Okay. Hello, everyone. Welcome to our very first hybrid
seminar. This will be a learning process, so please bear with us.
Welcome to the ESC seminar for today. It’s May 11th.
As we said, this is our first hybrid seminar.
So the way that we’re going to try to work this is that we have – we have
some people here in the room. We’re going to try to show you them.
There really are people here. I mean it.
And we have a bunch of people online. So Chad will introduce the speaker,
and then she’ll give her talk. We’re going to save the questions
for the end. Keep this a little simpler.
So, if you’re online and you have a question, put it in the chat.
And we will try to make sure that we can have people who are online
be able to ask their questions themselves by unmuting themselves.
We’ll see how that goes. Okay. So I’m going to hand it off to Chad.
Go ahead.
[silence]
- So it’s my honor today to introduce Alba Rodriguez Padilla.
Alba got her undergrad from the College of the Atlantic where she did research
on a variety of tectonic and geomorphology problems including a
thesis on the tectonic climatic evolution of the forearc of southern Peru, which –
actually, some of which was just published, I believe.
She then moved on to UC-Davis and joined Mike Oskin’s research group,
which is where I first got to know her. Her Ph.D. work explores a variety of
fault mechanics problems using a diverse array of techniques,
including field work, landscape analysis with remote data,
and finite element modeling. Most recently she was awarded
a NASA fellowship to investigate off-fault inelastic deformation
over multiple earthquake cycles. And, in addition to her Ph.D. research,
she’s done some really good undergrad mentoring work
doing research with undergrads primarily using data from the
2019 Ridgecrest earthquake sequence.
So, without further ado, I’ll turn it over to Alba tell her – to tell us
about her research on multi-fault earthquakes in southern California.
[applause]
- Thank you for that introduction, Chad. I’m happy to be joined by
a live audience today. This is really exciting.
It’s been a long time from giving in-person talks.
And today I’m going to be taking you on a tour of southern California from
east to west, where the joining nexus of this talk is multi-fault earthquakes.
We’re going to start east talking about the 2019 Ridgecrest earthquakes
and how inelastic deformation was distributed for those events and the
implications for how we think of the damage zone from those observations.
Then I’m going to bring in observations from prior events in the Eastern
California Shear Zone. And at the end, we will move
all the way east to Cajon Pass just north of L.A. where the San Andreas
and the San Jacinto Faults come together.
This is work done in collaboration with my Ph.D. adviser Mike Oskin,
and then with Chris Milliner [echoing sounds], Andreas [inaudible].
Okay, so without further ado [loud echoing] – well, actually,
before I start, I want to acknowledge that I know several of you
[echoing stops] have seen at least one-half of this talk before,
so thank you for being with me again one more time.
I hope these will still be interesting to see. And now actually,
without further ado, I’m going to start talking about a few damage zone models.
Why do we care about damage zones? Well, they are a key component of
the earthquake energy budget in seismic hazard assessment because
they constitute a permanent sink of strain energy. They modify the
elastic properties of the shallow crust. They threaten lifelines.
They amplify ground shaking during earthquakes.
We all care about damage zones. This problem has attracted geologists,
geophysicists, geodesists alike. But damage zones are defined
slightly differently for these fields of geoscience.
So geologically, we think of damage zones as the fractured and warped
volume surrounding a fault. And within this volume, fracture
density decays away from a principal slip surface until
it reaches some background level. As an example – can you see my cursor?
Yeah. Okay. Here’s an example from the field from Mitchell and Faulkner.
You can see fracture density on the Y axis and distance away
from the fault on the X axis. And you can see how that decays.
The same approach has been taken with aftershocks where damage zone
is defined as a volume where seismicity decreases until it reaches some
background level. An example at the bottom from Powers and Jordan 2020.
And different functional fits have been used to describe this decay.
Typically prefer something like a power law or an exponential decay.
And most of the knowledge from this approach to damage zone
comes from strike-slip faults.
Seismologically, damage zones are measured as a volume of decreased
shear wave velocity surrounding a fault that can host guided waves.
