Love and Rayleigh Wave Tomography of the Qinghai-Tibet Plateau and Surrounding Areas

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1 Pure Appl. Geophys. 167 (21), Ó 29 Birkhäuser Verlag, Basel/Switzerland DOI 1.17/s Pure and Applied Geophysics Love and Rayleigh Wave Tomography of the Qinghai-Tibet Plateau and Surrounding Areas YUN CHEN, 1 JOSÉ BADAL, 2 and JIAFU HU 3 Abstract Surface wave data were initially collected from events of magnitude Ms C 5. and shallow or moderate focal depth occurred between 198 and 22: 713 of them generated Rayleigh waves and 66 Love waves, which were recorded by 13 broadband digital stations in Eurasia and India. Up to 1,525 source-station Rayleigh waveforms and 1,464 Love wave trains have been processed by frequency-time analysis to obtain group velocities. After inverting the path-averaged group times by means of a damped least-squares approach, we have retrieved location-dependent group velocities on a sized grid and constructed Rayleighand Love-wave group velocity maps at periods s. Resolution and covariance matrices and the rms group velocity misfit have been computed in order to check the quality of the results. Afterwards, depth-dependent SV- and SH-wave velocity models of the crust and upper mantle are obtained by inversion of local Rayleigh- and Love-wave group velocities using a differential damped least-squares method. The results provide: (a) Rayleighand Love-wave group velocities at various periods; (b) SV- and SH-wave differential velocity maps at different depths; (c) sharp images of the subducted lithosphere by velocity cross sections along prefixed profiles; (d) regionalized dispersion curves and velocity-depth models related to the main geological formations. The lithospheric root presents a depth that can be substantiated at *14 km (Qiangtang Block) and exceptionally at *18 km in some places (Lhasa Block), and which exhibits laterally varying fast velocity very close to that of some shields that even reaches *4.8 km/s under the northern Lhasa Block and the Qiangtang Block. Slow-velocity anomalies of 7 1% or more beneath southern Tibet and the eastern edge of the Plateau support the idea of a mechanically weak middle-to-lower crust and the existence of crustal flow in Tibet. Key words: Surface waves, group velocity, shear-wave velocity, tomography, Qinghai-Tibet Plateau. 1 State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, 129. Beijing, People s Republic of China. yunchen@mail.iggcas.ac.cn 2 Physics of the Earth, Sciences B, University of Zaragoza, Pedro Cerbuna 12, 59 Zaragoza, Spain. 3 Department of Geophysics, Yunnan University, 6591 Kunming, People s Republic of China. 1. Introduction Intermediate-period surface waves constitute part of the response of the crust-mantle structure to the excitation produced by seismic events. These relatively long waves are useful for constraining the physics and geometry of the layers at crustal and mantle depths (OLIVER, 1962; DZIEWONSKI, 1971; KNOPOFF, 1972) as they transport the greatest part of the seismic energy, and key information about the elastic and inelastic properties of the Earth s crust and mantle (RITZWOLLER and LEVSHIN, 1998; RITZWOLLER et al., 1998, 21; XU et al., 2; YANG et al., 24). The surface waves observed on records of earthquakes are predominantly fundamental mode Rayleigh- and Love-waves and their group velocities along their respective great-circle propagation paths are mainly controlled by variations in the velocity structure of the crust and mantle. Therefore, in view of the dispersive character of the elastic medium, a detailed group velocity dispersion analysis of the waves recorded from earthquakes leads to a good resolution for depth-layered properties, and may be of great interest for the determination of regional seismic velocity structure and exploration of the contrasting geology between neighbouring geological formations (CORCHETE et al., 1995; BADAL et al., 1996; BONNER and HERRIN, 1999; MARTÍNEZ et al., 2; FREDERIKSEN et al., 21; MISHRA et al., 25; MITRA et al., 26). Surface wave velocity tomography (YANOVSKAYA et al., 2; HUANG et al., 23; YANOVSKAYA and KOZHEVNIKOV, 23) is a useful tool for this purpose and for monitoring nuclear explosions with discriminants (PASYANOS and WALTER, 21).

