Hello everyone, the time is 1:45p.m. on the West Coast and it is time to come back for a second round of Thunder Talks.
Second force, same as the first, and to bring us through the singalong, we have the fabulous Josie Nevitt. [WooHoo]
Alright, thanks everybody for joining us for the second session of Thunder Talks. We will have 11, 4-min talks given back-to-back
and live. This should leave about 10 to 15 minutes of discussion time at the end, so, please feel free to ask questions in the chat or you can save them
for the longer discussion period at the end. And so with that, we'll welcome our first speaker, Cliff Thurber.
Thanks. Good afternoon. I just want to spend a few minutes telling you about work
I'm doing with Taka'aki Taira, and Hao Guo, Avinash Nayak. Hao Guo is doing the heavy lifting on the work and my dog wants to join the meeting.
So we're basically updating a pre-existing Tomographic model using new data and a different method and seeing if we can fit waveforms better than the original
method.
It's not advancing.
Thanks. Okay, so the elements are as I say, we want to update a Tomographic 3D Velocity model as a sort of alternative to the geologically-based three model USGS has been working with.
And update it using new data and applying joint body wave surface wave inversion hopefully to improve resolution, especially of the Shear Wave model.
Starting model is my old 2009 Vp model and we're just using
an arbitrary Vp/Vs of 1.732 to create the starting S model. Both the P-wave and S-wave rival time data sets have been expanded
and we have a massive surface wave data set from ambient noise, courtesy of Avinash Nayak.
And so we're trying to assess the performance of the initial model, Thurber et al., 2009 model
versus the tomographically updated model and compare it to the original USGS 3D model. Just point out that, Evan Hirakawa has done work to update that model and will be incorporating some of his results as we go along.
And so this last part that's really just begun is the validation. So just a quick look at the model
covers the Bay Area plus some, so the greater Bay Area, the left is showing where these two cross-sections on the right are P-wave velocity
shown both in color and with some overlay contours. The dashed line is where it's well resolved above there
with some gaps. You can clearly see both the San Andreas and Hayward fault,
and the deep part of the Calaveras in the top slide. The bottom line you can see the dipping part of the San Andreas near Loma Prieta
and a beautifully aligned Calaveras fault.
This one is the S-wave Velocity model on along the same cross-sections, the same features. Don't really have time to go into any detail about the structure, but in particular the Great Valley of the GV up on the upper right is reasonably well resolved.
One of the ways we can assess quality that doesn't require the waveform validation, which is next, is just to look at how we're doing fitting the data.
The gray histogram, the gray bars and the histograms are the initial misfit.
And the blue bars are the final residuals and you can see the just regions are much more sharply peaked
afterwards and the misfit is basically dropped by a factor of 2 or more. In the case of the surface wave data, we had a IS initially, which has been corrected.
So that's very close to 0 mean with much reduced residuals, so from that measure, things are
going in the right direction. What we were really just getting going on is the simulation work that's just one example for one station for one event.
It's a magnitude 4.7 near Berkeley and a station near Fremont down at the bottom.
And I got 30 seconds to wrap up. We've got the initial model in green
and you can see that the red and the blue are both fitting the data better than the green model.
So we are improving the initial model significantly, but we still have work to do to try to fit the coda, which is much more energetic than either model.
But it's better in the USGS model. Thanks.
Oh, missed a slide that's okay.
You're muted, Artie.
Okay, let's try that again. This is already, I'm coming to you tonight from Zurich, Switzerland where it's almost tomorrow
and this talk will summarize some work I'm doing on waveform tomography for central and Northern California
to improve the model of wave speeds and I'm grateful for computing assets at Livermore and data from from UC Berkeley
and support from ETH. The goal of this work is obviously to create a 3D model of seismic waste feeds for numerical simulations of earthquake ground motions and adjoint tomography, images, subsurface structure using the data we record on seismographs rather than derived measurements and by design it improves the fidelity of simulations.
So we're applying to the region of central and Northern California and asking the question, you know, can we improve the structure starting with a simple model?
Can we fit periods of relevance for earthquake hazards and engineering. So the data come from moderate events, magnitude 4 to 6 from the, you know, that have, moment tensors from the Doug Dreger's coworkers and at UC Berkeley.
And we collect all the available broadband waveform shown on the map on the right. This first experiments we've done are using the GIL7 model from Doug that he uses for the moment tensors and most of the region.
And we're using although we're exploring some other starting models and we're using the Salvus software to simulate and invert the waveforms.
So, waveform inversion requires an iterative multiscale approach. We start with the longest periods and we
iterate until we converge and then we progressively add in shorter periods. In this case, we started with 15 seconcs minimum period.
Reduce that to 8 and 45 totally iterations and this animation shows the wave speed at the surface, the shear wave speed at the surface and how it evolves over the 45 iterations.
The minimum share wave is reduced from 1,500 meters per second to 800 meters per second and we see low wave speeds in the Great Valley sequence and the Delta east of the Calaveras, Hayward, and Rogers Creek faults and higher VS in the Franciscan rocks west of the San Andreas
and also in the Sierra Nevada. This plot shows waveform fits for an event and the map in the upper right here from eastern California propagating to the Bay Area for the initial model. The data is in black and the simulation
seismograms are in blue and the fits are progressively worse, especially at the largest distance.
