Estimation of the source process of the 2015 Gorkha, Nepal, earthquake and simulation of long-period ground motions in the Kathmandu basin using a one-dimensional basin structure model
© Kubo et al. 2016
Received: 30 October 2015
Accepted: 21 January 2016
Published: 5 February 2016
The source rupture process of the 2015 Gorkha, Nepal, earthquake was estimated by the joint kinematic source inversion with near-field waveforms, teleseismic waveforms, and geodetic data. The estimated seismic moment and maximum slip are 7.5 × 1020 Nm (M w 7.9) and 7.3 m, respectively. The total source duration is approximately 50 s. The derived source model has a unilateral rupture toward the east and a large-slip area north of Kathmandu with the maximum slip. Using the estimated source model together with a one-dimensional (1-D) velocity basin structure model, long-period (> 4 s) ground motions were simulated at a site located in the Kathmandu basin, where strong ground motions with predominant components in a 4–5s period were observed during the 2015 Gorkha earthquake. This simulation demonstrated that the major features of the observed waveforms can be reproduced by our source model and the 1-D basin structure model.
KeywordsThe 2015 Gorkha earthquake Source rupture process Long-period ground motions in the Kathmandu basin Joint source inversion Waveform simulation
The 2015 Gorkha earthquake occurred in central Nepal at 11:56 on April 25, 2015, local time (06:11 on April 25, Coordinated Universal Time). The moment magnitude (M w ) estimated from the centroid moment tensor (CMT) inversion by the Global CMT (GCMT) Project was 7.9. Based on the source mechanism and hypocenter, this event was a thrust-type interplate earthquake between the subducting Indian plate and the overriding Eurasian plate. This earthquake caused strong ground motions across Nepal with a maximum seismic intensity of VIII on the modified Mercalli intensity scale. This earthquake and the following aftershocks killed approximately 9000 people and injured more than 23,000 people. It was the worst natural disaster to strike Nepal since the 1934 Nepal-Bihar earthquake.
The source process of the 2015 Gorkha earthquake has been investigated using various datasets such as near-field waveforms, teleseismic waveforms, and geodetic data (e.g., Avouac et al. 2015; Galetzka et al. 2015; Grandin et al. 2015; Kobayashi et al. 2015; Yagi and Okuwaki 2015). In general, the resolution of source inversion with near-field waveforms (strong ground motion data or high-rate Global Positioning System (GPS) data) is spatially and temporally high because the near-field waveforms are expected to contain much information on the detailed source process. However, in the case of the 2015 Gorkha earthquake, the distribution of near-field stations was one-sided against the source region and this distribution is expected to reduce the source-inversion resolution or bias the solution. On the other hand, teleseismic stations are globally distributed and teleseismic data have a good azimuthal coverage, although the source-inversion resolution of teleseismic waveforms is generally lower than that of near-field waveforms (e.g., Yokota et al. 2011). Additional use of geodetic data (static displacements) together with waveform data makes the source inversion more stable (e.g., Wald and Graves 2001). Previous studies have shown that a more reliable source model can be obtained by using combined datasets in the source inversion (e.g., Yoshida and Koketsu 1990; Wald and Heaton 1994; Kubo and Kakehi 2013). In this study, therefore, we developed a reliable source model of the 2015 Gorkha earthquake jointly using near-field waveforms, teleseismic waveforms, and geodetic data.
During the 2015 Gorkha earthquake, strong ground motions with predominant components in a 4–5s period were observed in the Kathmandu basin, and the waveform comparison between rock and basin sites has shown that one cause of the characteristic waveforms is the site effect of the Kathmandu basin (Galetzka et al. 2015; Dhakal et al. 2016). The ground motions are also attributed to be the source effect because the 4–5s period components were predominant in the observed spectra even at the rock site (Fig. 3 in Galetzka et al. 2015). Thus, for understanding the long-period ground motions in the Kathmandu basin during this earthquake, both source and site effects should be considered. In this study, we investigated how well the long-period ground motions can be reproduced by the source model estimated from the source inversion and an available one-dimensional (1-D) underground velocity structure model for the Kathmandu basin. Although the source models of the 2015 Gorkha earthquake have been proposed (e.g., Avouac et al. 2015; Galetzka et al. 2015; Grandin et al. 2015; Kobayashi et al. 2015; Yagi and Okuwaki 2015), the waveform simulation in the Kathmandu basin using the derived source model has never been conducted.
