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Let's see.
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Jessica.
Mary.
But next week? Not.
Sorry, not next week in February, starting the first
week, so please rest assured, the seminar will be in great
hands starting then.
With that, I'll hand it over to Anne Wein, who will introduce
our speaker today.
Good morning.
I crossed paths with Emily Mongold on the topic of sea
level rise and liquefaction.
That was our study with Alex Grant to analyze the
liquefaction under changing sea level rise and it was advised by
Keith Knudsen as well.
But back to Emily.
She's a structural engineer, PhD student at Stanford University.
Sorry.
Yes, she is.
She completed her Bachelors in engineering at the University of
Delaware, where she became involved in natural hazards
research.
And I just heard hurricanes in particular. Working towards a
multi-hazard framework considering an array of coastal
hazards including earthquakes and modeled anticipated changes
due to sea level rise.
So we'll hear about this relevant work today and also
congratulate you, Emily, on your first publication and
earthquakes sketcher bit forged ahead on modeling posters.
Aster recovery, by accounting for rental and multifamily
housing.
So I present Emily, who is passionate about resilience,
sustainability, and solutions.
Awesome.
Thank you for the introduction and thank you all for having me
here today. My name is Emily Mongold.
And yeah, I'm a PhD candidate at Stanford University. Today,
I'm gonna be presenting mostly on this probabilistic regional
liquefaction analysis, but to give a little bit additional
background.
There we go.
Umm.
So yeah, I have a background in structural engineering, so when
I'm looking at natural hazards, I'm thinking from a risk-based
perspective, which means I'm thinking mostly about how
hazards impact the built environment.
I'm also working on having a focus on human impacts, so like
Anne mentioned, I did some work as well with focus on housing
and recovery of households and how households can actually
rebuild and how people are impacted by the hazards that
they experience.
So with the bigger picture, I'm working to include multiple
different hazards because people are impacted by multiple
hazards.
Umm.
And including sea level rise as a lot of hazards, impacts are
changing and I'm doing this with a focus on the impacts to
communities and working to calculate actionable outcomes to
understand how people are affected and what can be done to
help that.
So, when I think about this in the framework of liquefaction,
the difference between liquefaction hazard and risk is
that there can be a liquefaction hazard a lot of places and there
are different ways that liquefaction manifests.
Sand boils or uneven settlement and if this happens in a place
where there aren't people or or in a field somewhere, there's
still liquefaction hazard, but there's not really a
liquefaction risk because people aren't affected, infrastructure,
isn't affected, whereas, you can see on the right hand picture
that when liquefaction happens in a place like an urban
environment.
This is a picture from the 1906 San Francisco earthquake and we
can see that this has caused significant damage to a building
where it's settled backwards and it's fallen kind of on its
neighboring building.
And so this is where there becomes risk from the
liquefaction.
Liquefaction can cause damage to structures that would otherwise
withstand earthquake shaking.
We can see in this picture that the building itself is still
together.
It hasn't collapsed.
It hasn't exhibited any other damage other than the fact that
it's now settled back and and fallen on another building.
And in the past, largely because ground failure is difficult to
predict, but it hasn't been considered in a typical seismic
hazard and risk analysis.
And so I've been working to to combine it in and using some
empirical fragility curves.
Some researchers at the University of Washington
developed these based on empirical data, and so those are
useful to now be able to combine the way that we consider the
hazard with the losses that we expect and damages to buildings.
So at a phenomenological scale, liquefaction is really ground
failure that occurs during earthquake shaking and it
requires that the soil be saturated, sandy and of course
experience the shaking.
And so at that small scale, the particles have to be uplifted in
the poor water pressure, and that can cause the settlement
and the flow of the soil during during the event of
liquefaction. Liquefaction itself could happen under the
soil and not really have an impact that we would know about.
So because I'm focused on risk to to structures and risk to
people.
I'm focused on when liquefaction has a surface manifestation.
So that's like the sand boils and the settlement that I
mentioned before.
And there are many different ways that that past researchers
have considered measuring and predicting liquefaction, but the
liquefaction potential index or LPI is the metric that I've been
using, and this corresponds typically to either the
likelihood or severity of liquefaction.
It's been interpreted both ways by different researchers, so
typically an LPI above 5 would be where you might expect to
moderate liquefaction and LPI
above 15 would be where you might experience severe
liquefaction. So, I used those thresholds to kind of interpret
the data.
