We’re very excited to have four speakers today who are willing to
give their AGU talks a week early. So today we will have Fred Pollitz,
Evan Hirakawa, Jean Soubestre – who is visiting us from France,
and then Sara McBride. And before we start, I would just like to
announce that we will not be having a seminar next week because of AGU.
And then, on the 18th, we will have an AGU poster showcase during our
normal ESC Coffee Hour starting at 10:00.
So, if you are presenting a poster or have a poster from a recent conference
that you would like to just, like, put up and show off, please bring that.
And also let us know so we can contact you about exactly the rules
about putting them up here at Moffett.
And then, other than that, we won’t have a regular seminar
until January 8, I believe, so not until the new year.
And then – let’s see. For today, we’re going to
try to stick to the 15-minute timing. So I will be sitting over in that chair,
and I will be timing it. And, at 15 minutes, I will stand up.
So if you’re speaking, and you see me stand up,
that’s time for you to wrap up as quickly as possible.
And then also, just to keep things moving quickly, we probably
won’t be passing around the speaker that we have in this room.
So, if you get a question, can you just repeat the question
before you answer it?
Okay. And with that, we have Fred.
- Okay. Thanks for coming. I’m going to talk about the postseismic
deformation after the Ridgecrest earthquakes and what they imply
for the mechanics of postseismic deformation.
[inaudible] [beeping]
Okay. So the sequence is well-known, but on July 3rd, local time, it was
a magnitude 6.4 on a southwest- northeast-trending structure.
And that was followed 34 hours later by a magnitude 7.1
on a northwest-southeast-trending structure, which ruptured about
50 kilometers and produced a spectacular surface rupture.
Maximum surface slip was about 5 meters.
And the photo on the right, provided courtesy of Josie Nevitt,
was taken close to the magnitude 7.1 epicenter near the high-slip region.
To do geophysical modeling of this sequence of the foreshock and the
main shock, we need to define rupture planes, so I used these
two black planes for the magnitude 6.4 and three planes
in the heavy white line for the magnitude 7.1.
And everything has assumed right-lateral slip except for the
southwest-northeast-trending structure of the magnitude 6.4,
which is assumed to have left-lateral slip.
So it’s of interest to compare the postseismic motions that we infer
from the Ridgecrest earthquake to what happened 20 years ago
after the Hector Mine earthquake. So the processed transient motions
after the Ridgecrest earthquake are shown on the right.
And, even if you take away some of the large transient velocities that are due to
some of the campaign stations on here, the postseismic motions are 2-1/2 to
3 times greater after the Ridgecrest earthquake than they are after
the Hector Mine earthquake. And that could reflect more vigorous
afterslip, or it might be viscoelastic relaxation, or a combination of both.
So our objectives are to identify some of the usual postseismic processes
for elastic rebound, afterslip, viscoelastic relaxation of the
lower crust and upper mantle. Try to characterize the spatial
and temporal distribution of afterslip.
Constrain the regional viscoelastic structure of the ductile lower crust
and upper mantle. And all of this can be used to
infer time-dependent crustal stresses, which is of great interest,
although I won’t be talking about that in this presentation.
So the postseismic deformation data set.
This is predominantly a three-component time series
from continuous GPS sites and a number of survey mode GPS sites.
I used a total of one year’s worth of data to estimate the transient
postseismic motions, and that’s done to minimize seasonal effects.
So the purpose of the – of the processing is to isolate the transient
postseismic motions, which are over and above the background,
which was going on before the earthquakes actually happened.
There is additional information from InSAR and strainmeter time series,
which also do show substantial transients, but I haven’t folded that
into the modeling just yet, but we can compare with those kind of data.
So, looking at just the continuous GPS stations, over the first third of a year,
the transient velocity field is shown on the left for the horizontal motions,
and this looks pretty typical for what I’ve seen countless times for
viscoelastic relaxation of the lower crust and upper mantle, but it could
be partially shaped by afterslip as well.
And a lot of the gaps in the near field we will be filling in
with the campaign GPS. The vertical motions – there are
some patterns that seem to jump out, but the errors are very high, and I think
we need longer time series before they should be used in the model,
so I’m not using those – just the horizontals. So example time series.
One of my favorite stations is P595, and that time series for the three
components shows offsets due to the foreshock and the main shock,
as well as a change in the slope of the time series, which is hard to see in
this figure, but if we zoom in on the postseismic interval, this is referenced
to the pre-seiscmic velocity, so this is definitely a change in the
character of the postseismic crustal deformation field.
And the average change in slope is derived by fitting a simple
quadratic function to each of the postseismic time series.
Getting a message on this screen. Okay.
I fit in a quadratic function, and just using that to derive
the average change in slope over the first 125 days.
And we can go through the same exercise for another continuous GPS
site, where we see very nice decaying postseismic transients.
And similarly, another site west of the fault, which shows
nice decaying transients.
There’s also a wealth of campaign data,
and this is due to the very diligent efforts of field crews from the USGS
as well as the Southern California Earthquake Center, particularly given
the logistics needed to access the China Lake Naval Air Weapons Station.
So this field work was vigorous within hours following the foreshock and
throughout the month of July and has continued off and on until the present
time, and it yields a very valuable data set for studying the near-field
deformation. And, in combination with data from continuous time series,
it helps us resolve the different postseismic processes.
So this one time series that I show here from CLR1, that’s located …
It’s located right there on the fault. This shows probably the largest
displacements for many of the campaign sites, and it’s due very likely
to some kind of poroelastic rebound, and that’s corroborated by
interferogram analysis that our colleagues from
UC-Berkeley have been carrying out.
And we can do the same kind of analysis to estimate the postseismic
velocities at the campaign sites that we do for the continuous sites.
