were used. For example, the dashed black line is the final model produced from the initial model shown by the dotted black line. The range of models derived from different starting models is indicative of the uncertainties of the overall velocity profile. The best 1-D model is shown in purple. We found that the velocity model is robustly determined in the depth range of 8 2 km but that the resolution is poorly defined outside of this range due to the coverage of ray paths connecting events and stations (Fig. 12). Because the available ray paths provide poor coverage in both the sedimentary profile and the lowermost crust, we fixed the uppermost ( 8 km) velocity model and the Moho depth (45 km) to the velocities obtained from SWGV and RF inversion results. It appears, however, that in the depth range where the traveltimes and ray paths have the highest sensitivity, the two models compare extremely well. Station corrections are generally small, ranging from.16 s (at station MIB) to.13 s (at stations QRN and RDF), except at station FKI which has a correction of.76 s. The corrections progress from negative values in the northwest to positive values in the southeast. Earthquake epicentres are shown in Fig. 12, which shows the initial KNSN and relocated epicentres, along with the ray path coverage. The relocation and velocity inversion algorithm resulted in a 75 per cent variance reduction of rms traveltime residuals (to.17 s) and a data variance of.65 s 2. Within the network, we do not observe a major shift in hypocentres after relocating with the improved velocity model. Events are still concentrated near the northern and southern oilfields and show improved clustering, with

11 Comparison of the KUW1 Model Seismic structure of Kuwait Tert. 2 2 Depth (km) KUW1 CRUST2. CRUST5.1 WENA Sediment AP Velocity (km/s) Cret. Jur Velocity (km/s) Figure 9. The KUW1 velocity model and other nearby velocity models. The KUW1 model is shown in green. Other models are as follows: CRUST2. in black, AP model in cyan, WENA model in blue, sediment model in magenta, and the Kuwait model (from CRUST5.1) in red. Arrows to the right show the sedimentary column from Bou-Rabee (2). Table 1. KUW1: a velocity model for Kuwait. Layer Depth P-wave S-wave (km) velocity velocity (km s 1 ) (km s 1 ) Sediments Sediments Upper crust Middle crust Lower crust Upper mantle a tendency to estimate shallower hypocentres. The azimuthal gap of clustered events in the north is larger then the southern cluster, and we obtained larger residuals for those events. For events outside of the network, we observe shifts up to 17 km. 5 CONCLUSIONS In this study, we were able to estimate a reliable crustal structure for Kuwait based on joint inversion of receiver functions and surface wave group velocities following the method of Julià et al. (2). Results indicate that low velocity sediments cap the structure to depths of 8 km and the crust is 45 km thick. Within the crystalline crust, the velocities of the upper crust (8 25 km) are consistent with felsic composition while the velocities of the lower crust (25 45 km) are consistent with more mafic compositions (e.g. Christensen & Mooney 1995). The inferred profile for Kuwait is consistent with global averages for continental platforms (Mooney et al. 1998), however the thick sedimentary cover sets Kuwait apart from global averages. The model is able to fit and independent set of regional P- and S-wave traveltimes. Furthermore, inversion of traveltimes for local earthquakes, including event relocation, independently infers a

12 31 M. E. Pasyanos et al. Table 2. Summary of models for Kuwait and vicinity. Label indicates colour or symbol used in Fig. 9. Velocity columns give both P- and S-wave velocities. Model Label Sediment Average Crustal Upper-mantle thickness crustal velocity thickness velocity (km) (km s 1 ) (km) (km s 1 ) Laske sed. Magenta 7.3 Sed. profile Arrows 6.1 CRUST 2. DA Black / /4.7 Kuwait Red / /4.49 WENA Reg. 7 Blue / /4.58 AP Cyan / /4.55 KUW1 Green / /4.44 Travel Time (s) Distance (deg) Figure 1. Traveltimes of Pn (triangles), Pg (inverted triangles), Sn (diamonds), Sg (squares) and Lg (circles) phases for local events and regional events from the Zagros Mts recorded at station KBD. Traveltime fits to the data from model KUW1 are shown in solid and dashed lines. crustal velocity model that is extremely similar to the model derived from the joint inversion of receiver functions and surface wave group velocities. This paper demonstrates that, even with limited data, we are able to infer reliable crustal structure. Using the joint inversion of RFs and SWGVs by Julià et al. (2), we were able to significantly improve estimates of lithospheric structure in Kuwait and surrounding areas. One reason we were able to so readily capitalize on these data, however, is because of the surface wave analysis performed with existing regional network data. For example, simply making dozens of dispersion measurements at station KBD would not have yielded much information without the larger dataset of measurements that were made for the broader region. By adding these measurements, however, we can improve upon the model directly in the vicinity Pn Pg Sn Sg Lg Depth (km) VELEST fit KNSN KUW1 P- wave Model Velocity (km/s) Figure 11. P-wave velocity results for Kuwait from local seismicity. The blue line is the KUW1 model and the green line is the model originally used to locate events in the region. Dashed lines are outputs of VELEST program using various input models. Based on the range of output model, the velocities between 8 and 2 km are resolvable. The purple line is the final model from the VELEST inversion, which compares favourably to the KUW1 model. of the station. Similarly, receiver functions are a commonly used method for estimating structure from both temporary and permanent seismic deployments. Without the use of dispersion data in a joint inversion, however, the profiles from the receiver functions are often suspect. In short, while we might have a fair understanding of a region from existing data, we can significantly improve upon our understanding from even a single deployed station, as we have here in Kuwait. We further tested the model derived in this manner using local seismicity and VELEST model inversions and events relocations. In comparison to models that we could have derived without the deployment of station KBD, we find thicker sediments, slower crustal velocities, thicker crust and slower upper-mantle velocities. The RFs require a discontinuity near 1 km depth at the base of sedimentary column and the short-period SWGV provide

