Greetings! Come, everybody, join virtual hands as we join a community of community modelers, led by Jess and Andreas. Please take it away
Jess and Andreas.
Okay. Well, welcome back everybody to our next session, entitled, "Modeling is better with friends."
Update on California community models and we'll have a series of talks about a variety of fairly large-scale studies and community models in Central and Northern California, including the discussion of fault creep, earthquake monitoring and seismic velocity models. So I
just wanted to reiterate for those who are just tuning in, or may have forgotten that
we'll have our series of five talks, and then we'll have a 30 min discussion at the end.
We'd like for people to use the chat during the talk primarily for questions related to clarification that the speaker can answer while the talk is playing.
But please plan to save your more in-depth questions for the Q & A
session so that the chat doesn't distract people from actually listening to the talks.
So with that I'll hand it over to Andreas who will be introducing the individual talks.
Yes, thanks. We'll take care of
any questions in the talk and the chat will also be mentioned in the discussion session.
So the first talk here is by Josie Nevitt and Austin Elliott from USGS
on, "Shallow fault, creep within the Bay Area fault system
from near-field observations."
Many faults in northern California creep on the surface.
This map shows a spatial variation in the long-term creep rates measured by creepmeters as triangles and alignment arrays as circles.
Here are two conceptual models for what this creep might represent.
You can imagine the earthquakes at depth are driving creep towards earth's surface, whereas on the right the surface creep is arising due to the resolve
tectonic shears stresses, and may in turn load the seismogenic zone at depth.
So understanding the mechanics of shallow creep really is important for a fundamental knowledge of how faults work and the hazards they pose.
The USGS and partners have measured surface creep using a variety of tools over the last five decades. Today,
Austin Elliot and I will focus on the alignment array in creewpmeter networks describing their history, instrumentation, and the data sets that are publicly available.
The longest running, continual measurements of service creep on Northern California
faults come from the reoccupation and surveying of alignment arrays. With the retirement of Jim Lienkaemper
I have inherited stewardship of the alignment array data sets, and so we'll give an overview of them here.
Noting that all credit for their installation, routine measurement, and use goes to folks that come before me.
Alignment arrays, ultimately spelled with a GN or an
NE are networks of survey monuments installed on the ground surface straddling a fault trace by about a 100 meters.
These are reoccupied at some time interval to measure the relative changes in position among the established monuments due to fault creep.
Monuments generally consist of metal caps or nails with a precise dot, cross or hatches to enable unambiguous reset up of tripod mounted survey equipment with sub-millimeter accuracy over a known point. Good quality monuments are
embedded in concrete installed within a protective vault.
For various logistical reasons which I'll mention shortly.
monuments must sometimes just be survey nails pounded through existing pavement surfaces.
Some of the early alignment arrays in the 1960s and 1970s were installed as deflection arrays which are a line of about half a dozen or more markers perpendicular to the fault trace from which deflection by tectonic motion could be
measured. In contrast, for precision of measurement, ease of calculation, and efficiency, of surveying most modern arrays consist of just 3 points, 2 straddling default, and a third that sits off axis on the same side of the fault as the survey
instrument presumed to be undergoing the same fault
parallel motion as the instrument station. Motion of one side of the fault relatives to the other is calculated by measuring the change in the angle formed by these two survey lines.
For example, in the configuration illustrated here, right-lateral motion along the fault will result in an increasing angle between the end station and the off-axis station, as measured from the instrument station.
The arrays generally spanned a 100 meters across a recognized fault
trace and effort has been made to install them at sites where creep is localized on a single clear strand.
Most arrays are situated within roadways which benefit from stable paved surfaces, clear sight lines, and being orthogonal.
In addition to more clearly and unambiguously expressing a creeping fault.
trace. Often times city monuments can be used as logistical problems do arise from this setup, including the periodic removal of monuments by repaving as well as the introduction of obstacles like moving and parked cars as well as vegetation that can sort
of overgrow and interrupt the sight line. The availability and suitability of sites is not trivial, and so decades of careful work and consideration have gone into the insulation and maintenance of this survey, network. There're currently 90 alignment rates established around the broader
Bay Area region. A term, which I'm using a little generously here because they extend all the way up nearly to the Triple junction in the Bartlett Springs fault. The Hayward fault is the most densely monitored with a monumentary established roughly every 2
kilometers along its full length through the East Bay, pretty much all of our faults, except for the San Andreas
at this latitude exhibits some level of aseismic or interseismic creep, and so the network includes multiple arrays that have been established across all the major faults through impressive expansion by my predecessor here at the Survey
Jim Lienkaemper as well as Forrest MacFarlane and John Caskey, who head up the creep program at San Francisco
State University. These three maps illustrate the longevity and results from the alignment array sites the earliest were installed in the late 1960s after creep was formally recognized along the Hayward fault representing in some places over half a century of
data. One can see the 21st century expansion to cover the broader region.
The middle map shows the latest year during which each site was reoccupied a proxy for its status.
Some you can see in orange have been temporarily skipped due to the personnel and resource limitations from the COVID pandemic.
While a few others shown in red have been abandoned permanently due to destruction,
the loss of access, irregularities in measurements caused by soil and mass wasting processes or an absence of active creep
after many measurement [indiscernible]. On the right is a map of the avid creep rate recorded each site over its life span.
You can see that the highest rates are on the southern Hayward and Calaveras
faults. The data from the northern California alignment arrays represent the longest-running measurement of fault creep on the planet, a legacy which makes them valuable as long-term markers of steady deformation, as well as ground truth millimeter
precision records of annual creep. On the Hayward and Calaveras faults
these records go back over five decades as you can see from these time series, from all sites on the Hayward fault
alignment array measurements capture annual to decadal scale changes in creep rate, including local and regional responses to significant earthquakes as well as localized discrete creep events like the one on the southern Hayward in 1996. These plots rely
on interpolation from calculated velocities, as sometimes the accumulated displacement is not recoverable,
year over year. When, for example, a monument gets removed or paved over, and the site has to be reinstalled.
This plot, further, illustrates the variability of creep along the Hayward fault,
in both the spatial on the x-axis and temporal on the Y-axis dimensions, as measured from these alignment arrays phenomena that stand out in this unique data set include the slowdown at the southern end of the Hayward after the 1989
earthquake The low rates observed around the presumed rupture area of the 1868 Hayward fault earthquake and changes in creep rate, both positive and negative, associated with moderate magnitude 4 size earthquakes in the East Bay. I
believe there is a lot more to discover digging into these data more finely.
Further major role of alignment arrays is an earthquake response, as most of the faults in our region exhibit some level of aseismic slip
interseismically, it is expected that afterslip will form a large component of near-field deformation in future
Major ruptures. After the 2014 South Napa earthquake
Jim Lienkaemper and the earthquake geology crew
illustrated the value that quickly established and reoccupied monument arrays can play in capturing and forecasting continued afterslip. Deployment of new arrays is as easy as hammering survey nails into the ground and the regional network of existing arrays provide a
well-measured before and after baseline for these sort of 100 meter aperture, ground truth, measurements of offset. We expect to make use of alignment
array monuments in conjunction with complementary methods, such as repeat lidar and space-born imaging like InSAR.
New annual measurements are now published in a consistent USGS
science-based data repository each spring. These contain CSV files of each site's full measurement
history plus regional summary tables of new results. I am now the steward of these data
so reach out to me if you have any questions Forrest Mcfarland and John Caskey at San Francisco State University
are also major points of contact for these data sets.
Now we'll transition to the creepmeters.
A creepmeter measures line length changes along shallowly buried rod or wire at an oblique angle to the fault. The fault parallel displacement is proportional to one divided by cosine of the obliquity. The monuments are typically anchored to at least 3
meters depth, but this varies by installation. Because the crossfault aperture of creepmeters is narrow, generally less than about 15 meters, creepmeters are most sensitive to slip in the upper 5 meters and are generally insensitive to slip at depths greater
than 20 meters. The nominal resolution of the measurements is on the order of 10 microns, and the sampling interval is generally 1 or 10 minutes for most locations.
