This electronic supplement contains an additional description of the modeling techniques; tables of ScanSAR- and Swath-mode observations from ALOS-2, StripMap-mode observations, and fault-model geometries; and figures of W-phase results, inversion result sensitivity to geometry of the causative fault plane, earthquake relocations, PAGER fatality estimates versus media reports with time, line-of-sight displacements of resampled interferograms, population density of the Himalaya Front, an ALOS-2 path 048 Wide Swath interferogram, and a photo of a collapsed concrete building.
Here we expand upon the modeling techniques used in this study and summarized briefly in the main text. We also provide further background information regarding the discussion of historical ruptures along the Himalayan Front, which allows us to place the 2015 sequence into a seismotectonic context.
We use bayesloc (Myers et al., 2007, 2009), a multiple-event relocation algorithm formulated on Bayesian statistics, to relocate instrumentally recorded earthquakes from the Nepal region between 1964 and 2015 (Fig. S1). The bayesloc algorithm places probabilistic constraints on hypocenter locations, travel-time model corrections, measurement precision, and phase labeling and is therefore well suited to relocating large earthquake catalogs. Our catalog is comprised of 2246 M 3.5+ earthquakes over the region 79.5°–88.5° E, 24°–33° N, and contains a combination of arrival time data from the reviewed International Seismological Centre bulletin (http://www.isc.ac.uk, last accessed July 2015), including phase picks for historical earthquakes contributed by the Government of Nepal Department of Mines and Geology (DMG) National Seismic Center, the Advanced National Seismic System Comprehensive Catalog (http://earthquake.usgs.gov, last accessed July 2015), and 70 additional picks at local seismic stations for the Mw 7.8 mainshock, Mw 7.3 aftershock, and 31 additional aftershocks (Dixit et al., 2015). We use P, S, Pn, and Sn arrival-time observations in our relocations, totaling 149,787 observations. As travel-time corrections are incorporated into the bayesloc algorithm, we use ak135 (Kennett et al., 1995) for our starting travel-time model.
One benefit of the bayesloc algorithm is its ability to incorporate a priori constraints on prior distributions of earthquake hypocenter parameters. We therefore incorporate calibrated earthquake location data from earthquakes proximal (in space and time) to the 2015 earthquakes (222 events as of 12 May 2015), using the hypocentroidal decomposition algorithm MLOC (Jordan and Sverdrup, 1981; Hayes et al., 2013; McNamara et al., 2015) and a local velocity model (Monsalve et al., 2008). Of these earthquakes, we use 58 high-precision locations as strong constraints in our bayesloc inversion. We also place loose constraints on the depths of the Mw 7.8 mainshock and Mw 7.3 aftershock based on the depth determinations from the Global Centroid Moment Tensor catalog (Dziewonski et al., 1981; Ekström et al., 2012).
We compare these relocations to earthquake locations from the regional network of the DMG by associating their online catalog to our relocated database. This procedure is difficult because the DMG catalog lacks origin-time seconds and hypocentral depths and the epicentral locations are given to only two decimal places. Phase data are also unavailable, prohibiting a more detailed analysis. During this association process, we manually remove duplicates (situations in which multiple DMG events associate with one relocated event), using minimum distance, minimum time offset, and similarity in reported magnitudes as criteria to best match events. We are able to associate 116 of 206 relocated aftershocks with DMG locations over the time period of 25 April 2015–30 May 2015. Results are shown in Figure S2, indicating an average offset of DMG locations from relocations of 18.2km (median 13.5 km), in a general east-southeast direction (mean azimuth of relocations to DMG locations of 118°, median 80°).
Additionally, we derive moment tensors for the mainshock and eight aftershocks, using either the W phase (Kanamori and Rivera, 2008; Hayes et al., 2009; Duputel et al., 2012) or the regional moment tensor algorithm of Herrmann et al. (2011). The relocated catalog is available at ftp://ftpext.usgs.gov/pub/cr/co/golden/ghayes/Nepal2015_relocs.txt (last accessed October 2015) or by contacting the lead author of this paper.
Following the customary procedure for real-time implementation of finite-fault inversion at the National Earthquake Information Center (NEIC), we first model the earthquake source as a rupture front of finite width propagating on a 2D planar fault segment for which the prescribed orientations match the refined W-phase moment tensor inversion results (Fig. S3). We use a simulated-annealing algorithm (Ji et al., 2002) to invert azimuthally distributed teleseismic P, SH, and surface waves for the combinations of slip amplitude, rake angle (85°–125°; W-phase moment tensor rake ±20°), rupture velocity (2.0–3.0 km/s2), and rise time at each subfault element that best explains the teleseismic records. This procedure is followed for both the Gorkha earthquake and for its 12 May 2015 aftershock.