But after we run the inversions we get this fit so we see we can improve we go from this
to this. We improve the fits. So, this is very much work in progress. Some of the next steps are to consider other starting models, including different representations of the Moho.
What I showed is for a flat 1D model, but we're looking at models that conform to the Moho, we need to then, you know, we'll come up with several models and we'll have to then
quantitatively evaluate the performance, you know, with an independent validation data set. And look at the resolution and such.
And I believe we'll have to update the source parameters as we start to go to shorter periods.
That means updating the moment tensors and the depths and moments of the waveform that's required to fit the waveforms.
And we'll consider adding more data to improve path coverage, especially if we zoom in on the sub-region around the Bay Area.
Thanks.
Hey, sorry, I didn't realize I was next. Can you hear me?
Yep, you're great.
Thank you. Okay, so I'm just gonna describe something that I've been working on for the last few years.
Which is a means of looking in detail at fault zones using earthquakes and their aftershocks
after a large event. So this method [I don't believe that advanced] is called the virtual seismometer method.
There was a paper that went into detail about the mathematics of it by Curtis approximately in 2009 [can somebody else advance I don't seem to have control over this thank you] so what I'm focusing on here is the Japanese earthquake that happened on January 1st a large magnitude 7.5 and immediately after that there were thousands of smaller events aftershocks that occurred after. The map
that I'm showing here shows roughly the 70 largest best categorized ones that were present as of maybe 3 or 4 days after the initial event,
and when you get this nice, collection of seismicity, you can do interferometry on them to get an estimate of the green function,
and then you can use that green function to measure the physical properties of the fault zone beneath it. [Go to the next slide.] Thank you.
So this is just a snapshot of an hour's worth of data after the initial earthquake.
So about 20 min after the initial earthquake there's just a series of aftershocks that come in after and this continued on for quite a while.
I came in and grabbed data about 3 or 4 days into this. So I got some of the early data.
That was well categorized and I looked at it using the Japanese AHIH data to do the actual waveform measurements.
[So if we go to the next slide,
thank you.] So what you're seeing here is a map view. At about 12 km depth, which is where the bulk of the seismicity is.
It's been smooth to 8 km resolution because that's roughly what the resolution is across this entire image.
The resolution varies dramatically depending on how much seismicity there is in the subsurface. So as you get closer and closer to the fault, you can get higher and higher resolution.
But this is a decent representation. You can see the major fault access. You can see how the seismicity,
aligns to it and the green circle on the right most end.
That is the initial, the origin point for the magnitude 7.5. [So if we go to the next slide.]
So here we're just zooming in a little bit, to focus on that large event in the region around it
you can see all the black dots filled circles are earthquakes that I used. Data from there's some open circles which are other aftershocks that I didn't use but they represent where the default zone is.
So here you can see it's fairly smooth at this scale and you don't see a lot of detail at the fault but like I say you can enhance the resolution [so if we go to the next slide.]
Thank you. That's 4 km resolution and now you can start to see details of the actual fault zone
and you can see there's some separation, there's some variability in the structure
and if we go to the next one. This is now 2 km resolution, which is about the highest that I do for this one.
So here we can now see some some intricate details, different fracture patterns that these earthquakes are slipping on slightly different fault zones or slightly different fault fractures.
And that presumably is affecting the rupture pattern and why larger earthquakes occur at some points and then smaller ones spread out.
But those are details to be looked into. So this is the type of imaging, the details that you can start to see right along the trace of the fault zone from a back view.
But if we go to the next slide. To me the more interesting thing is to look at a vertical profile through the trend of the major fault zone.
So here we're seeing the top 35 km beneath the Noto Peninsula along the fault trace,
and this is a bit wider than what we were looking at before. And this is just a representation of Vp/ over Vs
in the subsurface and then I picked this particular one to show because it conforms pretty nicely to, the trend of the seismicity.
Seismicity tends to fall in the Vp/Vs zones of Vp/Vs that are less than 1.7 and the higher the Vp/Vs that zones tend to be aseismic
and we're seeing that represented here for this particular fault zone and you can start to see structural
details as well, which are really interesting for this particular fault zone.
And I'll stop there.
And that's just a Qp/Qs layer. Thank you.
Hello everybody. This is a Guoliang Li from the University of Southern California and SCEC community. Today I'm going to share your building the Multi-scale Velocity model in the Ridgecrest region with full-wave inversion of regional 2D and dense 1D seismic datasets. Now this figure shows in the Ridgecrest region after the two big events that this 2D
Array and the 1D array we're deployed in this region. I, the many are deployed in this region.
Many are deployed in this region. I, the interstation distance for those two deal rates above to 5 to 8 km.
And, the interested in distance for those two deals, it's about the interstation distance for those two deal rates above to 5 to 8 km and those linear re-crossing in the request thought.
I mean, it's only about 100 meters. You know, basically we use those 2D.
To build a reasonable system model and also to further use those linear, to be able to detail the for.
Well.
