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Toward a Unified Near-Field Intensity Map of the 2015 Mw 7.8 Gorkha, Nepal, Earthquake

  • National Society for Earthquake Technology-Nepal (NSET)

Abstract and Figures

We develop a unified near-field shaking intensity map for the 25 April 2015 Mw 7.8 Gorkha, Nepal, earthquake by synthesizing intensities derived from macroseismic effects that were determined by independent groups using a variety of approaches. Independent assessments by different groups are generally consistent, with minor differences that are likely due in large part to differences in spatial sampling. Throughout most of the near-field region, European Macroseismic Scale (EMS-98) intensities were generally close to 7 EMS. In the Kathmandu Valley, intensities were somewhat higher (6.5–7.5) along the periphery of the valley and in the adjacent foothills than in the central valley, where they were ≈6. The results are consistent with instrumental intensity values estimated from available data using a published relationship between peak ground acceleration (PGA) and intensity. Using this relationship to convert intensities to PGA, we estimate strong-motion PGA de-amplification factors of ≈0.7 in the central Kathmandu Valley, with amplification of ≈1.6 in adjacent foothills. The results support the conclusion that the Kathmandu Valley experienced a pervasively nonlinear response during the Gorkha main shock. [DOI: 10.1193/120716EQS226M]
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Toward a Unified Near-Field Intensity
Map of the 2015 Mw7.8Gorkha, Nepal,
Sujan Raj Adhikari,a) Gopi Baysal,a) Amod Dixit,a) Stacey S. Martin,b)
Mattieu Landes,c) Remy Bossu,c) and Susan E. Houghd)
We develop a unified near-field shaking intensity map for the 25 April 2015
Mw7.8 Gorkha, Nepal, earthquake by synthesizing intensities derived from macro-
seismic effects that were determined by independent groups using a variety of
approaches. Independent assessments by different groups are generally consistent,
with minor differences that are likely due in large part to differences in spatial
sampling. Throughout most of the near-field region, European Macroseismic
Scale (EMS-98) intensities were generally close to 7 EMS. In the Kathmandu
Valley, intensities were somewhat higher (6.57.5) along the periphery of the val-
ley and in the adjacent foothills than in the central valley, where they were 6. The
results are consistent with instrumental intensity values estimated from available
data using a published relationship between peak ground acceleration (PGA) and
intensity. Using this relationship to convert intensities to PGA, we estimate
strong-motion PGA de-amplification factors of 0.7 in the central Kathmandu
Valley, with amplification of 1.6 in adjacent foothills. The results support the
conclusion that the Kathmandu Valley experienced a pervasively nonlinear
response during the Gorkha main shock. [DOI: 10.1193/120716EQS226M]
The 25 April 2015 Mw7.8 Gorkha, Nepal, earthquake ruptured a 150-km-long segment
of the Main Himalayan Thrust (MHT) Fault, propagating directly below the Kathmandu
Valley at a depth of approximately 12 km (Avouac et al. 2015,McNamara et al. 2016). Whereas
the earthquake caused a serious societal impact in Nepal in terms of loss of life and property,
damage and fatalities were lower than experts had feared for an event of this magnitude in this
location. Although there were instances of catastrophic collapse, overall damage was especially
and surprisingly low within the Kathmandu Valley, where only a small percentage of buildings
sustained substantial to heavy damage. Studies have proposed that the relatively limited damage
resulted from long-period source radiation (Galetzka et al. 2015), pervasive nonlinear response
within the valley (Bhattarai et al. 2015,Dixit et al. 2015,Rajaure et al. 2016), and the distribu-
tion of main shock high-frequency radiation (Avouac et al. 2015,Hough et al. 2016). There is
thus an urgent need to better understand ground motions from the main shock and their
National Society for Earthquake Technology Nepal, Kathmandu, Nepal
Earth Observatory of Singapore, Singapore
European-Mediterranean Seismological Centre, Essonne, France
U.S. Geological Survey, Pasadena, CA
Earthquake Spectra, Volume 33, No. S1, pages S21S34, December 2017; © 2017, Earthquake Engineering Research Institute
implications for seismic hazard. Given that instrumental data are limited, it is further necessary
to consider intensity data derived from macroseismic observations, which are expected to reflect
relatively high frequency (15Hz) ground motions (Sokolov and Chernov 1998). In recog-
nition of the importance of gathering macroseismic data, intensity and/or damage surveys were
undertaken after the earthquake by a number of independent groups, using a number of different
approaches including direct surveys of intensities estimated from standard questionnaires
(Adhikari et al. 2015; this study), direct surveys of building damage and other macroseismic
effects (Mencin et al. 2016,Ohsumi et al. 2016), Internet-based questionnaires (Bossu et al.
2017), and assessment of media accounts (Martin et al. 2015,McGowan et al. 2016). In this
study, we focus on the intensity distribution in the near-field region, outside of Nepal. The
intensity distribution is constrained primarily by the results of Martin et al. (2015) as well
as intensities determined from Web-based questionnaires (e.g., Bossu et al. 2017).
Of particular note, an extensive Building Damage Assessment (BDA) was conducted by
the National Society for Earthquake Technology (NSET) with several international partner
agencies under three projects that were supported by various donor agencies. A total of
291,891 individual buildings were assessed in the Kathmandu Valley and throughout the
affected municipalities, to determine the observed damage grade according to the 1998
European Macroseismic Scale guidelines (EMS-98; Grünthal et al. 1998) and the building
type. Using this database it is possible to determine the statistical incidence of damage grades
for mud-brick masonry (assumed vulnerability Class A), cement masonry (Class B), and
reinforced concrete frame (Class C) structures at 847 individual locations within all munici-
palities impacted by the earthquake. We use these results to assign intensities using the
EMS-98 scale, hereinafter referred to simply as EMS intensities.
To assess intensities reliably from media accounts, it is widely recognized that it is crucial not
to give undue weight to isolated instances of damage, and to apply EMS-98 guidelines with an
appreciation for issues associated with macroseismic effects of earthquakes in the Indian sub-
continent (see Martin and Hough 2016 for summary). Even when the same general guidelines are
followed, intensity assessments from different approaches can differ because of either inherent
subjectivity in interpretation of accounts (e.g., Hough and Page 2011) or reliance on different
types of information. Hough and Pande (2007) showed that media-based intensities for the 2001
Bhuj, India, earthquake were generally higher than intensities assigned from direct surveys
(Pande and Kayal 2003) of macroseismic effects. This discrepancy is consistent with a reporting
bias whereby media reports are not necessarily overdramatized per se, but are preferentially avail-
able from locations with relatively dramatic effects (e.g., Bakun and Scotti 2006,Hough 2013).
