- Hello. I’m Ken Hudson. I’m a graduate student at UCLA
in the Department of Civil and Environmental Engineering.
And I’m going to talk to you guys today about the interpolation of
ground motion intensity measures using kriging at liquefaction sites.
So it’s important, especially in the Next Generation Liquefaction project
that I’m involved in, to get accurate estimates
of ground motion intensity measures at liquefaction sites.
Now, this is really important because it forms the demand side of the equation
for models – like, triggering models and manifestation models that we’re
trying to regress from our data sets that we have in the NGL database.
So we wanted to come up with a way to more accurately estimate the
intensity measures at each of these liquefaction case study locations.
So we’ve come up with the following workflow to do so.
First we estimate the intensity measures at the recording stations that we have
using ground motion models. So an earthquake occurs, it records
at recording stations different intensity measures, and we have those values,
and now we come back and we compute the estimated
intensity measures using a ground motion model or multiple
ground motion models of our choice that are fitting for the region.
Next we will compute the residuals of those intensity measures based on those
estimated values and the recorded intensity measures at those stations.
And the residual, R, total can be broken up into the event term, eta-E,
and the within-event residual is delta-W, shown in the equation there.
And that total residual is just the difference between
the intensity measures in natural log units.
Next we compute an empirical variogram from that spatial data,
and we fit a variogram model to it.
From that variogram model, we can generate a residual krige.
And the final step, then, is to look at the liquefaction sites that we’re
interested in, compute the estimate of the intensity measure using the
same ground motion models we started out with, and then querying that residual
krige at those liquefaction sites and modifying the estimated
intensity measures using the krige-queried residual
to get a final intensity measure estimate.
So I’m going to go through an example using the Loma Prieta earthquake.
Here is a satellite image showing the 1989 fault rupture in red.
The recording stations where we have the intensity measures recorded are
shown in yellow triangles, and the blue NGL sites where we
have liquefaction or no liquefaction observations
along with geotechnical subsurface data are located.
Those NGL sites often do not correspond with where we have
recording stations, which is why this interpolation step is so important.
Here we are showing the recorded intensity measure in blue.
The estimated intensity measure using the Boore, Stewart, Seyhan and
Atkinson 2014 ground motion model from the NGA-West2 project in orange.
And then, in green, we’re adjusting those orange estimates by the computed
event term, which is taken as the average of the total residuals.
On the right you can see the within-event residuals plotted
using that heat map scale at each of the recording stations.
And so you can see, in the Bay Area, San Francisco, and Oakland, there’s
very high residuals around 1 or higher. And elsewhere, they’re either around
zero or negative, depending on the location.
Next we compute the empirical variogram from that data set, and we
fit a variogram model, such as this stable variogram, to the data set.
Then we would need to compute the within-event residual krige
from that data. What is shown on the left is that krige with the stations
with the recordings overlaid on top of it, with the same color scale applied.
And on the right is the same krige with the same color scale showing
the within-event residuals along with the NGL sites and their
interpolated residual at each of those locations.
Then, at each of those locations, we can compute that same
BSSA intensity measure estimate and, taking the krige’s residual interpolation,
we can adjust that intensity measure to what is shown on the right in those
heat maps. The color scale is the same on both the left and the right.
On the bottom you can see, as a function of distance,
how the PGA changes.
- Today I’d like to introduce the application of passive surface method
using seismic ambient noise to delineate 3D S-wave velocity
structure at south and east San Francisco Bay Area.
Left-hand side is the site we carried out active and passive
surface measurement or [inaudible].
We have dispersion curves at these 62 sites.
Right-hand side is the site we measured H over V.
We measured H over V at 166 sites.
To build the 3D velocity model, we used H over V, dispersion curves
obtained from array measurements. Bedrock depths, H over V relationship
obtained from array measurement.
Vs30 information at about 100 sites obtained from active and passive
surface method downhole or crosshole seismic logging collected by USGS.