This equation for LPI on the right hand side, it's calculated
along the depth of soil.
So going from where there's groundwater, where the soil is
saturated, down 20 meters to a depth where you might still
expect it to impact surface manifestation, and the factor of
safety of liquefaction in there is where the information about
the soil, the ground shaking and the different soil properties
are all in that factor of safety FS.
LPI has been empirically fit by a lot of
geo-techs.
based on different soil data, but I'm using some that uses CPT
or cone penetration test data.
So there are a lot of different ways to calculate, but I've been
using two equations that use CPT data and that just stands in for
the the properties of the soil.
My case study is on Alameda, the island of Alameda, CA, which is
in the San Francisco Bay, directly across from San
Francisco.
And this is actually a map from the USGS that is based on the
soil deposits in the area.
We know that there's a lot of artificial fill in the Bay Area,
which is very susceptible to liquefaction and even the the
natural deposits that were there are in this medium liquefaction
susceptibility.
But this isn't based on the earthquake events.
This is based solely on these soil deposit types, but we also
know that the Bay Area and Alameda is subject to earthquake
hazard as well.
Of course, we have the Hayward fault to the east and the San
Andreas fault a bit farther away to the west. On Alameda there
are over 12,000 residential buildings, which are the
buildings that I analyze in my simulations.
So the questions that I aimed to answer with this work is how
much risk does Alameda face from liquefaction and how much loss
might we see in a future earthquake?
So first I'm going to talk through the methodology and the
end goal of this methodology is to get to an expected loss ratio
or the ratio of the damage to the total building value for all
of the residential buildings on Alameda.
And the first input that I start with is the ground motions using
open SSH, a database of known faults and possible ruptures on
those faults.
I produce ground shaking simulations using multiple
ground motion models to get simulated intensity maps where
we have PGA across the entire study area.
The second input is the groundwater level because the
soil has to be saturated in order for us to see
liquefaction.
So for this I have measurements of groundwater depth and I
interpolate the groundwater surface from those measurements
across the island to have a depth to groundwater.
And then the third input is the soil properties.
So at those same locations where there are groundwater
measurements, those are CPT boreholes where cone penetration
test data was obtained.
So those are along the depth of soil and I perform sequential
Gaussian simulations to simulate a 3D set of soil data.
In order to use that across that same area of the island.
These three inputs are then combined with that liquefaction
LPI equation that I mentioned before.
So the ground shaking, the ground water, and the soil maps
each play in to give a map of the LPI across the island.
And this LPI would be for one scenario.
So then this is done for multiple scenarios based on how
many faults and ruptures there are in the database.
Then for the risk analysis, I find the LPI at each building
location interpolating from from the total area and utilize those
damage and loss functions that I mentioned in order to get loss
ratio at each building for each simulation, which gives then an
expected loss ratio for the buildings.
So first I'm going to talk about the results from the hazard part
of the analysis.
The first is the annual rate of liquefaction across Alameda.
So this [indiscernible] L-value is the annual rate and we can
see that it's higher where the color is darker on the map.
This accounts for the rates of the triggering earthquake events
that are used in the simulations, which is a
combination of over 2000 probabilistic events and you can
see that the darkest areas with the highest hazard show up on
the western side of the island as well as on that southeastern
side, and this aligns very well with where we expect the highest
hazard to be from the geologic deposits.
But this is now taking into account the groundwater and
ground shaking as well.
So we can see that from a different data source to think
about, the soil properties, we still see a very similar map.
And using this map I've developed this regional metric
of regional hazard extent and severity.
So this is a way to consider the rate of exceedance of both an
area of the island and an LPI threshold that's exceeded.
So for example, if we're interested in 40% of the area
exceeding some threshold, we can look at this 0.4 along the X
axis, which is the portion of the land area, and then up the Y
axis we have the LPI that's exceeded.
We can look across at an LPI 5, where we might expect moderate
and liquefaction or look higher at an LPI of 15, where we might
expect severe liquefaction, and we can see that.
Obviously that's getting more rare to see larger areas and
combinations of larger LPIs.
But we can look at this for different portions of land area
that we might be interested in and see the the annual rate of
those events.
We can also see from this where either the extent or severity
are increasing so we can see on the top left of this where
we're kind of below 0.1 portion of the land area, but above an
LPI of 10. We see that these are very vertical.
