A magnitude 7.5 earthquake occurred on 28 September 2018 at 10:02:43 UTC
near the city of Palu on the Indonesian island of Sulawesi. It was a
shallow, strike-slip earthquake with a rupture extending to a length of about
150 km and reaching the surface. Moreover, this earthquake was identified as
one of very few events having a super-shear rupture speed.
Clear and long-lasting infrasound signatures related to this event were
observed by four infrasound arrays of the International Monitoring System of
the Comprehensive Nuclear-Test-Ban Treaty Organization as well as by one
national infrasound station in Singapore. Although these infrasound stations
SING (Singapore), I39PW (Palau), I07AU (Australia), I40PG (Papua New Guinea)
and I30JP (Japan) are located at large distances of between 1800 and 4500 km
from the earthquake's epicentral region, the observed infrasound signals
associated with this event were intense, including both seismic and acoustic
arrivals.
A detailed study of the event-related infrasound observations and the
potential infrasound generation mechanisms is presented, covering
range-dependent infrasound transmission loss and propagation modeling and
characterization of the atmospheric background conditions, as well as
identification of the regions of seismoacoustic activity by applying a back-projection method from the infrasound receivers to potential source regions.
This back projection of infrasonic arrivals allows one to estimate that the main
infrasound source region for the Sulawesi earthquake is related to the
extended rupture zone and the nearby topography. This estimation and a
comparison to other super-shear as well as large regional earthquakes
identify no clear connection between the earthquake's super-shear nature
and the strong infrasound emission.
Introduction
Indonesia is located in a region with a very high rate of natural seismicity
above a complex setting of plate tectonics. Subduction zones of convergent
plate boundaries in this region define the largest faults of the earth's
crust and the subregion of highest and most intense earthquake activity. In
fact, some of the strongest and most destructive earthquakes recorded during
the last decades have occurred in Indonesia, like the 2004 moment magnitude
(Mw) 9.3 Sumatra–Andaman earthquake and various other events with Mw larger
than 8 (Pailoplee, 2017). These strong offshore events can often generate large and
devastating tsunamis. Additional crustal-scale faults are also located on
the Indonesian island of Sulawesi, including the Palu–Koro fault transecting
the northern part of the island (Katili, 1978). The frequent seismic activity associated
with this fault was quantified using the United States Geological Survey
(USGS) nearby seismicity data link (USGS, 2018), resulting in at least 60 earthquakes
larger than body-wave magnitude (mb) 5 within the last 20 years and four events larger than
mb 6 prior to the event discussed in this study.
The 28 September 2018 Sulawesi earthquake occurred at 10:02:43 UTC near the
Indonesian city of Palu on the island of Sulawesi. It was estimated as a Mw 7.5 strike-slip earthquake (USGS, 2018) along the Palu–Koro fault with a hypocenter
location of 0.256∘ S and 119.846∘ E and a depth of about
20 km. Modeling indicates that the majority of the slip occurred shallowly on
the fault (above 10 km) with an offset of up to 7 m horizontal slip and a
dip slip of up to only 2 m (Socquet et al., 2019). The rupture zone of the event extended
north to south over roughly 150 km along the fault and through the city of
Palu, with a high rupture velocity of 4.1 km s-1 on average. This indicates it
to be a so-called super-shear event, having rupture velocities higher than
the corresponding shear velocities (see Bao et al., 2019; Socquet et al., 2019). The phenomenon is comparable to
the acoustic sonic boom, an effect where the source travels faster than its
emitted waves. Analogous to acoustics, the super-shear rupture generates a
shear-wave Mach cone, which may cause enhanced ground motion and result in
increased damage potential (Bernard and Baumont, 2005; Doan and Gary, 2009). The Sulawesi earthquake resulted not only in
intense ground shaking corresponding to intensity IX on the modified Mercalli
intensity scale, but also in liquefaction, landslides, and local tsunamis
within Palu Bay (see Heidarzadeh et al., 2019; Omira et al., 2019; Jamelot et al., 2019). A large number of precursory earthquakes as well as
aftershocks occurred surrounding this event.
The intense ground shaking of both the epicentral region and the topography
nearby the Sulawesi earthquake resulted in strong and clearly observed
infrasound signatures, which are the focus of this study. Infrasound, which
is the subaudible part of acoustic waves below 20 Hz, is generated by a
large number of natural and anthropogenic sources (e.g., see Le Pichon et al., 2010, 2019) and can
propagate over distances of thousands of kilometers with little attenuation recorded at highly sensitive infrasound arrays. Many sources of either an
explosive or an eruptive characteristic, or those associated with large mass
movements, can generate infrasound (e.g., Gibbons et al., 2015a; Pilger et al., 2018), including earthquakes.
Reports on infrasound from earthquakes in the USA (Mutschlecner and Whitaker, 2005) as well as in Peru,
China, and Chile (Le Pichon et al., 2002, 2003, 2006) indicate that the epicentral ground movement generates
infrasonic pressure waves. Further studies on the Mw 9.3 Sumatra–Andaman
earthquake (Le Pichon et al., 2005), on the Mw 9.0 Tohoku earthquake (Walker et al., 2013), and on Italian earthquakes
(Marchetti et al., 2016; Shani-Kadmiel et al., 2017; Hernandez et al., 2018) also highlight infrasound generated from tsunami waves hitting the
coastline and from secondary phenomena like remote ground motion of mountain
chains or of extended basin areas. This secondary infrasound by remote ground
motion is often called seismoacoustic waves, since the seismic waves
(longitudinal, shear, or surface) generated by an earthquake propagate to
distant terrain features where the wave energy is partly converted to
atmospheric acoustic waves in the infrasound frequency range (e.g., see Arrowsmith et al., 2010; Hedlin et al., 2012).