And deep 3D seismic velocity model based on
geological information compiled by USGS.
You can take a look all our measurements at our website,
seisimager.com. Left-hand side is Vs30.
It includes USGS database as well. So Vs30 is about 150 meters per second
along the shoreline and increase in the landward direction.
Vs30 exceeds 300 meters per second at Coyote Hills and on the northeast
side of the Hayward Fault and the southwest side of San Jose.
Right-hand side is H over V peak frequency.
Different color indicates different peak frequency.
Red to yellow circles indicate low peak frequencies that corresponds
to deeper bedrock site. And the green to blue circles indicate
high peak frequencies that corresponds to shallow bedrock sites.
At the site we carried out all our measurement, we calculated dispersion
curves and estimated Vs profiles using joint inversion together with H over V.
We summarized the relationship between H over V peak frequencies
and bedrock depths obtained from array measurements.
The site we only measured the H over V, we constructed initial
velocity model based on Vs30 information, geological information,
and bedrock depths estimated by H over V peak frequencies
then applied inversion to estimate Vs profiles.
This figure summarizes relationship between H over V peak frequencies
and shallow – 760 meters per second, and deep – 2,500 meters per second
bedrock depths as obtained from this study.
It is clear that both shallow and deep bedrock depth increase
as frequency decreases. The regression line, broken lines
for two bedrock depths are used to generate the initial 1D profiles of
the inversion at sites only H over V measurement was performed.
This figure compares Vs profiles obtained from this study with the
3D velocity model [inaudible] geological information compiled by USGS
At the South Bay, depths to layer with Vs of 2,000 meter per second
was approximately 1,900 meter and 500 meter at Cupertino and Alviso,
respectively. At the East Bay, depths to a layer with Vs of
2,000 meter per second were approximately 600 meter and
900 meter at Fremont and San Leandro, respectively.
We can say that the Vs profiles obtained from H over V and
other measurement were reasonably consistent with
the 3D velocity model based on geological information.
We combined all 1D Vs profiles and interpret to a 3D velocity model.
Left-hand side is interpolated Vs30. Here is the Coyote Hills
and Hayward Fault. It ranges 150 to 500 meters per second
and lower at shoreline to middle of South Bay and higher at mountainside.
Right-hand side is the shallow bedrock depths on top of 750 meters per second.
Here is the Coyote Hills and Hayward Fault.
Depths to the shallow bedrock ranges 20 to 400 meter, and it is
generally deeper at South Bay compared with East Bay.
At the South Bay, the bedrock is deeper at Cupertino and Evergreen Basins.
It is shallower around the Silver Creek Fault to Coyote Hills.
Left-hand side is Vs at 400-meter depth.
Here is Coyote Hills and Hayward Fault.
We can see Vs is generally lower at South Bay compared with East Bay.
Left-hand side is Vs at 700-meter depth. Here is Coyote Hills and
Hayward Fault. At South Bay, Vs is relatively higher at middle
of valley from downtown San Jose to San Jose Airport and Alviso.
The high-velocity reached in the South Bay appears prior to the
Silver Creek Fault and may continue northwest to Coyote Hills.
Now I take cross-sections at South Bay.
Here is a cross-section from Menlo Park to San Jose.
The shallow bedrock is clearly shallower at Alviso and deeper
at Evergreen Basin. Here is a cross-section
from Cupertino to San Jose. The shallow bedrock is clearly
shallower at SJC and deeper at Evergreen Basin.
Here is the conclusions. Note that the 3D Vs model
is preliminary model. More geological measurements
are needed to delineate the basin structure in greater detail.
Thank you very much.
- I’d like to present to you the Reno ShakeOut scenario,
which was published in BSSA by Eckert and others back in 2021.
This scenario predicts ergodic potential ground shaking
for one magnitude 6 event in Reno.
And incorporated into the geologic model that it uses are
gravity-derived basin thicknesses, which are mostly pretty smooth
and less than 1 kilometer in thickness.