In this study, we first estimated the source process of this event using the kinematic joint earthquake source inversion with near-field waveforms, teleseismic waveforms, and geodetic data. Using the derived source model, we investigated the relationships among fault parameters of the characterized source model of the 2015 Gorkha earthquake and compared them with empirical relationships for interplate earthquakes. Because there were few interplate earthquakes in the Himalayan region observed by the modern seismic observation network, it is important to examine whether the 2015 Gorkha earthquake obeys the previous empirical relationships of fault parameters. Then, we discussed its relationship with the interplate-coupling distribution, seismic activity, and past large events. Finally, using the estimated source model together with the 1-D velocity structure model for the Kathmandu basin, we carried out the waveform simulation of long-period (> 4 s) ground motions at a site located in the Kathmandu basin.
For near-field waveforms, we used three components of 5-Hz GPS waveforms at five stations produced by Galetzka et al. (2015) (Fig. 1a). Although Galetzka et al. (2015) also produced the waveforms at the Nepal Academy of Science and Technology (NAST) station (Fig. 1a), we did not use this data because NAST is located in the Kathmandu basin and its waveform data were expected to be significantly amplified and delayed by the effect of the Kathmandu basin. The observed displacement waveforms were numerically differentiated into velocity in the time domain and were band-pass filtered from 4 to 50 s. The time length of the near-field waveforms is 50–60 s, which depends on the record length at each station (starting 10 s before theoretical S-wave arrival). Green’s functions of near-field waveforms were calculated using the discrete wave number method (Bouchon 1981) and the reflection/transmission matrix method (Kennett and Kerry 1979) assuming a 1-D velocity structure model. The 1-D velocity structure model was constructed based on Monsalve et al. (2006), who developed 1-D velocity structure models in east Nepal and south Tibet to relocate earthquakes in these regions.
For teleseismic waveforms, we used P-wave parts of vertical-component broadband waveforms at 45 stations of the Global Seismograph Network (GSN) (Fig. 1b). The instrumental responses were deconvolved from the original recordings to obtain the ground velocities. The observed velocity waveforms were numerically integrated into displacement in the time domain, were band-pass filtered from 4 to 50 s, and were resampled at 5 Hz. The time length of the teleseismic waveforms is 110 s (starting 10 s before P-wave arrival, which was carefully identified by visual inspection). Green’s functions of teleseismic body waves were calculated using the program package of Kikuchi and Kanamori (2004) with the 1-D source velocity structure model (Monsalve et al. 2006).
For geodetic data, we used three components of static displacements at 12 stations produced by Galetzka et al. (2015) (Fig. 1a). Considering the difference in observation error between horizontal and vertical components, the relative weight of the vertical component against the horizontal component was set to 0.5. For Green’s functions of static displacements, we calculated the theoretical static displacements by a unit slip on each subfault assuming a homogeneous elastic half-space, as proposed in Okada (1992).
Results and discussion
Source model of the 2015 Gorkha earthquake
Fault parameters of the characterized source model
Fault parameters of the characterized source model of the 2015 Gorkha earthquake
Seismic moment (N*m)
Rupture area (km2)
Average slip (m)
Asperity area (km2)
Average slip of asperity (m)
8.1 × 1020
Relationship with interplate-coupling distribution, seismic activity, and past large events
Nepal has been struck by many large earthquakes, such as the 1505 West Nepal earthquake (M w ≈ 8.2), the 1833 Mid-Nepal earthquake (M w ≈ 7.6), and the 1934 Nepal-Bihar earthquake (M w ≈ 8.1). Considering the inferred source regions of these events (e.g., Bilham 1995; Ambraseys and Douglas 2004; Sapkota et al. 2013), the rupture area of the 2015 event seems to overlap the source region of the 1833 event (Fig. 7). Given that the convergence rate in central and eastern Nepal is 17.8 mm/year (Ader et al. 2012) and that this region has been coupled for 182 years at a coupling ratio of 0.8, the accumulated slip deficit at the time of the 2015 event was expected to be approximately 2.6 m. This value is comparable to the estimated average slip of 2.5 m for the 2015 Gorkha earthquake (Table 1), which suggests the possibility that the 2015 event was the reactivation of the preexisting asperity of the 1833 event.