We are here. I'm just assuming the reasonable bill, the reasonable cost models. There too much information here and I'm trying to summarize for you the most important feature is the loan.
We asked and hi VP as a normally along the garlic fault and also the, the risk restored.
And absolute depth. And besides, if you see clearly, if you use it carefully, you can also see the by material in the phenomenon along the riskressed region.
Just our model, our model, that's the those, those, those, those, beneath those, fordons can be able to you can extend to adapt our 6 kilometers.
And also a lot of other informations like the Basin, the in the B area, the velocity suits, low V, PVF and high VPV as anomalies and in the market area like the C.
The, it's so high VP, a high VPVF velocity and relatively low VPF anomalies.
And also our model also resolved the velocity contrast along the garg forwards. And here the velocity contrasts the defined as the will.
We have something of the faults divided. We have nothing over the thoughts. Here the map, in the lost contrast, built from from our model.
And we can see that this is the this is the But lost the contract forify along the along the strike and we can see that along the strike there are some priori already reors both along the strike and also another death rings.
Next, next I'm going to see you, show, show you the detail, the phone structures.
In this map, in in this map, so in the the, the, black, the black line here suits the surface nap rupture.
And here, Susan, the detail, the location of the B 4, V 1, 2 before linear read.
And here in this map, the dot, the white dotted line, so it's in the location, with the surface maps the rapture and we can see that beneath those beneath, beneath those, it's service mapped, Hi, be, no we asked And, hi, VPB as a nominate.
And also and also, and the size, the, the, and, or we, we resolve the very clear by material.
I'm a tutorial phenomenon here, a low loss structure in the south southwest part and the high velocity in the northeast part and also we also resolve with several localized the VPVs normally with the with the constitution of this and also here, Susan, the before already, you know, before I read the, the witchcraft bifurcated in 2 bronze.
And here the first bronze, the another bronze and also, and the, Bronx, we resolve with the low Mays and high VPS the ratio.
A normally and also localized the Hi, VP, the rest with the And, okay, that's our we just a result that Okay, that's all.
Thank you.
Hello everyone, I'm Weiqiang Zhu from Berkeley. So in 2018 I published I did my first public talk during my graduate school to present this
at the Northern California earthquake workshop. So the PhaseNet model is a deep Neural-Network-Based model used to pick P and S-waves.
Now this year, we try to propose an improved version about the PhaseNet model.
So on title, you can also see the improvements that we make we add more tasks to the model.
One is for not only picking sudden arrival times, but also including picking polarities and also estimating such parameters.
To give an example, so this is what the PhaseNet model is doing, given three components of waveforms, the model is used to predict the P-wave arrival and S-wave arrivals.
So this model was pretty good after [indiscernible] in Northern California earthquakes. It had been used to study many dense earthquake sequences and also volcanic earthquakes and induce seismicity.
In the PhaseNet past model, we added two tasks. One, is the polarity picking part? So for the first motion polarity, the model also predict if it's up and down.
And also we added a task to detect the earthquake event. So here the blue color is for the earthquake
event detecting time; the orange lines is used to predict the origin time.
So this has a larger origin time and this has smaller origin time. But adding these two tasks it can like, many used to serve two actual task.
So for the detection event and the prediction origin time part, we mainly try to solve the association task.
Also make it easier to count how many events. So the association task is like given a stance sequence our P and S picks, which can be generated by either [indiscernible] and [indiscernible] in the past,
we want to cluster them into separate earthquakes. So on contrary we use a different algorithm called GAMA to do this work.
But now, since adding the origin time prediction, also the P and S-wave are not well aligned, but if we predict the origin times they can be easily aligned.
So then based on this predicted origin time you can imagine we can easily do a clustering to class them into different earthquakes showed by different colors.
So by adding these task we solved the phase association task very easy to solve.
For the phase picking up parity picking part, we can definitely directly use it for focal mechanism inversion.
So because all these different models after training well it can be very sensitive to very weak signals,
so it can increase the number of priority picks and give us more focal mechanism solutions. And also for the fixed arrival time picking part we mainly use for detecting earthquake and get higher resolutions earthquake locations, but we can also use the origin fixed arrival times to do tomography studies.
So here we show some examples we tested all these first arrival pickings to test the rest of the models in the Bay Area.
So with all these models, we try to use physical parameters to solve the perception-based part as accurate as possible.
So the perception-based part is mainly into from the raw data measure all the parameters that we want to measure.
So in this part we have the [indiscernible] model. We want to do it in raw waveform when we want to measure the phase arrival times the phase polarity and also hopefully make the phase associations and also hopefully make the association task also easy and also hopefully make the association task also easy and for the DASA association task also easy.
And for the DAS data, we also have a model called the PhaseNet-DAS, which is used to pick a P and S as arrivals on DAS data.
And we are also exploring, like a multi-station-based method that tried to use not only single stations but multiple stations to do the phase picking and physics association tasks.
Then after we get all this accurate measurement, then we can get all the [indiscernible] constrain the accuracy of the parameters. I'll stop here.
Thank you.
Alright, good afternoon, everyone. So my name is Rob Skoumal. I'd like to talk just very briefly about a new algorithm and Python for computing focal mechanisms.
