Okay. So, a few notes. The history of the lightning talks.
About five years ago, Jack Boatwright started this tradition.
And so the themes of the lightning talks were, the first year, lightning.
Followed by scientists, mad scientists, and superheroes.
Followed by Jack’s choice of nothing but cute baby goats.
And then, last year, in memory of Jack, we did baseball.
The theme of this year’s lightning talks was chosen by Sara McBride.
It will be marine mammals.
I think they ended up being all seals except for one set of dolphins.
And then I believe – was it Jessie has already requested pandas for next year?
But, if people have requests for the year thereafter, please send them to me.
I always need suggestions.
Okay. So, lightning talks. Every talk will be one slide,
three minutes long. This is your microphone.
Please be sure that it is up here where your mouth is
when you come to give your talk.
So the names of the presenters are in the program.
They will present in alphabetical order so people who are giving lightning talks
can see where they are in the lineup. And you’re going to pay attention
to that because it is very important that you know when you’re supposed
to be here because your talk might start without you.
And the way the talks go is something like this.
First, we have a title slide that gives the name of the person and who they are.
Then they give a talk.
And, at the bottom of their talk will be a slider that goes from left to right.
It will take three minutes to go from left to right.
In fact, I got this template from Ken Hudnut. Thank you very much.
And then, when the slider gets to the full end of the slide, in three minutes,
your slide goes away. This will be your hint to leave.
[laughter]
Then the next speaker gets announced, at which point,
that speaker should already be running up here.
Because, again, their talk may start without them.
Okay.
Are we ready?
Here we go.
[laughter]
- Okay. I’m going to talk about strainmeter or strain data.
And I wanted to point out, on the heels of that last excellent session, that,
for the Ridgecrest sequence, there were strainmeters well within
rupture length of the sequence, as you can see on the map on the top left.
And you can see the waveforms immediately adjacent to those that –
at least this one station, B921, these instruments are linearly
sensitive to all frequencies from static to the highest frequency
you can sample without aliasing.
And that’s reflected, I think, in the waveforms there.
And there’s a lot of complexity in there, which brings us to the lower part of
that slide, which is a snapshot of the wavefield that Evan Hirakawa did
at a couple different times. And he used these strainmeter data
as part of the constraint on what the kinematics of this rupture were.
And then, to bring it to the Bay Area, I just wanted to show –
just keep notice of the map on the top – well, up there.
All those little dots, those are strainmeters that are actively running
at the moment in the Bay Area. So you can see that we have really
good spatial coverage in terms of capturing potentially a big event
on the Hayward Fault or the Calaveras or whatever you want to look at.
And so I’m – can we stop early? - Sure.
- Okay. [laughter]
- [inaudible] - Yeah.
[laughter]
[applause]
- Hello. I’m Phil Beilin. I have a GIS background –
oh, I have a GIS background. And I started here at the USGS
in landslides, and I’ve used GIS throughout my career.
I’ve moved into public safety over my career.
I’ve worked on data sharing, especially for public safety.
And this slide in my presentation to you is meant to encourage the scientists
and engineers of you to reach out to – they’re not really consortiums, but there
are groups of decision-makers for public safety, such as the fire chiefs,
the police chiefs, the EOC managers, in which they rarely get a – I’ll suggest
to you – a presentation focused for them to understand, in regards to
earthquakes, aftershocks, liquefaction, landslides that might be created
by earthquakes, what does it mean to them?
If you had to take all the science and distill it down to, in terms of the actions
that they need to be thinking about when they’re deploying their people,
that they don’t have enough information about – in a language
that they have understood. And I’m saying this because,
in Bay Area, we have tried to put on workshops, participate in state- and
national-level earthquake exercises, bringing together the fire chiefs and
police chiefs, the EOC managers, and get them familiar with the
kinds of data that are going to be created during an earthquake
and that are going to be derived from earthquake damage.
And then, how do they use it for where they’re responding to people.
They really don’t understand well the idea that there’s going to be aftershocks
and what that might mean to them. They really would love to hear from
geologists and engineers that, hey, just pay attention to, if you’re going to
be staging people, resources, don’t do that under a building
that’s seismically unsafe. And, in fact, you know what buildings
in your city might represent those pre-1980 buildings that you should
probably know which ones of those are unreinforced masonry and which aren’t.
Which one has hazardous materials? I’ll suggest to you, in the East Bay,
along San Pablo Boulevard, we have a lot of 1900, 1907, 1913 brick,
unreinforced masonry garages. And what do garages have?