So we're seeing the same amount of area has an LPI exceeding of
10, as has an LPI exceeding of 20.
So really, it's where we expect liquefaction.
We expect more severe liquefaction and so we can see
where the extent and severity are interplaying. And finally
another use in this is that we can find some equally likely
hazard scenarios.
So, if we look along those contours, we can see that it's
equally likely that 20% of the area exceeds an LPI of 5 or has
moderate liquefaction as it is that 5% of the area,
so as a smaller portion, but is exceeding saying LPI of 15 or
even probably an LPI of 20,
so that's how much more severe
liquefaction that we would expect.
And coming from that, we are looking to answer the question
of what earthquake sources are contributing to these high
liquefaction hazard scenarios.
So pulling, for example one of those from the last plot being a
20% area and an LPI threshold of 5,
we then perform a disaggregation, the same as we
would on earthquake hazard scenarios to see which
earthquake sources are contributing to that hazard.
So,
in this case, yeah, we're looking at that 20% of area
exceeding an LPI of 5, and we can map some of these ruptured
distances to the nearby faults.
So we know that between 0 and 20 and about 8 kilometers is the
Hayward Fault.
And and we know that at about 25 kilometers between 20 and 40 on
this plot, we would know that that's mostly coming from the
San Andreas fault.
And what's interesting in this particular map is that even some
of these distant ruptures, so up to 100, 120 km away, are
contributing to a moderate liquefaction hazard.
And now that map from the last slide is the upper left plot in
this set and we can create these for different areas and for
different LPI thresholds.
So, going left to right, we have an area of either 0.2 or 0.4.
So we're seeing higher area of the island expecting
liquefaction. And going from the top to bottom
we're seeing a higher hazard, so a threshold of 5 or a threshold
of 15.
And we can see that as we're looking at these higher and
higher hazard and area scenarios, we're getting closer
to the closer sources and the larger magnitude events, and we
can see really clearly that the Hayward and San Andreas faults
are coming out on both of these aggregations and this can help
inform what some high hazard events might be for scenario
selection.
So, we see that a M7.0 on the Hayward which is used a lot,
used by Anne Wein for the HayWired scenario.
So, that is definitely one that we're seeing contribute to this
high hazard.
And we are also seeing some San Andreas ruptures that are
contributing.
So, it might be interesting also to to see what a scenario would
look like from there.
Now I'm gonna move onto the risk results.
So that was all thinking about the hazard and the actual extent
of liquefaction and severity of liquefaction.
And now I'm looking at the losses and damages to
specifically residential buildings on Alameda.
So on this map, each of the colored dots are residential
buildings.
The gray-kind of building outlines are buildings of
different types, so maybe industrial or commercial
buildings and focused on the residential ones for this
analysis.
And you can see that they're concentrated on the eastern side
of the city.
And we know that some of the highest liquefaction hazard was
actually on that western side, but that's where there was a
former naval base
and so there's not residential buildings in that area of the
island.
And this plot the color is showing the expected loss ratio
of the building,
so combining all of the scenarios together and seeing
the damages both from ground shaking using HAZUS fragility
curves to see the expected damage to date and expected loss
ratio from those, and then using the
loss functions that I mentioned before for liquefaction, and the
maximum losses is taken from the two of those because of the way
that liquefaction damage is considered in those curves.
Basically, once the building is damaged, if they saw
liquefaction they can attribute liquefaction, but it's hard to
separate back out the damage from shaking versus
liquefaction.
So all the damage is considered together in that liquefaction
loss.
And yeah, based on those HAZUS fragility functions, this
depends on building type for the shaking.
So both the structural type of the building and the code year
or what level of code it was built to based on the age of the
building and for liquefaction and the damage is based solely
on that LPI at the building location because it's combined
for all residential structures.
And then what we can do is we can separate out the damage
that's coming from liquefaction.
So this is showing the expected loss ratio from liquefaction
over that expected loss ratio and the last map.
And again, because the maximum of both, we can see these very
high values, but that means that we are expecting a high damage
from the liquefaction itself.
Again, this is dependent on the hazard.
Based on those loss functions from the researchers at the
University of Washington and the liquefaction is including all
the damage to the building and and the locations with the
lighter color are less susceptible to liquefaction.
And it does follow that geologic deposit where we expect, lower
susceptibility, even though we still do expect a moderate
susceptibility in that location.