So, part of the motivation for this new algorithm comes from our improvements in earthquake detection, right?
So, with techniques like table matching or as we just heard about deep learning methods, we can commonly increase the number of detected earthquakes by about an order of magnitude when compared to traditional techniques.
But although we can detect these events, characterizing these events has proven to be a little bit more challenging.
So here in Northern California, we can commonly produce moment tensors for earthquakes of larger magnitude 4.0
For smaller earthquakes and in the kind of 2 to 3 range we can usually produce a block and strain focal mechanism,
but for earthquake that are smaller than that, we're usually out of luck when we're relying on traditional techniques.
So I've been fortunate over the past couple years to work with Dave Shelley and Jeanne Hardebeck and we've been trying to think of new techniques to improve our ability to resolve these focal mechanisms.
So this new algorithm has been designed with these techniques in mind. And then finally, whether or not you want to use these new techniques, the goal is to be able to produce focal mechanisms as easily and quickly as possible.
So just want to run through some of the new features of this code. So hopefully as the name of this algorithm applies it's you know a lot of the framework is based off of HASH.
So, with HASH, I think one of the coolest ideas is the idea to vary your input parameters to produce different suites of solutions representing a single event.
So with HASH one of things you can vary is the hypocentral depth of the earthquake.
Right, by varying that depth, you can get a variety of different takeoff angles. Now with HASH though the source receiver algorithm source is fixed in place.
So if you look at the takeoff angles you can get you'll get that little green line there on that mechanism.
Now with SKHASH, we've expanded this to the third dimension, right. So now we can vary that hypocentral location
according to your aerial [indiscernible] and as a result our piercing points are much more reflective of the true uncertainty.
Another feature that we added is ability to consider weighted priority measurements.
So traditionaly using a human analyst, those polarity measurements are pretty qualitative. So with HASH, you can kind of imagine that under the hood the clarities are treated as categorical variables, right.
Either the polarity is down or it's up. With SKHASH, we treat these polarities as floats, right.
So if it's a down polarity we're represented by a negative sign. If it's a positive or upwards, it's represented by a positive value.
And the absolute value that polarity measurement represents the weight, right. So this is really important, right.
If you have a like, functional polarity measurements, some that you're very confident about, some that you're not as confident about
you don't have to weight them the same because I could hurt your solution. At the same time, you don't want to exclude all the measurements
You're not completely confident about. Because there is value in those measurements. So especially when you're using these new techniques for a book when we're able to quantitatively determine the uncertainties concerning considering these weights is really important.
Another technique is the ability to consider, sort of missing data, right. So the idea is that
you have some kind of catalog of earthquakes hopefully you have lots of polarities and lots of down plolarities
but you're not gonna have qualities at every source receiver pair, most likely. So actually talked about this last year at this workshop and those idea was that we could use random force to make educated guesses for what those missing values will be.
Now with SKHASH, you can at this point either determine individual mechanisms for each of your earthquakes.
Or you can cluster them into families and they could produce composite mechanisms. So really the goal with SKHASH is you know, kind of no matter what your workflow is and what kind of data you're considering
the idea is that hopefully it's flexible enough to consider, those different work flows.
So in conclusion if you ever need to compute focal mechanisms in the future
I hope you'll consider getting this code a try. Everything is publicly available online. If you have any questions or suggestions feel free to email me at any time.
Alright, thanks.
Hi everyone. I'm Yifang Cheng from University of California, Berkeley.
Today I will share with you our recent work on the stress map of California. Here, is my view of the major tectonic units in California.
The relative motion between the Pacific Plate and North America Plate is mainly accommodated by the San Andreas fault and the partial-like accommodated by the Walker Lane Shear Zone and eastern California share zone.
And in Southern California the transverse ranges and the big band develop in association with the opening of Gulf California.
The right side shows a corresponding strain field. The geodetic strain field shows the temporal fluctuation of the crustal deformation.
But for the stratosphere it is the stress of accumulated across the long-term crustal deformation.
So it shows a provide a comprehensive perspective across the deformation. So how can we serve the stress map of California?
Here we use over 1.5 million earthquakes from both Northern and Southern California Assessment Network and we apply the REFOC hazard to calculate the focal mechanism.
This measure not only uses the polarity and S-wave the P-wave amplitude ratios that from the individual earthquakes, but also use the inter-events P with amplitude ratios and S/P with amplitude ratios to further constrain the focal mechanism.
In the end, we have obtained a high-quality focal mechanisms for over 50% of all catalog earthquakes.
And we use this focal mechanisms to perform the stress inversion and here is the result. For the Walker Lane Shear Zone and Northern San Andreas fault area is dominated by the transtensional stress and the Central San Andreas fault area is dominated by the transpressional stress.
While the Southern San Andreas Fault System is mainly dominant by the strike-slip stress regime.
And we also observe, very strong stress heterogeneity and the local stress rotation show clear association with the major fault
that might be related to the interval interactions, the petitioning and the major fault geometry. We further combine the stress tensor.
We obtain the waste, the major fault orientation to compute whether these major faults are optimally oriented to fail.