So this is an example for site GS26.
Here’s another example for site GS19.
Here you can see this one’s a bit noisier, but the signal is still fairly
robust for decaying – for detecting a decaying postseismic signal.
The verticals – this is probably a testimonial – the same kind of
analysis we’re doing for the horizontals probably shouldn’t be
done for the verticals, which I’m not including in the model.
There are postseismic interferograms, so this one is from July 10 to July 16.
It shows on the order of 10 millimeters’ displacement across the southern part
of the fault, which is much smaller than other signals that we’re looking at.
And that signal nearly vanishes in the July 16 to 22 interval.
So this is suggestive that afterslip, if it is going on, has a fairly short time
constant on the order of about two weeks.
So the goal in the remainder of this presentation is to interpret the
transient postseismic velocity field with models of either kinematic
afterslip alone or combine the afterslip and viscoelastic
relaxation of the lower crust and mantle.
So I’ve defined potential afterslip to be on the main shock and foreshock
rupture planes, extended downward from 17 kilometers all the way to the
cross-mantle boundary at 30 kilometers. And, when we insist that afterslip
is all that we need to explain the data, we get this fit that we see on the right.
And it’s a somewhat good fit, but there are systematic misfits,
namely the model velocities east of the fault are too small,
and the model velocities west of the fault are too large.
And that’s basically because there is an asymmetry in the
observed velocity field. And that is something that
we can address with the viscoelastic relaxation process.
So, to embark on joint afterslip in viscoelastic relaxation modeling,
we need source models of the foreshock and the main shock.
We’re going to look at two-dimensional viscoelastic structures,
and we’re going to take all that and try to fit the available data
with a combination of afterslip and the viscoelastic relaxation.
So this is one of the source models that we’ve derived, and we’re going to
be looking at this in greater detail, but essentially, we have up to
5 meters of slip in the – in the main shock at a fairly shallow depth.
And on the order of 1 to 2 meters’ maximum slip in the foreshock.
These models are going to be explored in greater detail in a manuscript and –
manuscript and preparation and an AGU poster by Jessica Murray.
So here I want to make the case that we need very strongly laterally
variable viscoelastic structure to explain the data.
And essentially, if we – if we look at pairs of stations, for example,
CCCC on the west side of the fault and P594 on the east side,
the displacements are much lower on the west side of the fault.
And we can look at other stations here at similar distances
and draw the same conclusion.
So, west of this boundary, the viscoelastic structure seems to involve
much longer relaxation times, much longer – much greater viscosities.
And that seems to be corroborated by the pattern of heat flow that is
shown here – much higher heat flow in the Basin and Range side
compared with the Central Valley side.
So we can define a viscoelastic model on the Basin and Range side
and Central Valley side with a number of parameters.
And that’s more parameters than we would like to work with,
so I simplify that by focusing on the transient rheology, which is basically
the Kelvin component of this analog rheology that I showed on the bottom.
And I assume that the ratios of the transient viscosities are 100 in the
lower crust to 5 in the mantle lid to 1 in the mantle asthenosphere.
So the weakest component is where the temperature is greatest
in the mantle asthenosphere.
The lower crust is assumed to be pretty strong, which is a result
we obtain from other postseismic studies.
So there’s really only two parameters to deal with
when we make this simplification. So, after running 60 different models
and doing a joint fit for afterslip and relaxation, in all cases,
we arrive at an optimal viscoelastic structure, which is shown on the left.
And that involves really low viscosities on the Basin and Range side.
The lower crust viscosity is still fairly high, but we have viscosities
less than 10 to the 18 pascal-seconds on the Basin and Range side, which,
in this kind of work, is pretty low. The contrast in viscosity is nearly
10-to-1 between the Central Valley side and the Basin and Range side.
And we really need that in order to account for the asymmetry in
the postseismic crustal motion. So, on the left, is the afterslip,
which is associated with that particular viscoelastic model.
And on the right is the fit to the data. And we are accounting for the
asymmetry across the fault in this case. So the picture on the left is the
best picture that I have so far of the – of the afterslip.
It’s not as illuminating as I would like it to be.
One thing worth pointing out is the shallow afterslip on the
middle section seems to coincide with a dip in the coseismic slip.
So the coseismic slip contours of 3 and 4 meters are superimposed here.
And there’s a bit of a dip in the coseismic slip where we’re
putting afterslip. So a tentative conclusion would be that afterslip
is filling in a gap in the coseismic slip there, as well as at depth –
well below where the coseismic slip happened.
And we can finally compare time series based on this best viscoelastic
and afterslip model, and we can look at the separate contributions
of afterslip and relaxation. So the combined processes
fit the time series as well as we might expect.
Afterslip is insignificant at this site.
We can do the same exercise for other sites.
This is the next to the last slide. [laughs]
So the time series that we predict at the campaign sites gives us
a handle on the time scale of relaxation. The assumed time scale is too slow
for this campaign site, but it’s too rapid for this site.
So that indicates different time scales of afterslip in different regions.
So, just to conclude, the main conclusion is that the viscoelastic
mantle relaxation explains most of the postseismic velocities,
but we also need afterslip. And with continued observations,
we can refine our models of post processes. Thank you.
[Applause]
- We have one question [inaudible].
- Is the viscoelastic structure that you assume consistent with, say, Hector Mine
and the SCEC [inaudible] or rheology model?
- And then, Fred, can you just repeat the question for the [inaudible]?
- Yeah. So, is the viscoelastic structure consistent with the …
- If you were to extend the Basin and Range over towards Hector Mine,
are you getting comparable values? Or you get more lateral variation?
- Oh, we would need more lateral variations.
