Hello. Thank you for the opportunity to speak here today.
Looking forward to telling you about our project looking at
aseismic deformation along the central San Andreas and Calaveras
Faults from repeat differencing of airborne Lidar topography.
This is a project I’ve been working on with my collaborators,
Steve DeLong and Ramon Arrowsmith.
By way of introduction, measuring creep along the San Andreas Fault
system indicates the moment accumulation that is available
for destructive earthquakes versus as less-damaging creep.
In this project, we’ve constrained the aseismic fault creep rate along
the central San Andreas and Calaveras Faults by differencing repeat airborne
Lidar topography data sets. We constrain spatially dense
creep rates every 400 meters and examine approaches for decreasing
noise in the resulting creep rates. We show that portions of the central
San Andreas Fault creep at over rates of 30 millimeters per year.
And this indicates limited strain accumulation.
Finally, we show, at Mustang Ridge,
the active fault trace is offset from the geomorphically mapped fault.
This shows a map of central California. Here is the San Andreas Fault
and the Calaveras Fault. The color of both faults represent
the creep rate determined from Lidar topographic differencing.
[sounds of birds in background] Slip rate has been measured
with a variety of instrumentation, however this instrumentation
typically lacks sensitivity to the dense 3D deformation field.
Lidar topographic differencing can fill this gap, as it resolves
deformation along and adjacent to active faults.
One application that we’re particularly excited about is performing
a slip inversion to solve for creep along strike and with depth.
To perform the 3D topographic differencing,
we use the iterative closest point, or ICP, algorithm.
This approach has been applied to many earthquakes over the past decade.
Originally, it gained a lot of attention because it could resolve deformation
along an adjacent active fault, where other geodetic data sets
typically lack spatial resolution.
The first step is to take a windowed subset of the Lidar topography.
This window – the length of the window is the resolution,
which is typically on the order of 30 to 50 meters.
The next step is to apply a 3D rigid body deformation,
which is used to align the point clouds. And the rotation and displacement
that’s required to align these data sets is soon to then be the 3D deformation
that occurred either during the earthquake or from fault creep.
And the final step is to calculate uncertainty on the 3D displacement.
For the differencing, we used four data sets.
This includes the 2005 B4, 2007 EarthScope, 2018 Salinas,
and Parkfield. Here are abbreviated acknowledgements for these data sets.
I invite you to look at our publication for the full citations.
I would like to acknowledge the foresight and coordination
that it took to collect, particularly, the B4 and EarthScope data sets
over a decade ago. And, critical to performing this
differencing was collecting both the 2018 Parkfield
and the 2018 Salinas data set. One of the challenges of performing
differencing is that a decade or so of creep produce significantly less slip
than the magnitude 7 or so earthquakes that this method has been applied to.
So we looked at several approaches for decreasing noise and ultimately
being able to better resolve slip along the fault.
A primary source of noise was changes in vegetation between
the two acquisitions. To mitigate the source of noise,
we used ground points only. There were a number of landslides
along the central San Andreas Fault. For example, here you can see,
in the topographic hill shade, landslide in this area.
And here is the displacement field, in this case, in the northeast,
or fault-perpendicular, direction. We see a very clear landslide signal
here. Ultimately, masked areas that had these very pronounced landslides.
And finally, flight line errors caused dominant source of noise in the
northeast and vertical directions. And so we focused our interpretation
on the northwest, or fault-parallel, displacement signals.
So here is a resulting displacement field.
This is showing the northwest, or fault-parallel, displacement.
And we see here, along the southwest side of the fault, that the area is
moving positive, or to the northwest. On the opposite side of the fault,
you see the opposite sense of motion with a clear,
very sharp boundary along the fault. The next thing we’re interesting in
was calculating the fault creep from the displacement field.
And that is shown here in this cartoon, where we essentially do a displacement
discontinuity approach. So we take the – calculate the average
displacement on each side of the fault, weighted by the error in the
displacement in each of the displacement measurements.
And then, to calculate the fault creep, we subtract, or find the difference,
in creep across the fault. The uncertainty in the creep rate
is the second norm of the weighted uncertainty across
each side of the fault. And then, to calculate the creep rate,
we divide the – or, we take the fault creep and we divide that
by the time between the Lidar acquisitions.
So we apply this method to the entire fault to calculate the creep rate,
and the results are shown here. This bottom plot shows distance
northeast versus distance from Parkfield. And the color that you’re
seeing is the northwest, or on average, right-lateral displacements.
In the top plot, we are showing creep rate in millimeters per year.
And I’d like you to pay attention to the blue, which is the right-lateral
San Andreas Fault creep rate, as well as the green, which is the
right-lateral Calaveras Fault creep rate. I will discuss some of the key features
that we can observe from this pattern of fault creep.
