Hi. Today I’m going to share some of my work on active faults
in northeastern California. Before I begin, I want to acknowledge
that this talk is just part of a larger project with collaborators
at a number of different science centers at the USGS
who have helped to study this region on different time scales.
So let’s start by taking a step back and thinking about the fault network and
seismic hazard in northeast California. In current seismic hazard models,
like UCERF3 shown here on the left, there are relatively few faults
and few geologic studies on those faults in northeast California.
So, in this image on the left-hand side, those red lines represent faults included
in the UCERF3 model, and the green dots represent those geologic study sites.
So let’s zoom in a bit more on northeastern California and
take a look at the Quaternary Fault and Fold Database.
This image in the center now shows the faults in the region colored
by the recency of fault activity. And already we can see a more dense
fault network than what was included in UCERF3.
And then, just for completeness, let’s take a quick look at the
2014 National Seismic Hazard Model source faults, which were
drawn directly from UCERF3. So they also represent simplification
of the faults systems and do not include many of the fault systems that are in the
Quaternary Fault and Fold Databases. So, from this, we can clearly see
that the fault network in the region is incompletely mapped and
incompletely characterized in seismic hazard assessments.
One of the reasons the fault network is incompletely mapped and understood
in this region is because northeastern California lies at the intersection
of multiple tectonic domains. In far northeastern California,
we have the Walker Lane transtension, marked by this green shaded area,
and westernmost extent of east-west-directed Basin and Range
extension marked by this pink shaded area.
From the west, there’s an influence of eastward-directed Cascadia
convergence, marked by this blue shaded area.
And all of this is overprinted by Cascade volcanism.
So these competing tectonic and volcanic influences on the landscape
make it challenging to decipher the fault patterns, kinematics, and general causes,
tectonic or volcanic. So now let’s zoom in a little bit more on this red box here.
So this is the region of northeastern California that we focused on.
The map on the right-hand side now shows the documented
Quaternary and Holocene faults in the region, again with those faults
colored by the age of most recent fault activity.
So we can see there is generally a north-south fault pattern representing
that westernmost extent of Basin and Range extension and
several northwest-trending faults that represent the northernmost
transtensional Walker Lane.
In addition, the region has some seismicity, noted by these numerous
blue dots, although admittedly seismicity in the eastern and
northeastern-most part of the – of the region is a little bit sparse.
Again, we have those three major volcanoes of the southern Cascades
that complicate our understanding of the tectonics in the region.
So we have Medicine Lake, Mount Shasta, and Lassen Peak,
marked by these yellow circles here.
So now let’s focus on the area within this red box, which shows a prominent
disruption in the overall north-south fault pattern.
So now we’re looking at a slopeshade map with the same Quaternary faults,
again, shown on the slide colored by most recent fault activity.
In addition, seismicity and focal mechanisms are shown that reveal
both normal faulting and dextral transtension in the region.
So, again, we see this overall north-south fault pattern.
But, even at this scale, I want to point out that we can see faults
that are incompletely mapped. They’re numerous north-south
faults and west-northwest faults that are including in Quaternary fault databases.
So now let’s zoom in on this yellow polygon here where we focused on
mapping the faults in this region. So, for the rest of the talk,
I’ll share some of our approaches and key results as we work to identify
and characterize the fault systems here.
So, as I mentioned at the beginning of the talk, we interrogated the
landscape at multiple temporal scales, which included bedrock mapping,
which is shown in the right-hand side of the slide and will serve as a base map
as we talk through some of our observations and interpretations
of the region. The key takeaway from this figure is that the pink colors
represent Miocene-to-Quaternary volcanics that are offset by these faults,
with the darker pinks representing older volcanic units and the lighter
pinks representing the youngest volcanic units.
But, for this talk, I’m just going to focus on our work on how we mapped
the faults, and then will only use these dated volcanic deposits to constrain
the most recent age of faulting. So we used two main approaches.
We had neotectonic remote mapping, which relied on four high-resolution
Lidar data sets, and three structure-from-motion outcrop
models of key but inaccessible areas. We then followed up the remote
mapping with field verification and mapping of some of the faults
in the area. And so those Lidar data sets are marked by these
red polygons now, so it covers a significant part of the study area.
From the Lidar data sets, we mapped and characterized more than
700 lineaments, most of which we interpret to be faults.
We observed primarily vertical offsets of deposits that ranged in age from
Miocene to latest Pleistocene, potentially even some younger
geomorphic surfaces as young as the Holocene.
