Earthquake impact is an inherently interdisciplinary topic that receives
attention from many disciplines. The natural hazard of strong ground motion
is the reason why earthquakes are of interest to more than just
seismologists. However, earthquake shaking data often receive too little
attention by the general public and impact research in the social sciences.
The vocabulary used to discuss earthquakes has mostly evolved within and for
the discipline of seismology. Discussions on earthquakes outside of
seismology thus often use suboptimal concepts that are not of primary
concern. This study provides new theoretic concepts as well as novel
quantitative data analysis based on shaking data. A dataset of relevant
global earthquake ground shaking from 1960 to 2016 based on USGS ShakeMap
data has been constructed and applied to the determination of past ground
shaking worldwide. Two new definitions of earthquake location (the shaking
center and the shaking centroid) based on ground motion parameters are
introduced and compared to the epicenter. These definitions are intended to
facilitate a translation of the concept of earthquake location from a
seismology context to a geographic context. Furthermore, the first global
quantitative analysis on the size of the area that is on average exposed to
strong ground motion – measured by peak ground acceleration (PGA) – is
provided.
Introduction
Earthquakes receive a lot of attention from the general public as well as
numerous disciplines across the natural and social sciences. With the notable
exception of seismology, most of them are primarily or even exclusively
concerned with the surface phenomenon and the impacts of earthquakes.
However, the literature commonly uses magnitude or other suboptimal measures
to quantify the natural hazard of earthquakes for impact research. The
physical phenomenon of strong ground motion does often not receive enough
attention and the literature lacks an interdisciplinary discussion of the
natural hazard of earthquake-related surface shaking.
Earthquake risk communication is generally considered a high priority topic
and many research and practical efforts are concerned with educating the
public and improving preparation. The Southern California Earthquake Center
(SCEC), for example, has started the Great California ShakeOut
, which has become an annual drill with millions of
participants. The Global Earthquake Model (GEM) project is an international
effort to develop a global model of earthquake risk as an open-source,
community-driven project https://www.globalquakemodel.org/,
last access: 10 May 2018, and the Global Seismic Hazard Map
project (GSHAP) has promoted a regionally coordinated, homogeneous approach
to future seismic hazard evaluation, including the production and
distribution of a global seismic hazard map .
Technological progress has also allowed for the emergence of real-time
seismology, which provides real-time information about an event during and in
the immediate aftermath of an earthquake .
For research on earthquake impacts, an appropriate understanding of the
physical hazard of past earthquake shaking as well as access to relevant data
are necessary. However, earthquake communication about past events is a
relatively neglected topic and many authors struggle with the
inconsistent – and sometimes inadequate – approaches in the social science
literature . This study utilizes USGS ShakeMap data
– a real-time seismology product – to create a dataset of
global past shaking exposure. Natural hazard exposure maps are necessary for
impact research, and they also allow one to spatially overlap the natural hazard
with social variables representing vulnerability or preparedness. This study
provides a discussion and quantitative analysis of global earthquake ground
shaking and variables of interest that can be calculated from such data.
Shaking data
Many different factors about an earthquake play important roles in what kind
of shaking is experienced on the Earth's surface.
classifies them into three categories: those related to the earthquake source
(e.g., magnitude, depth, or faulting mechanism), travel path (e.g., geology
can have a significant impact on attenuation), and local site conditions. The
prediction, estimation, and recording of strong ground motion parameters are
active fields of ongoing research and technological improvements
. SCEC, for
example, has a Ground Motion Prediction Working Group.
Strong ground motion can be expressed with different parameters, but it is
commonly characterized by peak ground acceleration (PGA), peak ground
velocity (PGV), and peak ground displacement (PGD). More sophisticated
measures such as response spectra or Arias intensity
generally provide a better characterization of strong ground motion
, but such data are not as easily available as PGA or
PGV. No individual index of strong ground motion is ideal to represent the
entire frequency range, but peak ground motion parameters are considered to
perform satisfactorily .
This study will focus on PGA as the ground motion parameter. However, PGA
alone only provides a limited representation of ground motion. To represent
the entire frequency range more appropriately a multi-parameter
characterization of ground motion is commonly used (e.g., for selected
earthquakes the ShakeMap product also provides response spectra maps at
periods of 0.3, 1, and 3 s – according to three Uniform Building Code
reference periods). Nevertheless, while appropriate in engineering, a
multi-parameter approach is not reasonable for many social science
applications. PGA is still widely use in earthquake engineering, and it
provides the advantage of a single-valued parameter with good data
availability. Expressing past shaking with PGA is also consistent with the
common approach of using PGA for earthquake hazard maps.
While more sophisticated approaches to calculate ground motion parameters
(e.g., those employed by SCEC) than the ShakeMap methodology exist, USGS
ShakeMaps are unique in providing consistent earthquake strong ground motion
data for a large number of events on a global scale and for several decades.