And geodetically, we characterize damage zones as areas of reduced shear
rigidity, or sometimes when we directly image earthquake ruptures, the area
where strain exceeds the elastic limit of the material, as shown from the work of
Chelsea Scott on the top plot here. The black lines denote the area that’s
in blue and yellow where strain has exceeded the elastic limit of rock.
A question that’s been persistent in damage zone studies has been
whether there are breaks in scaling within the damage zone.
And the reason we care about that is because, if there are breaks in scaling,
there may be different physics that are operating at different distances away
from the fault, dominating deformation. So, from their seminal work in 2010,
Powers and Jordan interpreted three zones from their decay and aftershock
density with distance away from the fault.
Close to the intercept, a zone where aftershock density stays
constant until a certain distance. This is a break in scaling.
This break in scaling is followed by an area where density decays with
a constant slope that ends where the dashed line is,
which is where this decay meets the background seismicity.
And these plots inspire the idea of an inner damage zone, shown in
the cartoons on the right, that contains one or several strands of fault core,
and the width of that inner damage zone is determined by
how many strands of fault core are within it.
And this ends in a break in scaling between the inner and
the outer damage zone. And some field geology studies have
also supported this conceptual model of an inner versus and outer damage zone.
A challenge in damage zone studies is that we typically think of damage zones
from a single data set – for example, a fracture or an aftershock distribution.
And these data sets often capture an integrated history of deformation
that spans multiple earthquake cycles.
So may have contributions from aseismic processes.
The 2019 Ridgecrest earthquakes provide this unprecedented view
of the distribution of inelastic deformation from an earthquake.
We had so many data sets covering it. So we want to incorporate different
data sets from the same earthquake to see if we can reconcile the view
of the damage zone from a bunch of different spatial scales that were
covered from these data sets. So we start by incorporating three
different independent rupture mapping data sets.
The first one is a rupture map of Ponti et al. 2020 that I know
many in this group actually collaborated in making.
It’s not shown in this slide but was included in this study.
This map was generated from field and geodetic observations.
The second one is our own map of the surface rupture that was generated
from post-earthquake Lidar data. I’m showing you that on the top left.
The north-south-trending features are related to the main shock broadly,
and the east-west-trending features are related with the 6.4 foreshock.
We supplement these two rupture maps with an additional rupture map
that covers five sections that are highlighted in orange.
And these sections were mapped from high-resolution aerial imagery either
collected from a drone or from the same airplane that collected the Lidar data.
And these are 2 to 20 centimeters-per-pixel resolution maps.
An example of one of those sections is shown below these two maps on top.
Okay. We also include two earthquake catalogs in our analysis.
For those that are into earthquake catalogs, these are the QTM and the
SESM catalogs, which are shown – the QTM catalog is shown on the top right.
Okay, so let’s just take a closer look at one of these high-resolution maps.
This is the middle of the magnitude 7.4 – 6.4, sorry, foreshock. And what I want to
highlight here is that the rupture is pretty complex at this scale.
You can see that there are two parallel fault strands, that things branch out.
In some locations, the rupture goes blind and distributed, localizes again,
distributes one more time. and I also want to highlight here
that the cracks go all the way until the edge of this footprint.
They’re not just limited to the primary rupture.
So the whole area is pretty heavily fractured.
This is just a field view of what that looks like.
You can see a big fault scarp here and all of this distributed
fracturing away from it.
This is just a 3D view of the surface rupture from Lidar point clouds.
I don’t know – the video doesn’t seem to want to play.
Oh, it is playing now. Okay.
So you can see there are two fault strands here.
You can see a few mole tracks in this view as well.
This is going to turn. You’re going to be able to see that there
are some sections where the Lidar point cloud penetrated into cracks.
So you can actually see the down-dip extent of some of these
fractures pretty neatly from the Lidar point cloud.
Okay, so I said we have three different surface rupture map data sets,
two earthquake catalogs, and we’re going to supplement these with maps of
the distribution of strain mapped from satellite imagery cross-correlation.
And here we use fault-parallel shear strains that are measured from profiles
that are oriented perpendicular to the primary rupture.
And these are stacked over 138-meter distance.
So each one of these – having a hard time moving the cursor here – okay.
So the fault is somewhere around here, and each of these pixels has 138 meters
along strike and 12-meter windows across strike.