The data is transmitted to Menlo Park once every 10 minutes; where it's processed and uploaded to the deformation website.
This map was made using data from an Open File Report by John Langbein, Roger Bilham, Andy Snyder and Todd Erickson
that will be released soon. This is a major update to the 1989 Open File Report by Sandra Schultz. Creepmeters
were first installed in the mid-1960s, primarily along the San Andreas fault. In the 1980s, in preparation for the next earthquake
the number of creepmeters in the Parkfield region increased to 12.
The 1990s saw the installation of 5 creepmeters along the Hayward fault, and over the last 30 years or so the number of active creepmeters has gradually declined primarily along the San Andreas fault. This map shows in yellow the locations
of active creepmeters today, not including recent installations by Heather Shaddox at al. in the San Juan Bautista region.
Here we're looking at the long-term creep rates determined from least-squares
regression of the creepmeter time series. For the Hayward fault
the creep rates ranges from about 3 to 7 mm/year.
On the Calaveras fault from about 2 to 10 mm/year, and then along the San Andreas
the rates increase from San Juan Bautista towards the southeast, from 9 to 15 mm/year reaching about 20 mm/year
in the central part of the creeping section and then decreasing again to 0
south of Parkfield, where the fault is presumably locked at the surface.
In addition to the variation in the secular creep, we see that there also is variation in how this creep accumulates over time.
For example, these two sites along the Hayward fault have similar long-term creep rates and seasonal variations, but the Point Pinole time series in blue varies much more smoothly compared to Fremont which has a more jagged or punctuated appearance.
Here's an example where you can see an abrupt increase in the creep rate, which is well above the long-term average.
These abrupt jumps are referred to as creep events, and we really don't have a good mechanical or real logical understanding of what leads to the smooth or continuous behavior versus this episodic behavior.
So John Langbein went through the full data sets at each site and calculated what proportion of the cumulative creep actually accrued as discrete creep events, and this analysis creep events were defined where the increment of slip in 1 day
exceeded the long-term average by 10 times.
What he found is that the Hayward fault is characterized by largely the smoothly or continuously creeping behavior
aside from Fremont; San Juan Batista is much more episodic, and Parkfield is a combination of both behaviors.
Here is all the northern California creepmeter data on one slide with the linear long-term trend in seasonal periodicities removed.
We see creep accumulating in a variety of time scales.
So again, the jagged appearance is due to the individual creep events, but we also see rate changes on the order of years, or even decades, and these often accompany regional earthquakes.
For instance, along the San Juan Bautista section of the San Andreas fault we see these three northern creepmeters have a reduced rate leading up to the Loma Prieta earthquake.
In an increased rate, following it. Meanwhile the creepmeters along the Calaveras fault recorded reduced creep rates following the Loma Prieta earthquake.
Of course, the Parkfield creepmeters recorded the post-seismic after-slip response from the 2004 magnitude 6 earthquake there.
However, these rate changes can't always be attributed to local or regional earthquakes.
For instance, along the Hayward fault, beginning in about 2008, there are increased rates along CTM;
and green and COZ and blue, and a decreased rate along CHP in pink, and I don't think that we have an explanation for this change in behavior.
An additional challenge to unraveling all of these records is understanding how shallow, normally unsaturated faults on materials interact with water.
We're studying this in Fremont, where a borehole crosses the dipping Hayward fault at about 21 meters depth, using an inclinometer system we're able to make institute measurements of the borhole deformation including at depths below the
water table and compare with the creepmeter records from the surface.
So this red curve shows the borehole fault
parallel displacement versus depth between 2003 and 2021.
where you can see there's been 12 cm of offset across the fault.
We also see that the shallowest portion of the borehole tilts in the opposite direction, the retrograde direction. The green curves [noise] show the results of an inverse model that's solving for the slip distribution on the fault using the borehole displacements and we find that in order to
match this retrograde tilt there needs to be a 30% routine in the shallowest slip, and this reduction is largely occurring above the water table, which we directly observe in the borehole and this is consistent with previous laboratory findings that faults and materials can be up to
twice as strong when they're dry compared to when they're wet.
Now this plot shows a fault parallel displacement versus time during a 3 mm creep event, and it appears that peak displacement occurs at depth prior to reaching the surface, so the inclinometer measurements peak and then plateau at day
285, while the creepmeter measurements continue to increase, and then even accelerate
during this big rain event. So again, this is consistent with the idea of inhibited slip in the shallowest unsaturated layer that's released when the fault zone is lubricated. I'll wrap up by highlighting some recent community advances
including new censored designs from Roger Bilham.
This includes the installation of gas sensors at several creepmeters, along with orthogonal paired sensors which Heather Shaddox talked about, and the design of multi-node sensors that would allow us to infer the shallow slip distribution. Here are three publications
that came out recently using the USGS data. Hirao et al. looked at dynamic triggering and communication between the northern and southern portions of the creeping section of the San Andreas. Gittens and Hawthorne used template matching to
identify creep events and estimate their long-strike size.
Jiang et al. looked at how afterslip and aftershocks evolved, following the Parkfield earthquake. If you're interested in viewing or downloading the data, it's available at this website, which I will try to remember to put into the chat right now so thank you.
Thanks very much. Nice presentation! Let's move on to our next
talk by Roland Burgmann on "Geodetic constraints, on spatio-temporal variations of crustal deformation and slow
fault slip in northern California.
Okay. So radial interferometry space-based observations of surface deformation
using InSAR has been around now for actually 30 years, and we've used this technology in a number of different satellite missions over central and northern California pretty much ever since to try to learn more about crustal deformation across the region.
And I would love to tell you all about all the recent studies that we've been undertaking, including a regional study using the ALOS-2 L-band satellite mission of northern California by Danny Lindsey and [indiscernible] integration of 8 different SARS systems to
illuminate the 3D variations of both fault creep and landsliding in the Berkeley Hills [indicernible], but in the interest of time, and with only 14.5 minutes remaining, I want to focus on two recent and ongoing
studies that I think nicely illuminate those space geodetic capabilities with a focus on time-dependencies and time scales of multi-annual variations, and even decadal-scale variations, but now become accessible with space geodesy and further enhanced with
observations in the field as well as seismicity repeating earthquakes that also tell us something about a fault slip at depth.
So first, I want to talk about work that Jessica published in JGR
this last week by Yushin Lee, former grad student here at UC
Berkeley and just started a postdoc at Caltech, and she looked at SENTINEL-1
InSAR deformation data. So with observations starting 2015 over the Triple Junction region between the San Andreas as fold and the southern Calaveras fault that almost merge and parallel each other, over quite some distance, here in the
southernmost portion. And so these 5-year record of surface velocities nicely agrees with GPS measurements of stations across the region.
So it captures deformation at just a couple millimeter per year
accuracy. And by differencing the line of site displacement across faults in the region, we are able to establish what the creep rate is along these faults, and so for the San Andreas and Calaveras faults we can map
what the creep, and even very slow, moving faults, like the Sargeant and Queen Sabes fault, seem to send a measurable signal of fault
creep, consistent also with field observations. So if you look at the distribution of fault creep rate along the fault as a function of latitude from south to north, both along the San Andreas fault and the Calaveras fault we can map out the spatial variation quite
nicely, and compare those with previous results from alignment array
measurements, creepmeters, and previous InSAR studies and there's some consistencies, but also some differences.
Now we're specially interested in temporal variations.
So here we're looking now at fault creep variation
deviations from average rate as a function of latitude
again on the San Andreas and Calaveras fault, but also as a function of time.
So here in these red regions, we have displacements that overshoot the average displacement, and in blue we have times where the rate is apparently decreased
leading to a less than average creep along the fault and you can see that in that southernmost portion of the San Andreas and Calaveras faults in the area where they overlap both of them have decreased slip rates in need 2017
to 2019 year period, with a slight difference in when that occurs.
So this is shown again here in these timeseries plots.