Although there are many studies about infrasound generated by earthquakes,
only a small number of earthquakes with a super-shear rupture speed have
been identified within the last 20 years (for Izmit, Turkey, in 1999, see
Bouchon et al., 2000; for Kunlunshan, Tibet, in 2001, see Bouchon and Vallee, 2003; for Denali, Alaska, in 2002, see Dunham and Archuleta, 2004; for Qinghai, China, in
2010, see Wang and Mori, 2012; and for Craig, Alaska, in 2013, see Yue et al., 2013), and only one publication known to
the authors identifies and investigates infrasound observations related to a
super-shear earthquake, namely the 2002 Denali earthquake (Olson et al., 2003). Therefore, a
main objective of this paper is to investigate the potential of a connection
between super-shear earthquakes and infrasound recordings of large
amplitude.
This paper is structured as follows: Sect. 2 describes the data and
methods applied within this study, Sect. 3 highlights the observations of
epicentral infrasound and seismoacoustic signatures at remote infrasound
arrays, Sect. 4 describes the modeling of infrasound transmission loss as
well as propagation and compares it to the observations, and Sect. 5 provides
a back-projection approach to identify the acoustic source regions of the
observed signals and discusses the event in comparison with similar
earthquakes.
Data and methods
This study mainly considers data recorded at infrasound arrays of the
International Monitoring System (IMS, e.g., described in Le Pichon et al., 2010, 2019) established under
the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The earthquake epicenter
and the nearest infrasound stations at distances of between 1800 and
4500 km around the event are shown in Fig. 1.
(a) Map of the Sulawesi earthquake epicenter (star) and the locations of the nearest surrounding infrasound stations (the circle corresponds to a single-sensor station and the triangles to multisensor IMS arrays; white-labeled stations registered the event, black-labeled ones did not). (b) Enlarged map of the epicentral source region showing in larger detail the rupture zone passing through the city of Palu. Map background was created using basemap data from the ESRI ArcGIS® Map Service.
The two IMS infrasound stations closest to the earthquake epicenter clearly
registered the event (I39PW in Palau and I07AU in northern Australia; see
Sect. 3). Two further IMS stations at larger distances found clear
indications of signals related to the earthquake (I40PG in Papua New Guinea
and I30JP in Japan, also see Sect. 3). However, two other Australian
stations (I04AU and I06AU) as well as all of the more distant IMS infrasound
arrays recorded no signals related to the earthquake source.
Additional data from a single infrasound sensor in Singapore (SING) were
investigated and also showed signatures related to the earthquake (see
Fig. S1 of the Supplement). However, due to a lack of array calculations
and directional information by only a single sensor, no further use has been made of these data.
The PMCC method (progressive multichannel correlation; see Cansi, 1995) used in this
study is available from the DTK-GPMCC application in the National Data Center (NDC)-in-a-Box
package. The main objective of the NDC-in-a-Box project is to offer the capability to process
and analyze seismic, infrasound, and hydroacoustic data to all
NDCs of CTBT member states, enabling them to become
active contributors to the verification regime of the CTBT. Technically, the NDC-in-a-Box project
consists of a number of automatic and interactive software tools which are
packaged in a virtual machine distributed by the CTBT International Data
Center (IDC). Among this set of software, DTK-GPMCC is the interactive array
processing tool. It allows users to configure and run the PMCC detector from
waveforms of any technology and to display and analyze the results. PMCC is
applied to the raw differential pressure recordings at each of the IMS
infrasound arrays' microbarometers to derive advanced data parameters like
back azimuth, apparent velocity, and frequency content of coherent signals
associated with different events (see Fig. 2). Back azimuth reflects
the horizontal direction of signal origin, while apparent velocity indicates
the arrival inclination, where higher values correspond to propagation from
higher-altitude ducts. Signals are identified as pixel information in
distinct time steps and frequency bands, and they are clustered to signal families
related to the same event. The 1/3 octave band configurations with an inverse-frequency-distributed window length are implemented between 0.01 and 4.4 Hz
(Garcés, 2013). Signals can be associated with a certain source by, e.g., applying cross-bearing techniques on the back-azimuth directions of two or more arrays
(Matoza et al., 2017). The seismic or acoustic origin as well as the propagation path of
signals, e.g., ducting via the stratosphere or thermosphere (Drob et al., 2003), can be inferred
from the apparent velocity and frequency content of the recordings.
In order to further investigate and understand the infrasound detection
pattern in the region following the Sulawesi earthquake, various simulations
were performed to compute acoustic transmission loss and to simulate
infrasound propagation between the source and the stations. Infrasound
transmission loss at the surface level (see Fig. 3) was calculated using a
frequency-dependent, semiempirical modeling technique coupled with
realistic atmospheric specifications along the infrasound propagation path
(Le Pichon et al., 2012; Tailpied et al., 2017) in order to draw a range- and frequency-dependent attenuation map
estimating the acoustic pressure loss between source and receivers in
decibels (dB). The transmission loss of the signal at each station is
associated with a confidence index that integrates uncertainties from the
propagation modeling and the atmospheric specifications. Infrasound
propagation (see Fig. 4) was modeled using a two-dimensional parabolic-equation method (NCPA PAPE; see Waxler et al., 2017) to quantify and visualize the ducting as
well as amplitude decrease between source and receivers.
In both the semiempirical and the parabolic-equation-based transmission
loss estimates, data from the European Centre for Medium-Range Weather
Forecasts (ECMWF) meteorological model are used to derive the effective sound
speed as the most important background parameter for infrasound propagation.