And, from these profiles, we see the transition from inelastic to
predominantly elastic happens around 10 to 70 meters away from the fault,
depending on location. And beyond a kilometer away
from the fault, the strains are too small to resolve from noise.
So we only use this data from zero to a few kilometers away from the fault.
So, to put these data sets together to say something about the distribution of
inelastic deformation from the fault, we are going to plot how fracture
density, aftershock density, and strain intensity for stacked
profiles across the rupture decay with fault-perpendicular distance.
And I guess it’s worth here pausing for a second and defining what I mean
every time I say “inelastic deformation.”
When I say inelastic, I just mean a point at which a material has yielded.
For most of these data sets, that is in the form of fracture from the rupture maps
and from the aftershocks, but also the strain maps may contain additional
processes that lead to yielding like warping or granular processes.
That’s just worth keeping in mind. Okay.
So here I’ve measured the nearest distance between each damage feature.
So a fracture, an aftershock, or a [inaudible] strain map.
And the – oh, whoops. That moved on its own.
And the nearest point on the fault. That is done in 2D for the fractures
and the strain maps, and that is done in 3D for the aftershocks.
And we fit these decays following the expression in Powers and Jordan 2010,
which is shown here on the slide. And the decay is defined by the fracture
density, d-naught, at the intercept. D, which is a break in scaling at
a given distance away from the fault.
Here for the aftershocks, it’s about 50 meters away from the fault
M, which is a parameter that defines the sharpness of that corner,
and we hold that equal to 2. And it doesn’t really change the
way the decay looks like, so to reduce the number of parameters
and following Powers and Jordan, we hold it equal to 2.
Okay, and then last, gamma, which is the decay –
sorry, the slope of the decay beyond that [inaudible].
This is shown here in green for the aftershocks.
I’m going to start slowly populating these plots.
I’m going to add the decay for fracture density from the
high-resolution imagery. And the thing that’s obvious here is that that
[inaudible] has moved pretty far inward, matched closer to the fault than it
was for the aftershock data. But the slope gamma is actually
pretty similar for both the imagery and the aftershocks.
Okay. I’ve populated this with all of the data sets now.
I have aftershocks, the Lidar cracks, the field-plus cracks, which are from
the Ponti map, the imagery, and the strain, which, instead of
density for strain, it’s strain intensity on the Y axis.
And you can see these look pretty similar.
Look at the distribution of those parameters from the Powers and
Jordan fit. Starting with Panel B here, I’m showing you the distribution of the
maximum likelihood values of gamma, which is that slope.
And it ranges from about 0.8 to 1.1, and you can see that these distributions
are different, but they overlap within uncertainty.
And we interpret the similar slopes to indicate that fractures, aftershocks,
and other processes that are captured by the strain maps are controlled by
the same mechanism and strain field given the overlap of these slopes.
Let’s all take a look at that parameter d, which is the corner – the breaking
scaling of the inner damage zone, which is shown in Panel C.
Here I’m showing you again the maximum likelihood values,
and I’ve added the uncertainty in the fault location at the surface and at depth
in the plots, which is shown by the orange shading in the background.
The corner position, d, is out farthest out for the aftershocks.
It’s 50 meters away from the fault, but it sits between zero and 12 meters
for the higher spatial resolution Lidar-, field-, and imagery-derived data sets.
And it cannot be resolved for the strain data.
And note that d is on the order of the spatial error of the main rupture trace
location at the surface, and the fits allow for the absence of a corner.
So d is allowed to be equal to zero.
Now, for the aftershock data, d is comparable to the
100-meter horizontal location uncertainty of events that are
used to define the subsurface fault geometry.
Okay. So, given that we have these distributions that span large spatial
footprints, we want to compare them to prior studies that looked at
inelastic deformation from the Ridgecrest earthquake.
So here I’m going to be plotting the results from these studies
at the bottom half of this plot. And what you want to look at here
is not really the Y axis of these, but it’s the X axis.
So the X axis denotes the maximum width of inelastic deformation
from each of these studies. So I’m starting here with the strain
maps from Chris Milliner’s work published in JGR last year.
And here, they were looking at the second invariant of the strain tensor.