Of course, petrochemical kind of things.
And they’re listed in hazardous material sites.
But having somebody bring that information together and explain to that
is part of what I wanted to address. I also wanted to mention that the
National Guard has exercises regularly, and they would love to have
participation we have from both utilities as well as some of the scientists to
sit in their JOC – their Joint Operations Center – and see
how they use your information. What questions they ask – they’re trying
to make decisions about deploying resources and where to deploy them,
and they need to learn more from you folks about what it is that you’re
going to be creating for them and how they should use it for their
purposes and for decision-making and supporting the state of California.
Because that’s what the National Guard does is support the government.
Period. Thank you.
[applause]
- So this busy slide is really best done in animation, but I’ll walk you –
so this – so the entire text here on the left just makes the point
that observational seismology is essentially exclusively,
or almost exclusively, done in the – in the far-field.
Where all the detailed information of what is happening within
rupture zone is lost. It’s never been good quality.
So the situation in earthquake physics is similar to where chemists
were before scientists started looking inside the atom.
We are doing far-field seismology. Chemists were doing – in the 18th
and 19th centuries, they were doing far-field chemistry.
Essentially just based on the concept of atoms.
Of course, once people looked inside the atoms, there were – that led to
development of a whole new theory – quantum mechanics – that led to far
greater predictability and quantitative framework for chemistry.
Similarly, I think that when we replace the far-field concept of just fault or
surfaces with fault zones and volumes, and we actually get detailed information
of what is happening inside these volumes during dynamic rupture,
we will get more quantitative and far more predictive theory.
So, to do that, I think time has come to actually instrument the entire plate
boundary with dense fault zone arrays. This concept is illustrated for southern
California, but it applies globally. Bay Area, but actually all faults.
So there’s certain type of information that has never been recorded.
As I said, we never actually observe dynamic rupture
in the near field within the rupture itself.
Now, the faults in southern California and in the Bay Area are all overdue.
So this is a very good time – this is something that we should be sort of
doing – thinking into the future. This is a good time to start
instrumenting the entire hazardous plate boundary faults.
And the basic instrumentation – this is all dense near-field.
Each line here is an array of seismometers and high-rate GPS
stations and cameras also that are just sitting here with densities,
are bracketing main potential ruptures, 100 meter on each side, and 350 meters
on each side, and then 500 meters. So there aren’t too many instruments
here. This is a – sort of a concept. But this – and they’re spaced
about tens of kilometers apart.
If we capture – if we instrument now the plate boundary, we should
be able to capture, for the first time ever, dynamic rupture
within the rupture zone itself. In the meantime, we will get
tons of information about fault zones.
So the instrument are not just waiting there. Thank you.
[applause]
[silence]
- Okay. This is not really a science talk, but I just wanted to present a new
capability we have in the Earthquake Science Center.
We have three-component nodal seismographs.
And, those of you who are not familiar with them, they are basically small
self-contained units, three-component. They have a GPS sensor on board
and a battery. So you can put these things out for 35, 40 days,
let them record, and the frequency range is pretty good on it.
You can go down to about 4 to 5 seconds all the way up to 1,000 hertz.
So you can use them for a lot of different types of seismic
investigations. And I think, for northern California, this is
going to be really – a really nice tool that we can do for a lot of different
investigations that we couldn’t previously do.
We currently have 268 of them. We hope to have about 300
by the summer, and then we can put out the large arrays.
And these are some examples of what we’ve done so far with them.
We’ve done some site characterization studies in southern California.
In the upper right, it shows the Ridgecrest sequence, where,
in just a day, we were able to cover the whole sequence – a 50-by-60-kilometer
array put out over the major seismicity and recorded that for a couple months.
We can also – we also did it – you can’t see very well across the
step-over on the Garlock Fault, where we put out an even denser array.
So you can do many different types of studies – earthquake monitoring.
You can do the site characterization. Reflection, refraction, ambient noise.
Tomography – just a lot of different things you can do with these that
we couldn’t previously do because you can put out such a large number
of instruments. Where, typically, you might put out,
you know, one or two broadband instruments in a day, with these,
you can put out hundreds. So you can really blanket the area
and see what you’re looking for.
So these instruments will be available to any of the scientists
in earthquake hazards or anybody collaborating with them.
So if anybody wants to use them, they’ll be there. Thank you.
[applause]
[silence]
- Good afternoon. Good afternoon, everyone.
Thank you for being here.
So the main focus of my research is investigating the usefulness
of incorporating strain data in earthquake early warning systems
in general and ShakeAlert system in particular.