Indeed, this parameter, defined as adiabatic sound speed modified by
horizontal winds in the propagation direction of the modeled sound, is used
to provide the atmospheric background conditions along the propagation path
between the source and the stations (Wilson, 2003). Ducting along tropospheric,
stratospheric, or thermospheric waveguides (Drob et al., 2003) can be estimated in the same
manner as the total amplitude loss from geometric spreading as well as
atmospheric attenuation (Sutherland and Bass, 2004). ECMWF values are used from 0 to 60 km altitude
and merged with temperature and wind climatologies above (MSISE00 and HWM07;
see Picone et al., 2002; Drob et al., 2008) to provide seamless effective sound speed profiles from 0 to 140 km
altitude.
Back projection of the coherent earthquake-related signals observed at
infrasound arrays to their source region is performed within this study
using a seismoacoustic method similar to that of Marchetti et al. (2016) or Shani-Kadmiel et al. (2017), which is also part of
the built-in capabilities of PMCC (see Fig. 5). Assumed is a conversion of
the initial seismic wave with crustal propagation velocities of, e.g., 4 km s-1
to acoustic waves with an average speed of, e.g., 0.3 km s-1 at certain terrain
features, like steep or flat topography such as mountain chains, islands,
cliffs, or extended plains. This method identifies the seismoacoustic
conversion areas and thus infrasonic source regions for the signals
observed, taking into account the arrival time and
back-azimuth direction relative to a point source in space and time, here
the Mw 7.5 earthquake epicenter, for each PMCC pixel. The cumulative sum and frequency of
occurrence of the back-projected origin locations therefore allows for the
identification of infrasonic source regions, either of epicentral or seismoacoustic
origin.
Observations
The 28 September 2018 Sulawesi earthquake was identified in the recordings
of four IMS infrasound arrays: I39PW, I07AU, I40PG, and I30JP. Differential pressure recordings from these stations for 4 to 6 h following the
earthquake origin time (10:02:43 UTC) are analyzed using the PMCC method
described in Sect. 2. Signal parameters related to the earthquake are
extracted from the PMCC results in terms of arrival time and duration as
well as direction of origin (back azimuth) and apparent signal velocity.
Findings from the observations of five infrasound stations and from theoretical distance–azimuth calculations to the Sulawesi epicenter. Main signal groups are labeled with “IS” (infrasound) and “SA” (seismoacoustic).
StationSINGI39PWI07AUI40PGI30JPDistance to epicenter (km)17881845268936044474Expected back azimuth (∘)94243322276213Expected 300 m s-1 arrival time (UTC)11:4211:4512:3213:2314:11Observed arrival time (UTC)IS 11:50IS 11:36 SA 12:34IS 12:08 SA 11:22IS 13:05 SA 12:37IS 14:30Observed signal duration (min)IS 10IS 25 SA 7IS 44 SA 16IS 24 SA 8IS 33Observed mean celerity (m s-1)IS 267IS 290 SA 200IS 304 SA 514IS 309 SA 380IS 263Observed mean back azimuth (∘)– (no array)IS 251 SA 257IS 319 SA 321IS 275 SA 276IS 209Observed mean apparent velocity (m s-1)– (no array)IS 383 SA 359IS 356 SA 371IS 351 SA 360IS 436
These observation parameters for the four IMS infrasound arrays and for the
earthquake-related signal also identified in SING station data are
summarized in Table 1. Furthermore, source-to-station distances as well as
expected back-azimuth directions and arrival times using a celerity
(epicentral distance divided by the travel time) of 300 m s-1 are presented for
comparison. A graphical representation of the waveform beams
(bandpass-filtered between 0.6 and 4 Hz, except for I30JP, where it is between 0.02
and 0.1 Hz) and the main PMCC findings for the four IMS stations are provided
in Fig. 2, highlighting not only epicentral infrasound arrivals and their acoustic
characteristics in the observations but also seismoacoustic and seismic
signatures related to the event.
Waveform beams and PMCC-derived results for the four infrasound arrays I39PW (a), I07AU (b), I40PG (c), and I30JP (d; stations are ordered by epicentral distance with three frames per station and station labels in the lower left corners). Shown in the corresponding stations' top frames are the observed back-azimuth deviations from the direction to the earthquake epicenter (see labels in the upper right corners), in the middle frames are the observed apparent velocities, and in the bottom frames are the waveform beams. The whole 360∘ back-azimuth observations are converted to the given deviation plotting of ±180∘. Apparent velocities are saturated above 1 km s-1. Beams are bandpass-filtered between 0.6 and 4 Hz, and 4 h of data is shown, with the exception of I30JP, where the beam is bandpass-filtered between 0.02 and 0.1 Hz, and 6 h of data is shown. Main infrasound (IS) and seismoacoustic (SA) signal groups (see Table 1) are highlighted in grey.
Initial seismic waves with high-frequency components (0.3–3 Hz) are found in
I39PW and I07AU data arriving 4 to 6 min after the origin time,
indicating apparent P-wave velocities of 4–10 km s-1, lasting about 2 min. These are followed by low-frequency (0.05–0.5 Hz), quasi-continuous
seismic waves observed in I39PW, I07AU, I40PG, and possibly I30JP, likely
related to seismic shear and surface waves, with velocities of 1–3 km s-1.