For this study 14 608 ShakeMaps of earthquakes from 1960 to 2016 have been
compiled into one dataset. Each ShakeMap consists of observed instrumental
ground motion parameters where available and estimates from models based on
ground motion prediction equations, nearby observations, and other data,
where no observations exist. The ShakeMap methodology is continuously
improved and documented in numerous publications, which can be found through
the USGS website
(https://earthquake.usgs.gov/data/shakemap/background.php, last access:
10 May 2018).
In December 2016 all online available USGS ShakeMaps were collected and
combined into one dataset for this study. The ShakeMaps were combined with
the GPWv4 gridded Land and Water Area to restrict the
ShakeMaps to only on surface land shaking. Except for one event in 1923,
ShakeMaps generally exist starting from 1960, and they are systematically
available from 1973 onwards. The representativeness of the sample of
earthquake ground shaking in the dataset compared to all earthquake ground
shaking in the time period is assessed by matching the ShakeMap data to two
different earthquake lists, which allows a cross validation. Since not every
earthquake has a ShakeMap, a reference dataset of all (or at least almost
all) earthquakes since 1960 is required. For this purpose the ANSS
Comprehensive Earthquake Catalog is used. More information on
the ComCat data can be found in Appendix . Finally, the NGDC
Significant Earthquake Database is applied as an additional
reference dataset and to assign impacts to events (the impact data will be
used in future research). The combination of the three earthquake data
sources can help to identify how representative the aggregated ShakeMap
dataset is for all global earthquake ground shaking. Details on how the three
datasets were linked with each other are described in Appendix .
As a result, the dataset can be considered to contain all relevant global
earthquake ground shaking from January 1973 to October 2016, a
reasonable sample from 1970 to 1972, and shaking from individual
devastating events from 1960 to 1969. However, the sample is more complete in
later years, in that it contains more weaker events. For smaller events the
dataset has a bias towards North American events, which can be avoided by
restricting the sample to only events with a magnitude of 5.5 or greater.
However, reducing this threshold to 4.5 is generally sufficient to avoid this
bias. Details on the representativeness of the ShakeMap dataset can be found
in Appendix .
Earthquake shaking history of 1973–2015. Panel (a) shows
the average annual maximum shaking exposure and panel (b) provides
the average annual number of “big” shaking events by location.
Panel (a) also illustrates some limitations of the data, such as the
edges of individual ShakeMaps and an over-representation of low shaking
events in more populated regions (e.g., Australia). It is also important to be
aware that the shaking history is a combination of observed and estimated
data for actual shaking exposure in the given time period.
Past earthquake shaking
Past earthquake shaking can be approached from two different angles, either
comparing different locations or comparing different events. First, we can
consider the exposure of a location or region to past earthquake shaking of
an individual event or to several events over a time period. The constructed
dataset allows one to visualize the global past shaking exposure.
term the average annual pixel exposure to maximum cyclone
wind speeds the “cyclone climate”. This representation of past exposure
to a natural hazard can be particularly useful in social science
applications. In a similar way, the average annual pixel exposure to maximum
PGA could be called the earthquake shaking “climate”. Since earthquakes
are not a climate phenomenon and past exposure maps are not equivalent to
future hazard maps, it is more reasonable to refer to it as the earthquake
shaking history. Figure illustrates the earthquake shaking
history of 1973–2015, the period for which the dataset is found to be
representative of overall shaking in the specific time range. However, some
limitations of ShakeMaps become apparent in Fig. a: (i) the cutoff
edges of the individual ShakeMaps are visible, (ii) some unrealistically high
outliers might skew individual pixels, and (iii) low shaking/impact events
are more likely to receive a ShakeMap when they are in areas of interest
(e.g., cities in Australia). The visualization in Fig. b avoids
these limitations by restricting to shaking above a PGA of 10 %g and
showing the number of events above that threshold. Earthquake hazard maps are
a common way to illustrate where and what strength of future earthquake
shaking is likely to occur. Such maps are usually expressed in PGA that is
expected to be exceeded at a certain likelihood within a given number of
years (e.g., ). Using the maximum shaking over time
instead of the average annual shaking allows one to compare the actually
experienced shaking (or estimates of it) with the probabilistic estimates
from hazard maps. To illustrate this potential application of ShakeMap data,
a comparison of the earthquake maximum shaking history with the GSHAP global
earthquake hazard has been conducted and can be found in
Appendix .
Second, the comparison of different past events can be the main objective
instead of comparing individual regional (or single coordinate) exposures.
Two crucial aspects of a particular earthquake are the location and the area
affected by the shaking. It is, however, not straightforward to define these
concepts. An earthquake is caused by the rupturing of a fault segment
(illustrated in Fig. ). The earthquake originates at the
hypocenter, but waves radiate out from the whole segment of the fault that
ruptures (rupture area). This results in the epicenter not being necessarily
at the center of the strong ground motion area. In the figure this depends on
the size of the rupture and the dip angle of the fault. Other factors such as
local site conditions and water bodies also affect where strong ground
shaking occurs. From a social science perspective the surface projection of
the rupture area could actually be considered to be more relevant than the
epicenter – which is the surface projection of the hypocenter. However, there
are numerous other factors that influence surface shaking and even two
earthquakes on the same fault can behave very differently due to their
underlying rupture processes.