The shear velocity within the basin is averaged to a one-dimensional model
between the surface and the basin floor. And that gives the basins themselves
less lateral velocity variability than was measured by Pancha and others in 2017.
However, above 30 meters – within 30 meters of the surface,
there is full geotechnical lateral velocity variation as measured,
which over small distances, varies by a factor of 2.
And that’s from Scott et al. in 2004.
This model, we fed it into an SW4 computation with help from Pitarka
and Graves’ stochastic source model. And we did that computation up to
3 hertz, so that required a 30-kilometer- wide grid with 40-meter sampling.
And it took 20,000 core-hours on the Lawrence Berkeley Lab’s Cori machine.
Now, the outcome of this model is that the whole urban basin gets severe
shaking. And that’s at a level of greater than 4/10 of a meter per second PGV.
Even though the basins are thin and less than 1 kilometer deep, there are
some spots of extreme ground shaking of greater than 1.5 meters per second
PGV over very small areas of less than 0.1 square kilometer.
And their location is very hard to predict.
The predicted shaking and amplification at this 3-hertz frequency is thus
quite peaky and can vary by a factor of 2 across less than 1-kilometer distances,
even when you’re not near the basin edge.
More in-basin scattering should – if we could include that in the model,
should dampen the peaks and extend the durations.
And that’s important because we’re only achieving 30-second
model durations, whereas we have 60-second shaking durations recorded.
So we started modeling some additional Reno quake scenarios for a nonergodic
view, and that plus the improved in-basin scattering will lead us to
some additional results pretty soon. Thanks for your attention.
- Hello, everyone. I would like to share with you briefly
some aspects of a five-year center operations proposal to NSF that
SCEC is developing on the coupled evolution of earthquakes, faults,
and geohazard in the San Andreas Fault System.
Using the entire San Andreas system as a natural laboratory of the next center
will include the appropriate boundaries for quantitative modeling of earthquake
processes in the system, including sub-parts of the system,
with increasing types of faults and slip modes and with increased
populations that can benefit directly from the research.
Analyzing earthquake faults and geohazard is coupled dynamical system
which is a major part of the proposal. We’ll provide quantitative
understanding of relations between diverse outputs that are generated
simultaneously in the crust, such as evolving seismicity, ground motion,
fault network configuration, evolving surface strain rates, and evolving
dynamic topography near faults. Using such diverse outputs for testing
and further development of models will reduce the
non-uniqueness of model results and will lead to improved hazard estimates.
The science plan of the proposal has four major research thrusts
with 12 topical elements, shown here on the left.
Thrust A aims to close critical data gaps, such as those associated with near-fault
regions in the very top crust and then synthesize the results in multi-scale
community models which will include northern California.
The second thrust is to develop rheologies that bridge the multitude of scales
and conditions of earthquake-related formation. This is two parts.
One is to develop effective constitutive laws for brittle deformation of fault
zone regions. And the second one is to develop effective constitutive laws
for long-term large-scale regional deformation.
Thrust C is to develop advanced virtual modeling frameworks that will use
the constitutive laws and the data sets to model to provide quantitative
predictive information on several phenomena.
So one virtual modeling framework would be to model tectonic
deformation. Another one is to model coupled evolution of
earthquakes and faults. This is – will be next-generation
earthquake simulator that will include evolution of fault structures during the
occurrence of earthquakes as well as with the ongoing tectonic loading.
And the third virtual modeling framework aims to improve estimate
of seismic hazard in California. This is essentially next UCERF.
And here SCEC involvement will be primarily science contributions
that can aid future UCERF simulations that will be done by the USGS.
The last thrust of activities is to improve the predictive understanding
of seismicity. This has several components including improved
forecasting of seismicity that can go beyond ETAS –
the next generation of ETAS. Another topic is tracking preparation
processes of large earthquakes that might lead to development of
hierarchical search for evolutionary behavior before large earthquakes.