But we see that it's a lot higher in that southeastern area
where there's high hazard as well as kind of the northern
portion.
And though, this is useful because it does take into
account the hazard, but it also does take into account from the
shaking side the building age and the building properties as
well.
And from this we can see that we can calculate total loss.
So on the Y axis we have the exceedance rate and on the X
axis is the loss.
The total loss in billions of dollars and the blue line is the
lost, sorry.
The darker blue line is the loss from ground shaking alone, so if
we ran this analysis just with the PGA and with the HAZUS
fragility curves, that's the loss exceedance that we would
get from that analysis. If we just took into account
liquefaction.
Then we will get that lighter blue line which we see captures
a lot of the more frequent rates because it is including kind of
the total damage.
but it's dropping off more quickly, which is likely because
we're not seeing then the damage for the buildings that maybe
don't experience liquefaction
very much, but would have damages from ground shaking and
those high return period events.
And then when we calculate them together, we get this black line
of the total loss and you can see that obviously neither
hazard predicts the losses as high as exceedances.
And it's also not additive of the two, so it's important that
we consider both in the same analysis to get that full
picture and to give an extent, an idea of the scale of this,
the total portfolio value is about $5 Billion USD.
So the very, very high.
And a very low exceedance rate events are getting to about 3
just below $3 Billion USD.
Now I'm going to pull a little bit from that work that Anne
mentioned the housing recovery
because
the way that I think about it is we can look at this loss, but we
don't really know necessarily the impact that loss has when
we're stopping at kind of dollar values.
So now moving a little bit into how we would think about
simulating the impact to humans.
So, this is some work that I did with Rodrigo Costa, Adam
Zsarnoczay, and my advisor Jack Baker.
And this is on some housing recovery modeling.
So this really connects the loss at each building location and
the dollar loss for each building to the human impact.
So the financing to repair from
an earthquake or from an event comes from some major public and
private funding sources.
And this flow chart is just, you know, not supposed to really be
able to read it, but it's just to give you an idea of kind of
the different sources and how it maybe splits into different
programs
and you have to go through all of these, all these different
programs to see, do you have enough money yet to repair?
Do you need to apply to something else?
And so it depends on income, of course, of the likelihood of
people getting things like bank loans and then our work has
included now also multifamily buildings.
So there are different sources for multifamily versus single
family buildings as well as the tenure or ownership,
so whether it's owner, renter, occupied makes a big difference,
And as far as renter occupied, its typically a landlord that's
applying for those repairs.
It's not the renters themselves.
So ths is all taken into account in a model that looks at these
actual processes and takes into account the steps that each
household or each owner of a building would take in order to
repair.
So I've applied this to.
the liquefaction damages in the in this regional study and.
that slide is running.
It's only including rental units because it's only single family
housing
Based on the liquefaction fragility, so the liquefaction
loss is only calculated for single family buildings.
But looking at all of the possible financing sources and
ability to finance repairs can get us an annual rate of unmet
need.
And so if a household has applied to all of the five major
funding sources, and they still don't have enough to repair.
Of course it's possible in reality that they figure it out
some other way, but this would still mean that it's a large
burden for them to be able to recover and so we use this unmet
need as a proxy for non recovery or just a proxy that it's a very
big challenge to recover and we can see this annual rate of
unmet need in the plot on top between the rental and owner
occupied units, the lighter colors of blue are lower annual
rates of unmet need and then the darker the color it's the higher
rate of having unmet need and we can see that on the right plot
on the right set of bars that the renter occupied buildings
are disproportionately having higher rates of not obtaining
funding and not being able to repair and this is useful. The
plot on the bottom is showing that annual rate of unmet need
and we don't necessarily notice it spatially but we know that
this is then taking into account the amount of hazard that the
different locations are exposed to and it's also taking into
account for the ground shaking damage the age of the building.
So it's not just the hazard, but the exposure.
And then also it's taking into account the income and the
renter 10 year status of the household that owns the
building.
And so we can see how all of those things are combined to
give us a bigger picture of how people are impacted by these
events.
So I might have gone fast [laughs] but
I will just give you some of the key takeaways from this.
First, I laid out this methodology of how we can
combine on a regional scale and run a liquefaction hazard and
risk analysis, combining some different data sources.
I showed you this new regional metric of liquefaction hazard
extent and severity where we can see both the portions of area
and the severity of flick of faction that we might expect.