Aftershock activity as well as seismic signals from other regional
earthquakes is also present in Fig. 2 for the hours after the main
earthquake; aftershocks include 12 events of mb 5 or greater and 40
events of mb 4 or greater within 6 h following the event
(USGS, 2018). Values for the arrival of seismic waves are not integrated in Table 1,
since the local microbarometer output generated from ground shaking of the
sensors is not the focus of this study. Nevertheless, the infrasound
sensors do work fairly well as seismic arrays for this event (e.g., see
Gibbons et al., 2015b) and the earthquake-related seismic arrivals can clearly be identified in
Fig. 2, having back azimuths towards the epicenter and apparent velocities
exceeding 1 km s-1 (drawn with dark blue colors in the middle frame plot of
each station, indicating seismic and not acoustic signal speeds).
Epicentral infrasound is clearly observed and produces the main signal with
the largest waveform amplitudes in I39PW and I07AU (beams are plotted in
Fig. 2 in the bottom frame plots of the respective stations, and signals are
highlighted by grey rectangles and “IS” labels). The analysis shows a
broadband frequency content (0.05 to 4.4 Hz) and long signal durations of 25
and 44 min (derived from the width of the high-frequency part signals
originating from epicentral directions in the PMCC analyses). These signals
are emphasized in Fig. 2, since the back-azimuth calculations as well as
the array beams are focused towards the earthquake epicenter (yellow colors
in the azimuth frame of each station indicate low to zero back-azimuth
deviations from this direction). The low deviations from the theoretical
back-azimuth directions (3 and 8∘; see Table 1 for the
corresponding values) confirm the signals to be associated with either the
epicenter, the rupture process at the surface, or the ground shaking of
topographic features on the island of Sulawesi. Crosswinds, as shown in
Fig. S2 of the Supplement, lead to certain back-azimuth deviations. An
azimuthal sweep is observed in the I07AU data from south to north
(directions of 316 to 323∘), consistent with the
north-to-south rupture over 150 km. Deviations from the expected
back-azimuth direction are largest in I39PW data (about ±10∘). The other stations only show weak or no such variations. See Fig. S3 of
the Supplement for a detailed representation of these findings using
absolute back-azimuth values.
For the more distant stations I40PG and I30JP, the epicentral infrasound is
consistent with the theoretical back azimuths (1 and 4∘ deviation) but mostly allocated with frequencies below 0.1 Hz, indicating
larger absorption of the high frequencies along the long-distance
propagation (see Sect. 4 for the corresponding propagation modeling). The
high-frequency pulses in the I40PG recordings around 12:40 UTC are
associated with a seismoacoustic signal, which is discussed at the end of this
section.
In general, the observed back azimuths fit very well to the theoretical ones
calculated for the epicenter for all four stations, allowing the application
of a cumulative back-projection method to locate the source regions of the
observed infrasonic signals in Sect. 5. The epicentral signals' mean
apparent velocities are all in the acoustic range valid for stratospheric
propagation (350 to 380 m s-1; see Table 1), with the exception of I30JP
having higher mean apparent velocities of 436 m s-1. This, together with low
celerity values of 263 m s-1 and the appearance of only low-frequency signals at
this station, strongly indicates thermospheric propagation for I30JP instead
of stratospheric. Thermospheric arrivals are expected to also be present in
the other stations' observations apart from the dominant stratospheric ones;
their later arrival time and lack of high-frequency content correspond to
the long-lasting signal families following the main signal peak for many
minutes in the low frequencies. These signal families can be observed
together with low-frequency seismic wave activity and low-frequency acoustic
components from the stratospheric ducting in frequency bands around 0.1 Hz.
They are discernible only to a certain degree by their apparent velocities and
arrival times, being the slowest and latest arrivals from the epicenter. The
celerities observed at I39PW, I07AU, and I40PG as well as the observed
arrival times and signal durations correspond well to the expected arrival
times calculated using a 300 m s-1 celerity of average stratospheric
propagation (see Table 1). The expected arrival times for these stations are
clearly within the main signals' observed time window and are only 2 to 6 min shifted from the respective midpoint of the observed arrivals' time
window (arrival time plus half of the signal duration).
Microbaroms, which are infrasonic signals from interacting ocean surface
waves (Donn and Naini, 1973; Ardhuin and Herbers, 2013), are also present in the recordings of I39PW and I07AU at around 0.2 Hz
and dominant before and after the earthquake signals. There is also surf or
potentially anthropogenic noise in I40PG and I30JP data at around 1 Hz during
the complete observation. These background (noise) signals can clearly be
separated from the epicentral signal by back azimuths (greenish colors in the top frame plots).
Seismoacoustic signals are identified in I07AU, I39PW, and I40PG data, coming
from nearly epicentral directions and having acoustic apparent velocities.
They have high-frequency content (above 1 Hz) and celerities below 200 or
above 380 m s-1, thus excluding purely acoustic waves propagating from the
epicenter at the time of the rupture and also those traveling through
the thermosphere or troposphere. These signals could be seismoacoustic arrivals
related to the earthquake (their signal parameters are provided in Table 1
and highlighted in Fig. 2 with the label “SA”). A conversion of seismic
to acoustic waves at certain distinct terrain features might be responsible
for these kind of signals. Islands between Java and Timor-Leste (south of
Sulawesi) could be the rough source region of the I07AU and I39PW signals,
while islands of North Maluku (east of Sulawesi) may be the source of the
seismoacoustic signals in I40PG. Further details on back-projecting and thus
identifying acoustic source regions are provided in Sect. 5. Nevertheless,
from the given observations it is not possible to certainly confirm these
signal locations as seismoacoustic source regions. None of the signatures
are observed at more than one station and smaller groups of signals come
from all regions around Sulawesi, including neighboring islands like
Borneo. These signals are not necessarily associated with the earthquake; they
could also originate from other local infrasound or ambient noise sources
and are just coincidental to the earthquake in direction and timing.