Earthquake location
Earthquake location is currently primarily discussed within and from the
context of seismology. However, a translation to a geographic language would
often be beneficial. Emergency response, hazard management, and regional
planning often rely heavily on geographic parameters and the use of
geographic information systems (GIS). coined the “first
law of geography” stating that “everything is related to everything else,
but near things are more related than distant things”. It is therefore not
surprising that the location is information about an earthquake that is of
great interest to the general public and disaster management. Furthermore,
the consideration of spatial effects (e.g., spatial autocorrelation and
spatial heterogeneity) can be crucial in econometric models
in the social sciences and requires to assign a
location to each observation. Thus earthquake location can also be a crucial
parameter in social science applications.
Illustration of a fault plane and wave intensity. The earthquake
nucleates at the hypocenter, but waves radiate out from every point of the
rupture area. For this reason as well as other factors (e.g., local site
conditions and water bodies), the epicenter is not necessarily in the center
of the strong shaking area.
The currently most commonly used and calculated points to characterize
earthquake location are the hypocenter and the epicenter. However, from a
strong ground motion perspective they are not the most interesting points.
The epicenter is not necessarily a good proxy for where strong ground motion
occurs, and it is thus not the optimal location choice for many applications.
When the epicenter is offshore it can also be far away from the strong
shaking region, and it is not straightforward to assign the event to a
country or region. Another earthquake location is the centroid location, such
as those calculated by the CMT project .
The centroid location is the average location in space and time of the
seismic energy release. However, the centroid location does also not account
for water bodies and its surface projection can thus often be far from the
strong shaking area as well.
In disaster management and planning as well as social science applications,
the desired location parameter should summarize the spatial component of
earthquake ground motion. While location information about earthquakes is
often supplemented with qualitative statements (e.g., “the epicenter is X km
offshore” or “the most affected region is X km south of city Y”), the
additional information does not necessarily enhance the digestion of
information. This is particularly the case when the information provides
details about the complexities of a rupture process. Details such as rupture
length or directivity are important aspects about an earthquake in seismology
and earthquake engineering, but they do not necessarily facilitate a better
understanding for individuals without the respective backgrounds and can even
contribute to confusion.
A simple geographic parameter that summarizes the shaking of an earthquake
can facilitate a translation from seismology and engineering to a geographic
context. Since many decision makers are familiar with such a geographic
context, this can enhance digestion of information by relevant individuals
and groups. Moreover, in some social science applications it is essential to
be able to assign coordinates to an individual event. Since the phenomenon of
interest in these applications is ground shaking, the purpose of this
location is to summarize shaking. So far, no formal definitions for
earthquake locations based on ground motion parameters exist. This study will
introduce two surface points other than the epicenter, both of which can be
considered different definitions of the earthquake surface location: the
shaking centroid and the shaking center. Both definitions are formulated such
that they can be applied to any ground motion parameter. Nevertheless, this
study specifically applies them to ShakeMap PGA data.
The “shaking centroid” (xSCt,ySCt) will be defined as the
average location (xi,yi) of shaking si weighted by the squared shaking
for a given ground motion parameter, only including locations that experience
at least 50 % of the maximum shaking smax of that event.
xSCt,ySCt=∑i∈{i:si≥0.5smax}(xi,yi)si2∑i∈{i:si≥0.5smax}si2
Restricting the included locations to the area with at least 50 % of the
maximum shaking is chosen for two reasons. First, it helps to avoid the
problem that ShakeMaps are usually cutoff before the shaking has completely
attenuated. Second, it ensures that the shaking centroid represents a
location that summarizes best the strong shaking area of the particular
event. The weaker shaking area is generally of less interest. The squares of
the ground motion parameter are also chosen to allow for a stronger weight of
the high shaking locations. Just like the epicenter, the shaking centroid
could be a location that does not actually experience any shaking (when it
falls in water) or only relatively low shaking itself.
In contrast, the “shaking center” (SC) is the point on the surface
which experiences the strongest shaking for a given ground motion parameter.
xSC,ySC={(xi,yi):si=smax:=maxisi}
The calculation of the shaking center provides a challenge when a ShakeMap
has more than one location that shares this maximum value. Details on the here
applied approach to handle this issue are described in Appendix .
Distances in kilometers between the epicenter (EC), shaking center
(SC), and shaking centroid (SCt), by whether the epicenter is in water or on
land for all 11 510 events with shaking and magnitude greater or equal 4.5.
EC to SC EC to SCt SC to SCt EpicenterMeanSDMaxMeanSDMaxMeanSDMaxOn land (46 %)713362510313713355In water (54 %)5353102259546822533445Total (100 %)3246102235486821628445
Figure provides an example of a ShakeMap with the three different
surface locations. The shaking center and shaking centroid generally do not
coincide with the epicenter. In particular when the epicenter lies in water,
it will definitely be distinct from the shaking center and it will very
likely also be distinct from the shaking centroid. The problems with the
definitions of the shaking center and the shaking centroid are that (i) they
depend on the choice of a ground motion parameter and (ii) any map of a
ground motion parameter – and therefore also the shaking center and the
shaking centroid – cannot be as accurately evaluated as the epicenter.
However, the shaking center and shaking centroid are locations of greater
interest for many applications.
