[Silence]
Good morning, everyone. Thank you for coming to our seminar today.
We just have a few announcements, mostly around the seminars next week.
We do have a bit of a seminar-a-thon next week.
Rob Wesson, who is a retired USGS employee, is going to be coming to
talk about his new book, which is on Darwin’s contribution to Earth sciences.
So that should be pretty interesting on Monday.
And then Kang Wang from UC-Berkeley is going to be
on Wednesday talking about InSAR observation of earthquakes.
So that’s going to be on Wednesday. But wait, there’s more.
We have [chuckles] Tom Mitchell on Friday, who is visiting from University
College London. And he is going to be talking about rock mechanics.
He’s visiting the rock mechanics lab here.
So we hope to see you at least one of the seminars next week.
And Fred Pollitz is going to do the introduction for our speaker today.
Thank you.
- Hello. It’s my pleasure to introduce Kathryn Materna.
She got her undergrad degree at MIT in 2014, advised by Tom Herring.
She’s a Ph.D. student at UC-Berkeley working with Roland Burgmann.
And she works on a lot of very interesting things in crustal deformation.
A lot of wide-ranging interests. One of the things was –
can you hear me?
Okay.
Susan is signaling over there.
Okay. One of the things is solid Earth deformation from the
stormwaters of Hurricane Harvey, which is pretty interesting.
Another thing is studying the source of the deep lower crust
Botswana earthquake from 2017. And she’s been doing a lot of work
around the Mendocino Triple Junction, which she’ll talk about today.
I’ve interacted with Roland’s group for many years, and I’ve known
Kathryn since she was a first-year student.
And now, it seems she’s the most senior member of that group, and she’s
always full of really good ideas. So I look forward to your talk.
- Okay. Thanks.
Okay. Good morning, everybody. Thank you for inviting me.
It’s always really great to come and visit the USGS.
Can you guys here me? Can the – okay, cool.
Yeah. It’s great to be here.
Today I want to talk to you about my research at the
Mendocino Triple Junction using mostly GPS to investigate
the styles of crustal deformation there.
I want to start out by showing a GPS time series to kind of
motivate the research that we’re doing.
Here’s a time series of a GPS station in the east component.
It comes from the Mendocino Triple Junction region.
And, in particular, the red time series here is one that we’ve corrected for
earthquake offsets, antenna changes, seasonal, and the normal tectonic trend.
So basically, we’ve corrected the easy stuff.
And we expected, at this point, to see a time series that was mostly flat
with some postseismic – potentially postseismic transients
because there are – there are earthquakes that
have happened in this region. And so this is the normal postseismic,
but after – but beyond that sort of transient, we were expecting to see
a flat line. And the surprise was that it doesn’t look very flat, and it looks like
there are systematic variations where sometimes the slope is higher,
and sometimes the slope is lower.
And this is the observation that we’re trying to explain.
This station is one of the stations – one of the GPS stations here in this –
in the blue arrows at the Mendocino Triple Junction,
which I think is an important region to study for a few reasons.
It is our closest subduction zone to here, actually.
It’s at the southern end of the Cascadia subduction zone and the
northern end of the San Andreas Fault. There is quite a bit of crustal
deformation in this region, both on land and out in the ocean.
And the region hosts more magnitude 6 and
larger earthquakes than any other part of California.
The southern end of the Juan de Fuca Plate is called the Gorda Plate, and it is
internally deforming intensely. As you can – and this results in
a number of offshore earthquakes that we see these magnitude 6.5
and larger earthquakes. There’s – they have occurred
every few years, and there’s two really important ones for this research.
One is the 2014 earthquake, and one is the 2016 earthquake –
magnitude 6.8 and magnitude 6.6.
The picture of this generalized subduction zone in Cascadia – and the
more specific picture in Cascadia – looks something like this.
And it’s important to keep this general picture in mind to interpret
the observations that we make. In the interseismic period,
the seismogenic portion of the subduction zone is generally locked.
And it’s – and, as a result of this interseismic locking, there is landward
motion that we detect in the GPS, basically everywhere on the continent.
But, as we go deeper into the subduction zone, there’s a transition region that’s
labeled here as long-term slow-slip events or slow creep, but it’s actually
pretty unclear what’s going on in this – in this region.
And then, in Cascadia, as you go deeper, there’s a well-known ETS zone.
It has episodic tremor and slip.
So seismically, we record tremor, and geodetically, we observe slip.
And, in this presentation, all of these zones are important
for understanding and explaining the GPS data.
The Mendocino Triple Junction does have some interseismic creep
on certain interfaces, and this is – I was actually here last year.