And the width of inelastic deformation from that was calculated to be about
30 to 100 meters away – wide, sorry – and that’s centered on the fault.
Okay. There are more data sets we can look at.
Here I’m plotting the relative surface displacements from the work of
Solène Antoine, also published last year. And relative surface displacements
just means fault zone width for a broad zone of deformation that receives
contributions both from elastic and inelastic processes.
You can see on the right plot each of the lines denotes the width
of that zone of deformation that, again, receives contributions
from elastic and inelastic processes.
And the width of the zones is about 600 meters to 2 kilometers, depending
on what section you’re looking at. And the black lines denote the mean
for the foreshock and the main shock, respectively.
We can also look at depth at the distribution of low-velocity zones
that were imaged seismologically. So here I’ve added two different lines
in green, both low-velocity zones. And then, within low-velocity zones,
the width of trapped wave zones that are enclosed by those low-velocity zones.
And you can see that those range from 100 meters to 2 kilometers in depth.
And I want you to note here that the corner is d, so that break in the inner
damage zone, that may or may not be allowed to be equal to zero,
does not match the width of any of the extents of inelastic
deformation that are imaged from these other data sets.
So, from this, we propose that d is actually an artifact in data resolution.
And the fault damage manifests in a continuum that doesn’t have a break
in scaling. So, in other words, there is no inner damage zone.
And something else worth pointing out here is that the decay of damage
is consistent out to pretty far distances away from the fault.
We can see cracks that are over 10 kilometers away from the fault.
So, to recap, from our overlap in gamma, we propose that these data sets
are showing that the damage zone is controlled by the same mechanism or
stress field up until pretty far distances away from the fault.
Now, we cannot use these decay exponents as the value of gamma alone
to distinguish between static and dynamic triggering processes,
but it looks like whatever is happening is pretty homogeneous up to pretty far
distances away from the fault. And a few questions that naturally come
out of this is, well, are these damage decays sensitive to lithology?
So, to do this, to investigate this, we filter fractures cutting through
sediment and bedrock. It is important to note that, as you
can see from the map on the left, the bedrock area we sample is really small
because the earthquake overwhelmingly ruptured through sediment.
But, even for this, we find the fracture density in sediment is 3 to 10 times
higher than that of bedrock at a given distance away from the fault.
But, however, the decay in fracture density with distance is identical.
So the slope of that curve is identical.
And something that may be leading to these decreased
fracture density in bedrock may be greater cohesion of the material.
Again, those similar decay exponents support that the mechanism responsible
for the nucleation of fractures operates under the same conditions
irrespective of material.
Another question that naturally follows from this is whether these damage
decays are sensitive to the magnitude of on-fault slip.
So, to evaluate this, we calculated the average slip and the slip variants,
which are shown here for each of the sections where we have
high-resolution imagery maps available.
And then, for each of these sections, I’ve plotted the fracture density
decays on the right. And what we find from this is that,
despite variability in the intercept, so in the fracture density at the overall
fracture density, we find no correlation between corner, slope, and overall
fracture density with slip magnitude. The place that has the highest fracture
density doesn’t have the highest on-fault slip and doesn’t have –
or any correlation to slip variability. However, we find that the decays
are relatively consistent throughout the rupture.
So we’ve earlier noted that the edges of these zones of inelastic deformation
that are defined by prior studies didn’t overlap with any of the
corners that are different data sets we’re able to define.
So we want to compare data sets in map view to see how these fracture
maps versus these geodetically mapped fractures overlap.
So here I’m showing you fracture density and strain intensity on the
left and the right plots, respectively, for two different regions.
What we find is typically the magnitude of strain, which here is conveyed by the
second invariant of the strain tensor, and fracture density overlap pretty well.
You can see that, as these zones narrows and widens, so does the zone mapped
from fracture density. But occasionally we find areas of high fracture density
that are disconnected from the main rupture – you can see a few of those
here – that do not appear on the strain maps. And this is because
the cumulative displacements of these fractures are too small.
They’re under 20 centimeters. They’re not large enough to
appear geodetically. But nevertheless, this deformation
is there, and it’s important to account for from a probabilistic displacement
hazard standpoint. So I just want to show you, to highlight the value of
high-resolution orthophotography in identifying individual fractures, which
cannot at all be discerned using these image correlation procedures.