From this mapping, we defined what we’re calling the Pondosa Fault Zone.
So that’s roughly shown here by this yellow box,
which is defined as a set of west-northwest-oriented faults
that disrupts the general overall north-south fault pattern.
We used the SfM outcrop models to help us better characterize some of these
faults in the Pondosa Fault zone because there were some pretty nice,
steep outcrops that allowed us to actually view the faults.
So these models were created from UAV and helicopter photos
in these inaccessible areas. So let’s take a quick look at this
northernmost model marked here by this yellow box and shown
in this image on the right-hand side. At a first glance, we can clearly see
several lineaments running across this orthophoto, and some are vegetation
lineaments, whereas others are just these kind of tonal lineaments.
In addition, we also see regions of different soil
or bedrock color and textures.
So, from these images, combined with our field mapping,
we interpreted four stratigraphic packages and at least four faults
that offset these stratigraphic packages.
In addition, we correlated these stratigraphic packages with units
that we mapped throughout the study area with our bedrock mapping.
Importantly, at least one of these faults that we were able to map
in the structure-from-motion model clearly correlates to a fault mapped
on the Lidar data, which we’ll look at in more detail on the next slide.
So now we’re going to be looking to the east-southeast
from this viewpoint shown here by this blue symbol.
So now we’re looking east-southeast at the outcrop model overlain
on the Lidar topography. We can clearly see this prominent
east-west-trending ridge coming toward us in the Lidar data.
That was the lineament that we mapped on the Lidar data.
We can also see these different textures and colors in the structure-from-motion
model, which represent these different stratigraphic units.
So we can throw on our interpretation, where we can clearly see at least four
faults that offset the stratigraphy. So now these different numbers here
represent these different stratigraphic packages, and the red lines
represent these different faults. So really just want to draw
your attention to this Fault B, with this fault zone that clearly aligns
with the fault mapped in the Lidar data.
We can also see from this angle that the stratigraphic packages
that are offset by these faults appear to have different thicknesses and
orientations across the faults. And so we interpret these changes in
thickness and orientation to represent oblique motion of these faults.
We also field-verified and mapped several dozen lineaments
in the Pondosa Fault Zone. So here’s an example of one of them
from the central part of the fault zone noted by this red box above here.
In this area, the faults generally trend west-northwest and offset this dark pink
Miocene-Pliocene Bartle Gap unit and the 1 million-year-old
lighter pink basalt at Pole Creek, in addition to some Quaternary alluvium
and younger geomorphic surfaces.
So, in the field, we observed scarp across Quaternary alluvium.
So now we’re at this blue symbol right here looking to the southwest
at this scarp in the Quaternary alluvium.
So, if we draw in yellow lines to mark the top and the base of the scarp,
also marked by the feet of the people in the photo, we can see that the vertical
separation is on the order of 2 meters, which matches with what we observe
on the topographic profile extracted from the Lidar data,
shown here on the lower right-hand side.
Then, if we move even farther southeast along this fault, we’re looking at another
profile, again going from southwest to northeast across the fault,
this time across the basalt of Pole Creek. We can see there’s 30 meters
of vertical separation on that older unit. So the increasing offset on older units
suggest to us that perhaps the fault has hosted multiple events.
So just showing you a flavor of some of the results that we’ve had,
but let’s pull this all together. So first let’s remind ourselves of
what is already known in the Quaternary Fault and Fold Databases
in this area, shown here on the right-hand side.
So, again, we see we have this general north-south fault pattern to the north
and south on this image with a zone of relatively few mapped faults in between,
specifically between this P and the Pit River to the south,
which is where we have that Pondosa Fault Zone.
So now let’s overlay the newly mapped fault network,
and we see so many more faults. Some of these faults we could see
at the scale of the 10-meter DEM, particularly in this region to the north of
the Pondosa Fault Zone, but the Lidar was really instrumental in identifying
a lot of them in higher terrain. We see that the zone of relatively few
mapped faults between Pondosa, this P here, and the Pit River,
actually does have a lot of faults. So, shown here on the right,
we now have the faults colored by the most recent fault activity,
which, as a reminder, was determined by the known or estimated ages of the
deposits that they offset that we derived from bedrock mapping.
And then, because part of our interest in this region was driven by the disruption
in the north-south fault pattern, let’s take a quick look at fault orientation.