I was lucky enough to give a seminar last year about studying creep on one of
the interfaces at the Mendocino Triple Junction, specifically the
Mendocino Fault Zone. But, in this presentation, in terms of
interfaces to focus on with our data, we’re going to be focusing on this part –
the lower part of the seismogenic zone and the transition zone of the
Cascadia subduction zone. And we’re going to look at
whether there’s time dependence in the coupling there.
So the GPS data that we are using in this study comes from the PBO and –
the Plate Boundary Observatory and University of Nevada-Reno solutions.
Basically, I used whatever solutions – large-scale solutions I was able to find.
And, in terms of basic processing, we took the time series,
de-trended them, and removed the offsets from earthquakes
and antenna changes and any changes in reference frame.
And we also removed seasonal components,
which we can see in this station. The east component here
has a lot of seasonality. And so, in a few ways, which I’ll
get into a little bit later, we tried to remove this
seasonal deformation component. And then, once we had a cleaner set
of time series for each station, we were able – we are trying to
investigate the effects of the offshore earthquakes on these time series.
So here’s about a dozen earthquakes – I mean – sorry –
about a dozen GPS time series. The east component on the left
and the north component on the right.
In this kind of messy plot, we’ve included the earthquakes and the
seasonal components. And then, after we clean up these time series for
earthquakes and seasonal components, they look something like this.
This is kind of the original motivating time series stack that we looked at.
And we thought, wow, there’s something really strange going on.
So these dashed lines here represent the times of big magnitude 6 or 7
offshore earthquakes. And this kind of curvature here
is a postseismic transient that we would expect.
So, 2005, 2010, 2014, and 2016. This is where the action is.
There’s something interesting in the time period between the 2014 and 2016
earthquakes that actually makes it appear in east component there is an
increase in slope during this time period, which we call T3.
And then, after the 2016 earthquake, which was December, so it shows up
as almost 2017, it looks like the slope returns, or goes more westward.
So this is interesting, and we see it at many stations, and we want
to understand why. The next step for us was to
solve for velocity at T3 and velocity at T2 and plot the differences.
So here on the left is the velocity change –
T3 minus T2 – at all of the stations.
We can easily apply these corrections and the procedure to stations anywhere.
And this is – this was kind of our big surprise.
We – so a few important things to point out.
T3 minus T2 – there is a velocity difference, and it is – and it manifests
as many stations moving more to the east than they were before.
The scale bar here is 2 millimeters per year.
So compared to the normal plate motion that we record at this – in this area,
about 20 or more millimeters per year relative to North America, these are –
this is about 10% of that velocity.
So it’s a smaller perturbation, but it’s spatially coherent.
And this is the – this is the earthquake that is the dividing line
between T3 and T2. On the right-hand side,
we have T4 minus T3, and the dividing line here is this 2016 earthquake.
Magnitude 6.6 and quite far away – about 200 kilometers away
from the center of this deformation pattern.
And, in this velocity difference, the stations are moving more to the west by
about 2 or 3 millimeters per year more than they – than they were before.
So, to summarize, we have observed in our time series normal postseismic
transients following two of the earthquakes in the record in 2005
and 2010, but a different sort of response following 2014 and 2016,
where, in these cases, there’s an oppositely signed velocity change.
And it’s kind of small – about 10% of the normal motion
at these stations. And it’s directed east-west.
We – so this was actually very interesting, and the
spatial coherence makes us really curious about what’s going on.
And just for background, typically what we would expect
in taking differences between velocities during the
interseismic period is we would expect almost nothing.
So we’ve tried a number of – so our first thought was,
is this an artifact of something? Seasonal corrections, for example.
So we’re trying here – we have the east component of a particular station.
This is the uncorrected east component, and we’re trying three different types
of filtering techniques to remove seasonals and a loading model
based on the GRACE gravity field. And what comes out of our analysis
here is that, in all of them, you can – you can remove the seasonals in
multiple reasonable ways and still resolve a velocity change.
So we think that overall it’s not the – it’s not just because we’ve removed
seasonals in a particular way. We’ve tried actually a few more now –
so five or six ways of removing seasonals,
and they all pretty much have consistent outcomes.
We were also interested in resolving from the data, what’s the time of the –
of the bottom of this V? Does it coincide with the earthquakes?
So we tried a filtering technique, passed a long-wavelength filter
through the data, and we resolved the point of maximum curvature,
or the place where the slope change should be relative to the time
of the earthquakes. So on these plots, if the symbol is yellow, it means that
the slope change happened at the same time as the earthquake.