In this work, we used thousands of earthquake records from borehole
strainmeters deployed along the West Coast of the United States
to find a relationship – empirical relationship between raw,
uncalibrated peak dynamic strains and earthquake moment magnitudes,
which is shown here on your left.
As a proof of concept to test this relationship,
we used strain data from the 2019 Ridgecrest sequence.
The figure on your left shows how magnitudes estimated from two
very close stations to the Ridgecrest main shock – this is magnitude 7.1
shown here as blue and green lines, how they compare with the actual
performance of ShakeAlert system shown here as a black line.
And what would have been the system’s response with perfect data,
with no data transmission latency, internal or external.
And I would like to thank Jen Andrews for providing this data to us.
So, whether we consider data transmission latency or not,
looking at the magnitude estimates from the strainmeters and their
close proximity to the – to the fault, we can see the strain data has the
potential to complement seismometer data for earthquake early warning.
In this case, I assume it might even help – it might have helped ShakeAlert
system reach the four-station alerting threshold faster.
The other work that we did in this regard – we also utilized strain data
from several recent earthquakes – I’m only showing one here –
in California to find the associated peak ground velocities.
So this is for the main shock again. And the strain-derived PGVs are shown
as the red squares, and you can see they’re almost indistinguishable from the
seismic-derived ones, which are shown as the – as gray circles here for the same
distance from the rupture. And putting all this together, we can –
it can mean that data from borehole strainmeters that are already there –
they’re already deployed – can be complementary to seismometer
data in estimating important parameters like earthquake
ground motions and magnitudes. And also maybe help avoid triggering
of false events by spurious noise by – with strainmeters providing
verification that there is actual shaking happening
and not just noise bursts on seismometers.
But what I’m really excited about in the future is actually the great
potential for scalability and offshore monitoring when we consider the fiber
optic strain-sensing technology for this. And that’s actually the main focus
of my current research. And, with that, I thank you very much for your attention
and invite you to check out my poster.
[applause]
[silence]
- Okay. So I have an update on a lightning talk I gave last year.
And I also have a poster next-door. So this is about a year on, and the
story is that we went on a hunt for relationships between the Cascadia
subduction zone and the San Andreas. Ten years ago, we published a paper
that showed that a number of events in both fault systems had radiocarbon
overlap, which made an interesting story, but not quite a smoking gun.
But we went on a hunt for the smoking gun, and we realized at some point,
in the upper panel, that we could – we’re capable or recording historic
earthquakes on both fault systems, which helped a lot.
So, on the Cascadia side, we have events in 1980 and 1992 that were
precisely dated with bomb carbon, and that’s in the upper panel.
And so that helped us realize that we had more resolution than
we thought in the Cascadia record. And so – and it also told us that,
if we can record historical events of that size – and we also found the
1906 earthquake in a – in a nice age model of – in Cascadia
in Trinidad Canyon, 100 kilometers north of the San Andreas.
So that told us, if we can record San Andreas earthquakes on the
Cascadia side, we should also be seeing Cascadia earthquakes
on the San Andreas side at a similar range in Noyo Canyon, which is
100 kilometers to the south. And so what happens is, though,
when you look in the Noyo Canyon cores at the – at around 1700,
when we have an earthquake on both fault systems – north coast
San Andreas and Cascadia – we don’t see two separate events.
What we see is one event. And it’s that doublet in the middle there
where we see a stacking of two separate elements of what may
be actually a single event. And, for several hundred years later
and several hundred years earlier, there’s nothing else in there.
And we should see both events. So what I’m proposing is that we’re
seeing actually Cascadia and San Andreas stacked together
in single turbidites with very little time between them.
And there’s seven other examples in the late Holocene like that.
We look in the time range, where we see – expect to see
San Andreas and Cascadia represented, and we don’t see that.
We see stacking of these odd doublets at those times.
And, for comparison, in 1906, just a single San Andreas event is on
the left, and that’s what that looks like. So we have eight of those doublets like
in the middle, and the idea is that Cascadia may be partially synchronized
and triggering San Andreas earthquakes eight times in the last 3,000 years.
But essentially not at all in the prior 7,000 years.
So partial synchronization, maybe a la Chris Scholz’s 2010 paper
talking about fault synchronization. So there’s the short version.
Come by the poster, and I’ll – and try to talk me out of it. [laughter]
[applause]
[silence]
- Hi. I’m Ruth, and I’m going to talk about a project that we’ve been working
on for a little while. Co-authors in the room, please raise your hand.