I showed initially that the transient deformation is about three times higher
than it was following Hector Mine. So the viscosities have to
be correspondingly lower. And so they may be jumping to
lower values across the Garlock Fault, by all appearances.
- Okay. Thank you.
Our next speaker will be Evan Hirakawa.
- Okay, thank you.
[inaudible] green box. It’s going to be really important.
[laughter] - Tried and failed to get it …
- Okay. So I’m going to show some results from some wave propagation
finite difference simulations I’ve been doing for the magnitude 7.1 Ridgecrest
earthquake using this kinematic rupture model that I’ve developed.
So not much background needed if you’re a seismologist.
This has been a pretty famous event. We were kind of startled by the
6.4 earthquake, and then, before we could gather ourselves, the biggest
earthquake in 20 years in southern California happened the next day.
It takes place – the tectonic setting is the Eastern California Shear Zone.
The specific fault is called the Little Lake Fault, and this is
kind of a immature fault zone. And so we have these very
geometrically complex faults. You don’t have these long,
straight faults like we have more in the western part
of California where it’s more mature.
And we’ve started to see now through a lot of the surface ruptures
and a lot of new studies that are coming out, that there’s a
very complex pattern of rupture. It’s likely that multiple fault surfaces
slipped during both events – during the magnitude 6.4
and during the magnitude 7.1.
And we’ve also, interestingly, heard, right after the earthquake, that the –
some of the real-time estimates at first underestimated the magnitude of the
large event, which, to me, suggests that maybe there’s some temporally
complex or, you know, slowly developing rupture going on.
So it’s turned into a very scientifically interesting topic – earthquake to study.
In addition, we’ve got pretty good station coverage right around the fault.
So these blue lines here is – this is the surface trace of the –
mainly from the 7.1 earthquake. Here’s the left-lateral fault
from the 6.4 earthquake. The star here is the epicenter of the 7.1.
And we see all these different kinds of stations are right around this area.
We have a large network of these Caltech strong motion accelerometers,
which are the red triangles. I’m going to be showing some data
from these GNSS high-rate GPS stations that Jeff McGuire,
Diego Melgar, and Brendan Crowell helped me obtain and work with
a little bit. And then there’s three PBO strainmeters
that Andy Barbour helped me work with and process.
So what I’m going to do is a finite difference simulation I used – SW4,
which is a 4th order elastic wave propagation code
developed by the people at Lawrence Livermore.
I’m going to have a 200-by-200- kilometer domain with 250-meter grid
spacing, so relatively coarse. And that’s because I’m only looking
at frequencies between 10 and 30 seconds. I’m simulating
80 seconds of wave propagation. And I’m using a 3D velocity structure
from SCEC CVM-H.
And these – and so that’s the color scale here.
So you can see – and this is the shear wave velocity
at 2 kilometers’ depth from the SCEC CVM.
I’m not using any attenuation here, which I think is relatively valid
at this frequency band. And the kinematic rupture model
I’m going to use I developed from Gavin Hayes’ solution
and kind of modified a little bit through trial and error.
So here is a picture of my fault slip model.
So here’s the Hayes solution that I pulled right off of the USGS event page.
And, through some trial and error, I kind of found slip patches that
needed to be added to match the GPS displacements.
So the black arrows here are the observed displacements from the
coseismic rupture from the 7.1, and the green arrows are my
final displacements based on the finite difference simulation.
And so, yeah, I’d say I, more or less, modified this by just amplifying
different patches of this to find which would make the arrows most hit the –
green arrows to the black arrows. And so the main feature here is that
we have a pretty large slip patch near the hypocenter, which has
been well-documented now by a number of authors.
Another very interesting part of this earthquake
is the time evolution of the rupture.
So, through the first couple models that I ran, I very quickly found that,
if the whole – if the whole fault ruptures at once, it’s very hard
to match the waveforms. And if you look at the coseismic
GPS displacement at, you know, one of these stations that’s – this is
station CCCC, you can see that there’s these different – what I would think
are maybe multiple subevents. So you have the initial big event,
but then you have other little ruptures that might be happening.
So what I did was I broke the fault into four different subevents.
So I have Rupture A, Rupture B, Rupture C, and Rupture D.
So the initial rupture is Rupture A.
This is the hypocenter, and it ruptures toward the north.
This color is the rupture time of each sub-fault.
And this – so this ruptures at – initiates at zero seconds.
This is a magnitude 6.69 rupture. Ruptures at 3 kilometers per second.
And unilaterally to the north. Then B is – has that
big slip patch that I showed. This is a 6.7 rupture.
It only takes 3 seconds after the initial rupture, but it ruptures slowly
at 2 kilometers per second. Rupture C is pretty small – 10-second
delay time, 2 kilometers per second, and ruptures unilaterally to the south.
And then Rupture D ruptures at 2.5 kilometers per second and
starts after 15 seconds, and it’s – this rupture is bilateral,
but it’s mostly toward the south. So here’s – this is going to be
a little animation of the rupture process.
So this is the fault, and it’s going to show the slip rate.
It’s going to kind of light up when it shows which part of the fault is
rupturing, and then over here, we’ll see the ground motions.
And I just kind of drew the fault traces on here for reference.
So the first part of the rupture is Rupture A.
This is going towards the north, and you see it shoots out this
wave pack here. And we had Rupture B.
It’s going to fast for me to explain. [laughter] I’ll show it again.
So we have Rupture A, Rupture B, Rupture C, and Rupture D
as this kind of higher-frequency [inaudible]. So let’s start over.
So Rupture A goes to the north, then Rupture B suddenly starts up at
3 seconds and has this big motion here. Rupture C is pretty small and has
this little boop of ground motion. And then Rupture D ruptures
bilaterally and has this sharp wave field of higher frequency motion.