As a first step toward synthesizing the results of independent studies, we compare the results from
independent surveys to determine whether they are generally consistent.
We further use a relationship between peak ground acceleration (PGA) and EMS inten-
sity (Hough et al. 2016,Worden et al. 2012) to generate a map of PGAEMS for the main
shock. We use this map to explore the amplification (and de-amplification) of PGA within
the Kathmandu Valley.
We review briefly the independent intensity assessments considered in this study (Table 1
and Figure 1): (1) The NSET study was undertaken by a team of researchers beginning in
May 2015, using an intensity questionnaire that was formulated considering building types
and local conditions in Nepal (Adhikari et al. 2015,Adhikari et al. 2016,Murakami et al.
2015). Intensities were assigned for a total of 265 locations. These locations are concentrated
in the Kathmandu Valley, but sites outside the valley were surveyed as well. (2) The Nanyang
Table 1. List of intensity surveys considered
Study Agencies Approach Locations
1 NSET Direct questionnaire survey 265
2 NTU Media-based 3,831
3 EMSC Internet-based questionnaire 106
4 CU Ground survey 72
5 DYFI-1 Internet-based questionnaire 68
6 DYFI-2 Media-based 16
7 NSET-BDA BDA 291,891
85˚ 86˚
85˚ 86˚
85˚ 86˚
6.57+/-1.03 BDA
6.74/-0.74 EMSC
(a) (b) (c)
(d) (e) (f)
Figure 1. Intensity values throughout the near-field region from independent studies: (a) NTU,
(b) BDA, (c) EMSC, (d) CU, (e) NSET, and (f) DYFI-1 and DYFI-2. In each panel, intensities are
shown for individual locations (circles; color scale shown in lower left panel), which in some cases
are averages from more than one questionnaire. The DYFI data include intensity values determined
from online questionnaires and values determined from either instrumental data (one location) or
from assessment of media accounts. Each panel also shows main shock rupture (Lindsey et al.
2015) (blue line), main shock epicenter (black star), epicenter of the 10 May 2015 Dolakha after-
shock (white star), and average near-field intensity 1σshown for each data set.
Technological University (NTU) study was undertaken immediately following the main
shock, relying on detailed media accounts made available over the Internet, including photo-
graphs and CCTV footage, to determine EMS-98 values for 3,831 locations within and out-
side of Nepal (Martin et al. 2015). (3) The European Mediterranean Seismic Centre (EMSC)
data set includes intensities determined automatically based on user-specified thumbnail
illustrations that correspond to observed effects at different shaking levels (Bossu et al.
2017). (4) The University of Colorado, Boulder (CU) study was undertaken immediately fol-
lowing the earthquake, relying on assessment of EMS intensities based on direct inspection of
damage and other effects, supplemented with assignments based on media accounts, at a total of
72 locations (Mencin et al. 2016). (5) The DYFI-1 study includes 68 modified Mercalli intensity
(MMI) values determined automatically from online questionnaires (see Wald et al. 1999).
(6) The DYFI-2 study includes 16 near-field MMI values determined in one case from instru-
mental data and for 15 locations from assessment of media accounts (McGowan et al. 2016).
Lastly, (7) the NSET-BDA study includes a total of over 800 intensity values determined from
the exhaustive BDA project, which involved direct surveys of 291,891 individual buildings.
The NSET-BDA study far surpasses all of the other studies in both the number of indi-
vidual locations surveyed and the rigor of damage assessments, providing the opportunity to
assign EMS intensities based on the observed statistical incidence of damage grades for
different vulnerability classes. In effect, the availability of this extensive data set, which pro-
vides far more detailed information than is usually available for investigations of macroseis-
mic effects, provides a unique opportunity to assess the veracity of the other studies, which
represent more commonly used approaches to assess macroseismic intensity.
Table 1lists the approach used to assess intensities in each study. In some cases, more
than one approach was used within the same study. The NTU study was overwhelmingly
based on interpretation of media accounts from outside and within Nepal, with a small
number of EMS-98 values based on both media accounts and available information from
ground surveys. The CU study was primarily based on ground surveys, supplemented with
a small number of intensities assessed from media accounts. Maps of intensity values from
studies 16 are shown for the near-field region (Figure 1) and for the Kathmandu Valley
(Figure 2), including the average and sample standard deviation for each data set.
Our initial comparison of the independent data sets (Figures 1and 2) reveals that the
NSET, BDA, NTU, EMSC, and CU studies are generally consistent to within 1σ. The
DYFI-1 data set includes few near-field values. DYFI-2 values, estimated subjectively
from a limited number of media accounts (McGowan et al. 2016), are higher than average
intensities from other studies. Qualitatively, the difference between the DYFI-2 results and
the other results are generally consistent with a reporting bias (e.g., Bakun and Scotti 2006,
Hough 2013,Hough and Pande 2007). Whereas the NTU study was also based overwhel-
mingly on media accounts, the reporting bias appears to have been obviated by the exhaustive
nature of the Martin et al. (2015) study, which involved a painstaking search of all available
data sources including CCTV footage made available over the Internet following the earth-
quake. Martin et al. (2015) further interpreted EMS values with an appreciation for issues asso-
ciated with assessment of intensities in the Indian subcontinent (see Martin and Hough 2016),
including the fact that masonry buildings in the Indian subcontinent are pervasively more
vulnerable than masonry buildings in Europe, where the EMS scale was developed.
Individual EMS values from the EMSC study (Bossu et al. 2017) reveal significantly
more variability than any of the other studies. This variability likely reflects the nature
of the data, which are derived from individual thumbnail questionnaire results with no spatial
averaging, and without information about building type or vulnerability. Reports from indi-
vidual locations with instances of catastrophic collapse would have been interpreted as high
(910 EMS) intensity. Of note, however, the average raw intensity value across the
Kathmandu Valley is consistent with the average value from all of the other studies
apart from DYFI-2. While DYFI data have generally been shown to provide reliable indi-
cators of ground motion and to be highly useful to investigate ground motions (e.g., Atkinson
and Wald 2007), the DYFI-2 values for the Gorkha earthquake were assigned based on media
accounts rather than the usual algorithm. Because the DYFI-2 data set is both small and an
outlier among the studies, we will not include it in our synthesis.