So here we have 3 sections of the San Andreas fault and in blue the D-trended and filtered with an 18 months filter curve of the creep rate variations as a function of time, and you can see this decrease below average displacement in
this 2017, 2018-year time grid. On the Calaveras fault we see a similar decrease, but it's slightly shifted by half a year
or so in time. Now you can also see on here creep, de-trended creep, cumulative creep, inferred from repeating earthquakes
from analysis by Taka'ki Taira
that seems to nicely mirror what we see in the InSAR data and then also showing here b-value of variations of seismicity in an [indiscernible] band along the San Andreas fault that also seems to have the same temporal trend. So there seems to be a persistent
multi-annual variation with a decrease doing this period with us somewhat
time shifted decrease, following on the southernmost Calaveras fault and Paicines fault.
So what might be producing this could reflect kind of a trade off of slip between the two faults.
Maybe as we keep looking this we'll see sort of a ping-ponging back-and-forth of slip on these two creeping segments of at least two faults.
It could have something to do with effects of hydrological forcing.
In 2016 there was a deep trout that ended, and a wetter period following.
So there could be changes in stress and/or pressure on the fault that resulted in these creep rate variations.
Now moving on to the second study, I wanted to discuss, looking at creep on the Hayward fault.
Here we have worked in a number of studies. That's probably a handful of papers taking surface deformation data and all repeating earthquakes and other geodetic observations to infer the distribution of creep along and with depth
on the fault, so here are two examples of studies that both used about 18 years of InSAR data along the Hayward, and also in this case, the Calaveras fault to map out using modeling the distribution of fault creep and locking in the subsurface both of which consistently show a
more coupled, locked region at depth that presumably might be the zone that slips again in future
large Hayward fault earthquakes. Now most of her Khoshmanesh, who worked with Mano Shirzaei, a few years back, looked at the 2015 to 2020 Sentinel-1 data to pretty much repeat that exercise, and and see how these more recent and very
high-quality data might change the picture and the goal was also to expand the analysis along the Rodgers Creek fault.
Again, the data agreed very nicely with GPS velocities and surface creep measurements.
Now in the model that most of I came up with the coupling looks very different.
Now we were still trying to dig deeper into possible differences in how the data were treated, the smoothing and the model inversion,
but there's certainly a first order decrease in coupling highest slip-rate overall in the subservice
inferred from this later data set compared to the early ERS and Envisage data.
So color contours on this upper plot indicate the apparent acceleration, positive acceleration along pretty much the whole Hayward fault from Point Pinole to Fremont.
Now to assess this, we're also looking at creep rates estimated from repeating earthquakes that Taka Taira analyzed along the Hayward and Calaveras faults.
So in the top panel we see the raid inferred from repeating earthquakes
prior to 2010 on the Hayward fault and converted through calibration equations to a great rate, and at the bottom we see creep rates following 2010 and these are in distance from Point Pinole.
So you can see on the Hayward fault there seems to be in many areas a somewhat higher creep rate; whereas on the Calaveras fault south of the junction of the two faults, the creep rate apparently decreased between these two time periods. We
can plot up the rate, change in zones where we had enough repeating earthquake before and after the earthquake.
And so we see this persistent acceleration of creep on the Hayward fault, deacceleration on the Calaveras fault, also seen in this pre- to post-creep rate estimate where the color indicates distance from Point Pinole. These higher rates along the
Hayward fault in the northern 50 or so kilometers, lower inferred creep rates from repeating earthquakes inferred on the Calaveras fault
to the south. Then we see similar patterns in surface creep rates, measured, using alignment arrays in many decades of effort by Jim Lienkaemper and a number of people from San Francisco State University and at the USGS. So you can see along the Hayward
fault from Point Pinole to Fremont persistently higher rates
Since 2010 compared to before. Again, an Xy plot shows this consistent acceleration.
So there appears to have been an increase overall in creep rate along the Hayward fault over the last few decades.
And so the question is, what might this be due to? This
could have to do with fault interactions and earthquake cycle effects.
We know the southern Hayward fault and Calaveras fault
decelerated due to static stress interactions with the Loma Prieta rupture in the aftermath of Loma Prieta, and since recovered some. And we also think that maybe the lingering stress shadow from 1906 could still be decaying over this
decadal time scales. Maybe again, that could be some climatic effect.
But there's does seem to be a persistent increase.
Now this reminded me a little bit of what was observed by Mavrommatis et al. in the decades prior to the Tohoku earthquake, where the faults surrounding the locked disparity that eventually erupted in 2011 accelerated
as evidenced by GPS, as well as repeating earthquake data in the decades prior to the rupture.
We cannot say anything about this having to do with this kind of precursory process, but it's an intriguing comparison that I hope you forgive me.
So in conclusion, we find changes in fault creep rate on the partial coupled San Andreas,
Calaveras, and Hayward faults on multi-annual to decadal time scales.
Those presumably reflect changes in the applied stress that might be either tectonic or non-tectonic in nature, or changes in fault strength that might reflect pore-pressure variations or frictional properties on the fault zone. And I'm looking forward to questions and discussions
with you following this session. Thank you so much.
Thank you. Yeah. I think we had a a good exchange of questions and discussion on the chat
already, and hope I have some more at the discussion section. Let's move on now to our next presentation
by Greg Beroza at Stanford, where he teaches, but also involved in other activities.
And I know it's a long list of co-authors and the presentation will be "Some recent developments in earthquake monitoring."
Hello! My name is Greg Beroza. I teach in the Geophysics Department at Stanford.
I want to thank you for the opportunity to give an update on our research [noise] and in particular, our efforts for improved earthquake development.
These continue to be driven by the methods of AI, and in particular, machine learning. We get support for this effort from the USGS, of course, but also from the Department of Energy and from the Air Force.
Research Laboratory. So as some of you may know we've been at this for a few years, but there's still a lot of work to do, and I think we're still on this steep part of the curve for making progress in developing what I like to call deep catalogs. I also think there's an
unmet need to explore these catalogs, both to understand what's in them,
what's not in them, how they differ by technique, and and also explore them in the sense that I think they have tremendous potential to teach us new things about earthquake processes.
So the basic task is, we are approaching it at least is to take continuous records of ground motion or waveforms, to develop a catalog of discrete seismic events, that is as comprehensive that is as complete as it can possibly be. This is classically represented as being done through a
workflow that looks something like this with a linked set of tasks that are, sort of modular, and we have developed machine learning models,
sometimes several of them, for each of these tasks, and these can be you know plugged in as desired, and in a modular manner.
So when we go about doing this, we use what we know about the nature of the test.
What we know is seismologists from that to inform the machine learning approach and architecture that we think should be best suited to carrying it out.
Of course, that changes with time and so these models, these methods are naturally change with time as well.
Once these have been reviewed in published we may make our models openly available through Github, and we continue to work to improve
there performance. So I think it's fair to say that AI in general and machine learning in particular, has had a dramatic impact on seismology.
Most of that has occurred through supervised learning. The blue part of the bars in this plot, in which neural networks learn complex relationships that are embedded in data by being exposed to and learning from very large label data sets that are full of instructive examples. So the examples
I will show in this talk are all supervised methods, and/or either that or they are data that are are organized and curated in such a way that they will support the development of supervised methods. Having said that I think there is great promise perhaps the greatest promise for the future, in
unsupervised methods, so we shouldn't forgive about those.
I should also mention that the for earthquake monitoring which tasks, which are sort of the lower half of this plot, has seen an outsized effort in the application machine learning methods and seismology, and I expect that that will broaden with time.
Okay. So the first one, Ian McBrearty, he's a grad student shown here on the left.
He's developed a graph neural network based approach to earthquake phase association that we call genie.
So graph neural networks are designed to work with non-Euclidean data.
And by that I mean that it's data that's organized on an irregular collection of nodes, without necessarily any canonical or natural ordering of seismic network is an example of an irregular collection of nodes that can be represented with a graph and it differs
From regular data, like a one-dimensional time series, which has a constant DT or a two-dimensional image which has regularly spaced pixels on those sort of data we can use convolution in a straightforward manner, but for irregular data
we need to do graph convolutions that take into account the structure of the data using things like adjacency matrices
so that our methods can learn things that relevant to say phase association like move out across an irregular seismic network.