Table compares the locations of epicenter, shaking center and
shaking centroid for PGA as the ground motion parameter for all 12388
ShakeMaps in the dataset with magnitude 4.5 or greater. About 57 % of those
earthquakes have their epicenter in water and some of those events do not
cause any shaking. Among the 11 510 events that do cause shaking about 54 %
have their epicenter in water. For the 46 % of those earthquakes that have
their epicenter on land, the average distance between the epicenter and the
shaking center is 7 km. This distance increases to 53 km when the epicenter
is in water. The full distribution of shaking center to epicenter distances
is shown in Fig. . The PGA at the epicenter is on average 13 %
weaker than at the shaking center, given that the epicenter is on land and
experiences any shaking (5285 events).
An example of a map of peak ground acceleration based on USGS
ShakeMap data. The figure shows the locations of the epicenter, the shaking
center and the shaking centroid. The shaking center in this example is 35 km
away from the epicenter. The distance between the shaking centroid and the
epicenter is even 53 km.
Strong ground motion area
In terms of the area affected by an earthquake, the literature so far has
mainly referred to the area exposed to certain levels of a qualitative
intensity scale for individual events. There has been no study on the global
pattern of the area that is on average exposed to strong ground motion
parameters for a given earthquake. This study provides the first summary of
global earthquake area size.
Comparing the epicenter (EC) and the shaking center (SC) in terms of
distance for all 11 510 earthquakes with shaking and magnitude 4.5 or
greater. In only about 9 % of earthquakes does the epicenter coincide
with the shaking center.
Attenuation across the world illustrated by average strong ground
motion area for 1973–2015. This map shows the average area that was exposed
to at least 90 % of the maximum PGA for an earthquake with the epicenter
at that location. For each 1.25 × 1.25∘ grid cell the
average area is calculated for all earthquakes of the 12 388 ShakeMaps with
magnitude 4.5 or greater, which have their epicenter in that grid cell. The
total number of earthquakes per grid cell varies significantly and the fact
that many regions have recurrence intervals of more than 43 years for strong
events makes this map particularly sensitive to the time interval of the
data.
Earthquake area compared to magnitude and maximum PGA. The four
panels in this figure show scatter plots (with scatter density illustrated by
color) to illustrate the relationships between these measures. Only
earthquakes with magnitude of at least 4.5 and maximum PGA of at least
10 %g are included.
The size of the area that experiences strong ground motion from an earthquake
is strongly dependent on the regional geology. Figure illustrates
how different the size of the area exposed to strong ground motion can be
across the world. In particular it shows the average size of the area that
was exposed to at least 90 % of the maximum PGA within each grid cell for
the time period 1973–2015 (see Fig. in Appendix
for reference to how many earthquakes are used in each grid cell to calculate
the average). For example, earthquakes along the west coast of South America
can generally be felt at far wider distances from the epicenter than
earthquakes on the west coast of North America. The west coast of South
America in Fig. also illustrates how earthquakes close to the
coast are spatially smaller, since the ocean restricts the shaking pattern to
one side. Water bodies are crucial in defining the area that can experience
ground motion, and therefore also for the area exposed to strong ground
motion.
Earthquake magnitude and distance are two of the most important factors in
ground motion prediction equations. It is therefore intuitive that magnitude
affects the area exposed to a particular shaking threshold, and it is indeed
easy to see this relationship in the data. However, as Fig. d
shows, other factors (e.g., geology and water bodies) introduce significant
noise in this relationship and make it thus less straightforward. This
highlights the importance of other factors than magnitude in determining
surface shaking. A more detailed summary of average shaking areas can be
found in Tables and . The
tables provide the average area exposed to 90 % of the maximum PGA for
each ShakeMap and the average area exposed to at least 10 %g PGA,
separately by magnitude and maximum PGA level. The total number of
earthquakes in each category, which is used to calculate the average, can be
found in Table .
Average area in square kilometers exposed to at least 90 % of
the maximum PGA, by magnitude and maximum PGA level.
A stronger magnitude event – keeping everything else about the earthquake
constant (i.e., depth, geology, fault type, hypocenter location) – will
result in larger areas exposed to any given PGA level. This is generally
confirmed by the data in Table . A high-magnitude event can only
have a low maximum PGA level when the epicenter is in water. It is, however,
likely that a still large area would be exposed to these relatively low
shaking values. Such events are responsible for the very large areas exposed
to 90 % of the maximum PGA in Table and
Fig. b. While an increase of the area above a fixed PGA threshold
with increasing magnitude is intuitive, Table and
Fig. b suggest that also the area exposed to a fixed percentage
of the maximum PGA increases with magnitude. This is most likely due to the
fact that large magnitude events tend to turn the large amount of energy
released not necessarily into stronger shaking, but into larger areas
experiencing strong shaking. A similar relationship does not seem to hold for
maximum PGA and area (see Fig. a). This can be
interpreted as the size of the area exposed to a certain percentage of the
maximum PGA being independent of the maximum PGA, but dependent on magnitude.
Earthquake magnitude therefore seems to contain more information about the
spatial extent of an earthquake than the maximum PGA. The relationship
between magnitude and the area exposed to at least 90 % of the maximum PGA
could potentially affect the global pattern of attenuation illustrated in
Fig. . However, the same figure only for earthquakes with
magnitude between 5.5 and 6.5 (see Fig. ) confirms the overall
pattern.