In light of the above considerations, our synthesis map for the Kathmandu Valley
includes the NTU, EMSC, CU, DYFI-1, NSET, and BDA results. The small differences
85˚15' 85˚20' 85˚25'
6 km
85˚15' 85˚20' 85˚25'
6 km
85˚15' 85˚20' 85˚25'
6 km
6 km
6 km
6 km
(a) (b) (c)
(d) (e) (f)
Figure 2. Intensity values in the Kathmandu Valley from independent studies: (a) NTU,
(b) BDA, (c) EMSC, (d) CU, (e) NSET, and (f) DYFI-1 and DYFI-2. In each panel, intensities
are shown for individual locations (circles; color scale shown in lower left panel), which in
some cases are averages from more than one questionnaire. Available DYFI data from within
the Kathmandu Valley (squares) are all determined from either instrumental data (one location)
or from assessment of media accounts. Average near-field intensity 1σshown for each data
set in each panel. Southern edge of main shock rupture shown in top right panel from Lindsey
et al. (2015) (dark line).
among the average intensities for these studies could reflect (1) systematic differences in
subjective interpretation, (2) random scatter, and/or (3) sampling bias. Among these five
studies, average intensity values across both the near-field and Kathmandu Valley are con-
sistent within 1σ. It is reasonable to conclude that subjective interpretations were generally
consistent among these studies, and consistent with the algorithms used for the EMSC and
DYFI studies, and differences result primarily from differences in sampling and/or spatial
averaging. Furthermore, to the extent that there might have been differences in subjective
interpretation, it is reasonable to average the results (e.g., Hough and Page 2011). We, there-
fore, develop synthesis maps by combining the six intensity data sets, without attempting to
calibrate individual intensity data sets. For the combined data set, we omit several isolated
EMS values of 910 from the EMSC data set: because these intensities are point values, we
expect that determination of a representative intensity for a given location will require aver-
aging, which requires sufficient spatial sampling. In Figure 3, we present the combined inten-
sity data set for the Kathmandu Valley. For the region defined by the map limits, the average
EMS intensity is 6.4 0.9.
Figure 4presents contoured EMS intensities throughout the near-field region. To gen-
erate the interpolated map, we use a Laplacian smoothing operator with a tension factor
of 1.0, which ensures that no maxima or minima are generated except at control points
(Wessel and Smith 1999). To improve the visualization of the variability of EMS values,
Figure 3. EMS intensity values throughout the Kathmandu Valley, omitting EMSC values.
in Figure 5, we show EMS residuals throughout the near-field region, calculated by sub-
tracting the near-field average of 6.6 from all values. (This average value is calculated using
all intensities within 84.686.4W, 27.428.3N. Within the estimated rupture perimeter the
average is 7.)
Figures 3and 4reveal that EMS-98 values were overwhelmingly within a narrow
range, 67.5, throughout the Kathmandu Valley as well as throughout the near-field region,
reaching values as high as 88.5 in only a few areas. This is consistent with the conclusions of
earlier studies based on more limited data sets (Adhikari et al. 2016,Hough et al. 2016,
Martin et al. 2015,Mencin et al. 2016). The spatial distribution of EMS intensities in
the Kathmandu Valley is qualitatively similar to that observed in earlier studies, with the
lowest values in the central, deepest part of the valley (Figure 3), and somewhat higher values
around the periphery of the valley and in the adjacent foothills. Figure 3does not reveal any
85˚15' 85˚20' 85˚25'
6 km
PGA-based EMS
6 km
PGV-based EMS
84˚30' 85˚00' 85˚30' 86˚00'
2601 345 78910
Figure 4. (a) (left) Contoured EMS intensities (color scale indicated) across the near-field region;
the main shock rupture (Lindsey et al. 2015) and the Kathmandu Valley are shown with solid and
dashed lines, respectively. Small gray dots indicate locations where intensities are estimated. The
intensity distribution in the northwest corner of the map, which is effectively unconstrained, is
masked. (b) (right) Contoured intensities in the Kathmandu Valley (same color scale shown in
Figure 4a). Filled circles in two panels indicate instrumental intensities estimated from PGA
(bottom) and PGV (top).
significant north-to-south trend across the Kathmandu Valley; high residuals are observed in
foothill locations in all directions with the possible exception of the southwestern corner,
where intensities are poorly constrained.
The spatial distribution of intensities throughout the near-field region reveals some differ-
ences with respect to the results of earlier studies (Hough et al. 2016,Martin et al. 2015).
Consistent with the earlier studies, we find intensities to be generally higher toward the northern
edge of the rupture, but unlike the earlier studies the swath of high intensities is less narrowly
concentrated along the northern edge; instead, there is a swath of relatively high intensities
close to the along-strike midline of the rupture. The average EMS intensity across the
near-field region is 7, slightly higher than that estimated by Mencin et al. (2016).
One can further consider the consistency of directly observed macroseismic data with
limited instrumental recordings of the main shock. Within the Kathmandu Valley, available
instrumental data include conventional strong-motion recordings from a total of six sites
(Bhattarai et al. 2015,Dixit et al. 2015,Rajaure et al. 2016) as well as high-rate (5 samples
per s) GPS from two sites (Galetzka et al. 2015, also see Hashash et al. 2016). Stations KKN4
and KTP are hard-rock reference sites, and TVU is a presumed thin-sediment site near the edge
of the valley. The rest of the sites are within the valley. The GPS data provide no constraint on
ground motions at frequencies above 2.5 Hz, and therefore no constraints on PGA, but the GPS
data do constrain peak ground velocity (PGV; Table 2). Instrumental intensities, IPGA and IPGV ,
can be estimated from recorded PGA and PGV values using an intensity-prediction relation-
ship; we use the relationships determined by Worden et al. (2012):
EQ-TARGET;temp:intralink-;e1;41;121IPGA ¼1.78 þ1.55logðPGAÞ,logðPGAÞ1.57
IPGA ¼1.60 þ3.70logðPGAÞ,logðPGAÞ>1.57 (1)
84˚30' 85˚00' 85˚30' 86˚00'
–1 0 1 2
Figure 5. Contoured EMS intensity residuals (color scale indicated) across the near-field region;
the main shock rupture (Lindsey et al. 2015) and the Kathmandu Valley are shown with solid blue
and dashed black lines, respectively. The intensity distribution in the northwest corner of the map,
which is effectively unconstrained, is masked.
EQ-TARGET;temp:intralink-;e2;62;412IPGV ¼3.78 þ1.47logðPGVÞ,logðPGVÞ0.53
IPGV ¼2.89 þ3.16logðPGVÞ,logðPGVÞ>0.53 (2)
where PGA and PGV are in cm/s2and cm/s, respectively. The values are given in Table 2and
compared to observed intensities in Figure 4b.
As shown in Figure 4b, we find good consistency between IPGA values and directly esti-
mated intensities. Not only is the overall shaking level consistent; both estimated and directly
estimated intensities reveal the same subtle difference between intensities in the valley versus
the adjacent foothills. In contrast, IPGV values are considerably higher than directly estimated
intensities (Figure 4b, top). Intensities as high as 9 are grossly inconsistent with the low
overall level of damage to highly vulnerable structures, revealing that the intensity-PGV
relationship cannot explain ground motions within the Kathmandu Valley during this
To further explore the distribution of ground motions, we now use the Worden et al.