I disaggregated on these different
combinations of area and liquefaction and LPI exceedance
in order to see which earthquake sources are leading the most .
to this high liquefaction hazard.
I calculated the loss from each of these events to see how much
damage and monetary loss the community is experiencing from
both the combination of ground shaking and liquefaction hazard.
And then I showed how this loss can be translated into unmet
need using a housing recovery model.
And how we can consider inequities or the impacts of the
actual damages and of the liquefaction events.
So I'll give you this.
Takeaway is that we can incorporate a liquefaction into
our regional earthquake hazard and risk analysis and if you're
interested in using any part of that workflow, the code is
available on my GitHub, which is emongold, and it's the same as
my email.
So if anyone is interested or wants to reach out about any of
this.
I would love to talk and love to take questions now.
3rd question.
OK, go.
Stuff.
Just take one second.
Alrighty, so let's open it up.
Thank you for a great talk.
I see a hand raised online.
Let's see.
Kevin Miller, do you wanna unmute yourself and ask your
question?
Yeah.
Hi, Emily.
Uh, first of all, great talk and I'm really happy to hear you're
using results from open SSH.
A uh, my question is about the the regional calculations you
It's.
showed around slide, I think is like 16 or so.
Umm, how do you account for spatial correlation in those
calculations?
When we do those types of analysis for ground motion
hazard, we typically need to account for the spatially
correlated ground motions, and I'm wondering how that plays
into your calculations and taking it might apply to
liquefaction susceptibility as well.
Umm. Yeah.
Thank you.
That's a great question.
So umm for the ground motions, the correlation models?
Uh, are you so that the Galatians themselves, the
FPGA's, are spatially correlated?
Uh, and then for the and soil model.
So for the 3D model of the soil across the island.
From this step, the sequential Gaussian simulations use a
variogram which is spread to the fit to the actual data, and so
that gives an idea of the correlation of the soil
properties in the vertical and then the horizontal directions,
and so that's applied to the sequential cache in simulations.
So we actually see, but some of this layering that we would
expect in the soil in the simulated soil data because they
have a higher correlation in the horizontal direction than in the
verticals.
So that we can.
So yeah, we capture the layering in the spatial relations.
Alright.
Josie, in the great talk in the hazard disaggregation that you
did?
I'm curious about the word distal sources and whether that
might represent something like the theater, and if you could
use that event on the comparison for your simulation.
Yeah, that's a that's a great point.
I'm not sure exactly what.
What faults of those came from but it, but it definitely is
possible that it's more similar to.
I'm afraid that I haven't done any any validation yet, and I
think that it would be.
Yeah, it would be slightly different because it would be
probabilistic, but still on that event.
So we need like multiple simulations of of the possible
both soil and and ground shaking.
But that to get kind of a balance of of what we would
expect versus what we're seeing.
But yeah, it's a.
Jim.
Yeah.
Thank you.
So wondering through the this slide here, did you keep the
groundwater level in the?
So much your constant, and if you didn't, did you see any
changes between?
Like say we are going to be in the winter versus the summer and
how that affected it.
OK, the diction.
Yeah.
Thank you.
That's that's a great question.
So I did very the water depth.
As part of the sensitivity study on this and and we only have
data from one set of from that same set of CPT.
So we don't have.
We don't have seasonal data necessarily, but but there are
other studies that have looked at at seasonal change and the
uncertainty on that.
And so based on that I used about 2 meters of shift and that
is one of the.
So the two most important variables that played in were
the equation that was used for for LPI, and then that
groundwater gap.
So yeah, that is important.
And that's also part of communications to include more
of the sea level rise also into that seems like longer term is
like this long term drought good for the for the vacation.
The longer the better.
Yeah, that could be a surprise.
Positive from from a long job.
Well, could you say more about how you calculate the action
potential index and do you need a cone penetration test to?
Obtained it.
Yeah.
So the LPI is based on, has been set with multiple different
empirical equations.
So the two that I use were both based on concentration test
data.
There are other equations of health Pi that are based on SPT
standard penetration test data and and.
Yeah, there is other research on on like more regional metrics
other than LPI.
It umm but for for this analysis I use Alpi based on CBT data and
that's what plays into both the.