So phase association is a deceptively difficult problem
it's instructive to show how genie is able to make phase associations for small events in which only a tiny fraction of a seismic network is illuminated.
That will be the case, for most earthquakes, which, after all, are small.
The left panel shows the associated phases blue for P and orange for S.
The middle panel shows the the total phases that they're associated from, and the vast majority of arrivals in this example are unassociated and perhaps false detection or artifacts.
And the right panel shows that it's relative to the events that their association were shown in green.
The red stations that have associated phases make sense.
If we have a larger earthquake, we need to associate that as well, and this shows the P and the S
wave associations for a magnitude 2 earthquake across the network, and so on.
This is for a magnitude 3, which it illuminates something like half of the network with detectable phases and an effective phase associator has to be able to deal with
all these contingencies. So anyway, that's the examples from the genie associator.
We applied it to a 100-day sample of data from the greater San Francisco Bay Area.
These got the orange events. These are not relocated.
So the the distribution is pretty fuzzy, but there's something like four times as many earthquakes as there are in the USGS catalogue, including many more aftershocks of the magnitude 4.6 event that happened on the creeping section during this time period.
So we can zoom in on that close in space and time to that location, and we can see that on the left, the not surprisingly, the newly detected events are small in the magnitude 0 to 1 range, and we detect many of them at least in part because machine learning phase pickers
like PhaseNet, which is used here, are super effective at detecting S-waves and S-waves for local earthquakes tend to be much larger than P-
waves, and hence emerge first above the noise for small events. On the right it shows that we have many, many more
aftershocks following the morey-like decay to this creeping section event.
Okay, if we form receiver gathers or we take those associated events, grab the seismograms and compare them to one another.
We can see that the waveforms are extremely similar, which gives us confidence that these are real, correctly associated
events. Now our goal in doing this is to do something like what Yongsoo Park did and published last year for Oklahoma Kansas.
So Yongsoo published a decadal catalogue tenure catalog for that region that had over 300,000 events in it.
That's something like a little over 20 times as many as in Comcat.
Most of the faults are near vertical strike-slip.
So, it is relatively easy to visualize what's going on.
We've seen lots of conjugate faulting.
We see features that affect the activity, such as the Nemaha Ridge, shown in yellow which is thought to be a barrier to flow in these earthquakes
that's relevant because these earthquakes are thought to be induced.
There are other features that are a little more mysterious, like the yellow boundary on the right to the east.
It shows us sharp cut off of activity without a clear geologic explanation for it.
That one processing this data, Yongsoo analyzed about 700 station years of continuous seismic data.
So it was a big, a big effort. Ian is going on order of magnitude larger.
So for northern California we have 20 years of continuous data.
He's looking at processing on the order of 10,000 station years of data and the sort of protoplasmic blob shown on the left indicates two things; one, it's, you know, in our locations and the other not ready to to show you in any detail but we're getting a handle
on the computation at this scale. So, without showing our, you know, approximate locations, we can see that the detections or the south Napa earthquake and the San Ramon swarm in 2015 indicate many, many, more detections in the catalog at a factor of 4 or 5
more than in the standard catalog.
So I mentioned supervised methods earlier in this talk, and how they need data.
They're data hungry. And so Leo Aguilar is developing an earthquake waveform database for the regional seismic phases
Pn and Sn. So we have developed and we have published a catalog of local earthquake waveforms a couple of years ago, which we called Stead.
Leo is doing this for the regional phases
Pn and Sn. So as seismologists in the crowd know well, these are important phases, but they're trickier
then the direct Pg and Sg phases are more spread out in time, so we've had to adjust our practice.
Leo has searched globally over multiple data centers.
Most productively, the ISC and the NEIC regional phase labels from around the world, and in doing so collected.
What's shown here over almost 6.5 million example
waveforms. Now, there's issues with these data. And so he developed a machine learning model that seeks to identify the interval between P
and S on a potential seismic waveform, and if it has sufficient quality, then with the labels that are defined from the PNS wave arrival, then he deems it acceptable, and if it has poor quality, then we reject it. So we're using machine learning
here for quality control, uses a pretty simple model, and and makes it pretty simple measurement. So why do we have to do that?
Well, we we actually reject the majority of the data more than half of the data.
It may be rejected because it's too noisy, because the waveforms weren't recovered properly, which can happen because the labels themselves are bad.
In the catalogs, or because there's more than one earthquake, which happens very frequently more than one earthquake in the 5 minute time window
we're using the last thing we want to do is to teach a machine learning model to detect small earthquakes as
one of the tasks were trying to do better. So anyway, there's this quality control step, which is also machine learning powered that we employ. After that process we're still left with plenty of data,
3 million examples, expresses 3 component 5 minute samples and an aggregate amount about 30 session years of label data.
So no shortage of data at all we can afford to pick. So for the earlier work, you may be familiar with Weiqiang Zhu phase model, which was sponsored by the USGS support and trained on NCSN data. It's since been used around the world
By many investigators to detect local earthquake phases very successfully. Now, Weiqiang treated arrival time measurement as a semantic segmentation problem.
He trained PhaseNet to classify continuous waveforms as either P-
waves, S-wave or noise. That's what we mean by semantic segmentation.
But he engineered it, and cleverly, in such a way that the P-
wave and the S-wave probabilities would peek at the arrival time.
Labels for P- and S-waves, and so the output is a time series of classification probabilities that peaks at the labeled arrival times from which we get the measurement.
Now postdoc, Artemis Novoselov has taken a different approach.
He's treating machine learning arrival time measurement as a regression problem in which the input is the same three component waveform.
But the output is a single number. The arrival time of a seismic phase.
This has some advantages. The arrival time is what we're after,
of course and there's a potential, at least in principle, to get some sample arrival
time measurements through this kind of approach. Now one of the things he did is to use something called subsampling.
It's an intermediate approach between ensembling and dropout.
His model, which he calls PhaseHunter out uses these subnetworks to develop multiple estimates to the arrival time such that he can create a distribution somehow captures the uncertainty of the measurement.
So this should be really helpful in giving the measurements appropriate
weights. This animation shows how phase picking uncertainty which is shown is this distribution varies as the noise increases, not surprisingly
noise as it increases, interferes with accurate arrival
time measurements, and that's reflected in the width.
The character of the distribution also due to its larger amplitude
it consistently picks S-waves more accurate than P-
waves, at least in this example. So I'll conclude by saying that there's a lot of activity and a lot of progress in earthquake monitoring using AI.
I've shown three examples of works in progress that indicate there's still a lot to do.
By this time next year we hope to have a deep earthquake catalog for northern California, using the available continuous data. We're working with archival data,
but there's no reason that we can't work with streaming data or
you can't work with streaming data and process things in real time.
And then, you know, I think it's gonna remain a dynamic environment in terms of catalog development.
And we have to get serious. We have to get creative about extracting understanding from these results.
So thank you for your attention, and I'll stop my talk there.
Thank you very much, Greg. Very fascinating to see all these latest advancements and earthquake detection.
So we'll move on to our next presentation by Russ
Graymer from USGS "On developing a geology-based 3D seismic velocity
model of the Central California Coast Ranges."
The presentation about the Central California Coast Ranges 3D geologic map and seismic velocity model my name is Russell Grammer, and I'll be taking you through the progress
we've made in the last year.
Years ago, we [indiscernible] of PG&E CRADA money and leverage that to create a initial 3D geologic model of the Central California Coast Ranges between the Western transverse ranges in the South and Monterey Bay in the north bounded on the east by the
San Andreas fault, and on the west by the coastline.
We used the 3D-fault model from the 2008 CRADA-funded effort.
The 3D block model from Jachens et al. he added some additional faults, ,
the Irish hills and around lenses of uplifted basement in major fault zones and then we constructed a top basement surface from sparse wells, widely space cross-sections and downward projections surface geologic mapping.
We also incorporated two more detailed models of the top basement surface that were available at the time.
In cyan, you see the Santa Maria Basin model; Sweetkind et al. and in magenta
you see the the model west of the San Andreas fault between Parkfield and Pinnacles.
By Roberts et al.