(2012) relationships to estimate PGAEMS (in cm/s2) from estimated EMS intensities.
Although the Worden et al. (2012) relationship was developed using data from
California, and using MMI rather than EMS, Hough et al. (2016) show that it provides a
good fit to available data from the Gorkha main shock for PGA values above 1%g.
Figure 6shows estimated PGAEMS amplification factors across the Kathmandu Valley,
estimated relative to the observed average, PGAAV E (e.g., PGAEMSPGAAV E ). Estimated
amplification factors are all in the range of 0.44.5, with 93.6%between 0.52.9 (2.5%
are below 0.5; 4.2%are above 2.9). The pattern of de-amplification within the central
Kathmandu Valley, with modest amplification around the periphery of the valley and in
the adjacent foothills, is clearly illuminated. Draping this result over topography (Figure 7)
Table 2. Instrumental data from the Kathmandu Valley. List of strong-motion
and high-rate GPS stations that recorded the Gorkha main shock, including pub-
lished PGA and PGV values (%gand cm/s, respectively), as well as IPGA and
IPGV estimates from Equations 1and 2
Latitude Longitude PGA PGV
Station (°N) (°E) (g)IPGA (cm/s) IPGV
KAT 27.713 85.316 16.5 6.6 107 9.3
DMG 27.719 85.317 17.8 6.7 ––
KTP 27.682 85.273 24.6 7.2 52 8.3
TVU 27.681 85.288 24.2 7.2 99 9.2
PTN 27.681 85.319 15.4 6.5 74 8.8
THM 27.713 85.377 15.0 6.4 90 9.1
NAST 27.657 85.328 ––90 9.1
KKN4 27.801 85.279 ––70 8.7
85˚15' 85˚20' 85˚25'
0.5 1.0 1.5 2.0 2.5
Figure 6. PGA residuals (amplification/de-amplification factors) relative to the average, calcu-
lated by dividing estimated PGAEMS values by the average across the Kathmandu Valley.
Figure 7. PGA residuals (amplification/de-amplification factors; same color scale as shown
in Figure 6) from Figure 6draped over topography (factor of 2 vertical exaggeration) within
the Kathmandu Valley. Dashed line indicates southern limit of main shock rupture from
Lindsey et al. (2015).
further illustrates this result. We note that residuals in the southwest corner of the region are
effectively unconstrained.
The amplification factors shown in Figure 7are calculated relative to the average esti-
mated PGAEMS across the region rather than relative to directly estimated hard-rock reference
values. The choice of baseline is subjective; alternatively, one could divide all values by an
average calculated for foothill sites only; however, the results could be biased if, as suggested
by Martin et al. (2015), topographic amplification was common. We note that this ambiguity
is not unique to this study, and has likely been an unrecognized source of uncertainty in many
past site response studies that relied on reference sites. We further note that Rajaure et al.
(2016) estimated amplification factors of 0.600.92 directly from strong-motion recordings
of the main shock from three sediment sites and one hard-rock site. While the baseline used
in this study might be open to question, results are thus consistent with instrumentally
constrained amplification factors.
Given the limited instrumental recordings of the 2015 Gorkha main shock, macroseismic
data offers the best available information to constrain the distribution of near-field ground
motions. In this study, we compare and combine macroseismic data sets, including the exten-
sive BDA, that were collected and interpreted by independent teams over the months follow-
ing the main shock. We find that assessment of EMS intensities done by independent groups
was generally consistent. The consistency of the media-based NTU results (Martin et al.
2015) with results from direct surveys indicates that media-based intensity distributions
can reliably capture representative shaking effects, if spatially rich macroseismic information
is collected and carefully interpreted.
Directly estimated intensities are consistent with instrumental intensities estimated from
available instrumental data in the Kathmandu Valley using the intensity-PGA relationship
developed by Worden et al. (2012), but are inconsistent with instrumental intensities using
the Worden et al. (2012) intensity-PGV relationship. IPGV values are not plausible in light of
the pervasively low level of damage to highly vulnerable structures in the Kathmandu Valley,
suggesting that, for this earthquake and this location, damage was controlled by PGA rather
than PGV. Two factors plausibly account for this result: (1) vernacular structures in the val-
ley, almost all of which are 1 to 4 stories, are not vulnerable to the longer period shaking that
controlled PGV, such that high PGV values were not generally damaging, and (2) high-
frequency energy levels and PGA were unusually low within the Kathmandu Valley for rea-
sons summarized in the following paragraph.
The combined EMS data set provides improved spatial resolution of the distribution of
ground motions during the Gorkha main shock compared to early studies based on indivi-
dual surveys. The distribution of near-field ground motions illuminated by this study con-
firms the earlier result that ground motions, and damage, within the central Kathmandu
Valley were lower than expected because of a pervasive nonlinear response of valley sedi-
ments (Bhattarai et al. 2015,Dixit et al. 2015,Hough et al. 2016,Rajaure et al. 2016), with
an estimated PGA de-amplification factor of 0.7 in the central valley. Whereas other fac-
tors, including source radiation and the distribution of high-frequency shaking might
have also contributed to the relatively low level of shaking in the Kathmandu Valley
(e.g., Avouac et al. 2015,Galetzka et al. 2015), it is clear from Figure 5that shaking in the
valley was significantly lower than in other regions along the southern edge of the main
shock rupture. The de-amplification factor within the central valley was sufficient to reduce
EMS-98 intensities from 7+ to close to 6, a significant difference given the vulnerability of
vernacular structures.
The overall distribution of near-field intensities reveals some similarities but also some
differences from the distribution revealed by earlier studies (Hough et al. 2016,Martin et al.
2015). As in earlier studies (Hough et al. 2016), we observe lower ground motions along the
southern edge of the rupture, and higher shaking in the forward directivity direction. The
swath of relatively high intensities, however, is less strongly concentrated along the far
northern edge of the rupture, instead extending from the approximate north-south centerline
of the rupture to its northern edge. We note, however, that the intensity distribution along the
far northern edge of the rupture, along the southern edge of the high Himalaya, is poorly
constrained (see Figure 4a). The improved intensity distribution will provide a useful con-
straint for future main shock rupture modeling in the Kathmandu Valley, as well as for devel-
opment of shaking scenarios for future large earthquakes in the region.