Alternatively, they could be due to uncertainties in the array processing or
back-projection methods.
Modeling results
Transmission loss calculations using firstly a semiempirical method for a
horizontal representation (map view, Fig. 3) and secondly a
parabolic-equation-based propagation model for a vertical representation
(cross section, Fig. 4) are performed in this section to confirm and
interpret the observed epicentral infrasound signatures as described above.
The semiempirical method is used to estimate the frequency-dependent
transmission loss of a signal reaching the different infrasound stations,
thereby characterizing its detectability. Propagation modeling is necessary
to identify observed and expected signal arrivals, and to associate them with
the prevailing atmospheric conditions between source and receivers, and the
corresponding ducting behavior.
Map quantifying the acoustic transmission loss in dB (color-coded), calculated for (a) 0.2 Hz and (a) 3 Hz source frequencies on a 0.5∘×0.5∘ grid. Arrows show direction and intensity of the stratospheric wind field averaged between 30 and 60 km altitude for 28 September 2018. The largest arrows represent a value of 25 m s-1. For figure symbols and station labels see Fig. 1. Country outlines are provided within the Matplotlib Basemap Toolkit (https://matplotlib.org/basemap/, last access: 2 December 2019).
Propagation modeling between the Sulawesi earthquake epicenter (plot origins at 0 km distance) and the infrasound arrays I39PW, I07AU, I40PG, and I30JP (respective triangles) using a range-dependent parabolic-equation method, quantifying the transmission loss in dB relative to 1 km for a frequency of 1 Hz. An averaged effective sound speed profile (veff) is shown for each station.
Summary of transmission loss values and uncertainties (in dB) derived for all stations within this study from the frequency-dependent, semiempirical method, as shown in Fig. 3.
The quantification of infrasonic transmission loss is shown in Fig. 3
using the semiempirical method (see Tailpied et al., 2017) as well as quantifying of the
stratospheric wind field in terms of intensity and directionality.
Simulations are performed within an 80∘×80∘ area using
0.5∘×0.5∘ spatial resolution around the earthquake
epicenter for source frequencies of 0.2 and 3 Hz. Most of the acoustic
energy is concentrated at the low-frequency band of 0.2 Hz. This was
calculated applying the Infrasonic Energy, Nth Octave (INFERNO)
algorithm (see Garcés, 2013) to the station data. It calculates acoustic
energy with frequency bands based on the American National Standards Institute (ANSI) and International Organization for Standardization (ISO) standards for noise
characterization for the acoustic range extended into the infrasound range,
and it is based on fractional octave bands. An example is shown in Fig. S1
of the Supplement. Within this band the transmission loss calculated is
similar for the closer stations SING, I39PW, I07AU, and I40PG (see Fig. 3a),
and their values are between 66 and 70 dB with uncertainties of about 4 dB (see Table 2). While values at these four stations indicate a
northwest-to-southeast corridor of signal amplitudes in the same order of
magnitude, the other stations in northeastern and southwestern directions
have slightly higher transmission loss values of between 73 and 79 dB (see
Table 2), indicating less favorable ducting conditions and detection
probabilities at these stations.
The similarity of the transmission loss values is consistent with the fact
that low-frequency signals are less affected by propagation effects along
the path. Drawing the same picture with a source frequency of 3 Hz (Fig. 3b) indicates a different situation: station values for SING, I39PW, I07AU,
and I40PG are now between 78 and 85 dB with uncertainties of 13 to 25 dB
(see Table 2). These values are still quite similar to the ones estimated
for 0.2 Hz, although the uncertainties for the calculation are increased.
The transmission loss calculated from the epicentral source into all
directions visualizes for the high frequencies a focal
effect in eastern and western directions with better observation conditions,
while leaving regions with increased transmission loss and thus more
unfavorable detection conditions in northern and southern directions. The
stations' values in the northern and southern directions are between 101
and 119 dB with uncertainties of 26 to 35 dB, indicating remarkably higher
transmission loss for these three stations due to propagation effects and
atmospheric conditions and explaining why no high-frequency signals (or
signals at all) are observed at the respective stations.
Stratospheric wind conditions affect the propagation, especially for the
higher frequencies, and point out the general possibility and effectiveness
of a stratospheric duct. This is consistent with the fact that high-frequency signals are more sensitive to the atmospheric conditions along the
propagation path, also explaining the higher uncertainties in the
calculation of these values. The stratospheric wind fields shown in Fig. 3
support this sensitivity by estimating the direction of the dominant
stratospheric wind regime, which is eastward at the Southern Hemisphere's
low latitudes, and the intensity of this 30 to 60 km average, which is up to
values of 25 m s-1. Strong tailwinds thus support the stratospheric
propagation to I07AU, while strong head- and crosswinds hamper it towards
I04AU and I06AU. Winds are weaker from the source towards the other
stations, mostly due to the equatorial wind situation of zonal stratospheric
winds changing their direction here, rendering possible the simultaneous
propagation in western (SING), eastern (I39PW and I40PG), and to a certain
degree probably even northeastern directions (I30JP).
The given transmission loss modeling provides a map-based estimation at the
surface level where stratospheric conditions are favorable or unfavorable
for infrasound ducting. Complementary to this, range-dependent propagation
modeling is conducted between the epicenter and the four signal-detecting
IMS arrays to estimate the loss of signal amplitude due to atmospheric
attenuation as well as geometric spreading over the considerably large
propagation distances of 1800 to 4500 km. This is performed to estimate if
stratospheric propagation is possible, even under weak ducting conditions or
conditions changing with distance.