We found that most of the area the major faults are optimally oriented to fail, but some region have some major forces that are not optimally oriented.
Some region may be caused by the local stress rotation like the region Northwest to the Clear Lake volcanic area, Southern San Andreas fault area.
Some area has the recent major ruptures that has already released a large portion of the stress, like the Eastern California shear zone.
And some major faults like the Garlock fault and the White Wolf fault, they show really high angle to the relative plate motion.
So they are already not really optimally oriented to fail under the current plate motion direction. So that's all I want to share with you today and welcome
further discussion by contacting me. Thanks!
Hi everybody! This is, Felix Waldhauser, I'm at the Lamont-Doherty Earth Observatory at Columbia University.
I'm presenting a summary of our recent developments in Northern California and a brief overview of our plan, our current plans
and this is work with Theresa Sawl, a student who was at Lamont and is now a post-doc at the USGS
at Moffett Field, Ben Holtzman, and David Schaff. So the overall objective is our current work is to detect and then monitor repeating earthquake sequences in real-time across Northern California,
So that we can track changes in parameters like recurrence, times, and size,
with respect to some baseline characteristics, and then monitor unusual behavior of individual faults. So we can take advantage now of three recent developments which I'm briefly
presenting here. One is we have finished a major update of the real-time double difference relocation system DD-RT for Northern California.
So this comes with a new high-resolution base catalog, which goes from 1984 to 2021,
with almost a million earthquakes in the locations are for new events are real-time after that.
So the most significant difference to the previous catalogs is that the hypocenter depth on our reference to the geoid
as opposed to some baseline station elevation. Adapting to the changes that the USGS made a few years ago.
So both catalogs, base catalog, and real-time solutions are available on a new server now, there's no call DD
[indiscernible] The second development is on the left and yellow dots is a catalog of repeating earthquakes
that we published in JGR in 2021 and it includes over 7,000 sequences with almost 28,000 repeaters it goes from a 1984 to 2014 and it will be used as the base catalog.
So the third development is a new method to detect repeating earthquake using an unsupervised machine learning
to harness variations in spectral features so Theresa Sawi has applied that tool
to a [indiscernible] long segment of the San Andreas fault and we text about a double set number of repeaters then compared to the ones in the catalog [indiscernible] shown on the left.
Both finding new repeaters sequences and also filling gaps in existing sequences
And so the current work right now is the plan to move the base catalog and the new detection workflow into the real time system so that we can update the repeating earthquake sequences in real-time
and expand on the current catalog. Thank you
Hi there, my name is Scott Marshall. I'm at Appalachian State University and on behalf of my co-authors Andreas Push and John Shaw I just wanted to tell everybody about the Skck Community Fault model.
And the with special emphasis on the fact that the community fault model is now going statewide because SECK is now the statewide California earthquake center.
So let's see if I can change the slide here. There's this area now that's of great interest, which is Northern California.
It always was of great interest, but now it's of specific interest because we have the Ske region is now expanded to Northern California as well and so as the Crescent Earthquake Center along with this group.
So what we're trying to do is make sure that our fault model is as accurate as possible. So what I wanted to do is give you a little bit a quick overview of what the CFM is.
What it is, it's hierarchically organized set of false. That currently covers Southern California, but You'll see it's about to cover Northern California.
The preferred version has about 450 faults. There's somewhere around 40 alternative representations included there.
This is all accessible, through the website. This is just the traces of the model, but the model itself is fully three-dimensional with variations in dip and strike with depth on the surfaces.
So on this get home page, which is linked down in the bottom right hand corner, you get simple maps or you can have fancy maps but the model itself is fully three-dimensional so we encourage people to actually download it in its 3D format.
In case you're familiar with other, large scale. BALL models. There's the US GSQ faults on the left, which is basically a collection of highly detailed surface traces.
So this is just a two-dimensional model. The CFM sort of situated in the middle here resolution wise and then the user of 3 or the national seismic hazard model, faults are on the right and those are sort of 2 and a half D they have a trace and a dip but there's no variations in the down dip.
Whereas the community default model has all kinds of variations wherever data justifies it. So just to compare these 3 side by side, they're not the same.
They're all different. And they have their own pros and cons.
Let's see if this works. This should be a 3D movie showing you one such fault to give you an idea of the complexity and the community fault model compared to some of these other fault models.
This is the Palace Verdas Fault in Southern California from Franklin Wolf and others paper in 2,022.
The plot on the bottom here is showing the dip angle of all the triangles in the mesh and the rows diagram up here showing you the strikes of those things.
And the color scale is going to show you the dip angles here. So let's see what happens.
Hey, it works. Awesome. So as we're rotating around, and by the way, the model is distributed in Go Cab format, but you don't need Go CAD to use the community fault model.
Again, this is made using MATLAB. You could do the same thing with Python or generic mapping tools, whatever you want.
So you can see this is quite the complicated fault structure. And one of the advantages of the community default model is we have a full three-dimensional representation.
The amount of intersections here cannot simply be represented by a fault trace and a dip angle. So that's one of the strengths of this model.
The downside is it's complicated.
I wanted to let everybody know we have an association service that runs automatically on the Caltech server.