Conclusion
This study provides a discussion of earthquake shaking data for an
interdisciplinary audience and with applications in earthquake impact
research, particularly with the social sciences in mind. It constructs and
utilizes a comprehensive dataset of global strong ground motion data to
define new concepts of earthquake location as well as strong shaking area to
help summarize the natural hazard of surface shaking. These concepts can help
to facilitate a more effective communication about the natural hazard of past
earthquakes that is focused on surface shaking. The concept of a shaking
center and a shaking centroid are introduced, which can often be better
suited location definitions for an earthquake than the epicenter in social
science applications and in disaster management.
More than 14 500 individual ShakeMaps were compiled into one comprehensive
dataset. The dataset can be considered to contain all relevant global
earthquake ground shaking from January 1973 to October 2016, a reasonable
sample from 1970 to 1972, and shaking from individual devastating events from
1960 to 1969. Observed or estimated shaking data of past events can be used
to compare hazard maps with maps of actual shaking occurrences. An example of
this application can be found in Appendix , which compares the
maximum PGA exposure for 1973–2015 according to the ShakeMap data with the
GSHAP hazard map of probabilistic estimates.
The dataset is applied to calculate the shaking center and shaking centroid
for all events in the dataset. The shaking center is particularly useful to
assign a country to an event, since it is always on land. The shaking
centroid, in contrast, is generally the best representation of the overall
location of shaking. It is the most reasonable choice for the assignment of
an event to a general region or to use as the
location in spatial regression models or other statistical tools. The CMT
centroid location could also be a relatively good predictor of the location
of strong surface shaking. An interesting future extension of this research
would therefore be to combine the ShakeMap dataset with the CMT data to
compare the shaking center and shaking centroid with the CMT centroid
location.
Finally, the dataset is also applied to calculate a number of different
shaking area variables. This work provides the first summary of global
earthquake strong ground motion area size. The average strong ground motion
area is shown to be a useful tool to visualize attenuation across different
regions of the world.
The constructed dataset can be used in future research to determine the
short-term and long-term impacts of past earthquakes. This will allow us to
investigate the impacts of earthquakes based on measures that represent the
natural hazard of interest: earthquake ground shaking.
The data sources used in this study are freely
available at the USGS website
(https://earthquake.usgs.gov/data/shakemap/ and
https://earthquake.usgs.gov/data/comcat/, last access: 10 May 2018;
), the NGDC website
(https://www.ngdc.noaa.gov/hazard/earthqk.shtml; ),
and the GSHAP website (http://static.seismo.ethz.ch/GSHAP/global/, last
access: 10 May 2018). The code that was used to process the data and create
the tables and figures, as well as more details about the raw data, is
available at the following GitHub repository:
https://github.com/slackner0/EQSurface.
ANSS Comprehensive Earthquake Catalog (ComCat)
For this work the list of all events with magnitude 4.5 or higher that
occurred between January 1960 and October 2016 is used. Additionally, events
below magnitude 4.5 during that time period were added if they do have a
ShakeMap according to ComCat. This results in a list of 225 429 earthquakes.
The data were downloaded in March 2017 with the ComCat online access tool
(https://earthquake.usgs.gov/earthquakes/search/, last access: 10 May
2018). The threshold of 4.5 is chosen since earthquakes outside the US below
this magnitude are not as systematically recorded in ComCat. We thus have a
reliable but not entirely complete list of global earthquakes of
magnitude 4.5 and higher for the chosen years. Since some of the earthquake
data sources in ComCat only provide data starting from certain years, the
data are more complete for more recent years. As the analysis will show,
particularly for the time period 1960–1972, the ComCat list cannot be
considered complete.
A more likely problem than the lack of events in the list are possible
duplicate events in the list. Earthquakes often occur in clusters. A big
event might have foreshocks or aftershocks. Sometimes two different
earthquakes occur at very close proximity and less than a minute apart.
However, a close investigation of the ComCat list reveals that some of those
particularly similar events in terms of timing, location, and magnitude might
actually not be separate events, but the same event with slight differences
in the estimated source parameters from different data contributors. I
exclude 33 events from the GCMT network for which another event within 1.5 s
at a distance of under 3∘ exists, since those events seem to be
duplicates. Furthermore, I also exclude six events that do not fulfill these
criteria but have been manually identified as most likely duplicates. There
are, however, most likely more duplicate events, as the representativeness
analysis shows. After excluding these events, we have a list of
225 390 events which can be uniquely identified by the combination of the
following parameters (rounded to specific accuracies in parentheses): timing
(to the minute), magnitude (0.1), longitude (1), latitude (0.25), and depth
(25).