The atmospheric ducting conditions and corresponding infrasound propagation
for the four stations are shown in Fig. 4. For I39PW, I07AU, and I40PG,
stratospheric ducting is modeled in good agreement with the observed mean
celerities of 290, 304, and 309 m s-1 (see Table 1). Following Negraru et al. (2010), celerities for
stratospheric ducting are expected to be in the order of 280 to 320 m s-1.
Corresponding ray-tracing calculations (not shown here) estimate the
celerities of those stratospheric ducts to be between 287 and 293 m s-1.
For I30JP, stratospheric ducting ceases along the 4500 km propagation path
due to more unstable ducting conditions and higher transmission loss (about
150 dB). This is also in good agreement with the observations, since only a
low-frequency signal is recorded at I30JP with a low celerity value of 263 m s-1 (ray tracing suggesting 244 m s-1), indicative not of a stratospheric but
of a thermospheric arrival.
Thermospheric ducts do not show up in Fig. 4, since this figure represents
a 1 Hz modeling case highlighting the medium- and high-frequency
stratospheric ducting and resulting in stronger absorption of thermospheric
effects. For lower frequencies on the order of 0.01 to 0.1 Hz,
thermospheric attenuation is considerably small (Sutherland and Bass, 2004), and acoustic signal energy
can propagate in the thermospheric duct over large distances with limited
transmission loss.
The availability of atmospheric ducts can be quantified using the effective
sound speed (veff) ratio between the stratospheric maximum (at 40–60 km) and the ground along the propagation path. This parameter indicates
favorable ducting conditions, when being equal to or larger than 1, and
unfavorable conditions otherwise. Nevertheless, Le Pichon et al. (2012) and Landès et al. (2014) point out that
veff ratios above 0.9 along the complete propagation path may also lead to
at least partially refracted energy in the stratosphere, whereas this
ducting becomes highly likely for values above 0.95. While classical
ray-trace modeling makes a strict separation between ratios larger or
smaller than 1 (leading to existing or nonexisting stratospheric ducts),
the parabolic-equation modeling used here also takes into account partial
refractions of acoustic energy at effective sound speed ratios near but
below 1. These partial refractions correspond to small-scale structures like
atmospheric gravity waves, which vary the atmospheric temperature and winds
and thus also influence infrasound propagation (Kulichkov et al., 2010; Green et al., 2011).
The veff ratios of the average profiles depicted in Fig. 4 are 0.96
(I39PW), 1.00 (I07AU), 0.99 (I40PG), and 0.93 (I30JP), fully supporting the
reasoning above. The propagation cases to I06AU
and I04AU are not shown in Fig. 4, having no observations of the event and accordingly low veff
ratios of 0.92 and 0.93, while the propagation to the single element station
SING is indicative of stratospheric ducting with a higher veff ratio of
0.98.
Discussion and conclusions
The main focus of the discussion of observed and modeled signals from the 28 September 2018 Sulawesi earthquake is on the source regions and source
mechanisms responsible for them. To support this discussion, a back-projection procedure (comparable to the one applied in Shani-Kadmiel et al., 2017, and in the Supplement
to Gaebler et al., 2019) is applied using the observed PMCC pixels and back-projecting them using
their temporal and directional information.
Back projection of the combined PMCC detections from I39PW, I07AU, I40PG, and I30JP. Considered is each PMCC pixel's back azimuth as well as a combination of 4 km s-1 seismic and 0.3 km s-1 acoustic celerities, resulting in seismic-to-acoustic conversion locations. Color-coded event density for these locations is shown on a 0.1∘×0.1∘ grid, highlighting regions with more than 200 back-projected pixels per grid node. The epicenter is marked by a star, and the rupture zone is traced by a dashed line. The map background was created using basemap data from the ESRI ArcGIS® Map Service.
The back-projection results towards the island of Sulawesi are presented in
Fig. 5 in terms of an event density map of the pixel-by-pixel information
on their most likely origin locations. A total number of about 107 000 pixels is used to derive the picture, combining the back projections of all
four stations' PMCC recordings towards the epicenter ±40∘
maximum deviation. Single station back projections can be found in Fig. S4. Seismic speeds of 4 km s-1, resembling the primary
propagation of crustal seismic waves, are combined with 0.3 km s-1 acoustic
celerities representing an average value of the station observations.
Uncertainties regarding the back-projected locations as seen by extended contour
regions in Fig. 5 are due to a number of potential influencing factors. The
choice of a fixed seismic speed and fixed acoustic celerity for all pixels
instead of individual values is supposed to introduce location deviations.
Measurement and analyses of back-azimuth directions may contain
uncertainties due to array configurations and due to crosswind influences on
the infrasound propagation. The method does not account for atmospheric
variability (as the forward-propagation approach of Fig. 4 does),
introducing certain location biases. The velocity-averaged back projection
nevertheless sufficiently emphasizes the major source regions and infrasound
generation mechanisms.
A region to the south of the epicenter is highlighted (yellow colors
representing the highest event density), well corresponding with the
earthquake rupture zone along the Palu–Koro fault line. Up to a certain
degree, this method also serves as a cross-bearing location procedure
although stations contributing to it are not equally weighted but weighted
by the number of pixels used from the respective stations (in this picture,
I07AU dominates the back projection, since it has the longest and largest
record of the event; also see Fig. S4). The location of the highest event
density is at 119.6∘ E, 1.0∘ S, approximately 80 km
south of the epicenter and thus halfway along the rupture.