2 1172 Y. Chen et al. Pure Appl. Geophys. There have been earlier attempts with surfacewave dispersion to determine laterally varying seismic velocity models of the crust and upper mantle beneath the Eurasian continent (PATTON, 198; FENG and TENG, 1983; SONG et al., 1991, 1993; WU and LEVSHIN, 1994; WU et al., 1997) and the Qinghai- Tibet Plateau (GUPTA and NARAIN, 1967; CHUN and YOSHII, 1977; YAO et al., 1983; BRANDON and ROMA- NOWICZ, 1986;CHUN and MCEVILLY, 1986;ZHOU et al., 1991; BOURIOT and ROMANOWICZ, 1992; ZHUANG et al., 1992; CURTIS and WOODHOUSE, 1997). In most of these studies only Rayleigh- or Love-wave dispersion was used and the resolving power was limited because of the sparsely distributed stations, the poor path coverage and the low quality of the analogue data. However, the integration of Love- and Rayleighwave data sets can provide more information that cannot be obtained from Love- or Rayleigh-wave dispersion measurements alone, and at the same time to place tighter constraints on possible models. In addition, the installation of digital broadband seismograph networks in China and adjacent regions has contributed to substantial improvement of both the path coverage and the data quality (HUANG et al., 23). Consequently, we are in a position to carry out an accurate determination of surface wave group velocity dispersion of the registered waveforms travelling across the Qinghai-Tibet Plateau and surrounding areas. Theoretically, the group- and phase-velocity dispersion curves must give generally the same information about the earth model. However, the inversion of surface wave group velocities leads to a better resolution of the layer shear-wave velocity and thickness than the inversion of phase velocities (KNOPOFF, 1972; KNOPOFF and CHANG, 1977). In order to obtain the three-dimensional SV- and SH-wave velocity structure of the crust and upper mantle beneath the Qinghai-Tibet Plateau and surroundings and then to address some of the outstanding questions on structure and tectonics of the probed region, we follow the conventional two-step method used by HUANG et al. (23) and YANOVSKAYA and KOZHEVNI- KOV (23). Fundamental-mode Rayleigh- and Lovewave group velocities between 1 and 15 s are first obtained through 2-D inversion for then inverting pure-path dispersion curves at each grid node to get 3-D SV- and SH-wave velocity models of the study region. Three notes characterize our working scheme: (a) we analyze relatively short source-station seismic trajectories which really sample the Plateau; (b) we carry out a detailed quantitative evaluation on possible impacts of event location and origin-time errors; (c) we obtain SV- and SH-wave velocity models simultaneously from the inversion of the same data set to avoid any incompatibility. 2. Brief Tectonic Setting The Qinghai-Tibet Plateau is largely in Western China, occupying almost one-fourth of the mainland. Undoubtedly, it is an outstanding product of the most spectacular and youngest case of continental collision on Earth, and both the Plateau and its neighbouring regions are ideal places for the study of the geological evolution of continent continent collision orogen (YIN and HARRISON, 2). The main body of the Qinghai-Tibet Plateau resulted from convergence and collision between the Indian and Eurasian plates over the past 7 5 Ma and consists of several tectonic blocks, namely from south to north: Himalayan, Lhasa, Qiangtang, Songpan-Ganzi and Kunlun-Qaidam Blocks. There are at least four suture lines among them: Indus-Yalu Suture, Bangong-Nujiang Suture, Jinsha Suture and Kunlun Suture, equally from south to north (YIN and HARRISON, 2; SHER- RINGTON and ZANDT, 24). The neighbouring areas are (clockwise) India, Pamir Plateau and Hindu Kush Mts., Tarim Basin, Qaidam Basin, Sino-Korean Craton, Yangtze Craton, and Burma (ZHANG et al., 1984; YANG et al., 24., LI et al., 28). Figure 1 shows a situation map of the Qinghai-Tibet Plateau and adjacent areas where the mentioned blocks and sutures can be seen. The Tibetan Plateau, between the Kunlun Mts. and the Himalayas, consists of tectonic blocks successively accreted to Eurasia. The Songpan-Ganzi Block in the northernmost part accreted to the Kunlun Block along the Ayimaqin-Kunlun-Mutztagh Suture (AKMS) during the Late Permian. Moving forward in time, the Qiangtang Block did as much again to the Songpan-Ganzi Block along the Jinsha Suture during the Late Triassic or Early Jurassic, the Lhasa Block to

3 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1173 Tien Shan Figure 1 Setting map of the Qinghai-Tibet Plateau and surrounding areas. The rectangular portion outlined by a short-dash line closes the study area. Tectonic lines depicted after YIN and HARRISON (2). Key to symbols: MBT Main Boundary Thrust, IYS Indus-Yalu Suture, BNS Bangong- Nujiang Suture, JS Jinsha Suture, AKMS Ayimaqin-Kunlun-Mutztagh Suture, HB Himalayan Block, LB Lhasa Block, QB Qiangtang Block, SB Songpan-Ganzi Block, KB Kunlun-Qaidam Block. Transects A A (83 E), B B (91 E), and C C (99 E) cross western, central, and eastern Tibet, respectively; profiles D D (33 N) and E E (31 N) cross the Qiangtang and Lhasa Blocks, respectively the Qiangtang Block along the Bangong-Nujiang Suture during the Late Jurassic, and finally the Indian Peninsula to the Lhasa Block along the Indus-Yalu Suture during the Middle Eocene. The Kunlun, Qiangtang and Lhasa Blocks are all underlain by Precambrian continental crust at least a billion years old (DEWEY et al., 1988). The Qiangtang and Lhasa Blocks came from the Gondwana land. Substantial southward ophiliolite subduction occurred across the Lhasa Block from the Bangong-Nujiang Suture during the Late Jurassic and from the Indus-Yalu Suture in the Late Cretaceous and Early Palaeocene. Palaeomagnetic data suggest successive wide Palaeotethyan oceans during the Late Palaeozoic and Early Mesozoic and a Neotethys that was at least 6, km wide during the mid-cretaceous (DEWEY et al., 1988; YIN and HARRISON, 2). The thickening of the Tibetan crust to almost double of the normal thickness occurred by northward migration, north south shortening and vertical stretching in the course of the Middle Eocene to the Early Miocene indentation of Asia by India (ZENG et al. 1996). Neogene strata are almost flat-lying and the rest conform upon Palaeogene or older strata. Since the Early Miocene, the northward movement of India has been accommodated principally by north south shortening in the north and south of Tibet. From the Early Pliocene to the present, the Qinghai- Tibet Plateau has risen by about 2 km and suffered east west extension. Little, if any, of the India-Eurasia convergence has been accommodated by eastward lateral extrusion (ZHANG and KLEMPERER, 25) Instrumentation 3. Data Acquisition The study area (22 N 44 N, 7 E 16 E) is monitored by different networks and the surface-