So this region between the north – the north and south faults
contains a lot of these west-northwest-, east-southeast-trending faults,
which is part of our definition for the Pondosa Fault Zone.
And, as shown by the structure-from- motion models, these faults have
evidence for lateral motion, suggesting that they’re likely oblique.
So now you might ask, well, okay, that’s great, but what about slip rates?
How fast are these faults moving? So coming up with a slip rate can
be pretty challenging here. First, this is a highly distributed
fault system with numerous individual strands.
Second, we were really only able to measure vertical separations.
We couldn’t identify good piercing points for lateral offsets,
so any slip rate may be an underestimate of the total slip rate.
Third, there’s uneven original volcanic topography and perhaps more recent
deposition in the hanging wall of some of the faults, which obscures the total
vertical separation that we can calculate. And fourth, we don’t know any of
the fault dips, so it’s hard to calculate a true slip rate on the fault,
and so we’re stuck with vertical separations.
But despite all these assumptions, let’s take a look at a transect across
the Pondosa Fault Zone just to get a ballpark estimate of rates.
So here we’re going from southwest to northeast,
across the Pondosa Fault Zone, so southwest to northeast on the right-hand side here.
Starting in the older Miocene-Pliocene Bartle Gap unit on the left with this
dark gray, going into these lighter grays for the youngest Late Pleistocene
to Holocene volcanic flows like the Giant Crater flow.
And what we see is that the apparent vertical separation rates on individual
fault strands and the net rates across different units are generally very low.
So, in the oldest unit shown here, the Bartle Gap unit, we have
vertical separation rates less than 0.1 millimeters per year based upon
an estimated 280 meters minimum vertical separation.
And then, even on the 1 million-year- old basalt at Pole Creek, the rates are
still only on the order of 0.1 millimeters per year
using an estimate of about 130 meters of net vertical separation.
So we have a very distributed fault system and a very low-rate fault system
with rates generally on the order of 0.1 millimeters per year or less.
Okay, so now let’s revisit the seismic hazard in northeastern California.
We already know the fault network is incompletely mapped in the region,
but the growing high-resolution topographic coverage will help us fill in
some of these gaps, like we were able to do with the Pondosa Fault Zone, to
identify and characterize many more of these distributed low-rate fault systems.
And so, shown on the right-hand side here, again we have that
Quaternary Fault and Fold Database shown with
the Pondosa Fault Zone now overlain on that.
So we filled in one of those gaps with a distributed low-rate fault system.
But now this begs the question of, how do we treat these zones
of distributed low-rate faulting in seismic hazard models?
Some of the challenges this region faces include incomplete mapping,
which we’ve touched on a lot in this talk, in addition to unknown slip rates
or recurrence intervals on these faults. And we also don’t really understand
or know the fault connectivity. So ideally, we’d be able to have
a lot of this information to know which fault strands may link up
during certain ruptures or better understand slip rates
across these systems. So, without some of that information,
what is the best way to incorporate these distributed systems?
The answer is certainly not clear and still an open question.
One potential pathway may be some sort of fault zone polygon that
encompasses all known or suspected fault traces.
But how something like this is incorporated into seismic hazard
models remains under development.
Another option, one that has been implemented so far in the
2023 National Seismic Hazard Model update, is a proxy fault,
shown here by this black line, for the Pondosa Fault Zone.
So a proxy fault is designed for geodetic modelers to collapse deformation
where a single connected fault trace is challenging to define.
However, a proxy fault may not be completely suitable for linking fault
ruptures in the broader fault network, nor may it be suitable depending on
the scale of the seismic hazard assessment being conducted.
So it’s certainly still an open question on how we want to incorporate
these distributed fault systems into seismic hazard models.
So, to summarize, here are the key points from this talk.
Northeastern California has distributed, sometimes poorly mapped,
low-rate fault systems. We were able to identify and
characterize one of these fault systems, the Pondosa Fault Zone, in our work.
This system generally trends west-northwest and disrupts an
overall north-south fault pattern, which I didn’t have time to touch on
today, but in a broader tectonic context, we think may represent
a left step in overall east-west-directed Basin and Range extension
or be part of a microplate boundary.
And finally, as we talked about the end,
characterizing and modeling these distributed low-rate fault systems
remains a challenge in seismic hazard assessments.
And with that, thank you for your time, and I’d like to acknowledge funding,
assistance, and discussion with the folks listed on the slide. Thank you.