And the last topic is associated with induced seismicity.
And here the focus will be on performing crustal-scale experiments
that can help understanding earthquake physics and
test time-dependent forecasting.
In addition to the system science, there are four other major activities in the
proposal that will – that are listed here. Number two is develop state-of-the-art
community modeling framework that will allow the science to take place.
Then we wish to develop impactful workforce development.
We want to develop multifaceted community engagement both within
our own community between scientists as well as between scientists in
various sectors of the public. And the last part is effective science
planning and coordination. We plan to increase activities
in northern California on all of these fronts in collaboration
with the USGS, CGS, universities, and industry.
And I thank you for listening and will be happy to have follow-up discussions.
- Hello. Welcome to a short presentation on the SCEC Statewide Community
Fault Model and automated earthquake-fault associations.
Let me briefly introduce you to the SCFM model in an overview.
What you can see here is a perspective view of the model
and the associated map. The model is a comprehensive
3D model of all faults deemed capable of generating destructive
earthquakes in the state. That’s the target. Currently it consists of about
450 faults in southern California and about 130 faults in northern California.
The tradition, for historic reasons, the model in southern California
is more mature and much longer developed.
The faults are geometric objects and are available as 3D triangulated
meshes from which also a fault trace shape file was derived,
which is available for GS purposes [inaudible] at that link.
Moving on to how this large model is organized and divided up into
geographic flood areas to manage a large model like this,
shown in different colors in this perspective view.
Each fault is constructed from various geologic and geophysical
constraints as they are available. For very well-constrained faults,
we’ll have subsurface data from wells and, in some cases,
seismic reflection surveys, mostly in the [inaudible] basins and offshore.
For others, we will use trace maps from
geologic mapping and cross-sections – conceptual cross-sections even if this is
the best-constrained we have. The model is regularly reviewed in large evaluation
efforts. And these reviews can then particularly pay attention to those faults
for which multiple different alternative representations are provided
in the model, which are ranked. And, in fact, we are currently inviting
experts to go through the process to evaluate the southern California
part of the model, the CFM, after we do that. It’s a peer review process.
It’s designed to make it easy for invited experts to go through the
faults and look at them. We use special tools developed for this purpose.
A map viewer which has a map and 3D option.
We use a web-based survey tool to show screenshots and allow ranking as
well as provide more in-depth source data for each fault to be evaluated.
This will focus on a more limited number of faults which have these
more meaningful alternative choices
of representation based on past experience [inaudible].
The results then will be collated and made available in the
preferred community model in the next release.
For the northern California portion of the model, we have 130 faults organized
into these six fault areas shown in colors in the perspective view.
The portion of the state that’s the – state border here that’s offshore that’s –
the large Cascadia subduction zone, for example, the San Andreas Fault,
give you a sense of the kind of updates we go through, I’ll show the last ones,
which are a result of a early review focusing on the offshore portion here.
All these faults which were added or improved in the situation from [inaudible].
Finally, let me briefly mention one use of the model, which is
an automated way to assign a source fault to an earthquake when it happens
in an objective automated way. For this, we developed a statistical
model trained from a training data set, which we have known associations.
This map shows that application of the model to a catalog.
The earthquakes are colored according to the fault to which
they were associated. It’s currently operationally deployed,
meaning that there’s a system which computes the probability of association
right after an earthquake occurs and it’s located.
You can actually sign up for a notification service,
which emails these notes with the earthquake. That’s how it looks.
And then a list of faults and their probabilities of association.
This is an example from last week from a fault on the San Jacinto system.
Thank you for your attention.
- This is Janiele Maffei, chief mitigation officer at the
California Earthquake Authority. The CEA is not an agency.
We are an instrumentality of the state, created after the 1994 Northridge
earthquake when insurance companies stopped writing homeowner policies
because California law required them to offer
earthquake insurance to their policyholders.