Yay. All right. Thank you for coming.
All right. So this paper – the manuscript is going to get submitted
to internal review this week. And this is looking at dynamic rupture.
So physically soft consistent models of large earthquakes on some
Bay Area faults. These are faults that have the highest – one of the highest
probabilities in this region. And what we do is we have a dynamic
rupture model that goes all the way from just opposite Gilroy on the
Calaveras Fault all the way through the Hayward Fault.
And to the Rodgers Creek Fault. It ends just south of Santa Rosa.
And, in this complicated fault geometry – 3D fault geometry,
we also include a branch over to the northern Calaveras Fault.
All of these – excuse me. All of these faults do creep,
so that makes it kind of interesting. And all these faults also have geologic
or historical evidence of large earthquakes on them,
which makes them scary. So we want to put together a model.
On the right side is some of the information that we’re including in this,
and this includes what the rocks – the 3D rock structure looks like.
And this is from the 3D GUMP model. We have fault friction information from
our Earthquake Science Center Rock Mechanics Lab, based on
surface outcrop measurements. We have creep information from
Gareth Funning and Estelle Chaussard’s slip rate models for these faults.
And then we put it all together into our favorite dynamic rupture code, which,
in our case, is Michael Barall’s FaultMod code.
And then we get simulations. The bottom three – Hayward nucleation
on the left, Rodgers Creeks nucleation in the middle, and then northern
Calaveras nucleation on the right. And it’s showing the ground –
3D ground velocity, so the 3D ground shaking of the Earth’s surface for
each of these scenarios – for three scenarios that we looked at.
And then, just under that, the bottom final slip is showing
what the fault slip would look like on the fault surfaces for each of those
scenarios. So these are a subset of the many possibilities that we ran.
All of our stuff does show that you can get large earthquakes.
Sometimes the creep – the effect of the creep does stop these events.
Sometimes it does not.
So manuscript going into internal review this week.
And if you have any questions, please come ask me or any of
my enthusiastic co-authors. Thank you.
[applause]
[silence]
- So cute.
Ooh.
Okay. Hi, everyone. My name is Alex Hatem.
I’m a Mendenhall in Golden – the USGS office there.
And I’m here to beat our drum of trying to collect more geologic data
to update the National Seismic Hazard Model.
The national model will be updated in 2023.
And we’re currently collecting geologic input data
that will be used in the model.
Our group is trying to collate about – well, many years of geologic data that’s
not been included in the model. California is in the best shape in the
country, so congratulations to you. But you can come by my poster and
see, actually, how little data exists in California.
So I’m hoping that people here can help me add to our database.
This is just a basic timeline for our progress.
We’re in the process of reaching out to regional coordinators that know
of new studies that have been done. And we’re currently compiling what
we have for the rest of the country as well as from UCERF in the state
of California. And then, we’ll be accepting these new contributions
until the end of May. And then, over the summer,
we’ll put together this new database that encompasses all 50 states,
which will be the first time that that’s been done.
And we’re hoping to get this submitted as a Open-File report or something
with a DOI that’ll be included in the national model.
So the data that we’re looking for are geologic slip rates, paleoearthquake
recurrence, slip per event, and any updates to fault geometries.
So really basic geology that I know we all have and love.
So I want to know what you know, so please come talk to me at my poster,
and you can contact me. My email is up there, and we
can talk more another time. Thank you.
[applause]
[silence]
- So I hope I made the point well with my talk earlier about the importance of
going to the field quickly to capture data that are perishable in nature.
And the Ridgecrest sequence provided us with a great example of that.
I know that probably not all of you would agree, and Chris said earlier,
he used to go rushing out to the field, but now he stays at the computer.
I know that, with seismic networks and open availability of data, that that
is the case in many senses nowadays, but I also think that being ready to go
quickly, either by ground-based vehicle or by helicopter, to do
reconnaissance can really pay off. And perhaps Ridgecrest was just
a special case. So perhaps, in general, maybe we don’t really need that.
Maybe we’ll get lucky with the right satellite images,
or somebody else will do some airborne imagery that we can use.
But, no, I think what I feel strongly about, and what I would like to
encourage and try to help with passing along the information I have
on this is that it is important to be ready to go as quickly as possible.
And it won’t be every time that those of us that, in general, are ready,
are able because, you know, maybe we’re away on a trip, or maybe we’re
sick or have something else that’s happening in our lives.
And so we need some depth to this group of people
that are really ready to go.
Okay, so that’s my main point. And I wanted to offer to have meetings.