4 1174 Y. Chen et al. Pure Appl. Geophys. wave data originate from 13 broadband digital stations: 6 belonging to the China Digital Seismograph Network (CDSN) or later to the IRIS China Digital Seismograph Network (ICDSN), 4 to the Global Seismograph Network (GSN), 2 to the GEOSCOPE Network and 1 to the Seismic Research Observatory (SRO), all of them in Eurasia and India. Some of these stations, such as KMI, LSA and WMQ, can be classed as CDSN stations at earlier time, but now they are classed as ICDSN stations. The GSN station XAN is now an ICDSN station too. Unfortunately, two stations, SHIO and GAR, were in operation for only a few years (until October 1985 and May 1992, respectively) and neither of them is recording at present Events and Seismograms We picked up 713 events for Rayleigh waves and 66 events for Love waves during the period January 198 to December 22. These Asian earthquakes registered by the installed arrays have magnitudes Ms C 5. and focal depths mainly \1 km, and all of them generated teleseismic surface waves in the broad period range 1 15 s. The focal parameters, in particular origin times used to measure dispersion relations, were extracted from the Harvard centroid-moment-tensor (CMT) catalogue, the CDSN catalogue and the International Research Institute of Seismology (IRIS) databases (which include several catalogues such asfinger,qed,whdf,mhdf,isccd).most of the seismic events, around 94%, have magnitude between 5. and 6.. The focal depth of approximately 8% of the events is less than 5 km; 1 12% of them are 5 1 km deep, and 7 11% deeper than 1 km. Generally, deep earthquakes do not generate surface waves and for this reason they were discarded at first. However, some events with deep focus were used to improve the dispersion measurements at some periods, especially at the longest ones, though they represent only a small part of the earthquake set. Considering the density of the array and distribution of the seismic stations, we collected up to 1,525 source-station Rayleigh waveforms and 1,464 source-station Love wave trains propagating along great-circle paths, with a high signal-to-noise ratio at most intermediate periods (1 7 s) Path Coverage A great number of raypaths sample the test area thus minimizing the impact of diffraction-related errors and undesirable contributions coming from outside velocity anomalies. Figure 2 shows the raypath coverage and the hit-counts computed from sized grid-cells for 5 s Love and Rayleigh waves across the study area. In both cases the pathlength ranges between 8 and 4, km, and the average is about 2, km. We have a dense and more or less even coverage with waves travelling in all azimuths sampling the curved front of the junction between India and Tibet and a variety of physiographic regions: Tien Shan, Tarim Basin, Western Himalayan Syntaxes and Eastern Himalayan Syntaxes. For Rayleigh waves the density of raypaths covering the Qinghai-Tibet Plateau ranges between 12 and 25 paths per cell, whereas for Love waves the path density becomes 1 25 paths per cell. For a period of 5 s, for example, the average density reaches 146 and 125 paths per cell, respectively. According to the previous density values, we can say that the region of our interest, the Qinghai-Tibet Plateau, is well illuminated by surface waves from the Main Boundary Thrust (MBT in Fig. 1). All the Love and Rayleigh paths go across the five tectonic provinces that form the main body of the Qinghai- Tibet Plateau, the 74 76% of their respective lengths falling into the Plateau. This high percentage guarantees that all the physiographic regions of our interest are sampled enough. Furthermore, according to the respective segment lengths in percentage of total path, all the tectonic units are equally probed by the waves, even though this percentage decreases slightly for the two tectonic blocks (Himalayan, Lhasa) in the southernmost part of the Plateau Data Pre-processing Wave periods shorter than 5 s (high frequencies) are scattered by shallow heterogeneity associated with complex structure and topography and do not take a significant role, and therefore were all removed

5 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1175 b a d c Figure 2 Ray coverage at 5 s for Rayleigh (a) and Love (c) waves propagating along great-circle paths (straight lines) across the study area. Solid triangles and full circles mark geographical locations of stations and seismic events, respectively. On the right, hit-counts computed from the trajectories travelled by the waves that supply a measure of the Rayleigh (b) and Love (d) path density, which is defined as the number of rays intersecting each cell (low-pass filtered) from the seismograms for noise reduction. In principle, within the available bandwidth, fundamental mode Love- and Rayleigh-waves sample well the crust and uppermost mantle. Both the vertical component and the two horizontal components of the Rayleigh and Love motion were corrected for instrument response and their respective traces reduced to ground motion. The horizontal N S and E W components of every record were, respectively, rotated to radial (along the great-circle path) and transverse (perpendicular to the great-circle path) motion. 4. Dispersion Analysis To minimize possible errors due to higher mode interference, scattering coda and noise, which would hamper the selection of clear dispersion curves, we use moving-window analysis on each seismic signal

6 1176 Y. Chen et al. Pure Appl. Geophys. to measure with least possible bias the dispersion of every wave train. We obtain the Rayleigh-wave group-velocity dispersion curve by applying a narrow bandwidth Gaussian filter to the ground-motion vertical component corrected for station response over many different periods (HERRMANN, 1973; PASYANOS and WALTER, 21). Similarly, we obtain Love dispersion measurements by applying the same filter to the data rotated and converted into transverse motion (RAYKOVA and NIKOLOVA, 23). To carry out a frequency-time analysis of the fundamental mode Love and Rayleigh waves and so obtain group velocity from the dispersed wave trains, we have used interactive software elaborated by HERRMANN (22). Examples of Love and Rayleigh waves generated by an event with the epicenter at latitude N and longitude E, recorded at LSA station after triggering at 2:37:12 GMT on 3 November 1993 (IRIS network), can be seen in Fig. 3. In this illustration, dispersion results for these 1 15 s bandpass filtered signals are also shown. The relative energy contour maps obtained by multiple filtering of the sample wave trains permit determination of the respective dispersion curves, as the group velocity of the fundamental mode is that associated with the observed maximum spectral amplitudes. A simple computer routine is used to locate all these spectral amplitudes whereby the corresponding group times that give us the observed group velocity dispersion can be extracted. The periods range generally between 1 and 15 s, although we after present group velocity tomographic images at periods of s. At the highest frequencies the models may be somewhat biased by propagation away from the great-circle path and possibly by mode-coupling in violation of the assumptions of the tomographic inversion. With respect to the longest periods it is worthy of note that, although the spectral analysis sometimes includes periods of 15 s or more, we restrict ourselves to shorter periods due to the gradual decrease of the number of paths travelled by the waves Clustering In further analysis we sort and then average measurements that share approximately the same source-to-receiver path in a cluster. In order to minimize data redundancy and off-great-circle propagation effects, those paths with nearby azimuth are considered to be similar if their starting and ending points both lie within 1.5% of the path length. For example, for an event 2, km distant from LZH station, we would cluster dispersion measurements from LZH station that lie within about 3 km from one other. The Rayleigh path coverage at intermediate periods is greater than the coverage for Love waves because the noise is stronger on the horizontal component records than on the vertical component records (VUAN et al., 25). After clustering, the number of Rayleigh paths is about 1,5 for periods under 7 s, which is slightly greater than the number of Love paths that is approximately 1,45; however, for periods above 7 s the number of Rayleigh paths is roughly 1,25, which is less than the number of Love paths that is 1,3 approximately. Clustering is useful to estimate the frequency-dependent standard deviation of the measurements within each cluster and to estimate the frequency-dependent standard deviation of the entire data set (RITZWOLLER, et al., 1998; VUAN et al., 25). In all cases the velocity values exhibit a similar amount of scatter and are inside a narrow standard deviation band that does not exceed.2 km/s. We hardly observe a rise in the uncertainties for the longest periods Impacts of Event Location and Origin-time Errors Our dispersion measurements are not free of source uncertainties, certainly. The use of short paths has both an advantage, smaller diffraction-related errors, and disadvantages: larger impacts of event location and origin-time errors. A statistical valuation of the uncertainties in origin time, focal depth and epicentral distance, gives error bounds of ±3.5 s, ±25 and ±1 km, respectively. The error in origin time (3.5 s) incorporates the error in the source time function; trying out an obviously high group velocity of km/s and an average path length of 2, km, the variation in surface wave group velocity would be of the order of km/s at worst. Furthermore, this type of error should be less than the one owing to a distance error of about