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(Received 7 December 2016; accepted 8 August 2017)
... Secondly, we suggest that near-field shaking was deamplified by a pervasively non-linear response of near-surface sediments. The zone of estimated deamplification is similar to that inferred in the Kathmandu Valley during the 2015 Gorkha, Nepal, earthquake (Adhikari et al., 2017). Our conclusion is also consistent with the qualitative conclusion of Trifunac (2003), who showed that near-field damage during the 1933 Long Beach, California, earthquake was deamplified in some nearfield regions by pervasively non-linear response of soft, water-saturated sediments. ...
... A growing body of evidence further suggests that near-field shaking in large earthquakes can be significantly tempered by pervasively nonlinear response of soft, water-saturated sediments (e.g., Trifunac, 2003;Adhikari et al., 2017). Non-linear effects pose a potential challenge for the development of intensity prediction equations, in particular if they are not well constrained for large magnitudes. ...
Seismic intensity data based on first-hand accounts of shaking give valuable insight into historical and early instrumental earthquakes. Comparing an observed intensity distribution to intensity-prediction models based on modern calibration events allows the magnitude to be estimated for many historic earthquakes. Magnitude estimates can also potentially be refined for earthquakes for which limited instrumental data are available. However, the complicated nature of macroseismic data and the methods used to collect and interpret the data introduce significant uncertainties. In this paper, we illustrate these challenges and possible solutions using the 1952 Kern County, California, earthquake as a case study. Published estimates of its magnitude vary from MW 7.2 - 7.5, making it possibly the second largest in California during the 20th century. We considered over 1,100 first-hand reports of shaking, supplemented with other data, and inferred the magnitude in several ways using intensity prediction equations, yielding a preferred intensity magnitude MI 7.2 ± 0.2, where the uncertainty reflects our judgement. The revised intensity distribution reveals stronger shaking on the hanging wall, south of the surface expression of the White Wolf fault, than on the footwall. Characterizing the magnitude and shaking distribution of this early instrumental earthquake can help improve estimation of the seismic hazard of the region. Such reinterpreted intensities for historic earthquakes, combined with USGS Did You Feel It? data for more recent events, can be used to produce a uniform shaking dataset with which earthquake hazard map performance can be assessed.
... However, as described by McGowan et al. (2017), for this earthquake a number of near-field intensities were entered manually based on the DYFI team's assessment of limited damage information. Adhikari et al. (2017) showed that the "enhanced" DYFI data set is inconsistent-with consistently higher near-field values-with intensity data collected by five independent groups from media reports or direct ground surveys. ...
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We thank David Wald (Wald, 2021; henceforth, W21) for his interest in our recent article (Hough and Martin, 2021; henceforth, HM21). Although different perspectives are vital in science, we are concerned that W21 misrepresents HM21 as an oblique criticism of the U.S. Geological Survey “Did You Feel It?” (DYFI) system, calling for HM21 to be retracted. Readers who are interested in the issues raised by HM21 and the statements made by us therein are referred to that article. In this brief reply, we respond to specific accusations made by W21 and return to the focus of HM21, calling attention to the extent to which macroseismic data sets and inferences drawn from them can be shaped by a lack of representation among individuals whose observations are available to science. HM21 never questioned the benefits of the community science DYFI project to science. HM21 noted, however, and we reiterate here, that community science also potentially benefits the community. Whether or not it matters for science, if participation in community science projects is unrepresentative across socioeconomic groups, it underscores the need for the scientific community to be proactive in its efforts to reach out to groups that have been underserved by current outreach and education programs. We appreciate this opportunity to continue the important conversation about representation.
... We, therefore, expect that documented shaking effects will overwhelmingly reflect the effects of high-frequency shaking, with frequencies higher than ∼3 Hz, which we expect to reflect PGA more than PGV. The insensitivity of small structures to longer-period ground motions (i.e., PGV) was illustrated by the 2015 Gorkha, Nepal, earthquake, during which even highly vulnerable small buildings in the Kathmandu Valley sustained surprisingly low levels of damage (Martin et al., 2015;Adhikari et al., 2017). ...
In this study, we revisit the three largest historical earthquakes in California—the 1857 Fort Tejon, 1872 Owens Valley, and 1906 San Francisco earthquakes—to review their published moment magnitudes, and compare their estimated shaking distributions with predictions using modern ground-motion models (GMMs) and ground-motion intensity conversion equations. Currently accepted moment magnitude estimates for the three earthquakes are 7.9, 7.6, and 7.8, respectively. We first consider the extent to which the intensity distributions of all three earthquakes are consistent with a moment magnitude toward the upper end of the estimated range. We then apply a GMM-based method to estimate the magnitudes of large historical earthquakes. The intensity distribution of the 1857 earthquake is too sparse to provide a strong constraint on magnitude. For the 1872 earthquake, consideration of all available constraints suggests that it was a high stress-drop event, with a magnitude on the higher end of the range implied by scaling relationships, that is, higher than moment magnitude 7.6. For the 1906 earthquake, based on our analysis of regional intensities and the detailed intensity distribution in San Francisco, along with other available constraints, we estimate a preferred moment magnitude of 7.9, consistent with the published estimate based on geodetic and instrumental seismic data. These results suggest that, although there can be a tendency for historical earthquake magnitudes to be overestimated, the accepted catalog magnitudes of California’s largest historical earthquakes could be too low. Given the uncertainties of the magnitude estimates, the seismic moment release rate between 1850 and 2019 could have been either higher or lower than the average over millennial time scales. It is further not possible to reject the hypothesis that California seismicity is described by an untruncated Gutenberg–Richter distribution with a b-value of 1.0 for moment magnitudes up to 8.0.
... One such initial study is documented in this issue. Adhikari et al. (2017) developed a unified near-field shaking intensity map for the M w 7.8 earthquake by synthesizing intensities derived from macroseismic effects that were determined by independent groups using a variety of approaches. The authors estimate strong-motion peak ground accelerations and describe the nonlinear response of the Kathmandu basin. ...
On 25 April 2015, a Mw7.8 earthquake struck near Gorka, Nepal. The earthquake and its aftershocks caused over 8,790 deaths and 22,300 injuries; a half a million homes were destroyed; and hundreds of historical and cultural monuments were destroyed or extensively damaged (NPC 2015). Triggered landslides blocked access to road networks, and other lifelines were significantly impacted. Damage occurred in the capital of Kathmandu and the surrounding valley basin, but the most heavily affected areas were in more rural regions of central Nepal where losses to some towns were severe. Recovery has been slow, but progress is being made in rebuilding and repairing lost and damaged buildings and infrastructure. This Earthquake Spectra special issue provides a compendium of research papers on the Gorkha earthquake. They are organized into five topics: (1) seismology, ground motion, and geotechnical issues; (2) lifelines; (3) buildings; (4) cultural heritage structures; and (5) social science and public policy related topics. This overview summarizes key aspects of the earthquake and highlights findings from the special issue papers.