So the factor of safety is the ratio of cyclic resistance to
cyclic stress, and so both of those are based on the the two
metrics, the QC, the yeah to persistence and a fast asleep
friction that come from the concentration test as well as
the the P GI and the magnitude event.
So our cone penetration tests available, why do you label for?
Areas that have liquefaction potential or are they rare?
Yeah.
So that is one of the the biggest challenges to applying
this.
This particular methodology is that there are lot, so the USGS
has a lot from the same data set on the East Bay.
Uh, but they they're not like coverage fully, so I'm working.
On a project also in San Mateo.
And there aren't these these public ones, but they are
somewhat commonly done by by developers in these locations.
So.
Yeah, getting getting the data can be more the challenge
though.
So I've been related question and by the way, very nice talk
and I'm very confident that I like.
So how do you extrapolate away from the limited number of CPT
soundings or SCPT soundings to the surrounding?
You know shallow structure, I mean I I know people use
velocity proxies, noninvasive proxies to use those sorts of
things.
There's a based upon the generally published job, you
know soil structures and subsurface maps.
Umm.
Yeah.
So it's it's not based on the geologic deposit maps, it's it's
interpolated from all of the CTS on the island and some other
research has shown that it's it's better to interpolate from
everything instead of separating and interpolating just within a
geologic zone.
And so, yeah, the very ground that I mentioned before is what
stands in for those spatial correlations.
So you assume a certain variability.
There are enough CPT soundings on Alameda.
You can get pretty good spatial coverage.
Umm yeah, there is.
There's 21 across the island.
Right.
Umm, there is a quick question in the chat from keep nutzen,
but I believe Steve your question covered the same topic.
So Keith, if if you have remaining questions about the
you know kind of going from point CPT tests to kind of
extrapolation for their beyond the point data.
Please chime in.
Yeah.
Thanks.
I I will great talk and I I really, really appreciate the
challenging nature of this ohh problem.
You're you're starting with the soils and the ground motions and
going all the way to probabilistic losses and
impacts.
So that's not easy.
Congratulations on all the work you're doing.
Umm yeah, so the the question Steve asked is really what the
one I I am interested in and I I think I understand what you've
done, but you just made a comment in response to Steve
saying that these sort of creaking based or geospatial
statistic approaches have been shown to do better than uh
geologic map based.
Uh, characterizations of the of the liquefaction susceptibility
and I'm not aware of those or maybe kick how, how do I learn
about these, these studies that have shown that?
Umm, so I don't know that it's better than like a different
type of of like perfection analysis in that sense.
I I can follow up with the the paper that I'm referring to that
I that I cite because it basically says that for the
liquefaction calculations that it doesn't make, it doesn't make
a difference if you separate them off and it's better if we
have the more data to to create the the full spatial spread
instead of trying to use much fewer points that are within,
umm one deposit.
OK.
Thanks.
Yeah, I I think it's always been a challenge deciding how much
how much of this kind of point data, LPI, for example, is
sufficient to adequately characterize an area.
And then if we change to a different area in a different
geologic setting, how much data do you need?
And I my personal belief is that marrying the two approaches of
using uh, at least few statistical approaches combined
with what we know about the surface geology.
Umm might be at the best product, but I I'd love to love
to hear what or I'd love to learn what?
They.
What reference here here exciting or just writing?
Here's your question.
Thank you.
Yeah.
I think for the great talk to your estimates of risk or loss
ratios have uncertainties and if they do, do you have a sense for
which component of the analysis is contributing the most of
this?
And yeah, so so on the, on the LPI.
On the hazard side, so not going all the way through to rest, but
but as far as the liquefaction hazard and the empirical
equation for LPI that's used is is very important so.
Uh, I've used too, and it would be interesting to to use even
more to to see how much they agree or how important that is.
And then the watered down as well as is important.
Right.
Ohh, first of all 'cause you didn't get it.
Keith is geologist.
Yep, yeah.
He was politely emphasizing that maybe you should revisit this.
A terrific talk.
It was really, really clear and I'm no expert in this field and
I felt like I learned a lot, so congratulations.
I have questions very similar to to Josie's you.
You did this aggregation to come up with, you know, from all
sources.
What?
What these various parameters are related to liquefaction loss
and potential.
Ohh, I'm interested in as a function of magnitude and
distance.
Can you look at sort of the threshold for for significant
liquefaction?
Uh Alameda Island or or in the other place?