The surface displacement field of the Gorkha earthquake and its largest aftershock were imaged by several spaceborne platforms, including the European Space Agency Sentinel-1a, Japanese Space Agency ALOS-2, and Canadian Space Agency Radarsat-2 Interferometric Synthetic Aperture Radar (InSAR) instruments. To image the coseismic and short-term postseismic fault slip of these events, we used ScanSAR- and Swath-mode observations from ALOS-2 (L-band radar) and StripMap-mode observations from Radarsat-2 (C-band radar) (Table S1). Together, these acquisitions span the full spatial area of both earthquakes independently. We omitted observations from Sentinel-1a because of uncertainties in line-of-sight displacement estimation from the Wide Swath mode of imaging that was acquired over the event. Similarly, we attempted to derive horizontal surface displacements from coseismic Landsat imagery through subpixel cross correlation using ampcor (Rosen et al., 2004); however, we were not able to successfully resolve coherent surface displacements.
ALOS-2 coseismic surface displacements for both events were acquired, processed, and made available by the University of California–San Diego geodesy group (Lindsey et al., 2015). Images were processed using the GMTSAR processing package (Sandwell et al., 2011). Radarsat-2 observations were processed with the GAMMA software (Wegmuller and Werner, 1997) and unwrapped with the branch-cut region growing algorithm (Goldstein et al., 1988). The topographic phase was removed with the 90 m Shuttle Radar Topography Mission digital elevation model (Farr et al., 2007).
To model each event, we follow the general methodology described by Barnhart et al. (2015),wherein an initial source geometry and location was inverted, followed by estimation of the static slip distribution. Coseismic surface displacements of the 25 April mainshock and the 12 May aftershock were modeled independently, because multiple independent interferograms are available for both events (Fig. S4). Details of the inversion procedure followed to model each event are described in detail below.
We first inverted coseismic interferograms spanning only the 25 April mainshock for the location, orientation, and dimensions of a single slipping fault patch with uniform slip (Okada, 1992) using the neighbourhood algorithm (Sambridge, 1999). We constrain the inversion to broadly sample the characteristics of the U.S. Geological Survey (USGS) W-phase solution for the shallow-dipping focal plane (strike 290°, dip 7°, rake 101°). The inversion was allowed to explore a broad parameter space of strike (270°–320°), dip (0°–40°), and rake (50°–130°), while the location, depth, and dimensions were left unconstrained. This approach led to solutions that did not agree well with seismological constraints, which had preferred dips that exceeded 30°. These steeply dipping values resulted from inversion tradeoffs between depth and dip, likely occurring because of the poor constraint on horizontal displacements afforded by the InSAR observations that would be significant in a nearly horizontal fault rupture.
To address this issue, we forced dip to match the W-phase solution and allowed all other parameters to sample the same model space as before. This resulted in a best-fit solution that better approximated the depth and magnitude of the W-phase solution. The misfit of this solution was similar to that in which dip was allowed to vary (Table S2), demonstrating that dips exceeding 30° are not required to fit the observations.
Our preferred solution was fixed in space, extended along-strike and down-dip, and then inverted following the approach summarized in the main text. We impose minimum moment regularization and allow rake to vary freely, only disallowing normal-type slip. Model uncertainties are estimated through a Monte Carlo approach as described in Barnhart et al. (2015).
We generated a geodetic slip distribution for the 12 May aftershock following the same procedure described above. As before, the inversion for the location and orientation of the aftershock fault plane from InSAR observations was impacted by tradeoffs between depth and dip. We again forced dip to match the W-phase solution (9°), allowed the inversion to explore the same model space as described for the mainshock, and inverted for distributed slip and model uncertainties. Additionally, we explored the possible coplanar nature of the mainshock and aftershock by inverting the aftershock coseismic displacements for distributed slip on the same fault geometry used for the aftershock. For a final inversion, we inverted a single ALOS-2 coseismic interferogram spanning both events, using the same fault geometry used for the mainshock inversion. In all distributed slip inversions, we imposed minimum moment regularization and allowed for variable rake.