7 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1177 a b b Figure 3 Two examples of Rayleigh- and Love-wave group velocity measurements: a 1 15 s band-pass filtered seismic signal corresponding to the vertical-component Rayleigh wavetrain generated by an earthquake located at latitude N and longitude E, and recorded at LSA station after triggering at 2:37:12 GMT on 3 November 1993 (IRIS network); b relative energy contour map as derived from the spectral amplitude-display obtained by the multiple filter technique; c filtered seismic signal corresponding to the transverse-component Love wavetrain generated by the same event; d analogous frequency-time diagram for the Love seismogram. The largest amplitudes of the envelope for each period are automatically picked within the normalized contours and plotted as continuous black lines on both energy maps, thus giving the Rayleigh- and Love-wave group-velocity dispersion curves km. However, the influence of the source finiteness and time function is rather small according to the magnitude of the selected seismic events. Intending to estimate the impacts of event location errors we have conducted a test by synthetic seismograms computed from a model earthquake and PREM model, from which we have obtained theoretical dispersion in the 1 15 s c d period interval. An event of magnitude Mw 5.3, epicentral distance 2,14 km and depth 35 km (the observed average values of source parameters) was taken as a model earthquake, and synthetic seismograms were computed by modal summation (HERRMANN, 22) after fixing other needed parameters such as the number of surface-wave-modes (NMOD), the strike of the fault plane (STK), the dip of the fault plane (DIP), the slip angle on the fault plane (RAKE), and the source-to-station azimuth (AZ), whose respective values were 1, 45, 45, 45, 45. The synthetic Rayleigh- and Love-wave group velocity curves due to uncertainties extending to ±2 km in epicentral distance and ±25 km in focal depth (as suggested by statistical valuation of the data), can be seen in Fig. 4. The four continuous lines in Fig. 4a, b, d, and e represent the dispersion curves obtained from the model earthquake. The remaining curves were generated considering smaller and greater distances and depths at a constant step of ±5 km. The dash lines define a standard deviation whose variation can be seen in Fig. 4c, and f. When we try out distinct epicentral distances the standard deviation does not exceed ±.28 km/s for Rayleigh and Love waves. In the case of varying focal depth the error bound does not exceed ±.15 km/s for Love waves and more or less twice this value, ±.3 km/s, for Rayleigh waves (even though a rise of ±.5 km/s in uncertainty is observed around a period of 2 s). In any case the statistical margin of error due to source mislocation is therefore very small.

8 dgroup velocity (km/s) egroup velocity (km/s) 1178 Y. Chen et al. Pure Appl. Geophys. a Group velocity (km/s) Rayleigh Epicentral perturbation of ±2 km b Group velocity (km/s) Love Epicentral perturbation of ±2 km c Standard error (km/s) Rayleigh Love 1 Period (s) 1 1 Period (s) Period (s) Rayleigh Love f Standard error (km/s) Rayleigh Love 2.4 Depth perturbation of ±25 km 2.8 Depth perturbation of ±25 km. 1 Period (s) 1 1 Period (s) Period (s) Figure 4 An event of magnitude Mw 5.3, epicentral distance 2,14 km and depth 35 km, just the observed average values of source parameters, was taken as a model earthquake. Theoretical dispersion induced by source mislocation: synthetic Rayleigh (a) and Love (b) group velocity curves due to uncertainty up to ±2 km in epicentral distance; synthetic Rayleigh (d) and Love (e) group velocity curves due to uncertainty reaching ±25 km in focal depth. The four continuous lines represent the dispersion curves obtained from the model earthquake. The remaining curves (dash lines) were generated considering smaller and greater distances and depths at a constant step of ±5 km. On the right, standard deviations that in the case of varying epicentral distance do not exceed ±.28 km/s for Rayleigh and Love waves (c), and in the case of varying focal depth are ±.15 km/s for Love waves and ±.5 km/s for Rayleigh waves (f) 5. Group Velocity Tomography We assume that the surface waves propagate in an isotropic earth as rays whose paths follow a great circle from the source to the receiver and sample the elastic properties of the medium down to mantle depths. Starting from an initial velocity model, we intend to estimate linear velocity corrections from a finite set of travel-time data referred to seismic trajectories crossing the region under study. The average group times corresponding to the epicenter-station paths travelled by the waves, are in our case the data to be taken into account. To estimate the lateral variation of the group velocity, the sampling region was gridded and the slowness on each grid cell was determined by a damped least-squares approach (AKI and RICHARDS, 198; DIMRI, 1992; JACOBSEN et al., 1996). Thus, we have obtained Love- and Rayleighwave group-velocity tomographic images of the Qinghai-Tibet Plateau and nearby areas, which provide an efficient mean to visualize the velocity changes both laterally and with depth. The resulting velocity variations at crustal and mantle depths are lastly analyzed at different periods and contrasted with the average dispersion concerning different tectonic units belonging to the widely probed continental domain Implementation As reference group velocity for a given period we took the average velocity calculated as the mean of all the velocity values determined for that period. The best damping factor for implementation is determined from the trade-off between resolution and covariance. That is, the optimum damping factor is that one with higher