I mean, for example is a A magnitude 5 1/2 on the Hayward
fault because efficient much liquefaction.
And then Jose's question for the lumber breed earthquake 8090
kilometers away, there was there was damage in the like
liquefaction.
So presumably you can pull that out and disaggregation.
Yeah.
Yeah, I think that's a lot of what we see from the designation
is that we can get that.
Probably even to to have very confident balance doing even
more probabilistic simulations.
So this is like one signing issue per.
Fracture scenario, yeah, but obviously doing more for each
scenario.
We give a little bit more idea.
It's like maybe a maybe a small range you could contribute to
that, but but we didn't see it in in this.
But yeah, there's I think there's desperation plots.
And if, if you're curious about.
About a different metric, so we can we can say the losses that
exceed a certain a certain barrier and disaggregate from
that and see see which sources it to that and so on.
Thank you.
Umm Ben Mason has a question.
Do you want to unmute yourself and ask your question?
Yeah.
Thanks.
So thanks, Emily.
I'm.
I'm Ben Mason.
I'm at the geologic Hazard Center and Golden Co.
Thanks so much for your talk.
My my interest was on the soil simulations.
And if you can add or how computationally expensive those
are, and if you could add those into your workflow so you could
consider multiple souls simulations and then consider
the uncertainty with that.
And on top of that, I'm wondering if you can also at the
same time and corporate geology into this this workflow somehow
because you know Alameda Island, I think it's a very smart choice
of a place to start, right?
There's a lot of data and we also know it's gonna liquify
spectacularly, but I'm curious, as you're moving to less base
heads and and harder sights if you can incorporate some of
these more sophisticated things into your analysis procedure.
Thanks so much.
Yeah.
Thank you.
And so to to address the first part of that question and this
is, yeah, so I do run sequential Gaussian simulations with with
uncertainty on that as well.
So the the variogram parameters are given ranges that are that
are reasonable based on the data.
To see how sensitive the the final local faction is from
that.
So when I mentioned before that the LPI creation in the water
depths aren't the most important, these certain
parameters for the variogram did not make a big difference.
They they were not important parameters and based on that so
in that sense every each of the 2000 runs does have a different
scenery.
Soil umm 3 I got 3D grid that you see there and and yes
definitely incorporating a different a different soil model
would definitely be great and you could still you could run
the whole analysis as long as if it either still outputs and LPI
or if you have a different damage or loss function from
that metric which I think that's one of the maybe the more major
holdups from my end of trying to look all the way through rest
and we have to have of course the data to run the the hazard
simulation but we also have to have the loss function to to
estimate damage to buildings so because that was the end goal
the loss function there aren't there aren't many that I've seen
the the one that the researchers at University of Washington are
working on is based on LBI so working backwards from that
that's why this particular model is used for the whole workflow
to come together.
But if you have these different pieces and and you have a
better, a better framework for the for the soil to faction kind
of calculation, then that would be great to to to plug in and
run well using that.
And a question for you.
I really liked your talk.
It was.
It was great.
Expanding on the last question a little bit, I've I'm wondering
if you've thought about running this type of analysis in in a
place where it is currently being developed.
Like I'm thinking about, for example, Treasure Island, it's
all Phil, but they're rapidly building apartment complexes and
houses to repurpose the land.
It'll probably liquify.
This is a broad conjecture, but maybe more so than Alameda
because it's all filled in the middle of the day.
What would you need to conduct a similar analysis aside from CPT
test and then maybe hand your results over to the engineering
firms to like appropriately design for housing?
Limitations and things like is that is that something you've
thought about it?
Umm yeah, so so I'm I'm very interested in how kind of this
can be applied in that sort of context.
Yeah.
And definitely like you mentioned that the CVT data is
is one of the main constraints.
And I think that it would be a question of what, what metric is
informative for them.
So if they're interested in OK as a as a meaningful metric to
to inform their development or if and and it's very easy to go
LPI to to probability of liquefaction or these
frequencies and rates, but.
Umm, as well as the loss, as long as so in this case the the
loss function is kind of the limiting factor because there's
one for residential.
But if you're thinking of like a tall, tall high rise, then that
wouldn't apply the same.
So yeah, that would be probably the main thing I would need.
Gotcha.
Cool.
Thanks.
Other questions in the room?
I'll talk.
Yeah.
Talk, Tom.
Yeah, I will.