Every time there's an earthquake, magnitude 3.0 or greater, detected by the USGS network.
I think the Comcast catalog. Automatically our code runs on that server and associates it with whichever CFM fault is closest.
So we're working to expand that statewide as we move the model statewide. And in my remaining seconds, what I just wanted to put out a call for is that we are moving statewide.
So the northern portion of the model is in progress. It's close to done, but before we have an official release we wanted to form these work groups.
So if you're interested in joining one of these work groups to help us assess the Northern California portion and tell us what we're missing and what we've done right.
Please reach out to me, Marshall ST at upstate. Edu and we'll get you hooked up with that process.
So thanks very much.
Hi, I'm Scott Callahan from SCEC and I just wanted to take a couple of minutes today to give you an update on upcoming CyberShake studies that are planned for northern California. The idea is to essentially take the same methodology that we recently applied in Southern California for CyberShake Study
2212. So this was end to 2022 started 2023 and use that same approach here in northern California.
It's been a while since our last Northern California study, it was back in 2018.
This time we're focusing on kind of a smaller area, kind of the greater San Francisco Bay Area, 315 sites.
And then so for each of these sites, we're planning to simulate seismograms for about 250,000 events.
This will be a broadband PSHA study, just like the one that we ran in Southern California.
So we'll be doing deterministic calculations up to 1hz and then combining those with stochastic simulations using the modules in the SCEC broadband platform up to 25hz.
So what's new and different with this upcoming CyberShake study? So here are a few things.
One is that we are adding a vertical component. In the past, because CyberShake uses reciprocity, we've done calculations for both horizontal components but not the vertical,
and we think it's time to add that. Another change is that we're reducing our minimum Vs. In the past.
it's been 500m per second. We're bringing it down to 400m per second because we know there's a lot of low velocity structures in the Bay Area.
We're also adding some new data products. We're going to calculate frequency dependent durations,
FOIA spectra as well as response spectra. I'm going to stop calculating the geometric mean because it seems like nobody's really interested in it anymore.
And as I mentioned, since we have a vertical component, we'll have three components sized grams instead of just two.
Another big change is modification to the events that we're considering. So traditionally with CyberShake, the way we decide what events we want to include in our simulations is we take our site of interest, we go out 200km, and we include any fault surfaces that are within 200km of that site.
We then include any events that occur on that fault surface. So what that practically means here is for the sites that are south of the red line in the map over here on the right, 200 km South will get you to Parkfield.
And so that means that we needed to include any events which ruptured the Parkfield section of the San Andreas,
and so in some cases, right, that is a wall-to-wall San Andreas event that starts down at the Salton Sea and ruptures all the way up.
We took a look at the hazard that those events contribute and both in the last CyberShake study up here and in attenuation relationships, the contributions are pretty low.
So we're removing all those events this time around and the effect is that it reduces the number of events when you need to simulate by about half
and reduces our simulation volume size by over half, so computationally it makes it much easier.
So right now we're in the process of getting this study ready to go. So we're evaluating different regional 3D models that we want to use as well as background models since many of our simulation volumes extend out into Nevada.
So right now we're doing looking at kind of profiles and slices to make sure that they pass, the eye test.
Once that's done, we're going to do the same thing we did in Southern California, which is to do validation using some of the SCEC broadband platform, Central-Northern California event,
so we're looking at Loma Prieta, Alun Rock, and San Simeon. We're also verifying the vertical component
to make sure all the processing pipelines, we're working correctly with that. And our timeline is that we're planning to start the study in the next few months.
So, if any of you have any feedback you'd like to provide, things you want to make sure we include or don't include or data products you'd like to see.
Or any of that, now is a great time to let us know and so you ought to get in touch with me.
Thank you very much.
Hello everyone, I'm Mark Benthien. I lead SCEC's education and outreach programs
and I'm here to give an update on the HayWired Scenario Exercise Toolkit that many partners have been involved with that came out last year
with support from Anne Wein, USGS and strong partnership with Monica Stoffel with
the California Resiliency Alliance. We developed this toolkit to provide an ability for businesses and other organizations to use the USGS HayWired Scenario in discussion-based exercises of many different types.
Of course, if you're not familiar with it, the HayWired Scenario is a comprehensive set of resources and reports about what might happen on a M7.0 centered near Oakland on the Hayward fault.
And it's really useful for exercising because there's so much great content, all the different impacts associated with multiple hazards.
And the cascading impacts of such an event and we there are many insights that can be drawn from these and planning not just for that earthquake but for any that might happen,
and so it's been designed for any organization even without experience to plan lead and learn from such exercises.
Again, many partners involved with creating it and providing feedback and we thank them all and many, many more to come who are using this now we're doing trainings with about the toolkit,
and rolling it out. What's interesting is what we've identified basically, 45 ideas across 30 themes that are
presented in the scenario that are ideal for these discussion-based exercises. We have currently 17 tools per 17 of those ideas and soon three more.
The tools have a discussion guide with how to run an exercise, who to involve, what questions to ask.