Linking the three earthquake datasets
The first step is to combine the ComCat earthquake list with the ShakeMap
dataset. Unfortunately, the ComCat list and the ShakeMap data are often
updated separately from each other and earthquake source parameters (e.g.,
magnitude, timing, location) can therefore differ between a ComCat event and
the ShakeMap for the same event. Also the earthquake “ID” does not always
agree between the ShakeMap and the corresponding ComCat event. The
differences in source parameters stem either from the data providing network
updating the parameters or from different networks being chosen for the
ShakeMap and the ComCat with slightly deviating parameters. Sometimes
the magnitude type might also be different, resulting in different magnitude
values. When possible events from the datasets are matched by timing (to the
minute), magnitude (rounded to 0.1), longitude (rounded to 1), latitude
(rounded to 0.25), and depth (rounded to 25). Such a match is possible for
7882 ShakeMaps.
The remaining ShakeMaps are matched to the remaining ComCat events (i) if
they are at most 60 s apart, at a (Euclidean) distance of at most 2∘, and have a difference in magnitude of at most 2.2 (0.7 if the
ShakeMap magnitude is below 5.5), or (ii) if they occur within 2 s and
at a distance of at most 2∘. If several events fulfill these criteria,
the event with the least time difference and the event with the least spatial
difference are identified, and if they are the same event it is assigned to
the ShakeMap. Otherwise the event with the least time difference is chosen if
that time difference is at most one-fifth of the next closest event (in terms
of timing). If that again is not the case, the spatially closest event is
chosen, given that it has a spatial distance of at most 1∘. For all so
far unmatched events of relevance (high magnitude of the ShakeMap or ComCat
event, or ComCat indicates that a ShakeMap should exist for an event) a
manual check and potential assignment is done. For 20 events a manual
assignment was necessary to match the right ComCat event and ShakeMap.
This process finally results in a total of 14 592 ComCat events with ShakeMap.
According to ComCat only 5310 events are supposed to have a ShakeMap. It was,
however, possible to find significantly more than that on the USGS website.
Nevertheless there are 127 events which are supposed to have a ShakeMap,
which is missing in the dataset. Most of them are missing because they were
produced after December 2016 when the ShakeMaps were downloaded and some also
because the ShakeMap files were corrupted. The magnitude of 67 of those
events is below 4.5 and for only three of the 127 events is the magnitude
higher than 5.5. It is therefore reasonable to assume that the exclusion of
these 127 ShakeMaps will not affect the representativeness of the dataset in
a significant way.
The second step is assigning each event from the significant earthquakes list
to a ComCat event. Again, the source parameters show slight deviations and a
similar approach as matching ShakeMaps with ComCat events is utilized. Each
significant earthquake event is matched to a ComCat event if they are at most
90 s apart, at a distance of at most 5∘, and have a difference in
magnitude under or equal to 2. If more than one ComCat event fulfills these
criteria the event with the smallest time difference and spatial distance is
chosen (they always agree for this dataset). However, for some events the
significant earthquake list has missing timing data (second, minute, or
hour). For those events, the timing has to be within the same day and the
spatial difference cannot be more than 2.5∘. Additionally, 14 events
were matched manually. Unfortunately some events in the significant
earthquake list seem to have typos (e.g., a drop in the leading
100∘ of
a longitude location). Some typos are identified manually and they are part
of the 14 manual matches, but there are potentially more typos or just
deviations in the data in terms of the timing. For all unmatched events with
no ComCat event within 90 s, we therefore identify matches if they are
within 24 h, at a distance of at most 0.2∘, and have a magnitude
that deviates by at most 0.2.
All but 152 of the 2130 significant earthquakes can finally be matched with
ComCat events. An additional 16 events of the significant earthquake list
that are not in the ComCat list were able to be matched with yet unmatched
ShakeMaps. It is unlikely that excluding the remaining 136 events will bias
the data in a problematic way. First, most of those events have relatively
low magnitudes and are therefore not in the ComCat list (114 of the 136 events have a magnitude below 5.5). Second, 86 of the 136 earthquakes stem
from the period of 1960–1972. This is a sign that the ComCat list for that
period is not as complete as for later periods, which we already expect from
the data availability of the ComCat data sources. Finally, considering the
impact of fatalities, 91 of the 136 events caused at least 1 death, but the
average among those is only 11, with a maximum of 80. After 1972, the largest
number of fatalities among these events is 14.
For the time period of January 1960–October 2016 we have 14 608 ShakeMaps
that are matched either to a ComCat event (13 061), to a significant
earthquake list event (16), or to both (1531). For those events we will use the
source parameters from the ShakeMap and disregard the potentially deviating
ComCat and significant earthquake list parameters. If no ShakeMap exists, the
ComCat source parameters will supersede the significant earthquake list
parameters in the dataset.
The representativeness of the ShakeMap dataset
Number of ShakeMaps in the dataset by magnitude and maximum PGA
level. The magnitude distribution of earthquakes with magnitude above 5.5
follows a power law, as expected for a comprehensive sample of earthquakes.
For lower-magnitude events ShakeMaps are not consistently produced and a less
consistent magnitude distribution is therefore observed in that range.
Epicenter locations of the sample of 12 388 earthquakes in the
dataset with ShakeMaps and magnitude 4.5 or greater. The map shows the number
of earthquakes in the dataset which have their epicenter in the corresponding
1.25 × 1.25∘ grid cell.
Attenuation across the world illustrated by average strong ground
motion area for 1973–2015 and earthquakes with magnitude between 5.5 and
6.5. This map shows the average area that is exposed to at least 90 % of
the maximum PGA for an earthquake with its epicenter at that location.
Number of earthquakes per year for given magnitude ranges. Before
1973 ComCat has a significant number of missing events, particularly for
events with magnitude below 5.5. Between 1960 and 1970 ShakeMaps only exist
for individual events with (high) fatalities. Starting 1973, ShakeMaps are
produced systematically, and from 2007 onwards almost all events with
magnitude 5.5 or greater have a ShakeMap.