Okay. So, so far we’ve taken a damage zone model, we’ve said something about
the shape of the distribution of damage from the Ridgecrest earthquakes.
And we’re interested on whether these damage distributions can be translated
into something that tells us about the physical properties of the damage zone.
So, based on the relationship between fracture density and shear rigidity,
we can estimate how much fracturing contributed to the rigidity decrease
in the volume of rock surrounding the Ridgecrest Fault. And this requires
both integrating the density and the length distribution of mapped fractures.
Okay. So we do that. An important – this is based on
the work of Budiansky and O’Connell. And an important part of this step is
that this approach does not distinguish whether the fractures were reactivated
during the earthquake or they were coseismically nucleated.
It just says how many cracks are there in this volume, what are their length
distributions, and how does that – what’s the shear rigidity decrease
that emerges from that.
So, from this, we estimate a decrease of about 40% in sediment immediately
adjacent to the fault and a decrease of about 20% in bedrock.
And these decline pretty fast to under 1% at 100 meters away from the fault.
These estimates are within the rigidity decrease that was geodetically
estimated by the work of Eric Xu.
And our estimates are based on fracture density mapped at the surface.
But the width of reduced rigidity that we predict compares very well
to the width and velocity structure of the trapped wave zones that were
observed postseismically at Ridgecrest. These zones typically extend
3 to 5 kilometers down-dip and remain constant in width.
And one thing that’s expected from this analysis is that there are low –
we can predict that there are low levels of rigidity reduction far away from
the fault. Because we see fractures extending pretty far away.
And this finding is supported by regional reductions
in shear wave velocity after the Ridgecrest earthquakes.
These are measurements from the far-field.
They’re averaging through a really large crustal volume.
And they show a ubiquitous small – around 2% velocity reduction
over this volume, which we think may be akin
to the widespread damage we’re mapping at the surface.
And these observations simultaneously reveal two things, which is, one,
fracturing generates this intense near-field damage.
And then widespread damage is accumulating in the volume
surrounding the fault. These two behaviors emerge from
the same nonlinear distribution. It’s the same power law distribution.
You don’t need any break in scalings to make these intensity near-field
damage and widespread rock damage on the overall volume.
We know the damage zones are pretty heterogeneous.
We’ve seen this from field observations and seismological constraints in
low-velocity zones again and again for Ridgecrest.
These low-velocity zones change along strike.
We know there’s variability. But, within this heterogeneity,
there emerge simple relationships that describe the average intensity
of deformation. And these may be useful
for developing generalized models of fault damage zones.
And there are, I think, still many outstanding questions from this work,
so I’m going to put a few of these out there for the room to see
if you can help me answer them. So first one says, how much of
the damage we map is inherited? How many of these cracks are
getting reactivated during the Ridgecrest earthquake versus newly nucleated,
and how does that distinction work for bedrock versus sediment?
Kind of the flip side to that is, how much damage is recovered
and healed after the earthquake? We see damage zones in the late
interseismic stages of faults, so we know that a portion of the damage
has to be permanent and irrecoverable. But how much of it is recovered
after the event? A 40% shear modulus decrease
is very high to maintain over long time scales, right?
Like, the cracks would be soup after a couple of earthquakes.
And then last, what happens to the crust in zones of dense faulting such as
the Eastern California Shear Zone? So here we’re seeing that there are
cracks that are 15 kilometers away from the main Ridgecrest Fault.
But those cracks are also 10 kilometers away from the Garlock Fault.
They’re 5 kilometers away from the next fault over.
So what happens in these zones of dense faulting that are
having frequent earthquakes, that are having inelastic
deformation over these really large footprints?
And then my last question is whether Ridgecrest is an exception.
We know Ridgecrest is weird. We have this orthogonal cracks.
We have this really large footprint that we were able to map.
There are cracks everywhere.
Is this a good model for thinking of earthquakes in general?
And the answer is, well, it depends what earthquake, obviously. [laughs]
So this is an example from the Madoi earthquake.
This is work that’s being done by Jing Liu in her group
at the China Earthquake Administration.
And what we find is that the Powers and Jordan model fits their fracture
density decays really well for subsections of the rupture.