This slide I kind of put here in case my movie didn’t work, but this is
another nice look at what I just showed. So, at about 10-1/2 seconds, you see
Rupture A is almost finished. Rupture B is actually also
almost finished. You have quite a bit of ground motion going.
Rupture C was small. It was this little part here.
And it just shot out a little bit of motion compared to the rest.
Here’s the wave front of Rupture D, and you see it’s starting to show
the ground motion part. And then here is after the fault
has finished rupturing completely that you see this strong motion
coming from the last southernmost rupture.
Just to look at some of the time series, I wanted to break them in and show
you the relative contribution from each one of the subevents.
So this is at the GPS station cccc. This is at p463.
And you can see, as I showed earlier, these very distinct subevents
come from these different – Subevent A, B, C, and D.
So, from Subevent A, we have very little displacement here,
but Subevent B is much bigger. Then this spike here comes from
part C, and then Subevent D has kind of the final lobe there.
And then similar story on this side.
Okay. And so now I’m just going to kind of go around the whole domain
and show different parts of the wavefield.
So this is showing kind of a move-out towards the north.
So most of these stations up here are mainly affected by Rupture A,
which ruptured towards the north.
The red curve – well, the black curves are all the data, but the red curve is
my synthetic strong motion, and the blue curve here is one
of these PBO strainmeters. And so you can see the first arrival,
at least in these north stations, look pretty good.
And some of these coda waves are coming from the southern –
the more southern parts of the rupture. And you can see that we’re able to
match the strain as well, which is not typically done
in these ground motion simulations.
Further off to the west, the first arrivals look very nice and
are dominated by that Rupture A. But Rupture B comes in
a little bit too strong. You can see the sharp spike here.
So something’s going on there.
And later arrivals are coming from the southernmost ruptures.
Going down this way, we see, again, the first arrivals look nice, but you
see these big motions that are coming in later.
And so I would attribute that either to – I would likely think it’s most likely
attributed to some kind of basin effect going on in this low-velocity area.
But it could be possible that there’s a re-rupture at some
other part of the fault up here that I’m not capturing.
In this line further south, you see that it’s mostly dominated by
Ruptures B, C, and D, which ruptured towards the south with
Rupture A giving a little energy because it’s directed off to the north.
But you can very clearly separate the beginning phases
at these closer stations.
And then, off further out here, you start to see that it gets a little bit jumbled.
But we still see that the first arrivals look decent.
To the south, this is where it gets a little more complicated, because I had those
three different ruptures all directed down this way.
And so you have a lot of interference going on.
But we see the main features still. We see the closest stations.
We see the first arrivals match. We see these very sharp spikes
coming from Rupture D in both the strainmeters and in the
strong motion stations and also in this GPS station here.
And if – and even farther out, you start to see that these synthetic
amplitudes are much larger. So there might be some kind of
attenuation or dissipation that I’m not capturing with the elastic model.
And then finally, off to the east here, we see – actually, there’s a very clean –
you can very cleanly pick out the separation between the
different subevents. This is one of the stations I showed earlier.
But it’s very nice that you can – that we can make use of the
GPS stations here in addition to those ground motion stations.
Definitely helps on this side where there’s
very low coverage of the accelerometers.
Just to look a little bit at the amplitudes. This is the synthetic PGV – the log
of the synthetic PGV on this axis. This is the log of the observed PGV.
And we see that it – on the dashed line here is a – is a 1-to-1 line,
so we see that it at least follows a decent trend.
Some of the stations out here, though, we are overestimating
the synthetic PGVs. And those are the stations
down here that I mentioned. So maybe there could be some
effects of attenuation down here, or there could be scattering attenuation.
So that could be a topic for a future model.
And then up here, again, you see that the observed motions
are much larger than the synthetics. And so these stations here are these
stations in these low-velocity areas where it’s possible that we’re
kind of underestimating the [inaudible].
Okay, and so, in summary, the magnitude 7.1 Ridgecrest
earthquake was well-recorded by many different types of data,
and we’re able to match them with a simple elastic wave propagation code,
at least in – at these relatively low frequencies.
These models are best fit by a roughly 50-kilometer rupture
with most slip concentrated by the hypocenter.
But the time series are best fit if we split the rupture into four
different subevents in this case. The first subevent nucleated to –
nucleated and ruptured to the north, and the subsequent events
all cascaded towards the south.
And the synthetic PGVs increased relatively well with the observed,
however future models might want to include more physics and things
that could break up these sharp wave fields.
Thank you.
[Applause]
- [inaudible] questions if anyone has one? [inaudible]
- Hey, [inaudible] never went back to the data [inaudible] magnitudes.
And I don’t know which magnitude you meant or whether those
magnitudes [inaudible] are related … - Even the real-time – the real-time …
- The real-time [inaudible]. There was multiple
[inaudible] magnitudes. - Yeah.
- But you never go back to it. [inaudible] it or …
- Okay. That’s a good – okay. - [inaudible]
- I meant to have a talk around the office about how the first
[inaudible] something, but maybe I should …
- Was that [inaudible] or was it … - Yeah. The [inaudible].
- Okay. And [inaudible] … - So maybe I should talk to you guys …
- [inaudible] - I don’t know that much about those,
so yeah. I’d like to chat with someone after this to either pull that out or …
- [inaudible] - It’s a complicated [inaudible].
- Okay. - [inaudible] a network that’s …
- So that’s not a physical – okay. - [inaudible]
- [inaudible] do a postmortem on [inaudible].
- Yeah. Jeff was the one I talked to, but I haven’t talked to you
since a few months ago, so … [laughter]
- Did we [inaudible]? - I really like how you match
all the different [inaudible] data. And then also that you used [inaudible].