The final ShakeMap dataset consists of 14 608 events. Table summarizes the number of
events in the dataset by magnitude and maximum PGA. Figure
provides a geographic overview of the earthquakes in the dataset by epicenter
location. Figure presents the geographic distribution of
the average strong motion area for all events in the dataset, and
Fig. provides a similar representation of the data, but
restricted to only events with a magnitude between 5.5 and 6.5.
The combination of the three earthquake data sources can help to identify how
representative the aggregated ShakeMap dataset is for all global earthquake
ground shaking. The first concern is whether “big” events – in terms
of either shaking or impacts – might not be in the dataset. The ShakeMap creation
criteria are supposed to ensure that this does not happen.
However, the significant earthquake list provides us with a tool to test this.
We would generally expect that a “significant” earthquake should have a
ShakeMap. Indeed, 1547 significant earthquakes in our combined dataset do
have a ShakeMap. We already discussed the 136 cases of significant earthquake
events that could not be matched to either a ComCat event or a ShakeMap.
Those events are either relatively small or from the time period of
1960–1972, suggesting that the ComCat event list is not complete for that
time period. Of the 447 significant earthquake list events which have been
matched to a ComCat event without a ShakeMap, 99 caused at least one
fatality. This is a concern, since events with fatalities should usually have
a ShakeMap. However, 84 of those events are from the time period of 1960–1972.
This is not unexpected, since ShakeMaps were not systematically produced
before 1973. The remaining 15 events after 1972 with fatalities but no
ShakeMap have on average 3 fatalities and a maximum of 11. We can therefore
expect that these events are sufficiently small to not miss a major impact
event. Of the entire 447 events, 207 are from 1960 to 1972 and they
therefore cause no additional concern beyond the already known unreliability of
that time period. For the remaining 240 events, only 73 have a magnitude of
5.5 or higher. Many of these higher-magnitude events are in remote locations
such as Antarctica or Alaska or occurred far offshore and did not cause a
lot of shaking. We have overall 9780 ShakeMaps with magnitude 5.5 or greater.
The 73 missing events therefore imply an error rate of 0.7 %, which is in an
acceptable range. Nevertheless, we can assume that the missing events would
have on average lower shaking and impacts than the included events, since
such events are more likely to get attention and therefore have a ShakeMap
produced.
Another question is whether the ShakeMap coverage is comparable across years.
To answer this question we first need to consider the reference data. As we
already discussed before, the ComCat list is most likely not as complete for
the time period 1960–1972 as for the years after that. In Fig.
we can see that the total number of events for “all earthquakes” (ShakeMap
events plus ComCat events without ShakeMap) seemingly increases over time.
However, this should in theory not be the case. The number of earthquakes per
year should be more or less constant over years. Part of the variation is
natural noise, but a lot of the increase can probably be explained by missing
events for the time period 1960–1972. This becomes particularly apparent when
events below magnitude 5.5 are considered. Another possible reason for the
increase is that duplicates in the ComCat database are more likely for the
time periods with more data contributing networks. Since additional networks
were added over the years, the number of duplicate events might also increase
with years. In particular the year 2011 looks suspiciously like it might hold
a large number of duplicate events, not only because the number of events is
exceptionally large but also because the number of events with magnitude 5.5
and without ShakeMap is surprisingly high for such a recent year. This needs
to be kept in mind when comparing the number of ShakeMap events with the
number of all events in Fig. . Nevertheless, we can clearly see
that from 1960 to 1970 ShakeMaps only exist for very few selected events, all
of
which have fatalities. Starting in 1970, ShakeMaps generally existed and
were
systematically produced from 1973 onwards. In 2006/2007 the share of events
with ShakeMaps drastically increases.
The ShakeMap data are increasingly complete in more recent years and it is
particularly incomplete before 1973 (and even more so before 1970). The extra
ShakeMaps in more recent years, however, come from more and more weaker
events receiving a ShakeMap. We can also confirm this with the distribution
of magnitude for events with and without ShakeMap in Fig. . With
increasing magnitude the share of ShakeMaps also increases. Most high-magnitude events do have a ShakeMap and the lower-magnitude (but above 5.5)
events without ShakeMap are on average older events than those with ShakeMap.
This figure also confirms that “big” events do generally have a ShakeMap
and we are not missing a significant number of high-magnitude events in the
ShakeMap dataset.
The distribution of magnitude for events from January 1973 to
October 2016. ShakeMaps are systematically produced for all events with
magnitude 5.5 or greater and the share of events with ShakeMap increases with
magnitude.
The share of events with ShakeMap (a) and the share of
North American ShakeMaps (b) by magnitude for events since 1973.
Below magnitude 5.5 ShakeMaps for North American events are more commonly
produced, and below magnitude 4.5 they are almost exclusively produced for
North American events.
A bigger concern is whether the ShakeMaps have a geographic bias. Since the
USGS is a North American institution, we expect that ShakeMaps for
low-magnitude earthquakes in North America are more likely produced than for
such events from different regions of the world. Comparing global maps of the
number of events in the ComCat list to the number of ShakeMaps does indeed
confirm a bias towards more North American events, in particular along the US
west coast. Figure helps us to investigate the North America bias
further. We can loosely define an event to be in North America when its
epicenter has a latitude between -170 and -60 and a longitude between 25
and 70∘.