9 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1179 resolution and lower covariance simultaneously. The trade-off between resolution and covariance is plotted in Fig. 5 for different damping. We have chosen r 2 = 3. as the optimum value for damping, in which case the covariance does not exceed.3 km 2 /s 4 and the resolution is close to.5. As the biggest error for travel times never exceeds 5 s, then the error made in the determination of period-dependent group-velocity models would be less than.9 km/s (sqrt(25*.3)). We start with the initial model and at each step of inversion the initially assumed velocity pattern is updated, and the variance between observed and calculated group velocity is computed to check the convergence of the process. The procedure is repeated until the difference between the new velocity values at grid nodes and the velocity values computed in the previous step converges to a minimum (less than 1.%) Assessment Theoretically, several factors may affect the results, among them we distinguish either systematic errors, as for instance instrumental errors, earthquake mislocations, or the error due to the origin time of the seismic event that incorporates the influence of the source finiteness and time function (LEVSHIN et al., 1999), or propagation errors and noise associated with the seismic signal, such as multipathing, modal contamination and azimuthal anisotropy (BADAL et al., 1992). Assuming that some of these factors (uncertainties in event location and origin-time) lead to rather small errors which can therefore be discarded, others have been either corrected (for instrument response) or properly minimized (by digital filtering and clustering). As for anisotropy, VDOVIN et al.(1999) observed anisotropy in South America only for longer period waves that are sensitive to this factor in the upper mantle. PILIDOU et al. (24) have shown that there can be a 1 2% trade-off between azimuthal anisotropy and S-wave speed heterogeneity in the North Atlantic. The role of crustal anisotropy in Tibet has been reported by several researchers who have found clear evidence for group-velocity radial anisotropy in the lower crust (LAVÉ et al. 1996;HUANG et al., 2, 24; OZACAR and ZANDT, 24; SHAPIRO et al., 24;LEV et al., 26). In a prolongation of this study we will discuss the role of radial anisotropy, which is a property required by the dispersion of horizontally polarized Love waves and vertically polarized Rayleigh waves, and is closely related to deformation and flow in the crust and topmost mantle (CHEN et al., 29) Checkerboard Tests In any case, smearing effects derived from insufficient ray crossings or consequence of the grid-cell size may be present. In order to check a correct partition of the explored area and to assess resolution Average Resolution Amplitude Rayleigh Average Resolution Amplitude Love x Average Covariance (km /s ) x Average Covariance (km /s ) Figure 5 Trade-off between covariance and resolution for inversion of Rayleigh and Love group times

10 118 Y. Chen et al. Pure Appl. Geophys. and smearing along raypaths, we follow a traditional checkerboard test, namely, the inversion of synthetic data with the same path distribution as the real data from an input model consisting of a regular pattern of alternating velocity perturbations, so that the similarity between the output inversion model and the input model is then taken as an estimate of the resolution power (YANOVSKAYA et al., 2;PASYANOS and WALTER, 21; PILIDOU et al., 24). We have tried several checkerboard tests with different grid spacing and computed theoretical travel times for all paths from the input models with alternating velocity perturbations. Here in Fig. 6 we only display the recovered models with the inversion grid spacing (*49,2 km 2 ) for 5 and 95 s Rayleigh and Love waves. The trial velocity perturbations for Rayleigh wave are ±7 and ±6% of the mean velocities of 3.13 and 3.63 km/s at periods of 5 and 95 s, respectively, whereas the trial velocities for Love wave are ±5% of the mean velocities 3.37 and 3.81 km/s at the same periods. These reference velocities were taken from real group velocity data. The limits of the velocity scale shown in Fig. 6 are all consistent with the minimum and maximum velocity of the input models. In all cases the synthetic travel times were then inverted using the inversion method described above and damping factor 3.. These configurations allow seeing in which direction smearing operates and prove that both the geometry of the junction between India and Tibet and the amplitudes are enough well resolved in most of the tested area, except on the Indian Plate (SW quadrant) and the Sino-Korean Craton (NE corner), as expected, as these two regions are just the poorly covered zones by raypaths (Fig. 2). However, LÉVEQUE et al. (1993) have indicated that this intuitive interpretation of checkerboard tests is dangerous and the resolution of fine features of the synthetic model does not necessarily imply equally good resolution of coarser features. The results of checkerboard tests primarily reflect the path coverage and do not account for noisy data, errors in earthquake source parameters or deviation from underlying assumptions of great-circle propagation and lack of mode coupling (PILIDOU et al., 24). Nevertheless, the checkerboard tests shown in Fig. 6 give an impression about the resolution achieved by the path geometry in conjunction with the final model smoothing. Regardless, we have taken an inversion grid spacing of , which since our viewpoint is the smallest grid spacing that would be acceptable considering a representative long wave of 245 km (3.5 km/s 9 7 s). A real *25-km-wide anomaly would be well resolved by tomographic inversion (with the exception of the mentioned zones), and therefore the true resolution that can be claimed for the study region is more like 25 km, although there might be some areas which are better resolved yet Resolution and Covariance Matrices Both the kernels of the resolution matrix, which express the exactness of the solution at a target grid node, and those of the covariance matrix, which give the error affecting the estimated solution, are calculated as usual (ZHAO, 21). To illustrate the quality of our group velocity patterns, we have computed these two matrices. In this context, each target grid node leads to a resolution/covariance representation in which the absolute maximum of any of these two functions should occur just at that node. Figure 7 displays 3-D mesh plots showing the best-fitting cones obtained from the resolution and covariance matrices calculated for 5 s Rayleigh wave at the grid node at north latitude 35 and east longitude 97 on the discretized area The kernels computed for other target nodes, periods and waves are of similar quality and imply a good agreement between the actual and calculated solutions for any wavelength Variance Reduction and rms Group Velocity Misfit The big or less fit to the dispersion curves produced by group velocity mapping can be expressed as two different measures of misfit (RITZWOLLER et al., 1998). One is the variance reduction relative to an earth model, for instance PREM model: variance reduction ¼ 1 ½R i ðui obs R i ðui obs U Þ 2 Š U pred i Þ 2 = where i is the path index, U i obs is the measured group velocity for path i, U i pred is the predicted group velocity for path i through the estimated group velocity map,