So do you think, in places where there’s not quite a match between
the [inaudible] and the synthetic, [inaudible] part of the velocity model?
Or do you think it’s more … - I think so, yeah.
Actually, there was some of these – some of these very distant stations
out here actually looked better if I just used the 1D velocity model.
I didn’t show that, but overall, I think, when I look at the whole domain,
the 3D velocity model looked the best.
But there’s definitely other velocity models to use.
There’s also the CDMS model. - So you think that they need
[inaudible] to update the velocity model based on your findings?
Or do you think that [inaudible] … - I wouldn’t go as bold as to say that.
I’d rather do something like I think Rob Graves did, where he simulated
a lot of the small aftershocks. So we get rid of all the source
effects and just see [inaudible] of each and [inaudible].
Yeah. - Okay. Thanks.
- Okay. Our next speaker will be Jean Soubestre from INVOLCAN.
[Silence]
- Okay. Thank you very much for giving me this opportunity
to present this work.
So I will tell you about a matter that we call CovNet,
for Covariance Network.
It is a method for the automatic detection and
location of the seismo-volcanic signals.
I’ve been working on this during my present postdoc
in Paris with Nikolai Shapiro and Leonard Seydoux.
And now I continue to work on this during my current postdoc
in Canary Island at the volcano observatory and trying to do
collaborations with many people. And that’s why I am here this week
to work with Phillip Dawson to try to apply this network on this date.
So this is a method designed for volcanic monitoring.
So volcanic monitoring – new methods face different challenges.
The first one is the fact to be able to be adapted to the different kind of
signals that we have on volcanoes from volcano-tectonic earthquakes
to the long-period seismicity. Another challenge is to be –
for this method is to be adapted to very low energy events
that cannot be picked or detected manually.
And, with the continuous growing monitoring networks that we have,
it’s important for new method to be able to deal with large amount of data
and, if possible, to do it automatically. So the method we developed
[inaudible] was – all those requirements.
So I will present you the method first and then I will focus on results
on four different volcanic contexts. The one cluster of volcanoes in
Kamchatka. The Teide Volcano in Canary Island. The French Mayotte
Island – coast of Madagascar. And the Mammoth Mountain volcano.
Okay, so first, we – so this is a network method, so first we get the –
we only use the vertical component. And we filter and downsample
our data. And then we divided – we divide the data into windows.
So here you have an example of the blue one and a red one.
And, on those window, we apply a spectral normalization.
Then we divide – so each normalized window is divided into sub-windows.
So here you have the example.
And we calculate the cross-spectra matrices between all the different
stations in Paris of our network. And we obtain finally the
covariance matrix of the average of those cross-spectra matrices.
So it’s the covariance – we call it the covariance matrix.
It’s in the frequency domain. But it’s the equivalent in the
frequency domain of the cross- correlation in the [inaudible].
And the interesting thing about this covariance matrix is that,
being Hermitian, it can be decomposed on the basis of
real positive eigenvalues associated with the complex eigenvectors.
And we use the eigenvalues to detect sources. And then, once we have
the detected sources, we can use the eigenvectors to clusterize them or to
locate them. Today we’ll just tell you about the detection and location.
So how we – how we detect sources, here it’s an example – you have
different seismic traces at the top. And you have a period of noise in
blue and a period with an earthquake in red.
And, at the bottom, you see the distribution of the eigenvalues
of the covariance matrix. And you see that, when you have
a period of months, the distribution is more spread than when you
have a dominating source.
So we defined a scalar parameter that we call the spectral width.
And that measure the width of the distribution of the eigenvalues.
And we use it as a proxy of the number of
dominating sources in our wavefield.
And once we have detected the source, we can locate it using the eigenvectors.
Our main hypothesis is that we do not use the full – the full covariance matrix,
but we rebuild one from its first eigenvector, with the hypothesis that
the first eigenvector contains the information about the
dominant source in the wavefield. So we rebuild a covariance
matrix from the first eigenvector. And then we go back in the time
domain by an inverse [inaudible], and we do a cross-correlation
based the location record. So we do it with a 1D velocity model
in order to be able to have sources at depth.
And then we perform a 3D grid search. So for each point of the grid, we can
calculate the travel times and the different travel times, and so we
can shift our cross-correlations. And if we – then, if we stack the
envelope of the cross-correlation at each point – each point of the 3D grid,
the point where the stack is maximum is the location of the source.
So let’s see results first on the – on the Klyuchevskoy volcanic group
in Kamchatka. So it’s in this peninsula at the east tip of Russia.
This is the largest cluster of subduction volcanoes in the
world with a lot of – a lot of composition and eruption styles.
And this is the network we are using to monitor those volcanoes.
So you have the five volcanoes – Kizimen, Tolbachik –
those are the white triangles. And we have 19 seismic stations.
And this is the results of the calculation of the
[inaudible] for 4-1/2 years of data.
So this is a graph in frequency and time domain.
This is not a spectrogram. This is – each pick cell of this
graph represent the width of the distribution of the eigenvalues
of the covariance matrix at that time and that frequency.
And when you have a warm color, it means that you have a dominating
source, and a cold color, you have noise.
So we detect many different kind of signals in those graphs.
But I would just focus on one. Those two very long signals in red
are tremors. So we know from a priori knowledge that there are tremors from
two different volcanoes that are Klyuchevskoy and Tolbachik.
But for sure, with just this analysis, using a scalar parameter, we cannot
distinguish if they are from two different source.
So that’s why we used the eigenvectors.
And what we did is that we did a location for every day of this data set.
So, when you have a day with noise, you end up with a unpure location.
But you have some day when you have an interesting location.
So this is an example. In 2009, we have – the tremor
is located below the Shiveluch Volcano at the north.