Most of you are familiar with our mitigation grant program,
Earthquake, Brace, and Bolt, or EBB. EBB has provided up to $3,000
to over 16,000 California homeowners to retrofit their older homes.
Our most recent registration period also offered supplementary grants
to low-income homeowners. We expect to be able to open
a new registration period in the first half of 2022.
The CEA sponsors research that investigates both earthquake
hazards and vulnerabilities. The seismic vulnerability targeted
in the EBB program is the older cripple wall house that has inadequate
sill plate anchorage and bracing in the crawl space.
Houses like this one damaged in the 2014 Napa earthquake were
obviously not suitable for occupancy. The typical repair time for these
houses after the Napa earthquake was two years.
The CEA sponsored a peer research project completed in 2019 that
confirmed seismic retrofits of cripple wall houses are effective.
The project quantified the savings of the cripple wall retrofit for scenario
earthquakes around California. This graphic shows the benefits
of retrofit for the two-story wood-sided house in San Francisco
after a magnitude 7.0 earthquake.
The $200,000 savings indicated by the red pin is for a 2,400-square-foot
house with a $200-per-square-foot replacement cost.
Of course, the actual replacement cost for houses in San Francisco
is upward of $400 per square foot, making the actual average savings
closer to $400,000.
The CEA research department has also been conducting an in-house research
project to evaluate the hazard models that are used to establish insurance rates.
The CEA was created by the California legislature and is governed
by the California Insurance Code. The code requires that insurance
rates be established using best available science. The USGS and CGS are
specifically identified as experts providing this best available science.
The current estimates of future earthquake ground motion hazards
in California presented in the USGS probabilistic seismic hazard maps of
California are based on two models. One, UCERF3, time-independent
model for earthquake – earthquake magnitudes, locations, and rates
or probabilities. And two, NGA-West2 model
for estimating future ground motion levels and their uncertainties.
The implementation of the UCERF3 and NGA-West2
also demanded and received additional information or judgments
on the part of the USGS implementation team.
UCERF3 had an impact on insurance rates, causing significant increases
in premiums for many California homeowners.
Two elements with the largest impacts were the inclusion of multi-segment
and multi-fault ruptures. As a direct result of UCERF3,
the CEA created the Best Available Science Committee, or BASC.
BASC is comprised of the CEA chief mitigation officer, chief risk and
actuarial officer, chief catastrophe response and resiliency officer,
senior science adviser, and director of re-insurance and risk transfer.
The first goal of BASC was to conduct an in-depth investigation of the UCERF
time-independent model features in light of the information and knowledge
which has been produced since the implementation of the model
and release of the seismic hazard maps for California.
The second goal was to explore and address issues related to
the USGS implementation of UCERF3 and NGA-West2 models
which resulted in the probabilistic seismic hazard maps of California.
As of the December 2021 CEA board meeting, BASC completed
its initial investigation, having conducted 13 interviews with
scientists and researchers involved with the UCERF3 project questioning
specific issues with UCERF3. BASC also interviewed various
representatives from each of the three modeling companies – CoreLogic,
RMS, and AIR, that are under contract with the CEA to implement the hazard
model developed in UCERF3 into models that provide
information used to establish earthquake insurance rates
and secure re-insurance and other financial products.
Based on this effort, BASC has ruled out the possibility of making short-term
or immediate modifications to the CEA’s view of best-available science.
BASC is currently working with the California Geological Survey and the
United States Geological Survey to identify ways in which the CEA and
other members of the risk modeling community can enhance their
participation in the development of the ongoing hazard model.
BASC has also determined that USGS is addressing the issues identified as
requiring further study in their ongoing work on the next model, UCERF4.
Additionally, BASC is reviewing the implementation and assumptions
underlying the demand-search component of the catastrophe loss
models. BASC will continue as a CEA committee reporting
frequently to a larger CEA modeling committee.
We look forward to this collaborative effort and would be happy to
meet with researchers studying segmentation and the creeping section
of the San Andreas Fault. Thank you.