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Structural risk-mitigation measures have been shown to significantly reduce earthquake-induced physical damage and casualties in various regions worldwide. However, these benefits remain unknown or inadequately quantified for potential future events in some hazard-prone areas such as Kathmandu Valley, Nepal, which this article addresses. The analysis involves modeling an earthquake scenario similar to the 2015 Gorkha earthquake (moment magnitude 7.8) and using four exposure inventories representing the current (2021) urban system or near-future (2031) development trajectories that Kathmandu Valley could experience. The results predict substantial losses (€8.2 billion in repair/reconstruction costs and 89,199 fatalities) in 2021 if the building stock’s quality is assumed to have remained the same as in 2011 (Scenario A). However, a partial improvement of the building stock’s quality in the present (Scenario B) can decrease financial losses and fatalities by 17% and 44%, respectively. Moreover, under a “no change” pathway for 2031 (Scenario C), where the quality of the expanding building stock remains the same as in 2011, and the number of buildings is larger to reflect population growth, financial losses and fatalities will increase by 20% and 25% respectively over those of Scenario A. In contrast, further upgrades to the building stock’s quality by 2031 (Scenario D) would reduce financial and human losses by 14% and 54% respectively, relative to those of Scenario A. In addition, the largest financial and human losses computed in the four scenarios are consistently associated with the low- and middle-income population. The main findings of this article can be used to inform decision makers about the benefits of investing in forward-looking seismic risk-mitigation efforts.
This paper is an analysis of the use of reported speech in six Tibeto-Burman languages from two closely-related sub-branches (Tamangic and Tibetic). The data come from a set of interview narratives about people’s experiences of the 2015 earthquakes in Nepal. The analysis begins with an examination of the relationship between reported speech, overt subjects and ergativity. We also look at reported speech and evidentiality, including grammatical reported speech evidentials. Structural features discussed include hybrid reported speech and multiple clause relationality. Interactional features discussed include the use of deictic shift, prominent subordination, and the multiple functions of reported speech forms, as well as zero-marked reported speech events. This analysis highlights the benefits of studying linguistic features such as reported speech in narrative context. We conclude with the implications of this usage-based analysis in the coverage of reported speech in descriptive grammars.
We map the dis­tri­b­u­tion of macro­seis­mic in­ten­si­ties from the Mw 6.9 Kani and the Mw 6.8 Chauk in­tra-slab earth­quakes in 2016 in Myan­mar us­ing the 1998 Eu­ro­pean Macro­seis­mic Scale (EMS-98) by in­ter­pret­ing data gath­ered from field sur­veys, com­mu­nity re­sponses sent via so­cial me­dia to the Myan­mar Earth­quake Com­mit­tee (MEC), and dig­i­tal news re­ports. Our macro­seis­mic maps for both events pro­vide bet­ter spa­tial data cov­er­age in Myan­mar, In­dia, and Bangladesh than com­mu­nity de­rived macro­seis­mic maps (e.g., U.S. Ge­o­log­i­cal Sur­vey's “Did You Feel It?”). In Myan­mar, this was dri­ven by im­proved telecom­mu­ni­ca­tion that has al­lowed so­cial me­dia such as the Burmese lan­guage Face­book por­tal of the Myan­mar Earth­quake Com­mit­tee (MEC) to reach into rural ar­eas from where re­ports of shak­ing ef­fects from earth­quakes have been pre­vi­ously un­avail­able. Our analy­sis of both the macro­seis­mic in­ten­si­ties and strong mo­tion ob­ser­va­tions from In­dia and Myan­mar sug­gests the two earth­quakes had dif­fer­ent source prop­er­ties. The com­par­i­son of our in­ten­sity data with in­stru­men­tal strong mo­tion records also sug­gests the peak ground mo­tion-in­ten­sity re­la­tion­ship by Worden et al. (2012) gen­er­ally per­forms well for both earth­quakes. In ad­di­tion, ground mo­tion be­haviour within the Burma and In­dian plates can be re­lated to dif­fer­ent ex­ist­ing ground mo­tion pre­dic­tion equa­tions (GM­PEs) and in­ten­sity pre­dic­tion equa­tions (IPEs) for sub­duc­tion zones and for sta­ble con­ti­nen­tal re­gions re­spec­tively. We there­fore sug­gest these ef­fects will need to be con­sid­ered in fu­ture re­gional seis­mic haz­ard mod­els or Shake Maps for this re­gion when eval­u­at­ing the im­pact of the fu­ture events.
The relatively low damage in the Kathmandu Valley caused by the 2015 Mw 7.8 Gorkha earthquake has attracted much attention. To gain a deeper understanding of this phenomenon, we conduct broadband ground-motion simulations for both the mainshock and the M w 7.2 Dolakha aftershock through a hybrid method that combines deterministic 3D synthetics at relatively low frequencies (< 0:3 Hz) and semistochastic synthetics at higher frequencies (> 0:3 Hz). Because they are summarized in a companion paper (Wei et al., 2018), the 3D deterministic synthetics were generated by embedding a finite-fault rupture model in a 3D velocity model that is characterized by a simplified basin structure for the Kathmandu Valley.We tested different weighting schemes using a finite slip model and backprojection results to weight the high-frequency sources. Our simulations were guided by fitting the observations from five strong-motion stations in Kathmandu Valley and the intensity and mortality distributions. Site effects were handled by amplitude spectra ratio derived from the vertical component of a hard-rock station (KTP). Our broadband ground-motion simulations show that (1) the stress parameter (3.8 MPa) of the mainshock was much lower in comparison to the M w 7.2 aftershock (23 MPa) that suggests the rupture process of the mainshock was relatively deficient in radiating high-frequency energy and different fault friction property between the mainshock and the aftershock; (2) the soft deposits in the Kathmandu Valley experienced a pervasive nonlinear site response during the mainshock and the M w 7.2 aftershock, which also contributed to the reduction of high-frequency motions; and (3) the high-frequency ground motions during the mainshock were primarily radiated from the down-dip rupture. Hence, we suggest considering the difference in the distribution of high-frequency radiation and fault slip in the broadband ground-motion simulations for scenario and historical earthquakes.
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This article summarizes recent advances in our knowledge of the past 1000 years of earthquakes in the Himalaya using geodetic, historical and seismological data, and identifies segments of the Himalaya that remain unruptured. The width of the Main Himalayan Thrust is quantified along the arc, together with estimates for the bounding coordinates of historical rupture zones, convergence rates, rupture propagation directions as constrained by felt intensities. The 2018 slip potential for fifteen segments of the Himalaya are evaluated and potential magnitudes assessed for future earthquakes should these segments fail in isolation or as contiguous ruptures. Ten of these fifteen segments are sufficiently mature currently to host a great earthquake (M w ≥ 8). Fatal Himalayan earthquakes have in the past occurred mostly in the daylight hours. The death toll from a future nocturnal earthquake in the Himalaya could possibly exceed 100 000 due to increased populations and the vulnerability of present-day construction methods.