If we go in time, and just to the beginning of – the end of 2009,
you have a quite deep source of tremor below the Klyuchevskoy.
And then, if you – if you continue in time and go at the end of –
the tremor at the Klyuchevskoy was about one year.
And if you go in time, you see that the source of the tremor is more shallow.
This was – this was at the beginning of the – of the tremor. It was quite deep.
And then it goes very shallow when you go close to the eruption.
And, yeah, we did it every day. So we also located a tremor in
the Kizimen. And finally, at the end, it’s a tremor at the other volcano.
And this is the same thing but representing the – for the third –
for four different volcanoes.
You have the depths of the source as a function of time.
And if you look at the second one from the top, it’s the Klyuchevskoy.
And you see that we can track the vertical migration of the –
of the tremor source, and it was – ended up very shallow close to
the eruption that was the red part.
Let’s skip to another data set that are different.
So the Teide Volcano is in Canary Island.
It’s an intraplate volcano, so very different,
with also different composition and eruption styles.
This is the network we used to monitor it.
We have about 12 stations – broadband.
And here you have the result of the spectral reach for about two weeks.
So here, again, we detect very – a lot of different sources.
Being on an island, for sure, we detect the oceanic sources –
so the primary microseism and the secondary one.
But if we focus on the volcanic source, just an example of a LP event.
So here, you cannot see the – if you zoom at that time,
you have a an LP event – very small one.
This event was not in our catalog because it’s impossible
to detect it manually. So here, this is the detection
applying a spectral widening that I show you.
If you apply no pre-processing, you cannot detect this event.
And this is with a one-bit normalization.
You never detect the event.
So you clearly need a way to enhance
the signal in all these frequency.
And I performed the location of this event, so it’s about 10 kilometer
below the cone of the data. And it’s quite coherent with
another location that we have from this [inaudible].
So it’s quite interesting because the method is able to locate those LPs.
Other volcanic context. The Mayotte Island
in the French Comores. So between Madagascar and Africa.
I’m sure you know that there is a new – a newborn volcano about 50 kilometers
east of Mayotte that was revealed with bathymetry campaign in 2019.
It’s about 800 meters high with 5 cubic kilometer of magma
in less than one year. It’s a monster.
And we had a very – a very highly energetic VLP – very long period
event on November 11, 2018, that was recorded all over the world.
And we did the analysis of the data during one year between April 2018
and May 2019. And, in reality, we detect a lot of self-similar VLPs.
This is the data I used – regional network from IRIS.
Here you have the VLP signal of the – of the 11 of November.
As you can see, it’s quite monochromatic.
And this is the detection for – so, for this day, the VLP detected here.
So we have large detection windows because this network is very big,
but we detect clearly the event. And we can locate it with the –
with the method. So it’s – we obtained a first location,
let’s say, that is east of – east of Mayotte.
And then I did the exercise using one year of data to try to
see if we detect self-similar events.
So here you have the stakeholders at the top.
And, at the bottom, you have three stacks in different frequency bands.
And this interesting one are the blue one at the bottom
is between 0.02 and 0.08. And the green one is centered around
the – from [inaudible] frequency of the VLP that is 0.065 hertz.
So we can – if we do the difference between the green
and the – and the blue line, we can – we can have a clear detection of this –
of those self-similar VLPs.
And just have a look – a zoom at the beginning of the – of the sequence.
So we – at the beginning, we had a seismic crisis with a lot of
volcano-tectonic earthquakes. And if we zoom later in November,
we have this kind of things where you can see that we have all those VLP.
Very monochromatic. With only [inaudible] at the 0.065 hertz.
So we are working on this VLP and its repeaters.
Here you have just the evolution of the fundamental frequency of this
self-similar events. And you see it’s decreasing at the beginning.
You have the major event in November – the yellow circle.
And then it increases.
Okay, and just to finish, just one result about Mammoth Mountain.
So you know Mammoth Mountain better than me.
So we used – we’re using two different network.
The blue one is the permanent network,
and the orange was a temporary network in 2013.
And here you have the results for 10 days of data.
At the top, the spectral width, and at the bottom, stacks in
different frequency range. But just – the interesting thing is the –
if you compare – so this is the result with the temporary network,
and this is the result with the permanent network.
So the temporary network being very close to the summit.
Clearly see the chair lifts of the ski station. [laughter]
But it also see – quite interesting [inaudible] here that we are
interested in now with Phillip – potential long period events
or maybe very small tremors.
So we are working on that with Phillip right now.
Okay, so, yeah, I presented you this method that is interesting
for volcano monitoring. This is running – currently running
as a routine at the volcano observatory in Canary Islands.
And I’m trying to apply the method on different data sets
now to [inaudible] other volcanic contexts.
Thank you.
[Applause]
- [inaudible] one question?
- I was – I’m actually interested in sort of your noise sources.
Have you tried looking at the noise sources to see if there’s
any variability in where the noise is coming from?
- Yeah. In [inaudible] in Canary Island, I’m trying to understand where are
the noise sources. And doing other kind of analysis
with polarization analysis and – because we also do ambient
noise tomography. So we have to know where
the noise sources are from. But, for the monitoring point of view,
I’m more interested in the volcanic sources. [laughs]
- Okay. Thank you.
[Silence]
Okay. And our last speaker will be Sara McBride.
- Good morning, everyone. Thank you so much for inviting me
to give this talk. Thanks, Grace and Jesse, for setting it up.
So this talk is less a research talk as it is a talk about research.
And what we’re doing here at the USGS about social science
and ShakeAlert.
So recently, Elizabeth Cochran and Allen Husker wrote a – sort of an op-ed
in Science called How Low Should We Go When Warning for Earthquakes?