Waveform simulation in the Kathmandu basin
Figure 8b shows the comparison of the observed velocity waveforms at KATNP with the synthetic waveforms produced by the structure models. These waveforms were band-pass filtered from 4 to 50 s. The horizontal components of the observed waveforms have large amplitudes and long durations as compared to the synthetic waveforms produced by the rock model (M-model). This difference is presumably caused by the site amplification of the Kathmandu basin. The horizontal components of the synthetic waveforms produced by the P-model have little effect on the basin amplification and are similar to the synthetic waveforms produced by the M-model, but not to the observed waveforms. On the other hand, the horizontal components of the synthetic waveforms produced by the D-model are significantly affected by the basin amplification and are similar to the observed ones. Thus, in the long-period band (> 4 s), the synthetic waveforms produced by the D-model can reproduce the observation much better than those produced by the P-model. We also found that there is little difference in the vertical component among the synthetic waveforms of the three models and that all synthetic vertical-component waveforms are similar to the observed ones. This means that there was little amplification due to the Kathmandu basin in the vertical component of the long-period band (> 4 s).
Thus, the waveform simulations demonstrated that the overall feature of the observed long-period ground motions at KATNP can be explained by our source model and the basin structure model of Dhakal et al. (2016). However, some discrepancies between the observed and synthetic waveforms still remain. For example, the polarity of the initial phase of the vertical component differs between the observation and synthetics, and this synthetic phase was mainly generated from the relatively large-slip area west-northwest of Kathmandu. This necessitates further investigations of the source model, particularly the slips west-northwest of Kathmandu. In addition, the waveform simulation in this study was conducted at only one station (KATNP). For further understanding of the generation mechanism of strong ground motions in the Kathmandu basin, it is necessary not only to improve the structure model of the Kathmandu basin, including the development of its 3-D model, but also to further develop the strong-motion seismograph network.
We estimated the source model of the 2015 Gorkha earthquake using the joint source inversion with near-field waveforms, teleseismic body waves, and geodetic data. The estimated seismic moment and maximum slip are 7.5 × 1020 Nm (M w 7.9) and 7.3 m, respectively. The derived source model has the unilateral rupture toward the east and a large-slip area north of Kathmandu with the maximum slip. Then, we investigated the relationships among the fault parameters of the characterized source model of the 2015 Gorkha earthquake and found that the fault parameter relationships of this earthquake are consistent with the previous empirical relationships of interplate earthquakes. The comparison of the final-slip distribution of this earthquake with the interplate-coupling distribution, seismic activity, and past large events indicated that the 2015 event could be the reactivation of the preexisting asperity of the 1833 Mid-Nepal earthquake. Using the estimated source model together with the 1-D velocity structure model of the Kathmandu basin, we simulated the long-period (> 4 s) ground motions at KATNP located in the Kathmandu basin. The waveform simulation demonstrated that the major features of the observation can be reproduced by our source model and the 1-D basin structure model of Dhakal et al. (2016).
We thank Prof. Kazuki Koketsu and the anonymous reviewer for their helpful comments. The Department of Mines and Geology, Tribhuvan University, and California Institute of Technology are acknowledged for providing us with high-rate GPS data. Strong-motion data observed by the USGS were gathered from the Center for Engineering Strong Motion Data. Teleseismic data observed by the GSN were collected from the Data Management Center of Incorporated Research Institutions for Seismology. The CMT solution estimated by the GCMT Project and hypocenter information estimated by the NSC were used. Topographic data were obtained from the Geographic Information Network of Alaska. Generic Mapping Tools (Wessel and Smith 1998) were used to draw the figures.