Let’s start out biweekly. Tuesdays at 10:00.
And we can try using Microsoft Teams, but we’ll probably need to use Skype
or maybe something else. But what I’d like to do is, with CGS and
USGS people and academic colleagues, just get together and talk about things
like – in the upper right there, it emphasizes teamwork.
And, you know, the steps that you need to be sure that you’re ready
to complete in a timely manner when the time comes.
And so this just shows all of the gear that I keep in my two bags – a backpack
and a flight bag. And that’s really all you need in order to
go to the field quickly. It’s all listed there.
That’s what I bring and what I would encourage others to bring.
So if you currently would like to participate in rapid-response field work,
this can be kind of a list of things that would be useful.
And basically shows, when you gear up, you bring your pack, and everything
else is on you or left behind. And so know what’s in your
go bags and be ready to go in a hurry. Because maybe next time, it’ll be you
that gets some really important data that otherwise we would never
know what happened. And so, with Ridgecrest,
between the 6.4 and the 7.1, this approach of going quickly
to the field really paid off. Let’s hope that, by sharing information
about how to do this, we can all be better prepared in the future. Thanks.
[applause]
[silence]
- Hello. So my slide shows an interesting correlation I found last fall.
But first some background. The SAFOD deep drilling project across
the central creeping section of the San Andreas Fault proved,
for the first time, that serpentinite and its magnesium-rich alteration
products will promote fault creep. So I – since SAFOD, which I worked
on a lot, I used what I learned there, started looking at creeping fault
sections and segments in northern California to see if they can
be explained in the same way. So the first stop was the
Bartlett Springs Fault – the creeping segment around Lake Pillsbury –
and that, indeed, seems to be caused by serpentinite there as well.
So what I – this past year, I’ve started looking at the Rodgers Creek Fault,
and the map in the center here is taken from a recent paper by
Shakibay Senobari and Funning. They developed a new technique,
which they used to kind of improve the identification of repeating earthquakes,
which are considered to be indicators of fault creep at depth in the fault zone.
And so the northern half of the Rodgers Creek Fault, shown here, is known from
a variety of data sources to be creeping. And they, indeed, found a large number
of repeating earthquakes with various degrees of confidence
in the northern half, extending down to a – this little outlier
a few kilometers southeast of Santa Rosa there.
So there are – geologic maps of the area show three places where serpentinite
outcrops within the Rodgers Creek Fault – and there are photographs
of them on the left side and the upper right over here.
And, when I went to put my AGU talk together last fall,
I wanted to use this map and plot their locations on them.
And it turned out that the three serpentinite outcrops all ended up lying
directly above some of the repeating earthquakes that they found.
More or less – they’re shown in the green circles here, more or less
bracketing the two end members – extremes of the creeping section here.
But there’s also one other point as well.
A few years ago, Belle Philibosian told me about a sliver of serpentinite
in the northern extension of the West Napa Fault.
And it’s a few kilometers to the northwest of the area that ruptured
in the 2014 earthquake. So it’s well beyond that.
I went and sampled it. That’s the photograph in the lower right.
Not much was known about it, but – so I sampled it but didn’t
do anything about it, but I went – looked at it, and also plots
directly above some of the repeating earthquakes.
So the question is, is this possible correlation a coincidence, or is there
some sort of genetic relationship between the serpentinite at the surface
and the repeating earthquakes at depth?
And that’s what I’m going to pursue next. Thank you.
[applause]
[silence]
- Hi. I’m Carol Ostergren. I’m with the National Geospatial Program.
And I wanted to give everyone an awareness of the Lidar
that the NGP has ready for your GIS.
The darker colors shown in the map – the dark blue and the dark green
are data that’s already available through national map servers.
The shades of blue in this map show Quality Level 1 data.
That’s eight points per square meter.
The green shades show Quality Level 2 data.
That’s two points per square meter.
Much of this data that you see across this swath of northern California
and the area down around Ventura and Santa Barbara was funded
through 2018 supplemental funds that we received.
The areas up around Plumas and the Camp Fire area are
now posted and available for download.
So one of the things that we’re watching this upcoming year is a new
line item in the governor’s proposed budget for $80 million.
And this would complete Quality Level 1 for the entire state
in partnership with the 3DEP program and with local partners.
So there’s a couple of URLs for looking for data, but I would really encourage
you to get a hold of me, and I can help you get the data that you need.
Thanks.
[applause]
[silence]
- Hi, there.
My name is Jessie Pearl. I’m a new Mendenhall postdoctoral
research fellow up in the Seattle field office.