And because of the filter wavelength that we’re using, which is a year,
we wouldn’t expect to see – to resolve changes better than a few months.
But the results are pretty much consistent with the idea that these –
that the timing of these velocity changes is coincident with March 10th, 2014 –
in this case, the time of the earthquake,
and December 2016, the time of the 2016 earthquake.
So, to take a step back, we have some strange observations
of velocity changes – interseismic velocity changes in Mendocino.
And we wanted to understand why, so we came up with a whole list
of hypotheses. And we’re trying to chase down which ones are most reasonable.
So our first hypothesis is that the offshore earthquakes –
perhaps the offshore earthquakes created static stress changes on
interfaces that are closer to land, and those changed the state of coupling.
So we performed Coulomb stress calculations to analyze the effect of the
static stress changes on the clamping of the onshore faults on the Cascadia
subduction zone in particular, and we found that this was not a very good
explanation because the Coulomb stress change from the 2014 event was
actually in the wrong direction to – compared to the – compared to
the eastward, you know, increase that we see.
And, in the case of the 2016 earthquake,
it’s just too far away, so the static stress changes would be very, very small.
So we also thought about viscoelastic relaxation or afterslip, but these
hypotheses struggle to explain the data because they – because the 2014 and
2016 velocity changes, they look linear with time as opposed to a decaying
exponential or logarithmic curve, so – and secondly, the velocity changes
actually extend quite a bit farther inland than we would expect from
viscoelastic relaxation and afterslip. We thought about potentially reference
frame issues, although, on intuitive bounds – or, on intuitive means,
it should not be a reference frame issue because we’re taking differences
of velocities. So it should be independent of reference frame.
But anything’s possible, so we wanted to check out the data in a lot of different
reference frames and see if this was visible across broader scales
or in any of these frames. But we didn’t really see much of a difference.
So I’ll show you here five different solutions that we’ve tried.
And the – and this is the signal of interest up here.
And it’s different between some of these different fields, and I think that this
solution in particular is pretty noisy, but there’s a lot of eastward motion
in the 2014 data in pretty much all the reference frames that we tried.
So the next really important – the next really important piece of data
to consider – or, hypothesis to consider has to do with hydrological loading.
And I want to spend the next 10 or 15 minutes talking about
hydrological loading because it – there’s reason to believe it could
be part of what we’re seeing.
In California, we know there was a massive drought between – starting after
2011 and ending around 2015 or 2016. And so there’s good reason to suspect
that, on top of the signal that we’re seeing, there is also a hydrological
loading component. So you may have noticed on the previous plot,
we expanded the map and did the same analysis for velocity changes for lots of
places in the western U.S. So we’ve – this is – this is a broader view.
In 2010, this is a time period where there was very little going on.
And these velocity changes are more – are smaller and more random.
So this is, like, a quiet time.
And, in this map of velocity changes – T3 minus T2 – this is the signal
of interest surrounding the 2014 earthquake, but there’s a few other
things to point out about this map. One is that Oregon has very little
activity in terms of velocity changes. So here the velocities are constant
across this time window. But, in the Bay Area,
there’s something – there’s something going on.
And it’s consistent in its eastward motion as well with Mendocino.
And, in the 2016 case – T4 minus T3 – again, nothing in Oregon.
This response here in the – in Mendocino.
And something else also – a westward motion, but maybe
a little more complicated in the southern part of California –
or, the central part of California. And so we thought it’s really
important to explain what this is. Because without understanding what
this is, we can’t really understand what’s going on in Mendocino.
So we performed a couple of corrections for hydrological loading.
First – and this is – this is a small detail, we did two kind of manual detailed
corrections for two stations – a station at – there’s a station
that’s 1 kilometer away from Lake Shasta.
And there’s a station that’s 1 kilometer away from Lake Oroville.
And for those particular stations, we performed a specific correction
for the lake loading effect of those two lakes. So here’s an example.
This is the lake level history for Shasta – for Lake Shasta and its modeled loading
effects in the vertical on this station. So this is – this is the mask that I used
for Lake Shasta, and that’s the station. And the modeled position –
I’ve copied it down here in red. So the modeled position is red,
and the GPS station position is black. And they actually – they track
very well, which tells us that, at these particular stations,
when you’re only a kilometer or so away from a major reservoir,
it’s important to consider the loading effects of that reservoir,
because they impact the station position quite a bit.
So we used this model as a correction, and we removed it from the time series,
and – first we thought, okay, well, we’re going to throw away the station by Lake
Shasta and the station by Lake Oroville. But when we realized how closely
these models can fit, we used the lake loading prediction as a correction,
and we recovered those stations. We kept using those stations.