An example of that is on top. But when you look at all of the
data collapsed together, it’s not a very good model.
It looks more like there are two – actually two distinct zones.
This is work that’s in the early stages, so it may change from now on,
but we’re already seeing that what defines Ridgecrest very well may not
be a good model for many other events.
But what if we look at Ridgecrest neighbors?
What if we look at the Eastern California Shear Zone?
I’ve looked at the fracture density decays for Landers, Hector Mine,
and El Mayor-Cucapah, together with Ridgecrest.
So I’m showing you those decays here with distribution of gamma
at the bottom. And we see that other Eastern California Shear Zone events
also exhibit similar fault-perpendicular fracture density decays and
widespread damage. So maybe this is a good
damage zone model to think about immature fault zones.
And I want to shift gears here and start talking a little bit more about the
multi-fault aspect of these earthquakes.
All these events have in common that they rupture several faults.
The earthquake have to transfer faults at several locations.
And multi-fault earthquakes are tricky. They are hard to identify in
the paleoseismic record. They’re hard to correlate across faults.
They are able to produce greater magnitude events and earthquakes
confined to a single fault, obviously. And they lead to longer sustained
more widespread strong ground motions and surface rupture.
When thinking of them in the long-term, they alter the stress state or probability
of events throughout the fault system, which makes them important
to deal with in long-term simulators.
For these events in the Eastern California Shear Zone, they happened
in pretty lower-populated zones in faults that have pretty slow slip rates.
But we know fault junctions that have faults that are slipping much faster and
are much more widely populated. And an example of that is Cajon Pass,
which is just north of L.A. where the San Andreas and the
San Jacinto Faults come together. These faults carry the majority of the
Pacific North America Plate motion. They’re fast-slipping.
And one of the things that have motivated thinking about multi-fault
events in the context of these two faults is the enigmatic 1812 event which has
been compellingly shown to have involved both faults from dynamic
models, but is there geologic evidence for this multi-fault rupture?
Well, we’re not the first ones to look for this.
And the challenge for identifying these joint earthquake paleoseismically
is that the northern San Jacinto has a very challenging geomorphic setting.
It looks like this. Where do you dig your trench?
The fault is obviously at the bottom of the valley, but there are
these huge boulders. We don’t really know where they are,
and there’s no good fine-grain material deposited for a trench.
And this has led to the northern section of the San Jacinto, which you
can see here a map of the San Andreas and the San Jacinto in black.
The dots represent different paleoseismic sites along strike
of these faults. And you can see that, from Colton all the way to Cajon Pass,
there’s these 30-kilometer gaps. You can fit a magnitude 6 event there,
the surface rupture, and not have any paleoseismic record of that event.
So today I’m going to be showing you some work regarding the Lytle Creek
Ridge Fault, which is a low-angle normal fault that we believe has
the potential to record multi-fault events through Cajon Pass.
This is a low-angle shallow normal fault.
It’s too shallow and too short to be seismogenic on its own
or to act as a transfer structure during multi-fault earthquakes, but it is ideally
positioned as a passive recorder of linked rupture.
So the question I’m going to be addressing today is whether we can
use these small faults to understand what the big players are doing at these
fault junctions where earthquakes may be transferring between them.
So let’s get to know Lytle Creek Ridge Fault, or LCRF, a little bit better.
Here I’m showing you the Lytle Creek Ridge Fault cutting
through the Pelona Schist. This is bedrock exposure.
Here I’m showing you the Lytle Creek Ridge Fault exposed in our trench that
I’m going to be talking about later. And last here’s the Lytle Creek Ridge
Fault localized along a serpentine band. So it has a lot of flavors along strike.
To test whether the LCRF is a good passive recorder,
we need to have a good sense of when does this fault slip.
So the LCRF is a low-angle normal fault, but it’s sitting on the middle
of the Transverse Ranges. So the first thing we need for this
low-angle normal fault to be activated in the strong regional north-south
contraction of the Transverse Ranges
is strong dilation that is able to overcome that contraction.
So I’m going to be considering three different scenarios here.
All of these are a combination of the suite of Coulomb stress models
and finite element models. And what I’m showing you here –
in orange, I’m going to have the fault that’s slipping in this model.