So I am happy that I have a chance today to talk about one of the tools that
I’m using up in the Washington area but also in coastal Oregon and northern
California to precisely date earthquake events, so – using dendrochronology.
So, back in the 1990s, dendrochronology was famously
used to the date the 1700 megathrust earthquake event with the idea that
coastal forests are subsiding after the – during the earthquake.
The saltwater, or even just the trauma itself, will kill those forests.
And we can go back decades or centuries later and look at actually
the physical characteristics of those rings in the tree.
The narrow and wide ring patterns act as a barcode.
If we can match that barcode with a long tree ring record from that area
that’s pretty much dictated by climate regionally, we can match exactly that
barcode and get a precise year, if not a season, on when that earthquake event
happened, when that ghost forest died. So this is a well-known technique,
and it’s a really wonderful and accurate technique, but it’s limited
by how long these reference chronologies are.
So how long we can make and push back these living tree chronologies,
or even extending it back farther using timbers or log cabins.
So I’m proposing that we turn to the chemical characteristics of the wood.
So we’re looking not only at flooding signals in the wood, but we’re also
looking at the radiocarbon within each tree ring.
So, similar to the bomb spike we think of in the 1950s, ’60s, and ’70s,
we see natural radiocarbon spikes. And, once we detect those within the
rings, we can precisely date some of these floating ghost forests, which will
allow us to more accurately date the penultimate earthquake and even other
earthquakes over the past 2,000 years, wherever we have those forests.
So another component to this research that I’m actually really excited about
here in northern California specifically is we can take trees that die in landslide
events – so there’s a piece of wood up there right above the graph of
the radiocarbon spike in 774 that’s from a landslide event at Red Lassic
Peak in northern California. So, using this wood, I can not only
precisely date when this earthquake occurred – or, when – sorry, when this
landslide occurred, but we can also think about what the
climate effect might be on the landslide versus seismic.
So there’s plenty of hydroclimate- sensitive tree rings chronologies
in northern California. We can think about the years preceding
this landslide were very wet or very dry and start to detangle the seismic
versus climate effects and what might be driving some of these
large earthquake events in the early part of the common era.
So I’m excited to talk with you all about some possible avenues
to explore in northern California using these techniques,
and I’ll be milling about. Thanks very much.
[applause]
[silence]
[laughter]
- Okay. I’ll just hold it.
So the – returning to Ridgecrest, the 2019 Ridgecrest earthquakes
have occasionally been characterized as surprising because only about
a third of the surface ruptures occurred on faults that were included in the
Quaternary Fault and Fold Database, shown in the figure on the left.
The black lines are the 2019 rupture, and the bold orange lines are the
parts of those that were in Q-Faults.
Along with my co-authors, Jessica Jobe, Colin Chupik,
and a number of others, we took on the project of attempting
to map out near-tectonic features that existed in the landscape
prior to the 2019 events. And, for that project, we used the
2-meter VM that was released on OpenTopography as well as
satellite and aerial imagery and recent – a compilation
of recent field observations that were made sort of as a byproduct
of mapping the 2019 ruptures themselves.
So our compiled mapping, based on that, what we find is that
pre-existing neotectonic features did exist in the landscape distributed
throughout basically the entire length of the 2019 ruptures.
And that – and, including, in fact, most of the secondary traces as well as
the primary traces. So what’s shown in the figure on the right – again,
the black lines are the 2019 ruptures, but they’re colored in red where there
were identifiable features pre-existing before 2019.
And the yellow lines there are actually additional faults that did not have
rupture reported in 2019 that’s sort of illustrating this broad
orthogonal fault network in that area.
So, based on our mapping, we estimate that between 50 and 70%
of the 2019 ruptures could have been identified as active faults if this
kind of a detailed study had been done prior to the events.
And this figure pretty much represents the punchline of our study.
But, for many more intriguing details, please come to the poster,
and we can talk about it. Thank you.
[applause]
[silence]
[laughter]
[silence]
- Hi, everybody. I’m Arben Pitarka.
I work at Lawrence Livermore National Lab and Rob Graves
and Arthur Rodgers are co-authors of this study.
This is just a snapshot of a long collaboration.
And we have a poster, actually, that will show probably more details,
but what we wanted to do is, we wanted to see how well the rupture generator
that we have recently refined works in predicting strong ground motion,
starting with the earthquakes that are already happening, and they
have really good recording data. In particular, our model includes
stochastic and deterministic features that are constrained based on
rupture dynamics but also recorded earthquakes.