On a broader scale, for a discussion of hydrology,
we thought it made sense to predict the station positions
using GRACE loading and see how they varied.
So here’s an example of a time series in east, north, and up for a station.
I’ve actually managed to cut off which station it is, so I’m not quite sure,
but somewhere in California. You can see the effects of the drought.
So in the vertical, GRACE is detecting less load due to groundwater,
soil moistures, snow during the drought. And that’s unloading off the surface
of the Earth, and the predicted position of the station is going up.
And similarly, the east and north reflect
the loading from a distributed mass as well.
And we take the – we take T3 – you know, T1, T2, and we make
the subtractions – the same subtractions that we do for the GPS data.
And the predictions are shown here across California and Oregon,
so – for the 2014 – T3 minus T2.
There were a few interesting features. But first I’ll point out is this scale bar
is very different from the scale bar of the GPS data I was showing you.
So this is 1 millimeter per year, and these are much smaller
than 1 millimeter per year. But what we noticed is –
I’ll start with the Bay Area.
It’s hard to see, but this velocity change is eastward-directed.
It’s – which is the same as the data, but it’s smaller by a factor of 10.
Similarly, a slight eastward and some southward direction here.
Oregon is different from the data. We saw no velocity changes in the data,
but certainly velocity change predictions from the hydrological
loading. And that’s something we don’t entirely understand.
So the GRACE – the GRACE loading model that we computed, we find that,
in the area where we’re very interested in understanding hydrology down here,
it has a similar direction to the data, but it’s too small by a factor of 10.
There are other loading products that we were considering as well.
And they are these two loading models called the GLDAS and NLDAS.
The PBO has a really – a really nice database of loading time series
that come from these two models. And so we used these as corrections
as well – an independent set of corrections to see if the slightly
higher resolution loading product from NLDAS, for example, to see
if that could explain hydrology in different regions.
And unfortunately, this slide is a little bit different from the previous one.
This is not the model from NLDAS. This is the data minus the model.
So the point is that the systematic eastward and then westward residuals
that were concerning down here are smaller after correction by NLDAS,
but not by much. So NLDAS had a very similar –
has a very similar velocity change pattern compared to GRACE, but –
a larger amplitude, but it’s still too small to explain all of the data.
It’s just too small by a factor of 3 instead of a factor of 10.
And another way to see this is by plotting the GRACE and NLDAS
and GLDAS models for a particular station against the GPS.
So the models for a station in Mendocino are these dashed lines here.
And we can solve for their fit – their best-fitting slopes and do velocity
differences between these time periods. Their velocity differences
generally agree in sign with the data but are too small.
And you can see that because the GPS slopes are here,
and they’re significantly greater. Like, the variation in actual data
in the GPS east component is kind of consistent with the – what you’d expect
from hydrological loading, but is way bigger in Mendocino.
So we moved on, basically, thinking the hydrological loading is part of the signal
that we’re seeing, and the drought is certainly something to consider, but we
can make corrections using NLDAS, GLDAS, or GRACE, and we still
see a large signal in Mendocino.
So there’s – so we think that there’s something else going on.
And the hypothesis that we’re running with right now and considering is that
the interface – the subduction interface beneath Mendocino, if it changes its
coupling state, and is sometimes more highly coupled and sometimes more
weakly coupled, we could explain the eastward motion of the GPS in a –
at a time when the coupling is higher. And we could explain the westward
motion of the GPS at a time when the coupling is lower.
So this is important to consider physically too.
How could this possibly be happening?
We took the GPS velocity change data and inverted it for a change in coupling.
And, in these two plots – this is for the 2014 case, and this is for the 2016 case –
the colors represent a change in coupling, but it’s important to note
that the sign is actually different. So, in this case, these coupling
changes of some number of millimeters per year are increases in coupling.
They increase the locking on the interface, and that
makes sense because the velocities are all moving to the east.
Whereas, in this case, these higher colors represent a decrease in coupling.
This is similar in concept to a slow-slip event, when we know that,
when the coupling decreases, the stations move to the west.
This one is the opposite. It’s kind of like an anti-slow-slip event.
But in order to explain the data, we have to appeal to both.
We have to say that in 2014, this is an increase in coupling,
and this is a decrease in 2016. And so this is – this is a strange result,
in a sense. And we wanted to understand why. So we looked
a little bit at the waveforms. I think there’s a lot more work
that can be done in this – in this area that we haven’t looked into yet.
But we plotted the waveforms of the different – of these four offshore
earthquakes, and these are the two that are of interest in the later time period,
on the same scale, at one of the stations in the Mendocino region.