And one of the calls the paper made was, we really need more social
science to understand what people want and expect from warnings.
And, we at the USGS, we agree. So about a year and a half ago,
we started out on a social science program,
and I’m the social science coordinator for ShakeAlert.
So we’re trying to do two things with our social science program.
One is to develop an understanding where populations are in Oregon,
Washington, and California in terms of – in terms of our
understanding around earthquake risk perception – protective action
knowledge. So do they know to drop, cover, and hold on, or what to do
if they’re in their car and there’s an earthquake? And basic earthquake
preparedness across the three states of where ShakeAlert operates.
And we also want to establish a monitoring evaluation plan
for ShakeAlert. And what that means is,
we want to say – we want to see, does ShakeAlert make any difference
over time to what people understand about earthquakes
and what kind of protective actions they take?
So we actually have seven active projects across nine universities,
and that includes five different states and two countries.
We might be expanding that further in the coming months.
And here, the first one I’m going to talk about is the most immediate
project we’ve got going on, which is the Ridgecrest earthquake sequence
research, which is Responses to the 2019 Ridgecrest Earthquake
Sequence and ShakeAlert. And this is being worked on by
Michele Wood, who is a longtime health communication
researcher at Cal State-Fullerton, and Jeannette Sutton.
So Michele Wood is going to be doing the quantitative component of it.
So we’re doing a survey, which means she’s going to be looking at
the numbers which people express. And Jeannette’s going to be
the qualitative lead, which means she’s going to be
looking at the words, which is the content.
And really what we want to understand in this purpose is to understand how
the public responded in southern California, particularly in the
Ridgecrest area and outside the Ridgecrest area with an
explicit focus on what people perceived about ShakeAlert.
We understand from some of the public feedback that people thought
that ShakeAlert was a failure – at least the ShakeAlertLA app was a failure.
Well, what about it was a failure, and how can we
make improvements going forward?
So the study’s aim is really to examine the perceptions
and self-protective actions. So if people felt the earthquake,
what kind of actions they took. As well as baseline knowledge
and attitudes – so where they’re at today in southern California.
And then the public reactions to ShakeAlertLA during this event.
So we’re taking two approaches, like I said, which is the main part of
this is going to be the quantitative research, which is going to be an
online survey, which Cal State- Fullerton is going to administrate.
And the sample’s going to be obtained by Qualtrics, which is a panel.
Basically, they have different panels that they access of people
who volunteer to be on these panels to be surveyed.
And so the first geographic region is going to be southern California, where
we have a sample size of 1,000 people. And then the second sample is going to
be within the Ridgecrest area, which is a sample size of 384 – thereabouts –
because we want to see what the different populations have to say.
The reason why the Ridgecrest sample is so much smaller is because the
population in Ridgecrest is so much smaller than in L.A.
And then we also have some open-ended questions, where is
where the qualitative research – the content analysis, looking at
themes and meaning and construction come in.
So hopefully, through this process that Jeannette and Michele are
coming through for us, is a better understanding of the cognitive and
behavioral responses of earthquakes and the immediate impact of alerts.
And they’re going to be doing some theory testing –
some social science theory testing. As well as, when they finish this,
they’re going to make some conclusions and suggestions at how to improve our
understanding of policymaking and organizational response to public
perceptions of warning limitations, particularly around things
like the threshold.
And then they’re going to be doing a baseline for us so that
we can track it over time. They’re also going to be
cross-training geoscience students. What we’ve noted in the past few
years is a lot of geoscience students are becoming
very interested in social science. And they want to have opportunities
to cross-train in social science as well as geoscience.
So we’re going to be achieving that with this.
So that’s that piece. Now we have the baseline survey,
which I’m a part of that research team. I’m the lead of that team.
And then we have Dr. Julia Becker at Massey University and Ann Bostrom
at University of Washington. Ann Bostrom is sort of – I hate to
use this term, but she’s kind of the grandmother of risk communication.
So we’re so lucky to have her on this project to help us out.
And really, the baseline survey is for Washington and Oregon.
So you know how we’re doing this for southern California and
a little bit of central California? Well, this is going to look at
Washington and Oregon in particular. And there was a survey done in Japan,
which was replicated in New Zealand, which is now going to be modified and
used in Washington state, so we can do a three-country analysis of how –
compare and contrast how these three countries – the populations in
these three countries understand earthquake early warning.
So first, we’re working on the survey instrument design.
Then we’re going to do survey implementation in Washington
and Oregon. And then we’re going to
have the first round of descriptive reports in April and June.
It’s a very aggressive timeline.
Then we have – we’ve invited an anthropologist to be a part of
our team – Dr. Beth Reddy, who is with Colorado School of Mines.
And she is looking at how the ShakeAlert community,
both internally within the USGS and externally with our university partners,
perceive and respond and understand how ShakeAlert works.
What we’ve come to understand is, each of us, as scientists and as
individuals, kind of perceive ShakeAlert and earthquake
early warning a little bit differently.
And so, what Beth’s role is going to be looking at the community of practice.
So how we come together, how we share ideas, and how
we understand ShakeAlert in terms of the different purposes of our
perception as a – as a group. And hopefully what she will come
together with is to create a shared understanding and language –
how to talk about ShakeAlert as a community internally within
the USGS and with our ShakeAlert partners.
Beth also completed this kind of work in Mexico City.
So this is not her first go at it. She also has worked really hard
on the Mexico City side as well and has published
a couple of articles about that.
So we also have earthquake early warning in schools, which is being
run by Lori Peek through the National Hazards Center.
And if anyone knows about Lori Peek, she worked with Dennis Mileti,
who some of you might know. Dennis is sort of the founder of
disaster sociology in a lot of ways. And she is one of his students.