So the new model we have by fault framework so revised the top basement surface to correct errors and add more detail.
We have subdivided the strata above the top basement surface into three stratographic packages.
Cretaceous, Heleogene, and Neogene.
And we've added additional detail in key areas. Here's an example of the modification of the fault framework in the version 0.1 model, the original model we had based our faults solely on downward projection of the map surface traces in some places like the Abel Mountain
Thrust this had led to problems. You could see the Abel Mountain Thrust map surface-trace ends in a hook around to the southeast.
This created an extension of that cut across geology and the gravity signal. For the version 0.2 model
we have added a blind fault extension to the southwest, based on details of the surface geology and the gravity signal that better honors the geologic relations.
Thinking to that same area. You can also see an example of how we have modified top basement surface.
The dots represent the structure contours of the original model; the red lines represent the new structure contours.
You can see in many places they are the same. But, for example, above the Abel Mountain Thrust they are quite different.
The much more detailed near the surface contact, in order to better capture the interplay between the topography and the modeled surface, and also more detailed
attempt to capture the folded nature of the context
on the southwest, and of course adjusted to fit the new fault framework.
Here are the structured contours on the tops of the stratographic surfaces that subdivide the above basement strata.
The green structure contours are on the top basement. The orange on Top Cretaceous and the yellow on Top
Paleogene. Of course, there's no structured contour on Top Neogene because that is the uppermost units of top of that where it exists is the topographic service.
Zooming into an area between the Santa Ynez and the Santa Ynez River faults in the Western Transverse Ranges, here's an example of the Top Cretaceous surface construction, you can see that it's quite a bit more detailed than the original
Top basement. We have both again added a lot of detail
near the surface contact of the unit in question to better incorporate the interaction between the surface and the and the topography.
You'll also see that we have incorporated
Intra-block faults that offset the stratigraphy, but are too minor to
have in the model as a block-bounding faults are now modeled as near-vertical on [indiscernible].
Basically we already have more than 70 fault blocks in this model
it's impossible to incorporate all of the faults that actually offset the modeled surfaces.
The near vertical approximation is decent, it's quite good for strike-slip faults decent for normal faults and not as good for reverse and thrust faults.
Sticking to that same area here's the Top-Paleogene surface. Again,
you can see those near vertical contacts that represent the faults internal to the block.
You can also, I think, hope see that the space between the structure contours is not the same everywhere, but it's variable.
It added lot of work to try to incorporate the along-strike changes in dip.
This is a big difference between versions 0.2 and 0.1 is 0.1
we assumed that the dip was uniform
across large stretches. Now we've done a much more careful efforts to incorporate local changes in dip along- strike. We've also incorporated places where the units truncate downward onto the underlying surfaces so in the northwest corner of this block
you'll see structure contours that appear to just stop.
This is actually where the Paleogene unit pinches out downward against the underlying Cretaceous.
Here's an example of an extra detailed area. We zoomed in on the Irish Hills.
You can see by how pixelated it is just how far we've zoomed in.
You can also see that there's quite a lot of detail here.
We've incorporated the multiple stacked, blind faults and overlapping Top Basement surfaces that we presented in the SSHAC meeting back in 2010.
All that detail is now incorporated into the 3D model.
The latest addition is now that we have a finalized 3D
geologic model working to bring in the seismic velocity relations with developing stratographic package specific depth versus velocity relations from velocity logs and oil and gas wells.
This map shows the blue dots are the wells, the straight vertical wells with velocity logs.
And this is a plot of the data from those straight vertical wells.
This is very preliminary data. I just got this last week.
We have some known quality control issues to this data. But I thought I would go ahead and show it.
The black dotted line is the exponential trend line for the
the light orange Neogene data. Most of the data that we have is in the Neogene.
You'll notice that our data only goes down to about 1,300 meters below sea level.
You see, this is a big difference between this area and the the Delta where our data went quite a bit deeper.
So we're going to have to figure out how to best project downward
below the volume where we actually have observe velocities.
We also are working to bring in additional wells that aren't straight vertical wells, but are deviated
that takes another step to bring in the deviation log to both to
calculate the true vertical depth, and to make sure that the lateral variation hasn't taken us into a different unit.
For example, drilling laterally across the fault.
So, to sum up, we now have a finalized 3D geologic map and we're working on a bringing in velocity log data to create a unit, specific philosophy depth relation.
We've learned a few lessons along the way. First,
this is a very large area, probably too large for a 3D model to sit.
The large area required many simplifications some of which we've already discussed.
We've learned that the widely spaced cross sections that we tried to use to to constrain the version 0.1 model were not sufficient.
You need to depict 3D geology if you're gonna use cross-sections, they have to be much more closely spaced, which obviously relates to the large area of the model much easier to get closely based cross sections in a smaller area.
It's also important to account for the effective topography and regions of fire relief.
You can't assume that the subsurface structure contours are parallel to the surface contact.
The next steps are to finish developing the depth versus velocity relations, and to integrate those with a 3D geologic model to create the 3D seismic velocity model and to get the whole thing peer-reviewed and published transforming
it from version 0.2 to version 1.0. Longer term we're hoping to revisit this model and to upgrade it by incorporating offshore geology.
I know we stop right at the shoreline, but we know that there's a depiction at least in the near shore of the offshore
geology, and we would also like to add even more detail in critical areas.
We also want to work to continue to try to integrate geology-based 3D seismic velocity models like this one with tomographic models.
I feel that each each approach has strengths and weaknesses, but that combined approach could produce velocity model that would be superior, that would be superior to that produced by any one approach. Thank you for your attention.
No, thank you. Russ, that's excellent progress. That's amazing,
it's really a large area, as you say, and an ambitious project, but with good progress.
So our last talk today will be by Evan Hirakawa from the USGS on the "Current state and future directions for the USGS San Francisco Bay Area region 3D
seismic velocity model (SF-CVM)."
Hello, everyone! I'm Evan Hirakawa from the USGS earthquake Science Center in Moffett Field.
Today I'm going to give a broad overview of the USGS
San Francisco Bay region 3D geology and seismic velocity models.
So these are really the primary velocity models that are in use for the San Francisco Bay Area, and in this talk I'm gonna touch on development history of these models, show some features of the model in the most current versions and things that were updated by myself and
Brad Aagaard recently and briefly talk about where we'd like to see the model go in the future.
Seismic hazard and risk is high overall in the Bay Area.
We've known this for hundreds of years since there's been multiple, disastrous and deadly earthquakes just in historic times.
But understanding the relative amount of hazard from site-to-site is very difficult, and can be highly variable.
One thing we've understood for a long time is that the subsurface geology plays a huge role in determining how the shaking may look at any given location, and this became very clear after the 1989 Loma Prieta Earthquake where we saw amplified
ground motions in Oakland above what's known as this very soft, low velocity Bay mud.
Relative to sites where the shaking wasn't as bad on top of bedrock, or even on sand and alluvium, and because of this the Nimitz freeway collapsed in one part above the Bay mud, but was actually left standing above the alluvium and so
capturing these kind of effects, and the variability is hard with typical hazard analysis, which is done mostly through empirical models, and we can reproduce some of these kinds of effects with 3D physics-based computer simulations.
But we really need a good representation of the subsurface to trust them.
So in light of this, and in preparation for simulations of the 1906 San Francisco earthquake on its hundredth anniversary, a group of USGS scientists led by Bob Jachens developed the USGS 3D geologic model in the San Francisco Bay
Region. So this is a model that includes many of the local faults with detailed fault geometry obtained from double difference earthquake relocations and stratigraphy, and other basin structures, and other geologic structures obtained from a combination of interpreting
geologic surface maps. Borehole logs and bringing some potential field
geophysics like gravity and and magnetics.
The resulting model has 25 fault services, 26 fault blocks, which are what we call these areas kind of grouped and split up by the faults, 33 different lithologic units, or zones, as we call them, which are shown by the different units, in the different colors
here. Each one of these is able to hold some kind of different seismic velocity property.
And then the way that we assign velocities to these zones are by what we often call velocity
verse depth rules which were originally fit by Tom Brocher from boreholes spread throughout northern California.