Figure 5 highlights that infrasound is radiated not only from a distinct,
epicentral point source alone, but from a region extended in north–south
directions following the rupture (in fact the event density values at the
epicenter itself are lower than those in the surrounding regions). Secondary
peaks away from the basin region around the rupture are identified north of
the epicenter and in the southern part of Sulawesi. The pixels of
this southern secondary color peak are mostly related to the early parts of
the main signal recorded at I07AU, while the central and northern color
peaks in the figure are related to signals arriving some minutes later. This
corresponds to the 316 to 323∘ sweep in I07AU data from
south to north, as described in Sect. 3. The two side maxima separated
from the main signal's colored region are related to the seismoacoustic
signatures described in Sect. 3. They are derived from a number of I40PG
PMCC pixels and point to a region near the North Maluku islands east of
Sulawesi (also see Fig. S4). Other side maxima, e.g., the ones between
Java and Timor-Leste, also mentioned in Sect. 3, are beyond the map borders
and not shown here but can be found in Fig. S4.
In general, the results observed and visualized by Fig. 5 point out that
an enlarged region, closely following the rupture and thus also the
topography along the fault, generates the acoustic signals recorded at the
remote infrasound sensors. This includes the rupture region itself suffering
most from the earthquake-related ground motion (offsets of up to 7 m
horizontal and 2 m dip slip) as well as an extended basin area around the
rupture, enclosed by mountain chains in mostly north-to-south directions.
Mountainous areas are a well-known source of seismoacoustic signatures (e.g.,
Arrowsmith et al., 2010) and correspond to the event density maxima in Fig. 5; the mountain
chains west and east of the Palu–Koro fault as well as the mountain area in
the south of the island with Sulawesi's highest mountains
(Mt. Rantemario and Mt. Rantekombola, both about 3500 m elevation) generate
large portions of the recorded signals. The less prominent but recognizable
regions north of the epicenter (Mt. Fuyul Sojol, 3000 m elevation) and on
the Maluku islands (e.g., Mt. Buku Sibela, 2000 m elevation) are also related
to topographic peaks. The most likely source mechanism for the generation of
large parts of the seismoacoustic signals is therefore estimated to be the
shaking of elevated or exposed topography, stimulated by crustal seismic or
surface waves reaching these areas and turning them into motion.
List of events similar to the 28 September 2018 Sulawesi earthquake, either in their super-shear nature or in their regional origin. The separation between “event detection” and “no event detection” is an estimation following data analyses performed by the authors of this study.
EventSource typeAvailable IMS stations up to 5000 km distance (with location, UTC date, magnitude, depth)Event detection (with distance and PMCC- estimated signal duration)No event detection (with distance)Denali, Alaska, USA, 3 November 2002, Mw 7.9, depth 4.9 kmSuper-shear earthquake I53US (156 km, 10 min) I10CA (3358 km, 30 min)I59US (4919 km)Sumatra–Andaman, Indonesia, 26 December 2004, Mw 9.3, depth 30 kmSame region, normal shear earthquakeI52GB (2852 km, 30 min)I07AU (4930 km)Qinghai, China, 13 April 2010, Mw 6.9, depth 17 kmSuper-shear earthquakeI34MN (1810 km, 10 min)I46RU (2480 km) I45RU (3273 km) I31KZ (3669 km) I30JP (3996 km) I39PW (4831 km)Craig, Alaska, USA, 5 January 2013, Mw 7.5, depth 10 kmSuper-shear earthquakeI53US (1294 km, 5 min) I56US (1443 km, 10 min)I10CA (2647 km) I57US (2795 km) I18DK (3509 km) I44RU (4236 km) I59US (4334 km)Porgera, Papua New Guinea, 25 February 2018, Mw 7.5, depth 25.2 kmSame region, normal shear earthquakeI40PG (1044 km, 60 min) I39PW (1759 km, 45 min) I07AU (1784 km, 45 min) I60US (3835 km, 45 min) I04AU (4164 km, 15 min)I22FR (3144 km) I05AU (4064 km) I30JP (4587 km)Kokopo, Papua New Guinea, 14 May 2019, Mw 7.5, depth 10 kmSame region, normal shear earthquakeI40PG (72 km, 10 min) I39PW (2379 km, 30 min) I22FR (2527 km, 10 min)I07AU (2649 km) I60US (3004 km) I05AU (4286 km) I30JP (4542 km) I58US (4803 km)
To qualitatively assess if the super-shear nature of the given earthquake or
the regional prerequisites (or both) are responsible for the intense and
long-lasting infrasound signals observed, the 28 September 2018 Sulawesi
earthquake is compared to three other super-shear earthquakes as well as
three other normal shear earthquakes from the same region (Indonesia and
Papua New Guinea). Shallow events between 5 and 30 km depth were chosen with
comparably strong magnitudes of Mw>6.5 so that infrasound
generation and detection can be expected. Table 3 chronologically lists
these six events and provides an estimation of the emitted and observed
infrasound for all of them.
The three super-shear earthquakes named after the Denali fault, the Qinghai
province, and the city of Craig, occurring in 2002, 2010, and 2013, are the
earthquakes most recent, most intense, and most similar in their super-shear
characteristics to the 28 September 2018 Sulawesi earthquake, also having
super-shear rupture velocities of 4 to 6 km s-1 (see Dunham and Archuleta, 2004; Wang and Mori, 2012; Yue et al., 2013). Although the IMS
infrasound network is not fully established yet (at the time of the Sulawesi
earthquake, 80 % of the stations were certified and operational, while only 8 % were at the time of the Denali earthquake and about 70 % during
the time of the other two earthquakes), at least one infrasound array was
able to unambiguously detect and characterize each of the mentioned
earthquakes.