Several tests were conducted to determine if the northeastern region of deeper slip could be explained by slip in the upper plate or on a steeper Main Himalayan thrust, akin to either slip on the Main Central thrust or on a steeper part of the décollement. Both data sets (geodetic and teleseismic) are fit best by slip on uniformly dipping décollement; however, we cannot entirely rule out the presence of upper plate, or steeper and deeper slip, with the available observations.
Loss estimates from version 1 of the PAGER report, associated with the earliest release of hypocentral information from NEIC (origin time [OT]+18 minutes; M 7.5; depth, Z=11 km) used ground-motion prediction equations employing hypocentral distance approximations for site-to-fault distance (Wald et al., 2008), that is, approximating near-source ground motions when fault dimensions are not yet available. Because the earliest magnitude estimates were low (M 7.5 versus the M 7.8 final), and because the epicenter of the earthquake was 80 km northwest of Kathmandu, initial loss estimates were noteworthy but relatively low (an Orange Alert; i.e., estimating between 100 and 1000 casualties); the highest modeled shaking was restricted to the more sparsely populated region to the west of the capital (Fig. 4 of the main article). The low estimates of impact persisted through the first four versions of PAGER; even with a rapid magnitude update (OT+50 minutes), fault dimensions scaled to an M 7.9 earthquake were not large enough to extend high shaking estimates into the more densely populated region of Kathmandu.
Version 5 of PAGER (OT+4 hours) included the first estimates of fault finiteness based on mechanism, aftershocks, and the tectonic framework, extending the source area of the earthquake eastward from the hypocenter beneath Kathmandu and increasing the shaking predictions in the city from modified Mercalli intensity VI to VIII (Fig. 4 of the main article). Fatality and economic loss estimates grew accordingly, changing the alert level to red and identifying potential fatalities close to 9000. Subsequent updates of the finite-fault extent (including updated teleseismic and geodetic models) and the addition of sparse regional data, “Did You Feel It?” reports, and other shaking estimates did not significantly change these estimates or the alert level (Fig. S5).
Interpretations of the extent of the 1505 event hinge on its identification in one trench several hundred kilometers west of Kathmandu (Mohana Khola; Yule et al., 2006), isoseismal studies (Ambraseys and Jackson, 2003; Ambraseys and Douglas, 2004), and records of damage to distributed monasteries along the Himalayan Front and in India (Bilham, 2004). Historic records show no evidence of this event being felt in Kathmandu (Pant, 2002). Sapkota et al. (2013) interpret a 1255 event from a trench just east of Kathmandu, as discussed further by Bollinger et al. (2014). The latter study also reinterprets a trench from Hokse (Nakata et al., 1998), ∼300 km east of Kathmandu, as recording the 1255 event (rather than an earlier, ∼1100 earthquake, as interpreted by Lavé et al., 2005). In this context, Bollinger et al. (2014, 2015 in prep.) interpret the 1255 event as the predecessor to the 1934 M 8.1 earthquake east of Kathmandu. On the other hand, Mugnier et al. (2011), and subsequently Mugnier et al. (2013), interpret two trenches 100–200 km west of Kathmandu as recording the 1255 event. If this is the case, then the 1255 earthquake is not a characteristic predecessor of 1934 and instead ruptured through the area that subsequently slipped in 1833 and 2015, toward (and perhaps overlapping with) the eastward extent of the 1505 event.
In between these 1505 and 1255 earthquakes, various authors also discuss large events in ∼1400 and in 1344. A great earthquake in ∼1400 is interpreted by Kumar et al. (2006, 2010), west of Kathmandu, extending over a distance of close to 800 km and coming within ∼100 km of the city (though they note the possibility that some of their observations could be related to the 1505 event). Other authorsreinterpret the Kumar et al. (2010) analyses as evidence for either the 1255 earthquake (Mugnier et al., 2013) or a 1344 event (Bollinger et al., 2015, in prep.). Mugnier et al. (2013) also interpret a 1344 event but farther west than shown in Bollinger et al. (2015, in prep.), correlated to a variety of trenches 500–800 km west of Kathmandu that Kumar et al. (2010) associate with the ∼1400 event.
Finally, Lavé et al. (2005) and Kumar et al. (2010) present evidence for a great ∼1100 event east of Kathmandu, over a length of close to 900 km. Mugnier et al. (2013) interpret the same event in the same location near Kathmandu but over a shorter distance eastward (∼400–500 km), while Sapkota et al. (2013) and Bollinger et al. (2014) reinterpret these correlations as being associated with the 1255 earthquake, over a length of ∼300 km.