So this figure demonstrates how we assign a different one of these kind of velocity depth rules which are shown in these curves
to each one of these different units, and we end up with seismic velocity models with all the known velocity interfaces, basins, and other structures based on the geology.
So, for example, here's the San Andreas fault and the Hayward fault, and you can see that there's some clear velocity contrasts across these which follows very closely with the mapped fault traces.
So a little bit of version history here the first version of the velocity model was version O5, that stands for 2005.
And like I said, this was originally developed to do these big 1906 earthquake simulations
and the way it also uses a name in simulations of the Loma Prieta earthquake to try to kind of validate the model, since we have actual recordings from that event.
But it's hard to tell which discrepancies in simulated motions are coming from the source and the geologic structure.
So one way to evaluate the model in a different way is to just use a much smaller earthquake.
So here's a kind of evaluation study by Artie Rogers.
[brief black screen] So, in order to eliminate the complexity coming from the source, they just use point source representations of these moderate earthquakes, and they found that the 3D model predicts the observed waveforms well and reproduces features that are arise from the basins, etc.
And they made some suggestions like, for example, they found that the shear velocity was on average about 4 to 5% too high
near the surface.
So these recommendations, and maybe some others, led to the 2008 updates.
So this was version 8.3.1. Let's address some of those surface wave velocities that I mentioned also made some changes to bring the velocities closer to what was being found by Cliff Thurber et al.
2007 tomagraphic model, and then also to reconcile with some more recent refraction and borehole data
down at the time. Modelers got quite a bit of use out of the 2008 version.
Here's for example, a suite of magnitude 7 earthquake simulations done by Aaggard et al., which led to the well-known HayWired Scenario continuing to be used as computing power got better up to these high resolution 4 hertz scenarios and is
also most recently was being used by this DOE ex-scale computing project where they're coupling these really high frequency
10 Hertz synthetic motions to actual structure simulations.
So this is where I came into the USGS around 2018 or 2019 when I worked on my first project evaluating the old version, the 2008 version of the velocity model, and most of this work is published in this BSSA paper Hirakawa and
Aagaard, 2022. But we're doing something similar to the last evaluation study I described where we want to just use a bunch of point sources, so we don't have these big earthquakes with source complexity.
We can see how just the synthetics look compared to the observations from these sources.
So we simulate 20 of these moderate magnitude 3.5-4.5 earthquakes, using SW4,
and we compare the synthetic waveforms with the real data via set of quantitative metrics that evaluate arrival time and intensity. So here's a clear demonstration of the change in waveform performance between zones this is a
magnitude 4.0 event on the Hayward fault with some stations in Berkeley that record the event shown by the triangles and the recordings are shown here in black, and the synthetics are shown in [brief black screen] light blue and you can see that for example, stations to the West
of the fault, like the station, BRK, [slideshows returns] the synthetic waveform matches quite nicely with the observations. But to the east of the fault, for example, at BKS the synthetics are late in the amplitude's too high.
So we made the inference here that velocities in this particular region are not bad
west of the Hayward fault, but they're probably too slow east of the
Hayward Fault. An easy way to address something like this where we think maybe the velocities in one zone isn't too bad, but an adjacent zone needs attention is just to reassign
those velocity depth rules that I described earlier.
So we can kind of swapping in different choices of the velocity, depth rules to different zones that we have, and in that way it's easy to keep the velocities the same in areas where we think it's fine and to modify them
in others, without actually touching the underlying, 3D geologic model without actually changing the actual, explicitly defined surfaces.
Stored in there. And so this is what we did, and one of the main changes, for example, was to increase the velocities to the east of the Hayward fault, based on the mismatch that I showed in the last slide where I thought maybe the velocities might be too low. Other things included T
decreasing the velocity in the Livermore Basin, where we saw that synthetic waveforms were arriving too high and dividing the zones along some straight lines, just so we could kind of set in a proxy for faults that are not actually existing in the model at the time.
Now, here's a big collection of synthetics for waveforms going through the East Bay Hills, and that modeling study I'm showing here,
The black waveforms are the observations; the light blue, are the synthetics using the old model, the versions, the O8 version and the red synthetics
are using our updated version, which we call Version 21 without spending much time on this, you can clearly see that the synthetic waveforms improve from version to version in relation to the travel time, and they also removed some of these large reverberation type artifacts that we see a
lot of the stations in the East Bay that are actually not there.
So a summary of the improvements for this version, 21.1 we improved synthetic arrival
times, peak amplitudes and cumulative motions in many parts of the East Bay.
Livermore Valley and North Bay. These are kind of some improvement maps where green means it got better, and red actually means it got worse.
So I encourage you to read the Hirakawa and Aagaard paper again, to learn more about this.
Models are available on science base. We also released Version 21.0, which is the same as version,
the 2008 version. But in this new Geo-model grid storage format that was developed by Brad Aaguard.
So this is a new software used for storing and querying
these raster-based models, and can handle topography, multiple geographic projections, and variable resolution.
And it uses a widely used HDF 5 storage scheme.
So you can take a screenshot of this slide to get these links.
So now what I thought I'd do is just kind of jump around the model and highlight
some of the special study regions that we're focusing on, in the sessions of this workshop.
So we heard about the Greenville fault in the Livermore Valley area, and so in the velocity model
Livermore Basin is represented by a large 20 kilometer wide basin, surround 7 kilometers deep at its deepest, but we found in our evaluation study that the velocities there were too high in the previous version. So here's a figure from the Hirakawa and Aagaard study, we
found synthetic arrival times were coming in too early; shown by the blue curves, so we manually slow down the velocities here and it
led to synthetics that arrive much closer to the observations.
And there's still work to do here, including modifying the base in shape and also modifying that geology of the Mount Diablo region, which has been more recently mapped in better detail, and also to include from geologic maps very soft quaternary sediments that are mapped in detail but
aren't explicitly in the model at the time
We learned about the Santa Clara Valley in a previous session.
So this is somewhere where we have started to more carefully add mapped quaternary sediments, and this is west of the Hayward fault and the Calaveras faults, and what we call the Central Bay Block where the shallow velocity structure is really over simplified and pretty homogeneous in
The current model. And this is despite the fact that we have pretty detailed geologic maps that separate things like bay mud which I mentioned earlier from sand and alluvium, which might have much different properties.
So I don't have time to really go into detail about how we did this, but the result is higher velocities in the alluvium and harder sediments, while we maintain the low velocities for the Bay mud. One result of this is to remove large
unrealistic shaking from synthetics generated with the current model shown blue.
Here, making a new synthetic shown in red look much more like the observations which again shown in black.
So we plan to wrap this up very soon and hopefully release a new model update this year, version 23.
And this is being prepared for a journal paper.
I mean lastly, I think tomorrow we're gonna hear about the Geysers.
The Geysers is kind of far out, close to the edge of the velocity model domain, but it is kind of in the realm of the North Bay, which I'm broadly grouping together, and I showed this in a previous slide where we updated this in the 2021 so the situation here that we
found is that the current geologic model is really oversimplified has high velocity,
Franciscan rocks spreading out way farther than it should, and completely overlooks the Sonoma volcanics, which we know is widespread in the North Bay.
So in the 2021 update, we drew some kind of crude minds here just to bring some of the synthetics to look more like these observations.
Just kind of cut up these blocks without actually changing the geologic model, and this got rid of some of these long reverberation-type artifacts that weren't in the data.
But it would be nice to explicitly include some of these mapped geologic features into the geologic model itself.
So this kind of leads into how I want to finish here.
So what else can we do in the future of the model?
We can refine the geologic model and get rid of some of these quick proxies like I showed on the last slide.
We also have 3D services that exist that we should be able to theoretically just kind of drop into the geologic model.
So here's an Eel River Basin model developed by Rob Grave is based on some cross-sections originally drawn in 1953 that we're using to model the Ferndale earthquake
right now. Add info from other regional inversions like tomography and attenuation, even though these might not have those sharp boundaries, we might be able to find out more about some of the areas that aren't underrepresented info from active source experiments so
there's a number of refraction lines that go across the East Bay, done by Rufus Catchings and Strayer et al. shows this, and then, of course, we have a ton of surface data, so we can reconcile the surface values with Vs30 or whatever other geotechnical info we have
to add in the so-called geotechnical layer to the model, which we know has a big effect on ground motions.