13 Seismic structure of Kuwait 311 Figure 12. Original (green circles) and relocated (red circles) epicentres. Ray paths between stations (blue triangles) and earthquakes are shown as grey lines. Events are concentrated in the northern and southern oilfields (shown as hatched areas). constraints on the shallow velocities. This model has been shown to be valid both locally (to relocate earthquakes) and regionally (predicting traveltimes). ACKNOWLEDGMENTS We thank analyst Flori Ryall for the phase picks that she made for events recorded at stations in the KNSN. We also thank Chuck Ammon for making his time-domain deconvolution code available, and Jordi Julià for sharing his joint inversion codes. We thank editor Torsten Dahm and two anonymous reviewers for their comments. This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract W-745-ENG-48. This is LLNL contribution UCRL-JRNL REFERENCES Agostinetti, N.P., Lucente, F.P., Selvaggi, G. & Di Bona, M., 22. Crustal structure and Moho geometry beneath the Northern Apennines (Italy), Geophys. Res. Lett., 29, 6 63, doi:1.129/22gl1519. Al-Awadhi, J. & Midzi, V., 21. The seismicity of the Kuwaiti subregion, Seismo. Res. Lett., 72, Al-Damegh, K., Sandvol, E., Al-Lazki, A. & Barazangi, M., 24. Regional seismic wave propagation (Lg and Sn) and Pn attenuation in the Arabian plate and surrounding regions, Geophys. J. Int., 157, , doi:1.1111/j x x. Al-Damegh, K., Sandvol, E. & Barazangi, M., 25. Crustal structure of the Arabian plate: new constraints from the analysis of teleseismic receiver functions, Earth Planet. Sci. Lett., 231, Al-Lazki, A., Sandvol, E., Seber, D., Barazangi, M., Turkelli, N. & Mohammed, R., 24. Pn tomographic imaging of mantle lid velocity and anisotropy at the junction of the Arabian Eurasian and African plates, Geophys. J. Int., 158, , doi:1.1111/j x x. Ammon, C.J., Randall, G.E. & Zandt, G., 199. On the non-uniqueness of receiver function inversions, J. Geophys. Res., 95, Bondár, I., Myers, S.C., Engdahl, E.R. & Bergman, E.A., 24. Epicentral accuracy based on seismic network criteria, Geophys. J. Int., 156, , doi:1.1111/j x x. Bou-Rabee, F., Site selection for the field stations of the Kuwait National Seismic Network, Seism. Res. Lett., 7, Bou-Rabee, F., 2. Seismotectonics and earthquake activity of Kuwait, J. Seism., 4, Christensen, N. & Mooney, W., Seismic velocity structure and composition of the continental crust: a global view, J. Geophys. Res., 11, Crotwell, H.P., Owens, T.J. & Ritsema, J., The TauP Toolkit: flexible seismic travel-time and ray-path utilities, Seism. Res. Lett., 7, DeMets, C., Gordon, R.G., Argus, D.F. & Stein, S., 199. Current plate motions, Geophys. J. Int., 11, Harris, D. et al., Calibration of seismic wave propagation in Kuwait, UCRL-JC , Lawrence Livermore National Laboratory, 21st Annual Seismic Research Symposium, Las Vegas, NV, 1 pp. Herrmann, R.B., Some aspects of bandpass filtering of surface waves, Bull. Seism. Soc. Amer., 63, Johnson, P.R., Tectonic map of Saudi Arabia and adjacent areas, Deputy Ministry for Mineral Resources Technical Report USGS-TR-98-3 (IR-948). Julià, J., Ammon, C.J., Herrmann, R.B. & Correig, A.M., 2. Joint inversion of receiver functions and surface-wave dispersion observations, Geophys. J. Int., 143, Julià, J., Ammon, C.J. & Herrmann, R.B., 23. Lithospheric structure of the Arabian Shield from the joint inversion of receiver functions and surfacewave group velocities, Tectonophysics, 371, 1 21.

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