Rupture model of Mw 7.8 2015 Gorkha, Nepal earthquake: Constraints from GPS measurements of coseismic offsets

Yadav, Rajeev Kumar; Roy, Prasanta; Gupta, Sandeep Kumar; Khan, Prosanta Kumar; Catherine, J. K.; Prajapati, Sanjay K.; Kumar, Amit; Puviarasan, N.; Bhu, Harsh; Devachandra, M.; Malik, Javed N.; Kundu, Bhaskar; Debbarma, Chandrani; Gahalaut, Vineet K.

doi:10.1016/j.jseaes.2016.04.015

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…According to some previous researchers, geophysical studies have detected lateral heterogeneity (vary the recurrence interval and intensity of seismic activity of high magnitude) and asperity zones in the Northwest Himalayan region. These asperity zones are identified as a zone of anomaly that considerably dismayed the crustal deformation (Srivastava and Mitra et al, 1994;Ponraj et al, 2011;Yadav et al, 2017;Dal Zilio et al, 2020). A long-duration seismic catalogue shows shallow depth seismic deformation at Higher Himalaya, while the lateral change in seismicity rate infers probable variability in MHT locking width.…”

Section: Discussionmentioning

confidence: 99%

Estimation of crustal deformation parameters and strain build-up in Northwest Himalaya using GNSS data measurements

Yadav

Kannaujiya

Ray

et al. 2021

Contrib. Geophys. Geod.

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GPS measurements have proved extremely useful in quantifying strain accumulation rate and assessing seismic hazard in a region. Continuous GPS measurements provide estimates of secular motion used to understand the earthquake and other geodynamic processes. GNSS stations extending from the South of India to the Higher Himalayan region have been used to quantify the strain build-up rate in Central India and the Himalayan region to assess the seismic hazard potential in this realm. Velocity solution has been determined after the application of Markov noise estimated from GPS time series data. The recorded GPS data are processed along with the closest International GNSS stations data for estimation of daily basis precise positioning. The baseline method has been used for the estimation of the linear strain rate between the two stations. Whereas the principal strain axes, maximum shear strain, rotation rate, and crustal shortening rate has been calculated through the site velocity using an independent approach; least-square inversion approach-based triangulation method. The strain rate analysis estimated by the triangulation approach exhibits a mean value of extension rate of 26.08 nano-strain/yr towards N131°, the compression rate of –25.38 nano-strain/yr towards N41°, maximum shear strain rate of 51.47 nano-strain/yr, dilation of –37.57 nano-strain/yr and rotation rate of 0.7°/Ma towards anti-clockwise. The computed strain rate from the Baseline method and the Triangulation method reports an extensive compression rate that gradually increases from the Indo-Gangetic Plain in South to Higher Himalaya in North. The slip deficit rate between India and Eurasia Plate in Kumaun Garhwal Himalaya has been computed as 18±1.5 mm/yr based on elastic dislocation theory. Thus, in this study, present-day surface deformation rate and interseismic strain accumulation rate in the Himalayan region and the Central Indian region have been estimated for seismic hazard analysis using continuous GPS measurements.

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Section: Discussionmentioning

confidence: 99%

Estimation of crustal deformation parameters and strain build-up in Northwest Himalaya using GNSS data measurements

Yadav

Kannaujiya

Ray

et al. 2021

Contrib. Geophys. Geod.

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“…One major advantage of our full Bayesian model exploration is that we don't need to impose such types of a priori regularization. The 2015 Gorkha mainshock has been extensively studied with seismic and geodetic data (Wang and Fialko, 2015;Feng et al, 2015;Yagi and Okuwaki, 2015;Yi et al, 2017;Liu and Yao, 2018 Galetzka et al (2015) and Yadav et al (2017). One Sentinel-1 ascending frame was used, collected by the European Space Agency and processed by .…”

Section: Bayesian Samplingmentioning

confidence: 99%

“…Supplementary material for Impact of topography on earthquake static slip inversions In this study, we use the static GPS offsets processed and provided by Galetzka et al (2015) and Yadav et al (2017).…”