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- Ader T, Avouac JP, Liu-Zeng J, Lyon-Caen H, Bollinger L, Galetzka J, Genrich J, Thomas M, Chanard K, Sapkota SN, Rajaure S, Shrestha P, Ding L, Flouzat M (2012) Convergence rate across the Nepal Himalaya and interseismic coupling on the Main Himalayan Thrust: implications for seismic hazard. J Geophys Res 117(B4):B04403. doi:10.1029/2011JB009071 View ArticleGoogle Scholar
- Ambraseys NN, Douglas J (2004) Magnitude calibration of north Indian earthquakes. Geophys J Int 159(1):165–206. doi:10.1111/j.1365-246X.2004.02323.x View ArticleGoogle Scholar
- Avouac JP, Meng L, Wei S, Wang T, Ampuero JP (2015) Lower edge of locked Main Himalayan Thrust unzipped by the 2015 Gorkha earthquake. Nat Geosci 8:708–711. doi:10.1038/ngeo2518 View ArticleGoogle Scholar
- Bilham R (1995) Location and magnitude of the 1833 Nepal earthquake and its relation to the rupture zones of contiguous great Himalayan earthquakes. Curr Sci 69(2):25Google Scholar
- Bilham R, Larson K, Freymueller J (1997) GPS measurements of present-day convergence across the Nepal Himalaya. Nature 386:61–64. doi:10.1038/386061a0 View ArticleGoogle Scholar
- Bird P (2003) An updated digital model of plate boundaries. Geochem Geophys Geosyst 4(3):1027. doi:10.1029/2001GC000252 View ArticleGoogle Scholar
- Bouchon M (1981) A simple method to calculate Green’s function for elastic layered media. Bull Seismol Soc Am 71(4):959–971Google Scholar
- Dhakal YP, Kubo H, Suzuki W, Kunugi T, Aoi S, Fujiwara H (2016) An analysis of strong ground motion and site amplification at Kantipath, Kathmandu from the 2015 Mw 7.8 Gorkha Earthquake, Nepal and its aftershocks. Earth Planets Space (submitted).Google Scholar
- Galetzka J, Melgar D, Genrich JF, Geng J, Owen S, Lindsey EO, Xu X, Bock Y, Avouac JP, Adhikari LB, Upreti BN, Pratt-Sitaula B, Bhattarai TN, Sitaula BP, Moore A, Hudnut KW, Szeliga W, Normandeau J, Fend M, Flouzat M, Bollinger L, Shrestha P, Koirala B, Gautam U, Bhatterai M, Gupta R, Kandel T, Timsina C, Sapkota SN, Rajaure S, Maharjan N (2015) Slip pulse and resonance of Kathmandu basin during the 2015 Mw 7.8 Gorkha earthquake, Nepal imaged with geodesy. Science 349(6252):1091–1095. doi:10.1126/science.aac6383 View ArticleGoogle Scholar
- Grandin R, Vallée M, Satriano C, Lacassin R, Klinger Y, Simoes M, Bollinger L (2015) Rupture process of the M W = 7.9 2015 Gorkha earthquake (Nepal): insights into Himalayan megathrust segmentation. Geophys Res Lett 42(20):8373–8382. doi:10.1002/2015GL066044 View ArticleGoogle Scholar
- Kennett BLN, Kerry NJ (1979) Seismic waves in a stratified half space. Geophys J R Astr Soc 57:557–583View ArticleGoogle Scholar
- Kikuchi M, Kanamori H (2004) Note on teleseismic body-wave inversion program. http://www.eri.u-tokyo.ac.jp/ETAL/KIKUCHI/. Accessed 28 Jan 2015.
- Kobayashi T, Morishita Y, Yarai H (2015) Detailed crustal deformation and fault rupture of the 2015 Gorkha earthquake, Nepal, revealed from ScanSAR-based interferograms of ALOS-2. Earth Planets Space 67:201. doi:10.1186/s40623-015-0359-z View ArticleGoogle Scholar
- Kubo H, Kakehi Y (2013) Source process of the 2011 Tohoku earthquake estimated from the joint inversion of teleseismic body waves and geodetic data including seafloor observation data: source model with enhanced reliability by using objectively determined inversion settings. Bull Seismol Soc Am 103(2B):1195–1220. doi:10.1785/0120120113 View ArticleGoogle Scholar
- Kubo H, Asano K, Iwata T, Aoi S (2016) Development of fully Bayesian multiple-time-window source inversion. Geophys J Int 204(3):1601–1619 doi:10.1093/gji/ggv540.