So in summary, the USGS Bay Area 3D geology and seismic velocity models are the most accurate community models in northern California.
The primary uses in simulations the simulations are used ideally to predict ground motions, but we also use simulations to evaluate and update the model itself.
Example, with these simple points source simulations. And there's a lot more data published and in progress that can help improve the model.
So we're going to be excited to see how these can be incorporated in the future, and I'll just finish by saying that we have a workshop coming up in a few weeks Tuesday, February 14th, and you can contact me if you would like to attend
this. Thank you very much.
Thank you. Great comprehensive overview of that update on a complex model already, which is now even more detailed
So that concludes the presentations.
Part of the session.
And will lead into our 30 min discussion section. There were a lot of questions and discussions on on the chat which now can be continued in person or more in the chat.
I have a couple of questions for most, because but if anybody wants to raise a hand with directions, partner at the bottom, just click on it and raise your hand to ask something question to anybody, most of the time to to try that
Perfect.
I won't say anything for a little bit to encourage reactions
Looks like Eric has questions.
Yes, that's a new 3D. Geology and velocity is really an impressive.
I wonder if the geometry of the of the heyward fault is still basically the same as in the previous model, so that you're only changing the you haven't changed the the geometry of the of the Hayward fault at all and it.
The question was for me or, Yeah, no, wait. So in that in those updates we did not change any of the defined geologic services or false services, so that the geometry of that the same
It looks like Alex had a question
Hey? Yeah, this question is, for I guess Evan and and rest both things, both of you, for nice talks.
I have a question about, obviously, I'll ask a question about faults.
So could you use either of your models to infer the presence of faults that are unknown at the surface, like buried faults, or inferred falls part of a structure?
Could you comment on kind of the utility of your models to cross check geology?
Understanding of 3 dimensional faults at that
Well, I don't think we could use our model a lot of our faults are downward, projected from surface faults.
The only places. Excuse me dealing with a bad head.
Yeah.
Cold here. The only places we have blind faults are places where those have been imaged or
Cool.
To take a break. Yeah, no, I can
Yeah, I'll pick up. I don't think it's when our 3D models there's really ways that we could discover new faults if that's what you're saying or infer.
Locations of faults there, usually the result of people like, you know, going out and discovering the new folds.
There are places where maybe a contact which is actually a fault contact in the real earth is kind of just represented as a depositional contact.
So actually? Or do you? Rogers brought this up yesterday when we were looking at the Los Angeles fault?
There's not really an explicit fault surface there in the model, but there's kind of a part where the bedrock dips down and looks like a fall kind of so there's areas like that where we can kind of AIM at.
And and say, Okay, maybe that's a target. But we should.
Use. What's there to build on this? But I think that's the the best direction that would address what you said
Thank you.
I see Laura has to stand up
Yes, and it it's very much related on the same kind of topic as well.
An extras mentioned so even, in particular, in your model, there you will have this place where you had to change the velocity profile.
But you don't want to. You don't change the gee to me.
Is a little bit weird, because shouldn't that be actually a motivation to see?
Actually, the geology is different from what we're thinking there, and therefore use that to.
We find Judge Korean France. In a way, that is what is the hangup about actually just changing that structural model that you're on as
Yeah, so I mean, so the line of re, yeah, you're right.
So the line of reasoning is that a lot of it surfaces and adept to the surfaces and the faults were constrained by kind of things.
Other than the method we were doing. You know these double difference relocations, or in some cases geologic maps, or you know, the potential field stuff.
So I think that it is a little bit dangerous to just change the velocities and say, Oh, we made the waveforms look better.
So that means that this is better. Yeah, you're right.
You should go back and change and re-examine the the actual geologic services
So that's something that, yeah, we'll be kind of a future direction.
But I think at kind of this resolution we we found that what we did was someone reasonable
Hmm.
We have that that leads to sort of today
Maybe what's on many people's mind is is their plan to, you know, combine or use the post ranches. The updated coast ranges model from us to enorm an update of the Bay area model
In many of these ways, with new falls and and and straight to grant.
Okay.
So right? Yeah, rest is the does the Coast Range model overlap with the Bay area a month or
Okay.
Yeah, it it a butts at the south end. The sorry sorry about the coughing fit, the the challenge would be the we've taken a slightly different approach to the subdivision of the above basement stratigraphy.
So we would have to figure out how to make those meld together.
We have some leverage there, in that. There's a lot of basement at the surface, so where there's basement at the surface, we don't have to worry about the above basement cause.
There is none. Once we have the velocity to depth, relations developed for the coast ranges and have that.
Integrated into the 3 Dg logic model. I think it would be relatively straightforward to compare along the edge
And see how we see how it compares
Once we once we have those 2 models stuck together.
Then we have a relatively continuous model from the transverse range is up to Point Arena. I think
So it would be a big volume to work with
I'm good
So there was
There was some discussion in the chat also about the possibility of including fault phone structures in these models as a added layer of complexity. And maybe there's more to say about that
Well, it depends on what we mean by falsehood structures.
The Central Coast Range model has tried to capture those places where there are flower structures that have been that have brought basement rock as sort of downward, pointing triangular wedges.
A long fault, we've tried to add a fault on either side, rather than having the single fault down the middle, so that we could have that lens of.
5 Velocity rock in the model. But if we're talking about things like zones of low velocity due to rock crushing along the fault that would be a scale probably too small for a regional model, and this
I I mean it would be possible to do. But someone would want to go in and do a more detailed start with our model and do more detailed modeling on top of it.
Rather than try to integrate that concept across the A model that's under zoom miles long.
I'd say the same thing in the Bay Area model, because, you know, like, so, a lot of default, planes, we're we're kind of divined in detail by the double difference earthquake relocations.
But a lot of this actual spread of them might be somewhere as wide as the phone, so that we don't know.
To add it into the geologic model would be pretty daring, I think, you know, but if on a case by case basis, you know, for a simulation or something, you wanted to add in a low velocity, fault zone, you could kinda just do that manually, but I don't think we're at the
Point where we can with confidence add that into the community model that everyone's using
We want to make sure to. Also, you know questions for some of the earlier speakers.
And so, if there are some other questions out there for them, please raise your hand.
There might be a few in the chat as well
So one of the things that came up in the chat during Greg's talk had to do with whether these methods that he was describing would be helpful for finding more repeating earthquakes in also how the models that are the he has catalogs coming from the different
methods might be validated against each other, and maybe you could say more about those topics
Yeah, you want. You want me to speak to those
Sure I think they were coming up in relation to your talk.
Yeah, they did. And and so the the notion of doing a comprehensive search for repeating earthquakes using machine learning is a is an interesting idea.
I have a student who's sort of doing that in his spare time.
He he has an idea for how to do that, and I will see if it works or not.
But we have, you know, the great catalog already for Central and Northern California to compare that with.
So so that should go pretty quickly. And the second part of the question had these 2 part questions always throw me so?
What? What was the second part? Jess?
About validation of the catalogs that come from different methods
Oh, can you hear me? Yeah. Yeah. Validation.
Hello! Oh, validate. Yes. Okay. So I I can hear you I'm I'm in a hotel on the Internet without.
Okay, so and one of the things about these deep catalogs, if you look at, say, the Ridge crest.
So there are 3 or 4, or 5 d. Catalogs for Ridgecrest, the Ridgecrest Sequence developed by different techniques.
And if you look at the earthquakes they have in common and the earthquakes they don't.
There's a big population that they don't have in common.
And so that's something we need to understand, at least for some things like like using them for e toos like earthquake forecasts.
So we're, you know. We sort of have a a hypothesis that these different machine learning data science methods have different sensitivities they have in.