First order, the creep rate is asymmetric.
We see higher creep rates here along the southeastern side of the creeping
portion of the San Andreas Fault. The highest creep rate of about
40 millimeters per year occurs near Slack Canyon in this area of the fault.
As we go towards Parkfield, there’s a very sharp decrease in creep rate –
a rate of about 8 millimeters – so, a rate of about, yeah, 8 millimeters per year.
At Dry Lake Valley, which is here, we see a rate of about 20 millimeters
per year. And this is a local minimum in the creep rate.
And here, where the San Andreas and Calaveras Faults overlap,
we see that there is a sharp decrease in creep rate along the San Andreas Fault,
likely relating to the partitioning of the plate boundary deformation
along those two active faults.
We then asked the question of whether the topographic differencing rates
are similar to the geodetic rates. The conclusion is yes, when the
difference in aperture of the instrumentation is accounted for.
This table shows the other geodetic rates that we compared to the
topographic differencing from a creepmeter with a 30-meter aperture
to Geodolites with apertures typically greater than 15 kilometers.
This plot shows creep rate versus distance from Parkfield.
And, for the most part, we see that the topographic differencing,
shown in black, are consistent with the other measurements.
Largest difference is the Geodolite instrumentation, but likely those
data sets are larger because of their larger aperture, which means
these instruments are measuring elastic strain accumulation.
This plot shows topographic differencing in creep rate versus
creep rate from other data sets. This is the 1-to-1 line.
There is a aperture dependence on the agreement between the
creep rate from the different data sets. For example, the topographic
differencing rates are higher than all of the creepmeter rates.
And, in contrast, the topographic differencing rates are lower than
many of the Geodolite [audio cuts out].
We then looked at Mustang Ridge to learn what topographic differencing
indicates in regions of complex deformation.
This region was first mapped by Rymer et al., who interpreted
distributed faulting as being responsible for creep rates that vary with aperture.
It was then mapped by DeLong et al., who interpreted that this distributed
faulting here produces an en échelon pattern of faults.
Topographic differencing is consistent and adds to these interpretations.
First, we see that, although there is about 10 kilometers of along-strike
en échelon faults in the geomorphology, the active deformation
spans about 4 kilometers along strike. These black and red lines indicate
the active portion of the fault mapped by DeLong et al. as well as
the USGS and CGS. However, topographic differencing
shows that the active fault trace is not here, but instead along this
portion of the fault, is located about half a kilometer to the northeast.
So this shows that topographic differencing is very important
at indicating which faults in the geomorphology are active.
Using the topographic differencing results,
we can look at off-fault deformation.
At Dry Lake Valley, the deformation is highly localized to the fault.
Consistent with this current work, Scott et al., in a JGR paper
from last year, also showed that deformation is highly localized
to the fault at Dry Lake Valley.
And this was shown by comparing surface fractures to topographic
differencing rates. And the localization of deformation was attributed to
the fault geometry, which is very well-aligned for right-lateral slip.
At Mustang Ridge, as we discussed on the last slide,
deformation is highly distributed. Along the central creeping San Andreas
Fault, we conclude that 20 to 50% of the deformation is accommodated
beyond a 30-meter aperture. This amount of high off-fault
deformation is highly unexpected for a mature and non-creeping fault.
We do see this clear dependence on fault geometry.
In addition, it’s possible that the high off-fault deformation
reflects a difference in mechanics dynamics
between a creeping and seismogenic fault.
I will now discuss applications and future work of these
topographic differencing results. We are particularly interested in
looking at strain accumulation. For example, by conducting a joint
slip inversion to quantify fault slip and depth-dependent changes in slip,
and topographic differencing would add a very important
intermediate-scale aperture.
We’re also interested in mapping active fault traces and comparing
the location of the actively creeping fault to Quaternary fault traces.
These displacements could also be used to better understand
fault displacement hazard. And finally, the fourth is application is
looking at the mechanical properties of the crust, sediment cover, and soil.
For example, does shallow rheology control the distribution of deformation?
So, in conclusion today, shown that we have constrained
creep rates at a dense spatial resolution along the central San Andreas
and Calaveras Faults from Lidar topographic differencing.
The topographic differencing rates are consistent with other geodetic data sets,
particularly given the different apertures of the instruments.
Discontinuities in the displacement fields indicate the active fault trace.
We see that, at Mustang Ridge, the active fault trace is not always
consistent with the location of the fault mapped by the USGS and the CGS.
And finally, 20 to 50% of the deformation occurs
off the principal fault. So thank you for hearing this presentation today.
I’d like to acknowledge funding from the NSF, ASU, NEHRP, and NASA.
And we are very excited to continue this work into 2021
with funding from NEHRP. Thank you.