Conference Paper
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Nepal was shaken by magnitude Mw7.8 Gorkha Earthquake on 25th April 2015 at 11:56 AM local time with the epicenter at Barpak, Gorkha, 80km North West of Kathmandu Valley. This Earthquake was followed by thousands of aftershocks including M6.6 and M6.7 within 24 hours and Mw 7.3 on 12th May 2015 which caused additional damage and casualties. According to official announcement, the impacts caused by this event included 8,969 people killed, and 22,321 injured in Nepal. In addition, significant damage to buildings, infrastructures and other critical services were well observed. There were very few strong motion instruments placed in different locations, especially in Kathmandu Valley before the earthquake, and data was recorded only in very few locations within Kathmandu Valley. So, only way to come up with comprehensive intensity distribution of the earthquake was through questionnaire survey. In this study, an attempt has been made to prepare the seismic intensity map of Gorkha Earthquake using the seismic intensity questionnaire survey. Study area was divided into several grids of different sizes 500*500m in highly dense area to some kilometers in low dense areas. The intensity survey summarizes the responses and average intensity was assigned to each grid. Questionnaire survey was conducted in key location starting from near to epicenter area to faraway places where smaller shaking was observed. Through the survey at more than 300 locations in different parts of severely hit area, intensity distribution map was created. The intensity distribution shows that significant part of the affected area was suffered MMI VI to VII intensity, some area with MMI VIII and very few areas with intensity MMI IX. The results will be useful for earthquake risk assessment as well as planning for further earthquake risk management activities in Nepal.
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The collection of earthquake testimonies (i.e., qualitative descriptions of felt shaking) is essential for macroseismic studies (i.e., studies gathering information on how strongly an earthquake was felt in different places), and when done rapidly and systematically, improves situational awareness and in turn can contribute to efficient emergency response. In this study, we present advances made in the collection of testimonies following earthquakes around the world using a thumbnail‐based questionnaire implemented on the European‐Mediterranean Seismological Centre (EMSC) smartphone app and its website compatible for mobile devices. In both instances, the questionnaire consists of a selection of thumbnails, each representing an intensity level of the European Macroseismic Scale 1998. We find that testimonies are collected faster, and in larger numbers, by way of thumbnail‐based questionnaires than by more traditional online questionnaires. Responses were received from all seismically active regions of our planet, suggesting that thumbnails overcome language barriers. We also observed that the app is not sufficient on its own, because the websites are the main source of testimonies when an earthquake strikes a region for the first time in a while; it is only for subsequent shocks that the app is widely used. Notably though, the speed of the collection of testimonies increases significantly when the app is used. We find that automated EMSC intensities as assigned by user‐specified thumbnails are, on average, well correlated with “Did You Feel It?” (DYFI) responses and with the three independently and manually derived macroseismic datasets, but there is a tendency for EMSC to be biased low with respect to DYFI at moderate and large intensities. We address this by proposing a simple adjustment that will be verified in future earthquakes.
Conference Paper
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The aim of this article is to present a computer-aided comprehensive strategy for the detailed damage assessment of the buildings and the optimal prioritization of strengthening and remedial actions that are necessary after a major earthquake event. Based on the visual screening procedures a building inventory was first compiled; then a vulnerability ranking procedure was specifically tailored to the prevailing construction practice implemented into a multi-functional, georeferenced computer tool, that accommodates the management, evaluation, processing and archiving of the data stock gathered during the post-earthquake assessment process, and the visualization of its spatial distribution. The methodology proposed and the computer system developed was then applied to the Municipality of Chautara, Sindhupalchok, Nepal, city which was strongly damaged during the overwhelming 2015 Gorkha earthquake.
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We use 21 strong motion recordings from Nepal and India for the 25 April 2015 moment magnitude (Mw) 7.8 Gorkha, Nepal, earthquake together with the extensive macroseismic intensity data set presented by Martin et al. (Seism Res Lett 87:957–962, 2015) to analyse the distribution of ground motions at near-field and regional distances. We show that the data are consistent with the instrumental peak ground acceleration (PGA) versus macroseismic intensity relationship developed by Worden et al. (Bull Seism Soc Am 102:204–221, 2012), and use this relationship to estimate peak ground acceleration from intensities (PGAEMS). For nearest-fault distances (RRUP < 200 km), PGAEMS is consistent with the Atkinson and Boore (Bull Seism Soc Am 93:1703–1729, 2003) subduction zone ground motion prediction equation (GMPE). At greater distances (RRUP > 200 km), instrumental PGA values are consistent with this GMPE, while PGAEMS is systematically higher. We suggest the latter reflects a duration effect whereby effects of weak shaking are enhanced by long-duration and/or long-period ground motions from a large event at regional distances. We use PGAEMS values within 200 km to investigate the variability of high-frequency ground motions using the Atkinson and Boore (Bull Seism Soc Am 93:1703–1729, 2003) GMPE as a baseline. Across the near-field region, PGAEMS is higher by a factor of 2.0–2.5 towards the northern, down-dip edge of the rupture compared to the near-field region nearer to the southern, up-dip edge of the rupture. Inferred deamplification in the deepest part of the Kathmandu valley supports the conclusion that former lake-bed sediments experienced a pervasive nonlinear response during the mainshock (Dixit et al. in Seismol Res Lett 86(6):1533–1539, 2015; Rajaure et al. in Tectonophysics, 2016. Ground motions were significantly amplified in the southern Gangetic basin, but were relatively low in the northern basin. The overall distribution of ground motions and damage during the Gorkha earthquake thus reflects a combination of complex source, path, and site effects. We also present a macroseismic intensity data set and analysis of ground motions for the MW7.3 Dolakha aftershock on 12 May 2015, which we compare to the Gorkha mainshock and conclude was likely a high stress-drop event.