And this presents a huge puzzle to us. How could an earthquake offshore,
at quite a distance, change the coupling on the interface?
And could it accomplish this dynamically?
This is a puzzle because the waveforms of these – of these two
recent earthquakes are kind of more or less similar in amplitude.
And waveforms – and dynamic shaking involves both positive
and negative changes in the stress over a time period of seconds.
So how can it be that a similar amount of shaking with both positive lobes
and negative lobes resulted in a change that was oppositely signed?
An increase in 2014 and a decrease in 2016.
So this is a big puzzle that’s been on our minds for many months now.
We wanted to look at other data sets that exist.
And maybe that could give us some sense of what’s going on.
So we spent some time playing around with the tremor catalog in Cascadia.
So, for those of you who have used tremor data before, we all know that
basically it comes from Aaron Wech’s website.
And that that is the – and this is basically the Aaron Wech catalog
that we have downloaded and plotted, and we’re interested
in looking at its evolution in time and space.
And the coupling change patch that I showed on the inversion results
is located about here at the up-dip limit of the tremor.
And we think that that’s interesting and related.
So we went to Aaron Wech’s catalog. We downloaded off the –
off this very amazing website here. And this is the catalog that we got.
Just by downloading – you can plot tremor catalogs in a –
in a variety of ways, but this is time versus latitude.
So the Oregon-California border is right here in the middle.
And this is southern Cascadia.
The Mendocino Triple Junction is right around here, and it’s very active.
So, over time, it’s kind of constantly rumbling off tremor.
And I’ve marked the time periods.
So we noticed a few things that were a little bit strange about this.
Well, first, obviously, there’s this big gap earlier in the tremor, which
means we can’t use this time period. And we talked with Aaron, and this is
very clearly the data just was not ingested for this region
of space and time. So that’s not that big of a deal.
The more concerning question that we were interested in is we noticed that
there’s a change in the character of the tremor at this – from here.
And then, at some point in 2016, it looks like something changes.
And then it’s very quiet after that. But the rumbling that’s constantly
happening doesn’t happen very much anymore.
So we asked Aaron to look into it and see what might be going on.
And he discovered that an instrumental problem right around the middle of 2016
reduced the detection capability. So there was tremor going on here,
it just was not detected. And the instrumental problem
had to do with five – about five stations here.
They’re the ones that are clear – that don’t have any data in them.
Sending more or less flat data instead of actual data.
And they were being sent into the detection scheme.
And, at the same time, many of these stations had telemetry problems as well
due to the fact that the stations will all – they send multiple stations’ worth of
data to a single analog telemetry device, as far as I understand it.
And occasionally, spikes that affect the analog telemetry device end up
at all of these four stations. If they’re correlated spikes at four
stations, they sometimes look like tremor, but it’s not.
So, in a – so Aaron helped us address these two things by re-running a period
of time in his catalog, where his detection of
analog telemetry issues is a little bit better, and where
he’s more aware of the stations that might be going offline.
And so he produced a new catalog for us, which was actually
very, very helpful. Here is the old catalog with the new catalog
pasted on top, basically. We have removed what was in this time
period and put in Aaron’s new catalog. And the rumbling tremor that we see
in the Mendocino region, it may get quieter in a way that’s actually –
that is, in fact, a real effect. But it doesn’t get quieter by quite
as much because some of this tremor was not detected before.
So we take this newer – this new tremor catalog –
a combined catalog – and we plot it here.
And we’re particularly interested in the way the tremor evolves in space and
time and how that might result – how that might relate to our coupling change.
So I have taken the tremor epicenters and associated each epicenter,
which is just latitude and longitude, with a depth on the McCrory 2012
plate interface – the nearest depth. And so now, in addition to latitude
and longitude boxes, we can study the tremor based on depth ranges too.
And, in this plot, I’ve decided on depth ranges, such as here –
24 to 35 kilometers. That’s this purple zone.
And then 35 kilometers and deeper is this orange zone.
And I can filter out tremor just in those boxes
and analyze how it occurs over time.
The reason I focused on this deeper – on the 24 to 35 kilometers is because
I wanted to isolate the region of the interface that is active
only during ETS events and not very active in the background.
So one of the features of the catalog that’s true of tremor regions around
Cascadia is that the deeper – the deeper region is more continuous,
and the shallower region is more episodic.
And we see that here too, where the deeper portion
of the interface is active at many times.
And the shallower portion of the interface is more stair-like.
The tremor rate per day in the purple zone is given here.
And this is – this is kind of an illustrative plot to show
the timing intervals. We can see that a major ETS event
happens in this region about every seven months.