And she focused on schools. She is now the director of the
Natural Hazards Center at University of Colorado-Boulder.
And we’ve hired her and – her and her team to just look at
schools because that is Lori’s expertise.
So right now, Lori is – Lori and her team are going to go up to
Anchorage, Alaska, and then go down to Ridgecrest to interview people
who are working for the school district to understand – so they’re looking at
school leaders as well as emergency managers for the
recent Anchorage earthquake. And they’re using a particular
framework, which Lori developed, called Ready, Willing, and Able
framework, to develop their survey design for California, Oregon,
Washington, and Alaska after they complete their initial interviews in
Alaska and California. Phase I is their qualitative interview research.
So they’re going to be doing that in January and February of next year.
And they’re really looking, like I said, at school district representatives and
emergency managers in these areas. They’re going to really focus on
pre-disaster mitigation and preparedness activities.
So what people did in Anchorage and Anchorage surrounds about the
earthquake as well as Ridgecrest. You know, did they do ShakeOut?
Did they have ShakeOut in schools?
How did they talk about it within the school’s community?
And as well as what people understood about the impacts of earthquakes.
And then how, given that Anchorage and Ridgecrest have had these
earthquakes, how this has changed their ideas about what earthquakes
can do and about their own preparedness within
the school environment. Then they’re going to be doing
that survey, like I talked about, which is the quantitative survey research.
And that’s going to be starting April 2020. And they’re going to be
just focusing on K through 12 public schools at a district level.
And so here is some of their sample sizes that they’ve shared with us
in terms of the schools and what they’re looking at responses.
So usually with surveys, you get between 8 to 10% of responses.
So, you know, for California, we might get 1,000 responses.
For Oregon, we might get 120.
So that will be enough for us to have a representative sample size
in most of these states.
So here is the project team that are working with us here.
Then we’re also working on analyzing CCTV footage.
This is a project I started out with Dare Baldwin at University of Oregon.
Dare is a psychologist. And she has a very prestigious career
analyzing CCTV footage of people’s behavior in different environments.
So we brought in Dare because we wanted her to assist us in analyzing
people’s behavior that we could record on Twitter and on Facebook
and on Instagram. You know how everyone shares,
like, here’s me in the earthquake. Well, we want to analyze that.
And that has been done before in Christchurch with Emily Lambie’s
work where she created a coding schema to analyze CCTV footage.
And the research value here is, we want to see what people did in
Anchorage and in Ridgecrest without getting an alert so that, when we
do have an alert, and we do have CCTV footage, we can take a look and
see what the differences are, just by people – just by watching people.
Oftentimes, the problem with surveys and self-reporting is people will tell you
what they think you want them to think they think. [laughter]
I’ll let you think about that for a second. It’s a bit of a mind twist, but it works.
So with the self-reporting aspect, people want you to kind of think that
they did the right thing, when actually they might have freaked out and not
done what they said that they did. So by analyzing human behavior,
we actually get to see what people’s actual behavior was.
So we’re working with Dare Baldwin’s lab up at University of Oregon.
Dare is going to be the North American repository for CCTV
footage for earthquake behavior. So we’re helping her set up that lab
and the studies for the students there. This is the final project – Analyzing
Ridgecrest – Media and Social Media Analysis for the
Ridgecrest Earthquakes. And this – I’m leading this research.
And so far, I’m doing this alone. So what I’ve done so far is I’ve
analyzed 70 media stories about Ridgecrest and about 2,000 tweets.
I’m using content analysis using an NVivo software, which means
I highlight and code different content, and then I see the connections between
frequency and who’s saying what, where, and when.
So if you want to know more about that particular presentation for ShakeAlert,
I’m presenting the preliminary results of that next Thursday from 4:15 to 4:20 –
sorry, that should be 4:30 – at Moscone South 157, upper –
at the upper mezzanine. And so I’ll be talking about that more.
I also have a couple of other presentations. I have two
invited presentations at AGU. One is the Failure to Alert, which is –
which is talking about ShakeAlert itself, and that’s the media analysis and
social media analysis that I just talked about here.
And then there’s another one – Stop the Presses, which is another
invited talk on aftershock forecasts. I’m going after Jeanne Hardebeck.
We’re going to do sort of a two-punch there, where Jeanne is going to present
the aftershock forecast, and I’m going to present how the media understood and
communicated the aftershock forecast, which is the same 70 media stories
I’ve already analyzed for ShakeAlert. And that’s on Thursday at 8:15 to
8:30 a.m., so it’s an early one, and it’s in Moscone South 157,
upper mezzanine. Same place as the other one.
And then this presentation – Social Science and ShakeAlert –
is going to be at a session called Automatic for the People, which I
got to use an REM album to title. And that’s going to be from
11:00 to 11:15 on Wednesday at Moscone South 301 to 302.
So does anyone have any questions?
[Applause]
- [inaudible] questions for Sara? - Hey, Jeff.
- Sara, is there anything going on that’s going to specifically target
monitoring the difference between [inaudible] versus [inaudible]?
[inaudible] populations.
- It’s a – that’s a really good question. We’re looking at some specific
questions on the surveys to ask, did you download the ShakeAlert app,
versus those who didn’t. Because we actually haven’t had
a WEA sent out yet, it’s a little bit more complex, whereas we have had
the opportunity for an app to have sent out an alert, and it didn’t
for threshold issues. So we actually haven’t
been able to send that – to focus on that yet.
But there are plans to look at them.
- [inaudible] questions?
All right. Let’s thank all of our speakers again.
[Applause]
- We will see you at the poster showcase during Coffee Hour.
[inaudible background conversations]
- Thank you. - Yeah. Thank you. That was great.
Thanks so much.
[inaudible background conversations]
[Silence]