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The Gorkha earthquake on April 25th, 2015 was a long anticipated, low-angle thrust-faulting event on the shallow décollement between the India and Eurasia plates. We present a detailed multiple-event hypocenter relocation analysis of the Mw 7.8 Gorkha Nepal earthquake sequence, constrained by local seismic stations, and a geodetic rupture model based on InSAR and GPS data. We integrate these observations to place the Gorkha earthquake sequence into a seismotectonic context and evaluate potential earthquake hazard.Major results from this study include (1) a comprehensive catalog of calibrated hypocenters for the Gorkha earthquake sequence; (2) the Gorkha earthquake ruptured a ~. 150. ×. 60. km patch of the Main Himalayan Thrust (MHT), the décollement defining the plate boundary at depth, over an area surrounding but predominantly north of the capital city of Kathmandu (3) the distribution of aftershock seismicity surrounds the mainshock maximum slip patch; (4) aftershocks occur at or below the mainshock rupture plane with depths generally increasing to the north beneath the higher Himalaya, possibly outlining a 10-15. km thick subduction channel between the overriding Eurasian and subducting Indian plates; (5) the largest Mw 7.3 aftershock and the highest concentration of aftershocks occurred to the southeast the mainshock rupture, on a segment of the MHT décollement that was positively stressed towards failure; (6) the near surface portion of the MHT south of Kathmandu shows no aftershocks or slip during the mainshock. Results from this study characterize the details of the Gorkha earthquake sequence and provide constraints on where earthquake hazard remains high, and thus where future, damaging earthquakes may occur in this densely populated region. Up-dip segments of the MHT should be considered to be high hazard for future damaging earthquakes.
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The magnitude 7.8 Gorkha earthquake in April 2015 ruptured a 150-km-long section of the Himalayan décollement terminating close to Kathmandu. The earthquake failed to rupture the surface Himalayan frontal thrusts and raised concern that a future Mw ≤ 7.3 earthquake could break the unruptured region to the south and west of Kathmandu. Here we use GPS records of surface motions to show that no aseismic slip occurred on the ruptured fault plane in the six months immediately following the earthquake. We find that although 70 mm of afterslip occurred locally north of the rupture, fewer than 25 mm of afterslip occurred in a narrow zone to the south. Rapid initial afterslip north of the rupture was largely complete in six months, releasing aseismic-moment equivalent to a Mw 7.1 earthquake. Historical earthquakes in 1803, 1833, 1905 and 1947 also failed to rupture the Himalayan frontal faults, and were not followed by large earthquakes to their south. This implies that significant relict heterogeneous strain prevails throughout the Main Himalayan Thrust. The considerable slip during great Himalayan earthquakes may be due in part to great earthquakes tapping reservoirs of residual strain inherited from former partial ruptures of the Main Himalayan Thrust.
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We thank Andrea Tertulliani and his colleagues for their interest in our article on the 2015 Gorkha earthquake (Martin, Hough, et al. , 2015), and for their comments pertaining to our study (Tertulliani et al. , 2016). Indeed, as they note, a comprehensive assessment of macroseismic effects for an earthquake with far‐reaching effects as that of Gorkha is not only critically important but is also an extremely difficult undertaking. In the absence of a widely known web‐based system, employing a well‐calibrated algorithm with which to collect and systematically assess macroseismic information (e.g., Wald et al. , 1999; Coppola et al. , 2010; Bossu et al. , 2015) in the Indian subcontinent, one is left with two approaches to characterize effects of an event such as the Gorkha earthquake: a comprehensive ground‐based survey such as the one undertaken in India following the 2001 Bhuj earthquake (Pande and Kayal, 2003), or an assessment such as Martin, Hough, et al. (2015) akin to other contemporary studies (e.g., Nuttli, 1973; Sieh, 1978; Meltzner and Wald, 1998; Martin and Szeliga, 2010; Ambraseys and Bilham, 2012; Mahajan et al. , 2012; Gupta et al. , 2013; Singh et al. , 2013; Hough and Martin, 2015; Martin and Hough, 2015; Martin, Bradley, et al. , 2015; Ribeiro et al. , 2015), based primarily upon media reports and other available documentary accounts. Tertulliani et al. (2016) do not propose to undertake a ground‐based survey; this would be not only an undeniably valuable, but also a difficult and protracted exercise. Nor do they suggest a different macroseismic scale that might be more appropriate than the 1998 European Macroseismic Scale (henceforth EMS‐98) used by Martin, Hough, et al. (2015). Martin, Hough, et al. (2015) essentially already follow their recommendation to make the best use of documentary resources, including video recordings, …
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To augment limited instrumental recordings of the M w 7.8 Gorkha, Nepal, earthquake on 25 April 2015 (Nepali calendar: 12 Baisakh 2072, Bikram Samvat), we collected 3831 detailed media and first-person accounts of macroseismic effects that include sufficiently detailed information to assign intensities. The resulting intensity map reveals the distribution of shaking within and outside of Nepal, with the key result that shaking intensities throughout the near-field region only exceeded intensity 8 on the 1998 European Macroseismic Scale (EMS-98) in rare instances. Within the Kathmandu Valley, intensities were generally 6–7 EMS. This surprising (and fortunate) result can be explained by the nature of the mainshock ground motions , which were dominated by energy at periods significantly longer than the resonant periods of vernacular structures throughout the Kathmandu Valley. Outside of the Kathmandu Valley, intensities were also generally lower than 8 EMS, but the earthquake took a heavy toll on a number of remote villages, where many especially vulnerable masonry houses collapsed catastrophically in 7–8 EMS shaking. We further reconsider intensities from the 1833 earthquake sequence and conclude that it occurred on the same fault segment as the Gorkha earthquake.
We make and analyze structural damage observations from within the Kathmandu Valley following the 2015 M7.8 Gorkha, Nepal earthquake to derive macroseismic intensities at several locations including some located near ground motion recording sites. The macroseismic intensity estimates supplement the limited strong ground motion data in order to characterize the damage statistics. This augmentation allows for direct comparisons between ground motion amplitudes and structural damage characteristics and ultimately produces a more constrained ground shaking hazard map for the Gorkha earthquake. For systematic assessments, we specifically focused on damage to three specific building categories: a) low/mid-rise reinforced concrete frames with infill brick walls, b) unreinforced brick masonry bearing walls with reinforced concrete slabs, and c) unreinforced brick masonry bearing walls with partial timber framing. Evaluating dozens of photos of each construction type, assigning each building in the study sample to an EMS-98 Vulnerability Class based upon its structural characteristics, and then individually assigning an EMS-98 damage grade to each building allows a statistically-derived estimate of macroseismic intensity for each of nine study areas in and around the Kathmandu Valley. This analysis concludes that EMS-98 macroseismic intensities for the study areas from the Gorkha mainshock typically were in the VII-IX range. The intensity assignment process described is more rigorous than the informal approach of assigning intensities based upon anecdotal media or first-person accounts of felt-reports, shaking, and their interpretation of damage. Detailed EMS-98 macroseismic assessments in urban areas are critical for quantifying relations between shaking and damage as well as for calibrating loss estimates. We show that the macroseismic assignments made herein result in fatality estimates consistent with the overall and district-wide reported values.