And that interval is actually pretty fixed. It doesn’t change very much.
Six to eight months, maybe, is the range. But it’s pretty well-regulated
for some reason. So, based on this information,
we came up with two potential connections, or hypotheses, between
connecting a tremor catalog with coupling changes on the interface.
The first hypothesis is that potentially the – if the location
of tremor, or the dominant location of tremor between one ETS event
and the next ETS event varies – if it moves up-dip or down-dip,
potentially that could look like a coupling change to the
untrained eye of the GPS. It could look like a coupling change.
So we wanted to investigate whether one ETS event or
the next had different evolution in space.
And the second hypothesis that could relate tremor to the occurrence
of coupling changes is that maybe the physical conditions on the interface
might change between early in an ETS cycle, during an ETS event,
or late in the ETS cycle. So perhaps the time since
the last ETS event is an important piece of information
to know about and maybe affects the physical conditions.
So Hypothesis 1 we could rule out pretty fast, actually.
We separated, say, eight different ETS events,
plotted the average depth of their tremor.
This pink one is a – is a weird ETS event
that I’ll show you on the next slide.
And all of the other ones were within 5 kilometers.
And I tried many boxes and definitions of regions,
and basically there was nothing systematically different
about one ETS event in terms of its depth compared to the next.
The eight ETS events that we chose – that we were
looking at are kind of laid out here.
We were also thinking about, maybe, what if one ETS event was
north-propagating and one was south-propagating?
Could that result in a different state on the interface? But probably not.
There’s – this is – this is the important one.
And ETS event that happened only two months – or, for some of the area,
only two weeks before the 2014 earthquake.
So we note from this part of the time history that the
2014 earthquake was early in the ETS cycle.
And here’s a case – this is the ETS event before the 2016 earthquake.
The 2016 earthquake was about four months after this.
So we were about in the middle of an ETS cycle when this
earthquake happened. However, this ETS was actually quite
weak in the area that we are researching. It was – it’s a major ETS event
that went all the way into Oregon in the north, but in the area of –
in our area of interest, it was actually – it did not produce a lot of tremor.
So to summarize that – and we can see it from this plot here too.
The 2014 earthquake occurred early in an ETS cycle,
while the 2016 earthquake occurred in the middle of
an ETS cycle, but the previous cycle was actually quite weak.
We have – so this leads us to think, physically, on the interface,
what needs to happen in order to achieve a coupling change?
And we’ve thought of a few ways that – two major ways
that you could change the coupling on the interface.
The question of whether these can happen dynamically or not
is a separate question. But it’s easier to see – to just lay out
in a schematic way that we could either – we could decrease
coupling on an interface by either increasing the fluid pressure in that
fault zone, which would decrease the normal stress, clamping the fault,
or we could decrease coupling by somehow – if it’s a – if it’s a
A-minus-B-positive – a fault that likes to slip, to creep at some velocity
interseismically, somehow we could increase that steady-state velocity,
and that would also look like a coupling change.
To achieve the opposite, increasing coupling, we would need to
decrease fluid pressure in the fault zone, which increases the normal stress and
clamps the fault, or we would need to decrease the steady-state velocity.
And somehow, at Mendocino, we actually need to do both.
Because one of these – one of the earthquakes resulted
in a coupling increase, and one resulted in a coupling decrease.
So our schematic interpretation of what’s going on is kind of based on
an interesting presentation of the – an interesting presentation
of the ETS – of physical conditions in the ETS zone.
In this paper here by Pascal Audet and Roland Burgmann, there’s a
presentation of what’s happening in the ETS zone where, throughout the ETS
cycle, dehydration reaction of fluids at this part of the interface results in a –
in a time-dependent change in effective normal stress due to
an overall increase in pressure in the – in the fault zone through time
and a release of that pressure during a slip event.
So we have kind of expanded this schematic idea to interpret what could
have happened in the 2014 and 2016 earthquakes where our cartoon
looks something like this. The key elements of the cartoon
are an ETS zone at a reasonably deep part of the plate interface.
And just up-dip of an ETS zone is a hydraulically sealed low-permeability
region, which is thought to exist to seal the high pressures that are likely present
in the ETS zone from the other, you know, more generic parts of the crust.
And the two basic ingredients are a time-varying pressure in the ETS zone
and a hydraulic seal that remains closed during normal times but opens, perhaps
due to increased fracture permeability, during dynamic shaking.
And if you have these two ingredients in the – in the fault zone, you can
make a cartoon picture where, early in the ETS cycle,
if there’s low pressure here, and it’s low relative to pressure just outside,
if this hydraulic seal opens, pressure-driven flow could, in fact,
move high-pressure fluids into the lower ETS zone
and increase the coupling just up-dip.