Really makes it easy as well as an imagery slide set pulled from the scenario reports. The three new ones that we're going to be adding are about economic impacts
and how that may impact on customers, may affect businesses financial recovery, about food access and insecurity the ability for employees and customers to even access food after an earthquake
and also payroll in terms of ability to pay employees. So those are discussions that businesses and many sizes might want to
engage in. All the materials you can find on EarthquakeCountry.org/haywired and we are rolling out a program of trainings and outreach this spring starting with the city of Newark coming up, but we are looking for other locations and venues in hosts for these workshops so please email me. There's my address: Benthien@usc.edu
If you want to host a workshop or know someone who might be interested. Those are the type of businesses and business associations, Chambers of Commerce,
nonprofits, and many others that might work. And also just want to end with, we also lead the Earthquake Country Alliance statewide support from FEMA and CalOES,
part of NEHRP and our next ECA Bay Area Workshop is on February 21st first thanks to Richard Allen and Ronald Bergman and others at UC Berkeley Seismological Lab.
We're going to be having our meeting there and if you aren't a member, we're going to be having our meeting there.
And if you aren't a member of ECA, you can go to EarthquakeCountry.org/join
and get on our list and learn about that and other activities. Thank you.
Alright, well thank you Thunder Talkers for a great session. And, we will have about 12 min now for, discussion.
So, I'm gonna kinda highlight a couple questions from the chat that weren't answered.
But please, if you want to ask a question, feel free to turn on your camera and raise your hand and we'll get to you next.
So there is a question for Felix from Cliff Thurber who asked if there's any idea of the cause of the previously missed repeaters.
Yeah, I think I, I answered that maybe. So most of the, well, many of the repeaters over missed from our initial catalog were That shallow depth and they have larger relative location errors.
And so I that then. The the spatial criteria that we had. At the beginning was, I guess, too strict.
And so with the, the unsupervised, feature extraction method we Essentially have a spatial, space independent.
Tool to go through the whole catalog so we can compare those fingerprints much much more quickly than that.
Doing a cross cross correlation. But then there are other reasons. So This is a paper on T and in the seismic record that.
This some of the causes.
Great. Yeah, and the link to that paper, I believe, is in the chat.
Right. Correct.
I can't put it in the chat
Great. All right, Emery.
We cannot hear you.
Yes. Just join over here.
Yeah. Yes.
No.
Hi, Veh, to hear your infinite echo.
Alright, can you hear us in here? All right, how's that?
Good.
Go forward.
Sorry we're trying here. Great. Yeah, thanks so much for all these thunder talks.
We saw a lot of different talks about velocity. Models across different scales. And I was wondering if folks who mentioned velocity models.
Use of velocity models or development of Blossom Miles wanted to comment on how. We should be merging the different models together, thinking about different scales and resolutions, and how we can.
Incorporate the variety of models into our uncertainty and use them. For simulations.
I can provide my thoughts on that. You know, it's clear to me that We can't resolve even with the including the surface wave data, we can't resolve the near surface well enough.
And so like other people I wanna be. Merging models so you know the USGS model especially their most recent one does really well.
And so I can imagine carving out basin structures down to the sediment base and interface. Embedding them in our topographically based model and then updating it.
Keeping the basin part. Fixed or trying to let it vary although I don't think we'd resolve it so that would make the demographic model.
Consistent with having the new basin. Stuck in it and fit the data. With that perturbation to the model.
Add it in. There are more clever ways of doing it that could also be explored these sort of model merging that are being pursued by a variety of groups.
So we'll try both. Just plain simple, stick it in and doing a more sophisticated merging.
Basically take advantage of what people have done. That has really nailed down the near surface top kilometer or so.
Yeah, could I add to that? I would, I mean, I'm, I'm thinking, I'm a big advocate now of waveform tomography because it inverts the data we're trying to predict because it inverts the data we're trying to predict the actual ground shaking.
Waveforms and it's very sensitive to shear waves. So, but the downside is it's difficult to fit the high frequencies and resolve the near surface.
So an approach is to use. Large scale waveform tomography like Claire Duty has a model of California in Nevada that fits periods of 12 s and then We can use that as a starting model for a smaller region.
As I'm doing as I presented and we can. Reduce that to, you know, hopefully the low single-digit.
You know, peer seconds of period. And then that provides a model of the deep structure that will predict.
The arrivals of the surface waves that are so key at large scales. And, and I agree with Cliff that the challenge is going to be their surface and we might have to work on a layering approach, you know, to, in place.
The geotechnical and you know upper few kilometers of the crust. And then test it and invert on it to make sure we are get something that's consistent with the data.
So one aspect is gonna have to be having essentially a multi resolution grid. You can't. Invert all of Northern California say with 10 metre note spacing everywhere.
So there has to be some cleverness. Hold it into that.
Yeah, but if we're gonna, if like if Scott is gonna run Cybershake, you know, with a 300 kilometer or 500 kilometer long rupture it would be nice to have a single model.
That, you know, is consistent across the domain for which you're trying to do the ground motion.
And I think we've formed to, gives us an opportunity to do that.
Great, thank you. We've got a couple more hands raised. Charles Scottthorne was first.
Thank you. My question is from Mark Bentine. Mark, the toolkit looks very useful for mitigation.