11 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1181 a b c d Figure 6 Checkerboard tests performed for surface waves crossing the study area. The represented model is gridded by sized cells varying in velocity by either ±7% of the mean velocity 3.13 km/s for 5 s Rayleigh waves (a), or ±6% of the velocity 3.63 km/s for 95 s Rayleigh waves (b). Analogously, these values become ±5% of 3.37 and 3.81 km/s for 5 and 95 s Love waves (c, d), respectively. Synthetic travel times were computed from these models and then inverted using the same grid spacing of The limits of the velocity scale are all consistent with the minimum and maximum velocity established for the input models. In any case the recovered group velocities suggest a good resolution in most of the tested area and U is the reference group velocity from PREM. The other measure of misfit is the rms group-velocity residual: rms misfit ¼½ð1=NÞ R i ðu obs i U pred i Þ 2 Š 1=2 ; where N is the number of measurements. Figure 8 shows these two measures of misfit to Rayleigh- and Love-wave group velocity. Variance reduction is similar in the 1 6 s range and practically is in excess of 95% over the useful period interval for Love and Rayleigh waves, even though progressive variance degradation, particularly in the case of Rayleigh waves, is observed at 6 s and beyond. At short periods, Fig. 8 shows a low misfit between.6 and.8 km/s, but also a relatively smaller variance reduction. At first glance, both facts in combination suggest that the initial reference model already provides a reasonable fit. Nevertheless, more appealing to us than this conjecture, the variance reduction at the shortest periods may be attributed to shallow structures, where weathered sedimentary basins with low seismic velocities and accreted blocks along suture lines are present. Off-pure-path propagation, which likewise can be important at short periods (LEVSHIN et al., 1994), event mislocation and wrongly reported origin times are other likely factors that also may contribute to variance degradation of group velocity. The rms misfit function takes very small values and changes similarly for Love and Rayleigh waves over the useful period interval, which is an excellent

12 1182 Y. Chen et al. Pure Appl. Geophys. a b 2 4 km /s Figure 7 3-D mesh plots showing the best-fitting cones obtained from the resolution (a) and covariance (b) matrices calculated for 5 s Rayleigh wave at the grid node at latitude 35 N and longitude 97 E. The plots for Love wave are very similar and therefore omitted result supporting the quality of the group velocity dispersion maps. Despite small increases from.6 km/s up to.16 km/s at periods gradually longer than 3 s, the misfit function remains under.16 km/ s for any period belonging to the considered interval. Regarding small changes related to long periods, a certain correlation between the progressive variance degradation (Fig. 8, upper part) and the increase in rms misfit (Fig. 8, lower part) is observed, albeit the variance reduction for Rayleigh waves degrades more rapidly than the rms residual passing from 97% (6 s) to 9% (95 s). This fact is unrelated to earthquake origin time and location uncertainties nor with an unappreciable low signal-to-noise ratio of the data, however it is probably caused by the smaller number of analyzed paths (from 6 s and beyond) that is a common bias to both types of waves. variance reduction(%) rms group velocity misfit (km/s) Rayleigh Love Rayleigh Love Period (s) Group Velocity Images It is common practice to provide tomographic images in terms of differential velocities with respect to an average value calculated as the mean of the velocities obtained for a given period. The physiographic characteristics of the crust and upper mantle based on seismic velocity anomalies may be more clearly featured by maps of Rayleigh- and Lovegroup-velocity percentages (% du/u) at various intermediate periods from 1.4 to 15. s. Figure 9 displays some of these maps of positive and negative anomalies which correlate with high and low group velocities and significant lateral velocity variations, thus revealing the complexities of the crust Figure 8 Two measures of misfit to Rayleigh- (solid line) and Love-wave (dashed line) group velocity: Variance reduction relative to PREM model and rms group velocity misfit and uppermost mantle beneath the Qinghai-Tibet Plateau At Crustal Depths Surface waves become more sensitive to sedimentary basins at periods shorter than 2 s for Rayleigh waves and 3 s for Love waves, and to the crustal thickness and the average crustal velocity at intermediate periods, 3 6 s for Rayleigh waves and 5 7 s for Love waves. For Rayleigh waves of period B15 s

13 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1183 Figure 9 Qinghai-Tibet Plateau: mapping of differential Rayleigh and Love group velocity percentages (% du/u) at the periods of reference. In all cases the reference speed (in km/s) is given in the bottom left-hand corner of each plot. Red color indicates relatively slow seismic velocity and blue color fast velocity