Which is consistent with the picture that we see in the 2014 earthquake.
In the 2016 case, the schematic is slightly different because the pressure
was higher, potentially, at the time of the – of the earthquake, which means
that, if that seal is broken, pressure-driven flow could, in fact,
go in the opposite direction, increasing the fluid pressure,
decreasing the normal stress, and allowing for more sliding
just up-dip of the ETS zone, which is, again, consistent with
the GPS – with the direction of the GPS motion.
But this is – this is a highly – this is speculative – highly speculative.
But it is important to think about these things because there are examples –
they’re rare, but there are a few examples that we’ve found,
and we’d love to learn about more, elsewhere in the world where coupling
changed, perhaps in a dynamically triggered way,
and we don’t really understand why. So I have a few examples that are –
that I think are interesting. One is a coupling decrease that
was apparently observed in Chile. And the – following the
Tocopilla earthquake in 2005. This is – this is the most important line.
This is the de-trended GPS at a east position of a station.
And after the earthquake, it obtained a new velocity.
And this is interpreted to be a decrease in coupling on the
interface that occurred as a – following – immediately following
the 2005 earthquake.
Chile continues to present more challenges for us, actually.
Because the second example I have is also in Chile, but in a –
in a few degrees farther south. We actually have recent observations
that are very similar to Mendocino and also are very difficult to explain.
There are GPS velocities inland here that moved up to 10 millimeters a year
faster towards the east than they did before the Iquique earthquake,
which was actually up here. So the Iquique earthquake was
several hundred kilometers away, and to the south, even after correcting
for any small postseismic that they had identified, it looks like the coupling
somehow increased on this part of the interface, driving everything to the east.
And the final example is actually in Sumatra, where, in the – where geodetic
data is often somewhat limited. But there is enough data from some
islands – forearc islands that suggest that before 2000, there was a certain
high coupling state in the southern part of this figure here – Enggano Island.
And in – and after 2001, the coupling state was – it decreased coupling.
And that’s the interpretation of the change in velocity on this –
in this station.
So there are – and it may have been dynamically triggered as well.
So there are a few examples – and I think the more we dig,
the more we may find, that the – that the coupling state
of faults may be more time-dependent than we realize.
So this – my implication slide is actually mostly just a series
of open questions that these data kind of bring to bear.
One is, under what conditions can coupling actually change
due to dynamic shaking? There’s even – there are examples
in the Bōsō Peninsula of Japan, where coupling changed in an
even more mystifying way.
It changed after slow-slip events where there’s no dynamic shaking.
So under what conditions can coupling change due to
dynamic shaking, or even independent of dynamic shaking?
And does Cascadia, in particular, experience long-term
coupling variations? We’re very familiar with the ETS
coupling variations that happen on the time scale of several weeks.
And they happen months apart. But, on a longer term, are there also
variations that we need to be aware of? And outside of Cascadia, what other
regions can experience these types of changes?
And the implications include – obvious implications include
time-dependent hazard assessments. Over time, can an interface
accumulate more strain or less strain than we expected?
And so, to summarize, I’ll leave this picture up and just lay it out that we
observe, in Mendocino, velocity changes of about 10% of the regular
velocity following offshore earthquakes that were
distant enough that it’s not a static stress type of interaction.
We think that these velocity changes can be most completely explained
by a coupling change on the subduction interface.
And we present a possible mechanism involving the flow of – the pressure-
driven flow of fluids near the ETS zone to move coupling up-dip and down-dip.
And the results suggest that perhaps fault coupling in southern
Cascadia may be time-dependent. Thank you.
[Applause]
- All right. Does anyone have any questions for Kathryn?
[Silence]
- Really interesting presentation. I guess I want to sort of ask you
to speculate a little bit about your first question.
And I guess my broader question is, is why don’t those other two earthquakes
in the area cause this sort of behavior? Because I would expect that they
occurred sometime during the ETS cycle, and so they
should have some sort of effect. And a more broad question would be,
why isn’t there any other dynamic triggering effect?
I’m sure that other large earthquakes that are, you know, much more distant,
but should have similar amplitudes in the same frequency range,
why wouldn’t that have an effect? - That’s a really good question that
we’re still kind of working out. We’ve been looking at a lot of the –
we’ve been looking at the catalog somewhat to see, maybe the
2014 earthquake – maybe it wasn’t the local one.
Maybe it was Iquique, which happened within the – within a few weeks.
The 2016 change, that occurred at a time when globally, there weren’t
very many, like, magnitude 8s or something like that that could cause it.
