- Open Access
Joint inversion of teleseismic, geodetic, and near-field waveform datasets for rupture process of the 2015 Gorkha, Nepal, earthquake
© Kobayashi et al. 2016
- Received: 31 October 2015
- Accepted: 11 April 2016
- Published: 26 April 2016
The 2015 Gorkha earthquake and its aftershocks caused severe damage mostly in Nepal, while countries around the Himalayan region were warned for decades about large Himalayan earthquakes and the seismic vulnerability of these countries. However, the magnitude of the Gorkha earthquake was smaller than those of historical earthquakes in Nepal, and the most severe damage occurred in the north and northeast of Kathmandu. We explore reasons for these unexpected features by performing a joint source inversion of teleseismic, geodetic, and near-field waveform datasets to investigate the rupture process. Results indicate that the source fault was limited to the northern part of central Nepal and did not reach the Main Frontal Thrust. The zone of large slip was located in the north of Kathmandu, and the fault rupture propagated eastward with an almost constant velocity. Changes in the Coulomb failure function (ΔCFF) due to the Gorkha earthquake were computed, indicating that southern and western regions neighboring the source fault are potential source regions for future earthquakes related to the Gorkha earthquake. These two regions may correspond to the historical earthquakes of 1866 and 1344. Possible future earthquakes in the regions are predicted, and the warning for Himalayan seismic hazards remains high even after the Gorkha earthquake.
- Rupture process
- Gorkha earthquake
- Coulomb failure function
Three types of datasets are available for this investigation: (1) teleseismic dataset obtained from the Global Seismographic Network (Fig. 2c) through the Data Management Center of the Incorporated Research Institutions for Seismology, (2) ground deformation dataset obtained at GPS stations (Fig. 2a) through the UNAVCO Data Center and from the InSAR image processed by Lindsey et al. (2015), and (3) dataset of near-field waveforms of strong motion (Takai et al. 2016) and high-rate GPS (Galetzka et al. 2015) stations (Fig. 2a). We used 37 vertical components of a teleseismic P wave, 12 horizontal components of six GPS stations and four vertical components of four GPS stations, line-of-sight deformations of 21 points from the InSAR image, and 18 components of six strong motion and high-rate GPS stations. We downsampled the InSAR image while keeping rough characteristics with intervals of 0.2°, a larger interval than the discretization of our fault model. We then selected points whose absolute line-of-sight deformations were greater than 10 cm. We did not use near-field waveforms of TVU, PTN, THM, and NAST because they are located on soft sediment and their waveforms, especially their horizontal components, are largely affected by sedimentary layers. Moreover, a detailed velocity structure of the Kathmandu Valley was not established.
First, the source fault was defined based on the hypocenter distribution of the main shock and aftershocks from the USGS data, as shown by the blue rectangle in Fig. 1. Strike and dip angles were set to 293° and 7°, the same as the Global CMT solution. The fact that the source fault region is limited to the northern part of central Nepal was inferred as the main reason for the smaller M w of the Gorkha earthquake in comparison with historical earthquakes, such as the M w 8.2 1505 Lo Mustang earthquake and the M w 8.1 1934 Bihar–Nepal earthquake (Ambraseys and Douglas 2004). Subsequently, a joint inversion of the abovementioned three datasets was performed to determine the rupture process of the Gorkha earthquake.
For this purpose, we used the multi-time-window linear inversion method with nonnegative least squares and smoothness constraints (Yoshida et al. 1996; Hikima and Koketsu 2005). We divided a fault into 18 × 11 subfaults with a size of 10 km in length and 10 km in width. Six 3-s ramp functions for the fault slip were set every 3 s for each subfault. The optimal number of time windows and their duration were determined by a trial-and-error approach. Rake angles were varied between 90° ± 45°. We determined the rupture front velocity, which controls the timing of the first time window to be 3.3 km/s, because this value minimized the teleseismic and near-field waveform variance. The weight of spatiotemporal smoothness constraints was determined by minimizing the Akaike’s Bayesian information criterion (Akaike 1980). The weights of all data points were set equal after flattening square values of each data point. Teleseismic, geodetic, and near-field waveform Green’s functions were computed by the methods of Kikuchi and Kanamori (2003), Zhu and Rivera (2002), and Kohketsu (1985), respectively. One-dimensional velocity structure model of CRUST 2.0 (Bassin et al. 2000) was used for these calculations. In this model, Vs around the source depth was 3.5 km/s. As such, the determined rupture front velocity was approximately 90 % of the local shear wave speed, indicating fast rupture propagation if rupture is primarily triggered in the first time window. In near-field waveform data processing, we integrated acceleration or differentiated displacement waveforms to velocity. In teleseismic data processing, we removed instrument response and integrated to displacement. All waveforms were band-pass-filtered between 0.005 and 0.4 Hz and resampled every 0.5 s. We used previously published geodetic data as it is.