And one of the features that we’re more interested is how – the unknown
for future earthquakes, which is the large slip patches will affect
the ground motion. So we did some sensitivity analysis,
and we noticed that, no matter how you put those patches into your slip
in general, you may fit the ground motion, on average, well.
But, when you look at the particular locations and time histories like they
are in the left – on the right panel, you can see that the performance of
each model kind of deteriorate, or it’s better than the others, and some
of them fit the data very well, some not. So what we notice is that, depending on
the depth of these patches, you may introduce or not really high frequency
into your rupture or – which is reflected in higher ground motion.
And, as Julian mentioned in this case, we need to take care of the fact that the
stress drop in our rupture model should be depth-dependent, and also the
rupture kinematics should reflect that. So that means that, for slip patches
that reach the free surface, which is one of those cases here, you may expect
really good behavior, and for the ones that have really deep patches,
then you kind of overestimate the effect of the high frequency generated
by the rupture. And then you have overestimate of ground motion.
So, for more details, you can come and discuss with us at the poster.
And I’ll be – I’ll be glad to show you more details
about this exciting research. Thank you.
[applause]
[laughter]
- All right. I’m going to talk about some work my colleagues and I are
doing at Livermore to model large Hayward Fault simulations with
high-performance computing. We’ve been working on simulations
of a magnitude 7 earthquake on the Hayward Fault for the last few years,
shown in the map view there in a domain centered on the Hayward Fault.
And you can see the PGV map in the background.
We used the code SW4, which is an anelastic wave propagation code.
It’s recently been enhanced to run efficiently on GPU systems.
And what used to be a challenging calculation to 5 hertz is now a routine
calculation. We’ve actually run 50 of those now in another study.
We got a opportunity to run on the Sierra supercomputer before it went
behind the fence of Livermore. And we ran a few calculations resolved
to 10 hertz, which I believe are some of the largest calculations that have ever
been done for an earthquake in California. And we get good agreement with
ground motion intensity measures – or ground motion models shown by
the epsilon values in the lower left there from 100 hertz or
0.01 seconds up to 10-second periods in PGA and PGV.
That’s for a network of over 2,000 sites at the surface.
But there are two important and related shortcomings to this linear elastic
modeling, or linear anelastic modeling, that have to do with representing
the geotechnical layer. Firstly, we assume a minimum shear
wave speed of 500 meters per second. And we also know that, when you hit
rocks with large accelerations, they do not behave as linear elastic solids.
We use – we’ve developed a method to correct for this based on a new ground
motion model from Jeff Bayless and Norm Abrahamson based on Fourier
amplitude spectrum that allows us to form a transfer function that essentially
we can have the numerator be the Fourier amplitude spectrum for
a nonlinear response for the in situ geotechnical properties.
The denominator is what we calculate, and that’s the before the Fourier
spectrum of 500 meter per second for linear only.
The black seismograms there are what comes out of SW4.
The green are after applying the transfer function.
The transfer function is shown in the middle plot there.
The dashed line is just the purely elastic transfer function.
That’s going from low – from 500 meter per second to the in situ for – this is
a case in Berkeley at 100 meters per second.
The black line includes nonlinear response,
which beats down those high frequencies.
And the response spectra are shown in the lower right there.
And geotechnical engineers will recognize that that shape is diagnostic
of a weak soil site, in this case Berkeley, at 100 meters per second.
So, when we perform this, we think we’re getting more accurate motions
and overcoming the shortcomings in linear elastic modeling as a intermediate
step to doing fully nonlinear modeling. And I’ll stop there. Thanks.
[applause]
[silence]
- Hi, everyone. I’m Valerie Sahakian,
and I’m presenting on some work on an earthquake hazards program
project that I’ve been doing with students Elias King and
Alexis Klimasewski. The goal is to estimate site parameters
in the Bay Area to use for ground motion estimation, and in particular,
we’re starting with kappa. Kappa is the slope of the
high-frequency fall-off on a site spectra that you can see in the
cartoon over there. So a high kappa means higher attenuation.
In order to compute this, we compile a database of records like the
seismogram on the top left. We get the spectra of each record,
and this represents contributions from the event and the site.
And then we put this into a large spectral decomposition to get event
and site spectra for every earthquake and station in the data set.
We then constrain that instead of using a reference site with a deviation
from a Brune spectrum. And then, using the site spectra
that comes out of that, we fit the equation on the bottom
to it in order to get kappa out of it.
We’re doing this in the Bay Area starting with just broadband stations.
So the map on the bottom left shows our data set.