On the top left, I have the slip vector on the LCRF.
Underneath it, I have the amount of stress that’s triggered on the LCRF.
And then, at the bottom of it, I have the amount of slip that’s triggered.
Okay. So first – sorry, the first scenario here is rupture on the San Andreas
Fault that goes past Cajon Pass, and it imposes mostly left-lateral
motion on the LCRF. So that’s not really consistent with normal motion.
And it triggers very little slip.
If we have rupture on the San Jacinto Fault that’s traveling southwards,
we impose normal sinistral motion on the LCRF, but it triggers
a relatively small amount of stress and slip on the LCRF.
Last, if we have a scenario that involves taper slip on both
the San Andreas and the San Jacinto, this triggers double the stress change
on the LCRF than the scenario that involves the San Jacinto Fault alone.
So, from this, we hypothesize that the LCRF is most likely to host the large
event with really large slip, like tens of centimeters, which, even if these
events on the San Jacinto or on the San Andreas only are also stored –
sorry, also triggered the LCRF, they impose too little slip
for that to be recognizable in a paleoseismic trench.
So we dug a trench in the middle of the San Gabriel Mountains.
This is an 18 meters long, couple meters wide, 3 meters deep at a couple sections.
And the fault zone is all concentrated in 3 meters within the fault.
So most of that trench was actually useless to dig.
It was just a good workout. [laughter]
This is the trench record. We found – I’m speeding up here
because I realize I’m going over time. We have three events.
The event horizons are highlighted in yellow here.
And each of these event actually have tens of centimeters of slip each.
So they’re fairly large events.
So we took charcoal samples from each of these different trench layers,
and we dated them. And, from this, we were able to
constrain ages for events number 1, number 2, and number 3.
And I want to focus on the most – this is three events over 2,000 years,
by the way, but I want to focus on the most recent event.
And here the distributions from the charcoal dates get very multi-modal.
So can we – can we refine these ages by adding constrains from invasive
pollen species, which are – we know the introduction dates to California of
several pollen species pretty well, so can we look at the different samples
here and say what the most recent event on the LCRF is?
And there are two candidates here. There’s 1812, which has been previously
shown compellingly to have dynamically bridged the gap.
So we expect 1812 to be there. But there’s also 1857, which traveled
pretty far south on the San Andreas Fault, and it traveled south past
Cajon Pass, shown in the work of Olaf Zielke.
So which one of these two events is the one in the trench?
Well, we were able to retrieve three different samples of significance
that have species that we know with well-introduced dates.
These are tamarisk, Spanish broom, and Russian thistle.
And particularly, tamarisk and Spanish bloom are on the un-faulted
unit that caps the most recent event. And those were introduced to
California in 1839 and 1848, respectively, which really make 1812
a really good candidate to be the most recent event in the trench.
So the recurrence interval of the LCRF from these three events
is about 660 years. And we can compare our data here to chronologies on the
San Andreas and the San Jacinto Faults to – if we assume that the record
in the trench only contains large events that represent joint rupture,
we can look at the number of events in each of the paleoseismic sites
north and south of Cajon Pass on the San Andreas and the San Jacinto,
and we can compare the numbers.
We cannot correlate specific events across the three faults because the
probability density functions are very abrupt, particularly
the ones on the LCRF. But we can get an understanding
of the frequency of joint ruptures through these earthquakes.
So if I count all of the earthquakes on the Wrightwood, Mystic Lake,
and LCRF trenches, we end up finding that 20%, so three out of 15,
and 23% – 3 out of 13 – of the events are shared respectively
between these San Andreas the San Jacinto Faults.
We did a bunch of modeling to determine the mechanics of these
earthquakes, but I will only go there if people want to ask at the end
because I don’t want to use your time. So, to conclude this part of the talk,
the San Andreas and the San Jacinto Faults have ruptured together in the
past 2,000 years three times, most recently in the historic 1812 event.
And the Lytle Creek Ridge Fault ruptures passively every 660 years
or so with 20% and 23% of the ruptures in the paleoseismic record of the
San Andreas and the San Jacinto Fault respectively are being shared.
And, with that, I’ll take any questions.