We know they have inconsistencies, and how well they they pick even the same picker at different times and slightly different signals and when you're working down to the detection threshold you know there's a big population of earthquakes that are marginally detectable so we think that's what's
Going on, but we're not sure so there's but there's a there's some interesting work, I think, to be done in that space, and then that's that's you know.
Of course, related to validating that these are legitimate events in the catalog
Okay. Great. Thanks.
Yeah.
Thank you with the question. Well, I see Alex has her hand up
Yeah, I just post a question in the chat. And and Mr.
Grendel has asked me to turn on my camera. So this is for Austin.
Josie and Roland altogether. Us and Josie presented some great Usgs resources that compiled all of these creep observations from from the field, and Roland was talking a lot about other methods to compile creep observations.
Is there like some database of sar observations?
I know there's the the vertex tool that makes it so much more accessible to get a interference.
But terms of on fault measurements through time. Is there some sort of I don't know if it's a collaborative database effort, or something to put it all together
So the isn't exactly what what do you have in mind?
Wednesday at no earth scope has a database of processed data products.
And so data products and that could include quick time series.
So so we don't have anything comprehensive.
The the data that using produce partly, you know, funded by the Usgs external program.
That is in a database. You know that via Sinodo that that's fully documented.
Probably the raw data at Windsor and the creep data publicly available.
But I I think it's a great idea to to to have that kind of system that's more comprehensive.
That's a really good idea
I know Justin Kai Johnson went through the effort of compiling a lot of creep observations for the Nshm Update that's been ongoing and they did an excellent job of compiling it.
But it would be in in the interest of keeping all of these models as living, breathing models.
It's it's an interesting idea to keep it rolling through time
That's a great idea.
So I noticed girl, in that you posted yourself a question in the chat.
Would would you like to address your question?
Well, well, you know the th. This is from the these wholesale creep rate changes, you know.
We only became clear to us 2 days before the deadline of submitting the video.
So so that made me feel like, oh, you know, if if I could have shown everything today that would be great.
But anyways, am I able to share my screen? I want to show just really quickly a figure, if that's possible.
So right. I showed you this comparison of creep rates from repeating earthquakes that Tuckogue produced on the Haywood fold northern southern, and then northern central color areas, and so on.
A/C acceleration, acceleration. So this plot here on the left, essentially takes all the data of between the arrows.
Here no! Then he would fault, so many would fold down here, and for the color various.
The same thing. And so here, now, you nicely see how the Southern color is fault.
The repeating earthquake data such as that deep acceleration all the way since the 1984 Morgan Hill earthquake on the northern part of the central centre is so the alum lock segment you can nicely see a decaying trend from 19
84, 1988, and the 2,007 along Mark Earthquake, and then on the Hayward fold.
You see that acceleration on the Southern Heywood fault in the Northern Haywood fault very nicely, and then bums, when we had magnitude 4 earthquakes in 2,007 2,011 2,018 that you see on the right so that gives sort
Of a much better picture of of these temporal variations over the course of almost 40 years, that that seem very significant.
And so then the question is right. Is this all related to the effect of these and other earthquakes?
And you can strain rates that are reflected by the qui ball or is there something else going on?
So I thought I I was excited by this, and thought I would share that with you.
I I would just add, in the context of bringing these sort of near near field surface observations and the geodesy and everything altogether, there is a pretty strong complementarity naturally among the different scales of measurement that may help understand right.
The processes. I think, Josie, alluded to this very well, that you know when you're measuring the group over 30 meters, it's different from over one or 200 meters, and it's different from insar pixels with a regional scale and there may be very different processes
going on that modulate the the rates at different scales, different places along the fault. So it's good to have all the sets
Good question for for Chosia 30. Thought I heard you mentioned that they number of creep meters is declining over the years.
Is that right?
It has over the last 30 years or so, just and I don't know the reasons for all of them.
Some of them you know. We're never telemetered data.
It was always manually made. And so those sites in particular are not sort of maintained in the record anymore.
The real, you know, expert in this is John Langbine, who has did decades of work on the Crete meters. So if he wants to chime in on that, he's welcome to
Yeah.
Yeah, I guess so I'll chime in. Basically, it's personnel back in the 19 eighties.
I guess there were. I don't know how many people were on the creek project, probably about 3 or 4, and then eventually I ended up being the master ceremonies arguably, probably in the late nineties and early 2 thousands.
There are essentially 2 of us at times. It was just one of us, so it was basically a program.
You know, there's just not enough people to go around like maintain these things.
And we're arguably we're at this point now.
Eric's, and normally the one who's supposed to maintain the Us.
Gs stuff, and he's torn amongst a whole bunch of different projects.
So that's where it is.
No see there was. Oh, go ahead, John!
That's yeah, are you weights? A strain meter programs do is having the same problem
There was some mention in the chat, I believe, about the idea of Measuring Creek below San Pablo Bay, and I don't know if you have any commentary on on what might be possible, or in the works there
Well, there are ideas in the works, and the kind of the first field visit is happening within the next couple of weeks.
So it's kind of early days. But you know, Roger and and John and others have designed creep meters to be, you know, able to be flooded in their vaults.
So the water isn't necessarily a prohibiting factor, and if Janet wants to chime in, if she's still around, that would be great but yeah, no, it's exciting.
It'll be really interesting to to have a sensor there to see you know what's going on in the bay.
The faults continue there
You can give us an update next year.
Yeah.
Yeah, it looks like we have, maybe just a few more minutes left.
If there are any remaining questions out there.
It's there, like a systematic plan to have a additional phones with alignment.
Array so creepiness. It seems like, you know, it's hard enough to maintain what there is already
Yeah. So I'll speak to the alignment there is.
There's been a in the last couple of decades, I think I mentioned this in the talk that there there's been a pretty massive expansion of the alignment arrays to sort of beyond Hayward and San Andreas.
Faults, and so there are a lot more ways spread in the region, but that has to some degree outpace to the capacity of the people involved in the project to keep up with it.
And so you see, when you look at the data that a lot of them have gone from sub annual sampling to well, just annual sampling.
And you you. You know you lose a lot of resolution, of discrete events that are going on, which you can detect in the earlier years of some of the records.
And oh, yeah. Jim retired for us to the program.
Has some we're trying to breathe, some some new life into the personnel that are available to go and do these I know we faced some of these challenges with just trying to get enough people to go out and and visit the sites.
In in recent years. And so, yeah, if people are interested in joining the joining the party on the project.
So. Yeah, right now, Miss Ben, have this hand up, and can probably speak to this as well
That's a final question by Ben.
Hi, everybody. Yeah. Just comment on the San Pablo Bay Underwater Creek meter.
We've been thinking about that for a few years. Todd.
Ericson's an ocean engineer by trade, and we've been designing some going through the process of designing some instruments along talking with Roger about it.
I think. Key fast in the chat about. If the location of the fault is well known enough in the bay, and Janet Watts work.
It was a science advances article on that with a shallow seismic showed really clearly where the fault was heading north from Point Panel up towards up towards the northern portion of Santa Monica Bay, where it seems to take a sort of a right step and becomes less clear so
In sort of the southernmost two-thirds of the bay.
It seems pretty clear where it is, and we'd like to install more than one creep meter there to see if we could.
You know image, shallow slip, propagating, though one of the advantages is that the water is very shallow, there at at low tide it gets to be, you know, a few meters.
That's also disadvantaged, though to just in terms of potential traffic and and things hitting your your devices.
So yeah, state.
Great. Well, I think we're at the end of our time.
Block here. So thanks everyone for really interesting talks and discussion.
I don't know if there are any instructions that Sarah or John want to give about the breakout session that's coming up
Well as always. Thank you to all the amazing speakers and moderators.
That was wonderful. That concludes the official, workshop agenda for today.
So you are all free to go, but if you would like to not go
you can stay with us, and we'll go to have a special break out discussion on a SCEC statewide
expansion plans, we will have a few short little talks, and then we'll talk about how we are
going to coordinate and move forward and make sure it's the bestest expansion ever.
So I say, take 3 minutes to go grab a cup of coffee, stretch your legs, do a little, dance in your own home, and let's start the quote... unquote breakout at 4:18 p.m., and you don't have to go anywhere.