We can then see that for all events since 1973, almost all ShakeMaps with
magnitude under 4 are from North America and there is a strong bias towards
North American events until about magnitude 4.5. Between magnitude 4.5 and
5.5 still relatively more North American ShakeMaps are available, but the
bias is in a reasonable range. For events with magnitude greater than 5.5 no
apparent North American bias exists. Panel a in Fig. has some
outliers with a much lower than expected ratio of high-magnitude events
having a ShakeMap. The most pronounced outliers are even for the share of
North American events being low for some relatively high-magnitude values. It
is unlikely that there are actually that many high-magnitude events
(particularly in North America) without ShakeMap and we are most likely
seeing the effect of duplicates in the ComCat list artificially increasing
the denominator.
It is advised to restrict the dataset to ShakeMaps with magnitude above 4.5
or even 5.5 for many applications to avoid the North America bias. Events
with a magnitude under 5.5 can still occasionally cause severe impacts. We
therefore do not want to exclude all of them, particularly since the events
often have a ShakeMap if they did indeed cause significant impacts.
Nevertheless, it is important to be aware that the sample is geographically
biased for earthquakes below the magnitude threshold of 5.5. If we only
considered ShakeMaps from events with a magnitude of 4.5 or higher, our sample
size would be 12 388.
Calculation of the shaking center
In the sample of 11 510 ShakeMaps with positive shaking and magnitude 4.5 or
higher, 10 401 ShakeMaps have a unique maximum PGA location, 656 events have
two grid cells with the maximum PGA, and 453 ShakeMaps have more than two
grid cells sharing the maximum PGA. For the earthquakes with more than one
grid cell as potential shaking center, it is necessary to define a consistent
way to pick one of them as the shaking center. The here applied approach to
tackle this problem is to incrementally add the surrounding cells of the shaking center candidate cell and calculate the average
shaking value in that square. Only those shaking center candidates that reach
the highest value for that measure are kept, until only one location remains.
In this manner, the location with the maximum shaking that has the strongest
shaking in the area surrounding it is chosen as the shaking center. This
procedure reaches the edge of the ShakeMap in only 47 cases when PGA as the
ground motion parameter. We then assume that the average shaking outside of
the ShakeMap is the same as the average shaking of the added cells that are
still in the ShakeMap at the same distance to the potential shaking center.
However, for 24 of the 47 events still no unique shaking center can be found,
since they occurred in small island regions and only caused shaking in very
small areas (those events have on average only 17 km2 exposed to any
shaking). For those events the location closest to the shaking centroid that
experiences maximum shaking is chosen as the shaking center. This procedure
results in a unique shaking center for all 11 510 events. In cases where
this would not be sufficient, the smallest distance to the epicenter can be
considered.
Comparison of earthquake shaking history with GSHAP hazard data
ShakeMap data can be used to compare probabilistic shaking estimates for the
future (hazard maps) with actual shaking occurrences. To illustrate this the
earthquake maximum shaking history calculated here (maximum PGA exposure for
1973–2015) is compared with the GSHAP global earthquake hazard map in
Fig. . The scale is cut off at a difference of 10 %g,
presenting any higher values in the same color as a difference of exactly
10 %g.
Since only limited documentation about the GSHAP data could be found, it is
necessary to make some assumptions about the data before it can be combined
with the ShakeMap data. In particular, (1) it is assumed that the coordinates
refer to the center of each grid cell, and (2) the data for the longitude 193
are dropped due to repetition of the longitude column (longitude
-167∘ is kept in the
data). For this comparison, the maximum shaking history is calculated at the
resolution of the GSHAP data of 1/10×1/10∘. Each grid cell
is assigned the maximum PGA value exposure that occurred anywhere in that
grid cell at some point between 1973 and 2015 according to the ShakeMap data.
Comparing the earthquake maximum shaking history with the GSHAP
hazard map. Panel (a) shows the maximum shaking experienced between
1973 and 2015, and panel (b) plots the GSHAP data of probabilistic PGA
estimates that will not be exceeded with a 90 % chance within 50 years.
Subtracting (b) from (a) results in the difference between
actual exposure and the probabilistic estimates, which is displayed in
panel (c). The scale is cut off at a difference of 10 %g. This
comparison reveals that the maximum shaking history for the given time range
tends to exceed the GSHAP estimates in most seismically active regions of the
world. It is important to keep in mind that the shaking history is a
combination of observed and estimated data for actual shaking exposure in the
given time period.
The author declares that she has no conflict of
interest.
Acknowledgements
I want to thank John Mutter, Art Lerner-Lam, Douglas Almond, Amir Jina, and
George Deodatis for their advice on this work. I also thank Timothy Foreman,
Jesse Anttila-Hughes, and Markus Riegler for helpful comments and
discussions. I am grateful to David Wald and Michael Hearne for advice on the
ShakeMap data and for providing some MATLAB code that helped process the
data.
Edited by: Maria Ana Baptista
Reviewed by: two anonymous referees
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