The infrasound signals for the Denali earthquake indicate a high signal-to-noise
ratio at the nearby I53US station as well as a much weaker signal at I10CA
much further away. This event was a good opportunity to track the
infrasound back to its generation region in the Alaska Range mountains along
the Denali fault where the rupture occurred (observed in I53US data, Olsen et al., 2003) and
to the Rocky Mountains southeast of it (observed in I10CA data), where
similar observations were made for the 1964 Alaskan earthquake (see
Young and Greene, 1982). The strong movement of local and remote topography generated the
infrasound in good agreement with the Sulawesi case. However, no indication
is given that the super-shear characteristics of the Denali earthquake
specially favor the generation of infrasound. For the Qinghai and Craig
earthquakes, also reported to be super shear, much weaker and shorter-duration infrasound is observed at stations at distances of 1400 (I53US
to Craig) to 1800 km (I34MN to Qinghai), compared to Sulawesi where
stronger and much longer infrasound signals were observed between 1800
and 4500 km. Again, these do not indicate any connection between those
previous super-shear earthquakes and extraordinary infrasound generation.
The Sulawesi earthquake is also compared to three strong earthquakes within
the same region, most prominently two nearby Papua New Guinea earthquakes
(near the Porgera mine, 2018, and Kokopo city, 2019) of the same magnitude
occurring half a year before and after the Sulawesi one, showing clearly
observed infrasound signals with high signal-to-noise ratios at multiple IMS
stations as well. These infrasound signals are observed up to distances similar to the Sulawesi case and also provide long-duration, strong-amplitude wave energy associated with infrasonic and seismoacoustic arrivals
coming from the two earthquakes. Clear seismic signals are also present in
the recordings (as in most cases described before, apart from Qinghai), and
an association with topographic features such as infrasound source regions is
possible (the mountain chain in central Papua New Guinea for Porgera and the
mountain areas in New Britain and New Ireland for Kokopo). For the Sumatra–Andaman earthquake of 2004, strong infrasound with long signal durations was
observed and could be back-projected to topographic features of islands and
shorelines, especially where the follow-up tsunami reached the shoreline of
the Bay of Bengal (see Le Pichon et al., 2005). None of the presented earthquakes were super-shear
earthquakes, but all of them, especially the two very similar Papua New
Guinea earthquakes, generated strong infrasonic signals comparable to the
signals of the Sulawesi event.
It can be concluded from comparison with the other events mentioned above that strong
infrasound generated by an earthquake is not mainly or exclusively linked to
the earthquake's super-shear characteristic but most likely to the nearby
existence of mountainous topography. This topography serves as a large-area
resonating membrane in terms of large masses brought into motion by a
triggering earthquake. These mass movements produce large amounts of
acoustic energy, which can be recorded at nearby or remote infrasound
stations given conducive propagation conditions.
The given super-shear event represents one of only a few large-magnitude,
shallow earthquakes generating pronounced infrasound. It therefore provides
a unique opportunity to study earthquake-generated infrasound in terms of
the source mechanisms, signal characteristics, propagation conditions, and
ducting behavior. It also supports the improved understanding of the process
of infrasound radiation by mountain shaking from large earthquakes and the
conversion of seismic to acoustic energy.
Measurement uncertainties within this study are due to the instrumentation
and methods applied; modeling uncertainties are due to assumptions applied
within the models and to multiscale atmospheric variations between
source and receivers, leading to uncertainties in the transmission loss and
propagation calculations. Taking into account these uncertainties allows for the
improvement of methods and models to cope with such issues in the future. This
will help develop novel and enhanced insights about infrasound observations
and modeling in general and earthquake-generated infrasound in particular.
This will also help to optimize seismoacoustic observation networks in terms
of better understanding instrumental needs and better evaluating the
signatures observable by them. Finally it will support seismoacoustic studies
of natural as well as anthropogenic infrasound sources in the future and
thereby support the infrasound monitoring for treaty verification purposes
of the CTBT, as have other CTBT-related studies about infrasound observation,
propagation, and signal characterization (Assink et al., 2016; Bowman, 2019;
Gaebler et al., 2019).
Data availability
Information about earthquake magnitude, location, and frequency of occurrence
in the region of interest is retrieved from the online-accessible archive of
the USGS; see https://earthquake.usgs.gov/earthquakes/ (last
access: 2 September 2019; USGS, 2018).
Atmospheric wind and temperature profiles are derived from operational
high-resolution atmospheric model analysis, defined by the Integrated
Forecast System of the ECMWF, available at https://www.ecmwf.int/ (last
access: 2 September 2019; ECMWF, 2018).
Waveform data for the infrasound arrays of the CTBTO IMS (https://www.ctbto.org/, last
access: 2 September 2019) used in this study are available to the authors, being members of National Data Centers for the CTBTO. Waveform data for the SING
infrasound station are available to the authors, being members of the Earth
Observatory of Singapore.
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-19-2811-2019-supplement.
Author contributions
CP analyzed the waveform data, performed the propagation modeling, wrote the
manuscript text, and coordinated the coauthor contributions; PG compiled the
data, generated the figures, and helped with finalizing the manuscript
layout; LC provided first ideas and initiated the collaborative study; ALP
provided expertise in earthquake infrasound, comparison to other events, and
initiated the collaborative study; JV analyzed the waveform data and
performed propagation modeling; AP analyzed the waveform data and provided
manuscript text; DT performed the attenuation modeling and provided
manuscript text; BT provided first ideas and initiated the collaborative
study; all authors supported and improved the draft by proofreading,
commenting on, or correcting the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative (EOS contribution no. 249).
Review statement
This paper was edited by Maria Ana Baptista and reviewed by Sven Peter Näsholm and Alex Iezzi.
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