The circles are earthquakes. The triangles, which are
a little hard to see, are the stations. We have about 30 stations,
3,800 earthquakes. And it’s about 20,000 recordings.
We do this inversion with a number of different parameters.
So, starting with signal-to-noise ratio of 3 to 5, different waveform lengths
from the S wave arrival, as well as number of frequency bins
that we run the inversion on.
In the right-hand side, you can see some of the results and also
some of the future work. So those two middle plots are
site spectra for all of these different inversion runs for
station CVS in the North Bay and EMR in the delta.
You can see the variation in these and also the variability and uncertainty
that we get out of the model that also goes into uncertainty on
the kappa values themselves. Another thing to point out is that there
are regional variations in kappa itself. The top right map shows the stations
colored by their kappa value. So you can see that stations in the delta
tend to have higher kappa values on average than other stations elsewhere.
One of the problems is, if you can see the units on the color bar there,
the kappa values are a little higher than we would expect.
And we think that this is because there’s seismic attenuation
that is not removed by this inversion that we have.
So one of the next steps is to remove an average Q from the records
before we invert them. Or to find a path-specific Q from
a Q model like Eberhart-Phillips in the region and remove that first.
And then finally, what we would like to have ideally is an interpolated map of
kappa values and their uncertainties like exists for New Zealand.
So, in order to do that, we need more data points.
So one of the next steps is to redo this calculation with a new database that
also includes records recorded on strong motion stations and then create those
values to obtain both a kappa map and a kappa uncertainty map.
I do not have a poster with me, but I have it on my computer, so if
you’re interested in learning more, please come find me. Thanks.
[applause]
[laughter]
[silence]
- Hi, everyone. I think I’m the last talk. So, last, but hopefully not least.
I’m Maureen Walton. I’m a postdoc at the USGS in Santa Cruz.
I’m here representing our team today to essentially advertise that we have now
put together a compilation of offshore faults offshore of California.
So basically, Quaternary faults offshore – a database
offshore of California.
And the reason that we started doing this is because there’s lots of updates
that are needed to the current Quaternary Fault and Fold
Database offshore. And, in the last decade-plus, we’ve gotten a lot of new
high-resolution data sets offshore that we’ve been able to do new mapping
from, both in our group at the USGS but also with external collaborators as well.
So new data sets include high-resolution bathymetry
and high-resolution multi-channel seismic.
And we’ve been working on these data, mapping new faults
for, yeah, 10-plus years. And lots of new publications
have come out, and so we decided it was high time to compile
all of the new mapping into an offshore database.
And, as you all know, offshore mapping of faults is also important
for accurate hazard assessments. And so a lot of the – again, all of these
faults come from published sources. So a lot of the faults in northern and
central California are from Sam Johnson’s work through the California
Seafloor Mapping Program. And then a lot of the faults that we have
in our database offshore of southern California have been published in
various peer review publications through the USGS West Coast Marine
Geohazards Project led by Danny Brothers out of my office.
And we have – just like the Quaternary Fault and Fold Database, we have
comprehensive attribute information that we’ve kind of optimized for the
offshore environment, including fault age, slip rate, and slip sense
where we know it; mapping accuracy;
and also the reference from where that mapping came from.
And we have designed – we’ve been working closely with CGS to get this
database out there. And it’s been designed for easy ingestion by partners
and coordinated products at CGS, the USGS Quaternary Fault and Fold
Database, SCEC, National Seismic Hazard Maps, among others.
And our database will be versionable and capable of accepting published
input from the community, hopefully, down the road.
And we are starting with California, but we’re hoping to also add Cascadia –
offshore Cascadia faults next and then also Alaska.
And we do have a poster, so please come and see us at our poster.
And, just so everyone knows where we’re at, this database has been –
it’s going to be a USGS data release.
It’s in internal review now, so it should be out within the month, I would say.
So, yeah, come see us at our poster for more.
[applause]
[laughter]
[silence]
[laughter]
[silence]
- Okay. Thank you, everyone. That was an amazing lightning talk
session. So all of our speakers – let’s give them all a big hand again.
[applause]
So now we have a break to go look at posters.
A couple of them have been advertised in the lightning talks you just heard, but
there are many, many more out there. So definitely go check them all out.
And, if you haven’t checked out the fire truck yet, and it hasn’t
gone home yet, you should check that out as well.
And then we’re going to meet back here at 3:15 – is that right –
for the last session of the day, which is earthquake early warning.
Okay, see you in a little while.
[laughter]
[inaudible background conversations]