Source process analysis by Grandin et al. (2015) is most similar in terms of datasets. The difference between their and our resulting slip distribution is that over 4 m slip area expanded to about 50 km east of Kathmandu is only seen in Grandin et al. (2015). This slip can be observed in other studies that used InSAR data (Feng et al. 2015; Galetzka et al. 2015; Hayes et al. 2015; Kobayashi et al. 2015; Lindsey et al. 2015; Wang and Fialko 2015); however, Avouac et al. (2015) also used InSAR data, and their resulting slip distribution does not show the slip in question. This is probably because one of the two InSAR images used by Avouac et al. (2015) does not cover the eastern region of Kathmandu. As mentioned previously, the M 6.1 and M w 6.7 aftershocks occurred in the east of Kathmandu. InSAR data are very dense to include local deformation by the aftershocks. Our discretization of InSAR data probably reduced the effects of the aftershocks.
Here, we focus on regions surrounding the source fault because we cannot discuss potential future earthquakes only through ΔCFF. No large earthquake is expected in the northern deeper part because this part is creeping (Mugnier et al. 2013), and the M w 8.1 1934 Bihar–Nepal earthquake (Ambraseys and Douglas 2004) already occurred in the eastern region (Sapkota et al. 2013). Considering that the convergence rate and interseismic coupling between the Indian and Eurasian plates are spatially uniform in Nepal (Ader et al. 2012), remaining southern and western regions (dashed magenta ellipses in Fig. 6) might correspond to large earthquakes listed in the history of earthquakes damaging Kathmandu (Mugnier et al. 2013) and recent earthquakes felt in India (Szeliga et al. 2010). The 1866 earthquake (M 7.2 or 7.6) occurred in the southern region (Khattri 1987; Szeliga et al. 2010), and trenching at the MFT in the western region showed an event in the thirteenth or fourteenth century (Mugnier et al. 2013). These southern and western regions where historical earthquakes in 1866 and 1344 occurred were likely stimulated by the Gorkha earthquake, increasing the possibility of generating a large earthquake. Between the two regions, the calculated maximum ΔCFF of the southern region is one order of magnitude larger than that of the western region. Thus, the southern region was more likely stimulated.
In view of the abovementioned details, three scenarios were considered related to the Gorkha earthquake. First, if the entire western region was ruptured reaching up to the MFT, an M8-class earthquake, such as the 1934 Bihar–Nepal earthquake, would occur in accordance with its area (e.g., Murotani et al. 2008). Second, if the western region was ruptured with a partially similar situation to the Gorkha earthquake, an M7-class earthquake, such as the Gorkha earthquake, would occur. Third, if the southern region was ruptured, an M7-class earthquake, such as the 1866 earthquake, would occur. Galetzka et al. (2015) reported that the Gorkha earthquake was modest over a short period and large over a long period. The abovementioned three scenarios are plausible future earthquakes in this region, but it cannot be predicted if they will have the same features as the Gorkha earthquake.
Our inversion results show that the Gorkha earthquake mainly ruptured a relatively deeper part of the MHT with maximum slip in the north of Kathmandu. The total rupture duration was approximately 60 s, and the rupture propagates at a relatively high speed of approximately 3.0 km/s. The calculated ΔCFF from the Gorkha earthquake suggests three possible scenarios in the southern and western regions for the source fault of this earthquake. Thus, a warning of high Himalayan seismic hazards, which may include the three scenario earthquakes, should be continually issued to Nepal and countries around the Himalayas.
HK conducted the analysis. HK, KK, and HM drafted the manuscript. NT and MS acquired the strong motion data at the Kathmandu Valley and participated in the discussion. MB and SNS participated in the interpretation. All authors read and approved the final manuscript.
We would like to thank Yasuhiro Kumahara and Seiji Tsuno for providing us the data of MFT and the instrumental response of strong motion sensors, respectively. We also thank Martin Mai and two anonymous reviewers for their helpful comments. We used the Generic Mapping Tools (Wessel and Smith 1991) for drawing the figures. This study was supported by the SATREPS program of JST/JICA, the J-RAPID program of JST, and MEXT KAKENHI.
The authors declare that they have no competing interests.
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