Section: Perspectivesmentioning

confidence: 99%

Impact of topography on earthquake static slip estimates

et al. 2020

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Our understanding of earthquakes is limited by our knowledge, and our description, of the physics of the Earth. When solving for subsurface fault slip, it is common practice to assume minimum complexity for the Earth's characteristics such as topography, fault geometry and elastic properties. These characteristics are difficult to include in simulations and our knowledge of them is incomplete, leading many to believe that there is minimal advantage to be gained by accounting for these effects. However, 3D structure can be easily included with the newly-developed software package SPECFEM-X, and topography and bathymetry can be measured directly and are well known all over the Earth's surface. Accounting for topography thus seems to be an efficient strategy for incorporating accurate and extensive information into the inverse problem. Here, we explore the impact of topography on static slip estimates, with a particular focus on how topography may impact slip resolution on the fault. We also wonder if the influence of topography can be accounted for by simply using a receiver elevation correction. To this end, we analyze the 2015 M w 7.5 Gorkha, Nepal, and the 2010 M w 8.8 Maule, Chile, earthquakes within a Bayesian framework. The regions affected by these events represent two different types of topography. Chile, which contains both the Peru-Chile trench and the Andes mountains, has a greater elevation range and steeper gradients than Nepal, where the primary topographic feature is the Himalayan mountain range. Additionally, the slip of the continental Nepal event is well constrained, whereas observations are less informative in a subduction context. We show that topography has a non-negligible impact on inferred slip models. Our results suggest that the effect of topography on slip estimates increases with two main factors: poor observational constraints and high elevation gradients. In particular, we find that accounting for topography improves slip resolution where topographic gradients are large and the data is less informative: at depths for the Gorkha event, and near the trench for the Maule

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“…7). Several GPS sites in Nepal and southern China showed coseismic offset caused by the earthquake 12 . Four GPS sites in the Indian region, immediately to the south of the rupture, showed significant coseismic horizontal offsets ranging between 3 and 7 mm towards north 12 .…”

mentioning

confidence: 99%

“…Several GPS sites in Nepal and southern China showed coseismic offset caused by the earthquake 12 . Four GPS sites in the Indian region, immediately to the south of the rupture, showed significant coseismic horizontal offsets ranging between 3 and 7 mm towards north 12 . The aftershock distribution, including several seismic events of >M w 5.5, extended east of the hypocentre of the mainshock, and a M w 7.3 aftershock occurred in Kodari region, Central Nepal, approximately 150 km east of the hypocentre of the main shock (Figure 1).…”

mentioning

confidence: 99%

The 12 May 2015 Kodari Earthquake (M<sub>w</sub> 7.3) in Central Nepal:Delayed Triggering by the 25 April 2015 Gorkha Earthquake (M<sub>w</sub> 7.8)

2018

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The 12 May 2015 earthquake of M w 7.3 occurred in the Kodari region, Central Nepal, 17 days after the 25 April 2015 Gorkha earthquake (M w 7.8) along the Himalayan plate boundary. Both the earthquakes were associated with predominantly thrust faulting on the Main Himalayan Thrust (MHT). This is the largest aftershock of the 2015 Gorkha earthquake which occurred approximately 150 km east of it. Our analysis suggests that the 2015 Gorkha earthquake significantly increased the Coulomb stress on the shallow unruptured and updip part of the MHT, further west of the 2015 rupture and also in the hypocentre region of 12 May 2015 M w 7.3 aftershock. In the following 17 days period, Coulomb failure stress increased further by the relaxation of coseismic pore pressure on the eastern side of its coseismic rupture, where the 12 May 2015 aftershock had occurred.

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Rupture model of Mw 7.8 2015 Gorkha, Nepal earthquake: Constraints from GPS measurements of coseismic offsets

Cited by 17 publications

References 16 publications

Estimation of crustal deformation parameters and strain build-up in Northwest Himalaya using GNSS data measurements

Estimation of crustal deformation parameters and strain build-up in Northwest Himalaya using GNSS data measurements

Impact of topography on earthquake static slip estimates

The 12 May 2015 Kodari Earthquake (M<sub>w</sub> 7.3) in Central Nepal:Delayed Triggering by the 25 April 2015 Gorkha Earthquake (M<sub>w</sub> 7.8)

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