It's interesting in the problem of who's going to pay for this,
and I think at one point you seem to think that Windows would
be putting up part of the money to to be build housing and that
that kitchen you problems.
How much is damage fixable versus how much is total?
A good boss and also time to fix it.
Because that takes years and and the better can't wait years, let
alone put in money into something really ready to use.
So it was I.
In that case, it's starting on the whole economic issues and
having thought about this stuff and I and I know that's not
true, she refused.
This is all of that where I hope it's not.
It is take 450 years.
You could you give us a little feel for, I mean, on renters.
Did people really think renters?
So that put up money.
Umm, so that's a great question.
Yeah, you don't have a home anymore.
Yeah.
So yeah, so so in the case of renter buildings, we simulate
the but the owner like the tenant, the tenant, the
landlord.
So the landlord would be the one that's repairing structural
damages.
And for renters, like their insurance is typically just for
contents.
And and this we don't consider contents damage, so it would
still be the owner of the building, but it's different
because they're not an owner occupant.
So they're repairing that as as like their business.
And so that causes them to have different, umm, different
funding that they can get and then also a different process.
So if one landlord owns multiple buildings, then they can't get
the same funding for every building, so they may be able to
get part of the funding, but some of the rental buildings but
still be.
Be unable to be repaired.
Do you have any idea how long for one of your scenarios at 7
1/2?
It was in so much with the you're living the idea how long
it would take to redo that and what it is today. Umm.
So that's a good question.
So.
So the hazard recovery model in this this paper is is just for
earthquake and then the separate results were from liquefaction.
So the results in this paper, I don't remember the exact
timeline, but we do have recovery times for the buildings
on Alameda from simulations of a 7.0 on on the Hayward.
And so that, yeah, that's that's definitely in the years that
goes everything out to four years or so.
And some of the scenarios.
And that's in a case where that's another metric that we
can get out of the house and recovery model is to see not
only the funding, but it takes time to obtain the funding
depending on the sources as well.
And then it takes time to repair after getting funds and and that
depends on how many contractor crews are going to be available
to work on houses in parallel.
Umm.
And and so that's.
Yeah, that's all included in in this hazard recovery model.
But but I don't have a sense with the liquefaction case of of
the time.
Yeah.
Would it even be repairable like usable again? Right?
Because sometimes, like in Christchurch, they didn't.
Maybe gonna end at all.
Yeah.
Uh, they Anne.
Could you speak?
Sorry.
Yeah, I said.
My mask.
I think that this could take a lot of time to figure out the
permitting for the the rebuilding, which you may not
have built in yet, and it was an issue after learning.
Create it and and Christchurch they had areas where they would
not rebuild because the risk was too high to going in the future.
Yeah, it would be a lot of time.
We'll go.
Yeah.
On the on the land issues, yeah, I think so.
I think part of with Christchurch because they were
hit by so many aftershocks that they were able to see very
quickly that this was a repeating issue.
And I think that maybe if there's if there's not
aftershocks, then then it might not be seen and the present as a
repeating issue necessarily, even though we know that that it
that it would be realistically again.
So that might be another.
Can I pick up on that aftershock, please?
So in your source model, you just have one rupture, right?
One big earthquake and then you propagate that through to the
next, uh parts of your workflow for each simulation, right?
And like that, earthquakes can come and sources and but it it's
just one.
And we we know that.
Earthquakes are clustered and and that there will be
aftershocks after the earthquakes.
So do you have a sense of well first, could you include that in
your I'm workflow to have lots of sources that are like somehow
temporarily and spatially correlated and then what kind of
impact do you think that would have on some of your downstream
outputs like annualized losses?
Yeah, that's a great point.
Yeah, after shocks are not are not included in this and I think
that the biggest thing from umm from the whole workflow
perspective like from the risk perspective is that we don't
really have good data on what the additional damage is going
to be to buildings.
So if you know a building already has damage and Ben has
more.
We don't have necessarily a good enough understanding or enough
data on on that to then see how much higher the damage would be
and and a lot of them do if if they already have high damage
then we know that depending on the time, like if it, if it's if
it soon after another they're trying already starting to to
build something new then.
If there's already complete damaged then it then it doesn't
make a difference, but it would be interesting.
I just.
Yeah, I don't.
I don't don't have the the data for that loss story seems like
an exciting research gap.
That is so self serving.
With that, let's thank Emily one last time for a great talk.