Thank you for that. I noticed in your table that there was no it was blank across the board for firefighting earthquake.
So I just curious if you can comment on that. Thank you.
We actually do have a mitigation, suggestion idea on there. May have been covered in what you saw or may not seen and we have a discussion tool for it.
Thank you.
For fire after the earthquake. So if you go to earthquake country.org slash haywired, you can look for that tool.
Thanks. Sorry, I missed it. Thank you so much.
No worries.
Thanks, and Elizabeth Cochran has her hand up.
Yeah, thanks, Josie. I had a just a question for, about, I noticed there was a lot of small-scale spatial variability in the Stress orientations and the fault slips stability.
I was wondering how much of that do you think is sort of scatter in the in the results and how much of that is real and what might that be a attributed to.
Thanks for a great talk too.
Thank you for your question. Yes, we did check that, by, perturbing the, the focal mechanisms.
And based on their uncertainty and the perform the stressing version again and most of the grades have the uncertainty less than 10 degree.
And in the meantime, we also calculated for those focal mechanisms we use the first stress inversion.
Whether those focal mechanisms are all optimally oriented or not the mistress angle between the No, the best sleep direction and is a safe direction of the focal.
And we find the main sleep, deviation. Is about a 30 degree. Most of them are around like a 20 to 50, which is caused by the real process like the local spatial temporary stress heterogeneity in that grade.
So which means, yeah, it's like the real process is perturbation may affect it more and even though the focal mechanism has large uncertainty, but once we have a large group of them and do the stressing version.
Yeah, the yesterday is less than 10 degree, which is pretty, reliable. You can't answer your question.
Thanks. Yeah.
I have another question for you thing, which is you mentioned stress release. I've been reflected in sort of Eastern California share zone.
I was curious if it would be, you know. Plausible and maybe if you've tried this to look at different time intervals before and after major earthquakes to see that transition.
Yes, we really hope to, but actually for this major squeeze sequences, they have limited the foresharks, especially in the time in the several years before the main shock.
So it's really hard to compare, but indeed the people really find this stress stress notation compared with the earthquake people in after the main shocks.
But this kind of opinion will be more obvious in some like while monitor the small scale like increase the sesame state or like some some some whatever it can be more easier to see like before and after fluid injection and a lot of micro earthquake so it can be more obvious but for this kind of major earthquakes that are currently on a certain form to during the inter-sessment time period.
Yeah, some of them are pretty quiet.
That makes sense. Okay, I also I wanted to ask, Guo Yang Lee, a question, Ruth Harris.
Had asked whether the earthquakes, were relocated and I think you answered that the ones that you had shown were not.
I was also curious if you had compared the fault orientations mostly their dip that you've seen the tomography with other models or inversions of fault geometry such as from flesh at all, just to see if you're seeing the same sort of changes in depth along strike.
For the rich cross faults.
You mean that the the deep the deeper angle of the the board.
Yes. In the dip direction.
Yes, so I think of the for the risk question, the, the, the, the, the deep ankle almost, you know, a, so I We, you know, our mother, we have, we just get more, we still did such kind of work.
So yes, I haven't.
Okay. So we are just about at a time. I thought I would just.
Also ask Scott Marshall as we're kind of extending the community models statewide if there are you know lessons learned from doing this in Southern california that could maybe expediate the process.
Kind of help us produce these community models. A little more effectively up here.
Hmm, lessons learned. Interesting question. Well, I guess there's a couple lessons.
One of them is pretty obvious, which is don't duplicate efforts. So we already have a lot of data that exist for Northern California.
For example, the Bay Area has some three-dimensional fault surfaces. We have the Q false database.
We have the national seismic hazard model. So we're gonna definitely be using those resources as we build these.
Faults northward. The other thing I would say that's been a really useful but complicated lesson learned is the importance of community evaluation.
A lot of models that, produces are considered community models because lots of people work on the code or something like that.
But the community fault model, I mean the the metadata for it has hundreds and hundreds of references.
So there's not one single person that can really say they know every fault in California. So what we do is periodically just about a year and a half ago we had a community evaluation of the model and it's clear that there's not uniform agreement on all faults in Southern California, surprise.
But it's important to have these evaluations. So when there is disagreement, we can make alternative representations.
So I think that hopefully that kind of answers your question.
Yeah, it's great. Yeah, we're looking forward to Thanks more community models up here, so that's great.
Yeah, so if people are interested, we need more expertise in the North. So if people are interested in helping out, we're forming, evaluation groups to sort of look at small pieces of the Northern California model that we've put together.
So, email me, let me know. Marshall st@appstate.edu.
Great. Well, thanks again to our speakers. We are, looks like a couple of minutes over, so I'll turn it back to Sarah, I believe.
Who will release this for a break. Alright.
Thank you so much, Josie, and all of our wonderful speakers in Vendo Talks, 2 of 3.
So, right now we get to have another little break unless you all speak on our next session.
So everybody, please enjoy 15 min unless you all Hoshiva, Husco, Cochrane, Edwards, Boston, Mcbride, or Sandals, in which case stick around and let's get you set up.
See the rest of you