But that’s a really good question about the 2005 and 2010.
I think it’s possible that the 2010 event may have just been smaller – 6.5 –
and it seemed to not have very much surface wave energy.
But, in terms of what frequency range is most effective and what
duration of shaking, for example, we really don’t know yet.
- But what makes those two so special? - These two?
- Yeah. - The latest two.
Perhaps there were velocity changes. So we’re – the 2005 earthquake
may have had velocity changes too. But there were only about five stations
installed a few months before that earthquake happened.
And when we look at their time series, it’s suggested that there may have been.
And the 2010 earthquake, I think, may have been too small.
- Two quick questions. Is there episodic tremor and
slip down in the Chile examples that you were showing?
And might that – you know, how does relate its characteristics
to what’s happening in Cascadia?
And the other question was, what about the Bay Area?
You showed that hydrologic loading didn’t really, you know, explain the
magnitude of the arrows up in the Mendocino area in the Bay Area.
Any speculation on what might make similar-magnitude changes
in motion down here? - Yeah. Chile is kind of an anomaly
of subduction zone because it doesn’t have tremor, as best as we know.
It may have some longer-term slow slip or variations even beyond the two that
I showed, but we haven’t detected any ETS activity.
Which means that perhaps what’s going on there is different –
just, like, qualitatively, a different situation.
And your section question – oh, in the Bay Area. Yeah.
So what we’re working on now is taking the hydrologic loading
models and scaling them up. Because it’s possible that they’re –
in their overall characteristics, the loading models are correct,
but in the local site response, they could be off by some factor.
So our latest results that didn’t end up here were, station by station,
increasing the amplitude of the loading correction until it matches the amplitude
of the GPS annual signal and then using that as a correction.
And what it showed us, just quite recently, is that, if we have that extra
degree of freedom, we can make the – we can explain
the Bay Area residuals pretty well. And the Mendocino effect is still there.
- [inaudible]
- Any other questions?
[Silence]
- Yeah. Thanks again for a really interesting, thorough talk.
I was curious what the sensitivities are with respect to that loading calculation.
I think you said you used the PREM velocity model.
- Mm-hmm. - What happens if you allow for
shallow structure that deviates from that significantly or something?
I’m not sure what the key parameters are there.
- Mm-hmm. Yeah. We haven’t tried forward-calculating
the load on another structure. But there are groups in Europe who are
doing this, and they suggest, actually, that it may affect horizontal.
So it’s something that’s definitely worth looking into.
It affects horizontals and phase, actually.
[Silence]
- Any other questions?
[Silence]
- Yeah. The hypothesis with the coupling change due to the –
you know, this fault valving behavior where the permeability
changes and the water escapes somehow, I think, is common for
reverse faults and stuff like this. But these earthquakes are on
those transform faults offshore. So it – are you saying that maybe
the permeability change and this dynamic triggering happens just
due to the shaking from that? And is that – do you think that
shaking is strong enough at the depth of the ETS zone?
- We’re not sure. It’s – that’s something we wonder about a lot too.
The shaking at the surface – it’s maybe not – it’s not easy to
take the measurements that we make of seismic shaking at the surface
and just claim that it’s exactly the same amplitude of shaking
on the interface itself. There could be extra structure
and a wave guide effect or something that make it
hard to predict exactly the amount of shaking.
Something that we want to look into further is going back, I think,
to Justin’s question that perhaps other seismic events – big earthquakes
elsewhere in the world may have had a similar amplitude of shaking.
And it’s definitely an important question to push forward.
Why didn’t that cause the same thing?
- Just to follow on Evan’s question, is there any evidence that those
two earthquakes triggered tremor dynamically at that same time?
- Not very much, actually. The website catalog does have some
dynamically triggered tremor on the day of the 2014 earthquake.
But with Aaron’s help, we looked at it more closely,
and it seems like that’s false detections from aftershock activity.
So on the – when we looked at the 24 hours surrounding that earthquake, there
was lots of earthquake stuff, but nothing that was a systematic tremor detection.
We got really excited when we saw lots of tremor on that day.
We thought it – there was movement everywhere, but it seems to be false.
- Okay. Any last questions?
Great. So let’s thank Kathryn one more time. Give her a round of applause.
[Applause]
If anyone is interested in coming and having lunch with us,
please come up to the front of the room, and we will arrange a lunch.
Also, Kathryn still has some spots available in the afternoon.
If you want to arrange meeting with her, please contact Fred or myself or Jeanne,
and we will make that happen. Anyway, thank you.
We’ll see you here next Monday.
[Silence]