14 1184 Y. Chen et al. Pure Appl. Geophys. Figure 9 continued

15 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1185 and Love waves of period B2 s we can see Tarim Basin as the most notable area of relatively low velocities and negative anomalies (up to 8 9%) as a consequence of a powerful sedimentary structure. Other smaller zones located westward just in the confluence of suture lines, also characterized by low velocities and negative anomalies, can be seen emerging at shorter periods than 2 3 s for Rayleigh waves and periods from 3 to 4 s for Love waves. For 3 6 s Rayleigh waves there is a clear band of low velocities along the Western Himalayan Syntaxes, Qiangtang and Lhasa Blocks and part of the Eastern Himalayan Syntaxes. For 5 7 s Love waves we meet negative anomalies associated largely with the main body of the Qinghai- Tibet Plateau north of the Indus-Yalu Suture. In these regions of thick crust the group velocities are sensitive to the slow crustal velocities opposite to the fast mantle velocities of the outlying regions (PASYANOS and WALTER, 21) At Mantle Depths From 6 s Rayleigh waves and 7 s Love waves, we mostly are starting to sample the upper mantle. Although the difference of Rayleigh wave group velocity among different tectonic units becomes less at longer periods, negative differential velocity percentages permit clear distinction of the outlined Qinghai-Tibet Plateau. The low-velocity structural feature is more notable with Love waves than with Rayleigh waves. In contrast, the group velocities beneath the Indian Plate and Tarim Basin are higher than in other areas for both types of waves. 6. Inversion for Shear-Wave Velocity Structure 6.1. Method and Procedure Local group-velocity dispersion curves on each grid cell covering the model region are inverted for SV- and SH-wave velocity profiles. We approximate the distribution of the elastic constants in the crust and upper mantle by a finite number of planehorizontal, homogeneous layers overlying a halfspace: four 5-km-thick layers at shallow depths, six 1-km-thick layers, seven 2-km-thick layers, one 3-km-thick layer, and one 5-km-thick layer to 3 km depth. The number of parameters or degrees of freedom specifying the initial discrete earth model is reduced using the relationship a = [2(1 - r)/ (1-2r)] 1/2 b between compressional wave velocity (a) and shear-wave velocity (b), where r is Poisson s ratio. We have considered the approximation r *.25 giving the relation a * 1.73b. A connection is also made between density (q) and compressional wave velocity (a) via the simple approximate relationship q = 1.7?.2*a, which is a trade-off relationship between the Birch s law and the Nafe and Drake s empirical relationship (LUDWIG et al., 197). We parameterize the initial model by layer thickness and shear velocity from the PREM model, and perform an iteratively differential damped least-squares inversion until the shear velocity-depth model converges to a stable solution (HERRMANN, 22). To start the computation a same SV-wave velocity model is used for all the nodes belonging to a same tectonic unit; afterwards, the final (inverted) SV-wave velocity models are used as initial models to obtain SH-wave velocity structure. The inversion of Rayleigh and Love velocities is performed with damping factor and the convergence of the process is achieved with the model standard deviations less than.1 km/s after about ten successive iterations in the first case and about six in the second one. Figure 1 shows examples of period-dependent surface-wave speed at a grid point of latitude 83 N and longitude 29 E located on the Himalayan Cordillera, together with the respective shear-wave velocity models obtained by inversion. The differences between both velocity profiles can be clearly seen and also the excellent coincidence between measured and synthetic dispersion. The Love Rayleigh discrepancy in shear velocity at relatively shallow depths B45 km is small, less than 1.7%, in comparison with what takes place at deeper depths wherein it reaches larger values around 4.5 5%, due in part to the strength of anisotropy in the Moho transition zone and uppermost mantle (NISHIMURA and FORSYTH, 1989; RITZWOLLER and LEVSHIN, 1998; YANOVSKAYA et al., 1998, 2).

16 1186 Y. Chen et al. Pure Appl. Geophys. Group Velocity (km/s) a b R-measured dispersion R-synthetic dispersion L-measured dispersion L-synthetic dispersion Period (s) 1 Depth (km) Anisotropy cofficient (%) R model L model PREM model Velocity (km/s) Figure 1 Examples of Love- and Rayleigh-wave group-velocity dispersion curves (a) and shear-wave velocity profiles (b) obtained by inversion at latitude 83 N and longitude 29 E. Both observed (circles) and theoretical (continuous lines) dispersion (a) computed by forward modelling from the inverted velocity-depth models (b) are also plotted. The starting PREM model (dash line) also has been included. The shading horizontal bars superimposed to the SV- and SH-wave velocity models display the strength of VTI anisotropy estimated from the Love Rayleigh discrepancy. The values displayed are percentage anisotropy, defined as 2(v SH - v SV )/(v SH? v SV ) 6.2. Tests of Reliability It is known that the seismic velocity structure obtained by inversion of surface wave dispersion data may be affected by lack of uniqueness and poor resolution owing to nonlinearity, especially when a single surface-wave mode is used, and smoothing introduced by theoretical approaches (HUANG et al., 23; YANOVSKAYA and KOZHEVNIKOV, 23). However, the following criteria were adopted to assess these drawbacks which are inherent to any inversion process: Variance Reduction At each step of inversion the S-velocity structure was updated and the variance between the observed and calculated group velocity was computed to check the convergence of the process. The model with the least variance was considered as a representative model at the target grid node. In Fig. 1a we compare the observed Love/Rayleigh group-velocity dispersion and the theoretical dispersion computed by forward modelling from the final velocity model, and we can see an excellent agreement between both curves Resolving Kernels The kernels (rows) of the resolution matrix are usually calculated at various depths and reference grid points and plotted to illustrate the quality of the solution, since they are a measure of the exactness achieved when the solution is calculated at a target grid node. We show in Fig. 11 the resolving kernels for the SV- and SH-wave velocity models plotted in Fig. 1. The sharper the peak of the kernel with respect to the baseline at each depth, the better the agreement between final model and actual model, and hence the greater the reliability of the inversion result. As can be seen, the depth circa 22 km that is resolvable by Rayleigh waves is slightly greater than