- Monsalve G, Sheehan A, Schulte-Pelkum V, Rajaure S, Pandey MR, Wu F (2006) Seismicity and one-dimensional velocity structure of the Himalayan collision zone: earthquakes in the crust and upper mantle. J Geophys Res 111(B10), B10301. doi:10.1029/2005JB004062 View ArticleGoogle Scholar
- Murotani S, Miyake H, Koketsu K (2008a) Scaling of characterized slip models for plate-boundary earthquakes. Earth Planets Space 60:987–991View ArticleGoogle Scholar
- Murotani S, Satake K, Fujii Y (2008b) Scaling relations of seismic moment, rupture area, average slip, and asperity size for M ~ 9 subduction-zone earthquakes. Geophys Res Lett 40(19):5070–5074. doi:10.1002/grl.50976 View ArticleGoogle Scholar
- Okada Y (1992) Internal deformation due to shear and tensile faults in a half-space. Bull Seismol Soc Am 82(2):1018–1040Google Scholar
- Pandey MR (2000), Ground response of Kathmandu valley on the basis of microtremors, Paper. Proceedings of the 12th World Conference on Earthquake Engineering, Auckland, New Zealand, 30 Jan 4 – Feb 2000, Paper No. 2106.Google Scholar
- Pandey MR, Tankudar RP, Avouac JP, Lavé J, Massot JP (1995) Interseismic strain accumulation on the Himalayan crustal ramp (Nepal). Geophys Res Lett 22(7):751–754View ArticleGoogle Scholar
- Pandey MR, Tankudar RP, Avouac JP, Vergne J, Héritier T (1999) Seismotectonics of the Nepal Himalaya from a local seismic network. J Asian Earth Sci 17:703–712View ArticleGoogle Scholar
- Sapkota SN, Bollinger L, Klinger Y, Tapponnier P, Gaudemer Y, Tiwari D (2013) Primary surface ruptures of the great Himalayan earthquakes in 1934 and 1255. Nat Geosci 6:71–76. doi:10.1038/ngeo1720 View ArticleGoogle Scholar
- Somerville PG, Irikura K, Graves R, Sawada S, Wald D, Abrahamson N, Iwasaki Y, Kagawa T, Smith N, Kowada A (1999) Characterizing crustal earthquake slip models for the prediction of strong ground motion. Seismol Res Lett 70(1):59–80View ArticleGoogle Scholar
- Wald DJ, Heaton TH (1994) Spatial and temporal distribution of slip for the 1992 Landers, California, earthquake. Bull Seismol Soc Am 84(3):668–691Google Scholar
- Wald DJ, Graves RW (2001) Resolution analysis of finite fault source inversion using one- and three-dimensional Green’s functions 2. Combining seismic and geodetic data. J Geophys Res 106(B5):8767–8788. doi:10.1029/2000JB900435 View ArticleGoogle Scholar
- Wessel P, Smith WHF (1998) New, improved version of Generic Mapping Tools released. EOS Trans Am geophys Un 79:579View ArticleGoogle Scholar
- Yagi Y, Okuwaki R (2015) Integrated seismic source model of the 2015 Gorkha, Nepal, earthquake. Geophys Res Lett 42(15):6229–6235. doi:10.1002/2015GL064995 View ArticleGoogle Scholar
- Yokota Y, Koketsu K, Fujii Y, Satake K, Sakai S, Shinohara M, Kanazawa T (2011) Joint inversion of strong motion, teleseismic, geodetic, and tsunami datasets for the rupture process of the 2011 Tohoku earthquake. Geophys Res Lett 38(7):L00G21. doi:10.1029/2011GL050098 View ArticleGoogle Scholar
- Yoshida S, Koketsu K (1990) Simultaneous inversion of waveform and geodetic data for the rupture process of the 1984 Naganoken-Seibu, Japan, earthquake. Geophys J Int 103(2):355–362View ArticleGoogle Scholar