Figure S1. (a) Earthquake relocations for the Gorkha sequence, versus (b) original locations and (c) relocation shifts. Relocation uncertainties are shown in (d), shaded by time (light gray, 1964–1999; medium gray, 2000–2014; dark gray, 2015). Epicenters are sized by magnitude. In panels (a) and (b), the white star represents the relocated Gorkha earthquake hypocenter, overlain with best estimates of rupture areas for the 2015 earthquakes. Dashed and solid ploygons represent approximate rupture areas for the 1505 and 1934 earthquakes, respectively. The surface trace of the Main Frontal thrust (MFT) is shown in white, from the Global Earthquake Model Faulted Earth database (Berryman et al., 2014).
Figure S2. Difference between our earthquake relocations and the Government of Nepal Department of Mines and Geology (DMG) regional network database. (a) Horizontal offset between relocations and DMG locations. (b) Offset azimuth between relocations and DMG locations.
Figure S3. W-phase results for (a) the Gorkha earthquake and (b) its M 7.3 aftershock. Fixed double-couple inversions allow us to test the sensitivity of long-period body-wave data to the geometry of the causative fault plane. At each combination of strike and dip, solution centroid moment tensors (CMTs) are color-scaled according the root mean square (RMS) misfit, relative to the minimum misfit. CMT mechanisms and moment magnitudes are listed at selected strike/dip combinations where there is a change from the adjacent solution.
Figure S4. Line-of-sight (LOS) displacements of the resampled interferograms (Table S1) versus geodetic model predicted surface deformation (second column) and misfit (third column) for (a) the 25 April mainshock and (b) the 12 May aftershock. In all cases except the Radarsat-2 interferogram, positive displacements are motion toward the satellite. The Radarsat-2 interferogram is opposite because of an LOS convention difference.
Figure S5. (a) Version 1 of the USGS NEIC PAGER report for the Gorkha earthquake, and (b) final version (v.7) of the USGS NEIC PAGER report for the Gorkha earthquake.
Figure S6. (a) PAGER fatality estimates versus various media reports with time. Media reports were collected from BBC (www.bbc.co.uk), CNN (www.cnn.com), Huffington Post (www.huffingtonpost.com), New York Times (www.nytimes.com), CBC news (www.cbc.ca/news), ABC news (www.abcnews.go.com), and the Times of India (www.timesofindia/indiatimes.com), all accessed on 1 June 15. (b) Modified Mercalli Intensity difference, ShakeMap versions 1 versus 7, illustrating the effects of finiteness on shaking estimates.
Figure S7. Population density of the Himalaya Front (Landscan population database; Bhaduri et al., 2002), overlain with historic slip distributions.
Figure S8. (a) ALOS-2 path 048 Wide Swath interferogram for the Gorkha earthquake (Lindsey et al., 2015), and (b) a cross section through the same vertical deformation field. In (c) we show the predicted vertical displacement for the 6 February 2013 M 8.0 Santa Cruz Islands subduction zone earthquake and in (d) a cross section through the same vertical deformation field, illustrating how the Gorkha earthquake can be considered to be an onland, subduction zone event.
Figure S9. A multistoried reinforced concrete building built on slopes, which collapsed due to ground shaking during the M 7.3 earthquake on 12 May 2015 near Sanghachok, in Sindhupalchok district. (Photo courtesy of Kishor Jaiswal, as part of the Earthquake Engineering Research Institutes' Learning from Earthquakes field reconnaissance mission to Nepal.)
Table S1. Summary of InSAR imagery used in this study. Instrument and Imaging Mode describe the satellite and image acquisition mode used for each independent image. Pre- and Post-Event dates detail the preseismic and postseismic acquisition dates.
Table S2. Summary of fault slip model geometries for the April 25 mainshock and May 12 aftershock detailed in this study. All models are derived from geodetic observations (Table S1). All models are a single fault plane with uniform strike and dip. Fixed rake (slip direction) value is shown where applicable. Free rake inversions allow for thrust, right-lateral, and left-lateral slip (no normal-type motion). Models: italic indicates preferred model (Fig. 2 in the main article); SP, single patch model with uniform slip; DS, distributed slip model; U, geometry is unconstrained; C, geometry is constrained. Model aDS-C3 of the May 12 aftershock is the same as model DS-C of the mainshock. Longitude/Latitude indicates the vertices of the top of the fault plane. RMS is the root mean squared misfit between the predicted model and observed surface displacements. (§, dip is fixed; ||, depth is equivalent to centroid.)
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