17 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1187 Layer Depth (km) a Layer Depth (km) b Layer Depth (km) Layer Depth (km) Figure 11 Resolving kernels for the SV-wave (a) and SH-wave (b) velocity models shown in Fig. 1 the depth of 17 km to which the Love waves allow the identification of features of the models. Below these values degradation of the resolution is expected because the number and quality of measurements decrease at longer periods, especially for Love waves. 7. SV- and SH-Wave Velocity Images Local group-velocity dispersion curves on each grid cell covering the model region are inverted using Herrmann s codes (HERRMANN, 22) for 1-D depthdependent SV- and SH-wave velocity profiles, which are then smoothed laterally across cells using the continuous curvature surface gridding algorithm provided by Generic Mapping Tools (GMT) software (WESSEL and SMITH, 1995, 1998) to obtain the 3-D velocity images. The 3-D structure is basically displayed by: (a) SV- and SH-wave differential velocity maps at various depths to 19 km due to the reliable depth range for Love and Rayleigh waves; (b) velocity cross sections along three north south profiles A A (83 E), B B (91 E), C C (99 E) across western, central and eastern Tibet, respectively, and two west east profiles D D (33 N) and E E (31 N) across the Qiangtang Block and the Lhasa Block (Fig. 1). The tomographic images can be seen in Figs. 12 and Horizontal Slices At shallow depth of 8 km the patterns displayed by the differential velocity maps confirm two major sedimentary areas of negative anomaly (low velocity): Tarim Basin (8%) and Burma Arc (5 7%); although the Rayleigh map reveals anomalies of the

18 1188 Y. Chen et al. Pure Appl. Geophys. %dv/ v %dv/ v %dv/ v %dv/ v %dv/ v %dv/ v Figure 12 SV- and SH-wave velocity anomaly maps in terms of relative velocity perturbation at the indicated depths. In all cases the reference speed (in km/s) is given in the bottom left-hand corner of each plot. The color scale is sometimes slightly different in order to emphasize the details on each image. Red color indicates relatively slow seismic velocity and blue color fast velocity

19 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1189 %dv/ v %dv/ v %dv/ v %dv/ v %dv/ v %dv/ v Figure 12 continued

20 119 Y. Chen et al. Pure Appl. Geophys. IYS BNS JS Altyn Fault Elevation (m) Depth (km) Depth (km) A MBT Lhasa Block A' Himalayan Block Qiangtang Block Tien Shan Indian Plate Tarim Basin (a) (b) Latitude (Degree) Shear wave velocity, km/s Depth (km) Depth (km) Elevation (m) 6 B 3 Indian Plate AKMS Altyn Fault MBT Himalayan Block Lhasa Block Qiangtang Block B' Latitude (Degree) (a) (b) Shear wave velocity, km/s 4.8 Figure 13 Shear-wave velocity sections obtained from Rayleigh-wave (a) and Love-wave (b) group velocity along the N S transects and W E profiles indicated in Fig. 1. A thick line represents the Moho discontinuity and it is assumed to coincide with the velocity isoline of 4. km/s for both SV- and SH-wave velocity patterns. The grey scale is always the same for any section to a better interpretation of the results. In all cases the topographic relief, the names of the crossed physiographic regions and the boundaries intersected by the profiles have been included (top). The topographic data were taken from the GTOPO3 digital elevation model (DEM) data set (U.S. GEOLOGICAL SURVEY, 1993)

21 Vol. 167, (21) Surface Wave Tomography of the Qinghai-Tibet Plateau 1191 JS AKMS Elevation (m) Depth (km) Depth (km) 6 C Qiangtang Block Songpan-Ganzi Block C' Qilian Mounts. 3 North China Latitude (Degree) (a) (b) Shear wave velocity, km/s 4.8 Elevation (m) Depth (km) Depth (km) D 6 Indian Plate MBT Himalayan Block IYS BNS JS Lhasa Block Qiangtang Block Songpan-Ganzi Block Longitude (Degree) D' (a) (b) Shear wave velocity, km/s 4.8 Figure 13 continued

22 1192 Y. Chen et al. Pure Appl. Geophys. Elevation (m) Depth (km) Depth (km) E Indian Plate MBT IYS BNS JS Himalayan Block Lhasa Block Longitude (Degree) Qiangtang Block Longmen Shan Thrust E' (a) (b) Shear wave velocity, km/s 4.8 Figure 13 continued same sign in Songpan-Ganzi (5 7%), west of Qiangtang (*5%) and Lhasa (6 7%). From the named zones the remaining anomalies are slightly positive or zero, with the exceptions of Pamir and Hindu Kush that display a comparatively strong negative velocity anomaly (CURTIS and WOODHOUSE, 1997;JEMBERIE and MITCHELL, 24). Lhasa and Qiangtang Blocks and Eastern Himalayan Syntaxes are the regions that clearly support slow-velocity (negative) anomalies at depths of km and depict the strength of the continental collision between India and Eurasia. At greater depth levels (*13 km and beyond) the maps change completely and the velocity patterns become very complex, however the large-scale structures occupying the central part of the images with more or less enveloping contours exhibit small velocity anomalies of contrary sign. This picture seems to persist down to 19 km which is yet a well-resolved depth, for disappearing at deeper mantle depths, albeit a remarkable slow-velocity anomaly (nearly 3 4%) beneath the North Indian Platform seems to be the predominant feature in the models. PRIESTLEY et al. (26) have presented a new 3-D SV-wave speed model for the upper mantle of Eastern Asia from multimode surface waveform tomography. Except for some details, the main features of the horizontal depth cross sections are fairly consistent with the results given by these authors. We find low velocities at shallow depths, although relatively fast with respect to that reference model at deeper mantle depths beneath Tibet. Also, these authors report slow speed at 75 1 km and fast speed at 15 2 km. However, in our maps low velocities extend throughout the crust and uppermost mantle (18 11 km), while fast wave speed exists at depths exceeding 13 km. These minor differences may result from the different data set used by PRIESTLEY et al. (26, 28), who assumed a known crustal structure and inverted for upper mantle structure only Vertical Sections In all SV- and SH-wave velocity sections the topographic relief (taken from the GTOPO3 digital elevation model, U.S. GEOLOGICAL SURVEY, 1993), the names of the crossed physiographic regions and the boundaries intersected by the five profiles (Fig. 1),