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Hello, everyone. My name is Kevin Milner.
I’m from the Southern California Earthquake Center at USC.
I’m going to be talking to you guys today about operational
earthquake forecasting with UCERF3-ETAS.
I’ll be sharing some lessons learned from the 2019 Ridgecrest earthquake
sequence and implications for future UCERF models.
This work is part of a broad collaboration, and some of
those collaborations are – collaborators are listed here.
Let me begin with a tale of two earthquakes,
each alike in magnitude – let’s say magnitude 6.
Now, the Reasenberg and Jones model, which is a very useful model,
forecasts an approximately 5% probability of each of these
earthquakes triggering a subsequent magnitude 6 or larger earthquake
within one week. Well, what if I were to tell you that one of those earthquakes
was right on the San Andreas Fault with a really high – or some other
high-slip-rate, well-studied fault. Another one was kind of
out in the middle of – in the middle of nowhere.
Our intuition suggests that aftershock probabilities are greater
for earthquakes near active faults. So maybe we might think that the
one on the left would have a higher probability of triggering something
truly large than the one on the right. Now, the UCERF3-ETAS model was
constructed precisely to formalize this sort of intuition into a usable model.
Now, UCERF3-ETAS is an extension of the Third Uniform California
Earthquake Rupture Forecast, or UCERF – that’s the time-independent
model used by the National Seismic Hazard Maps, building code, etc.
Then that model is extended with a time-department model –
UCERF3-TD, which adds Reid’s elastic rebound theory, where the
probability on a fault is dependent on the date of last event.
So maximum probability gains here, for example, on the southern
San Andreas and also up in the Hayward and Calaveras can get
up to, you know, a factor of 2 over the long-term model.
And they also decrease in the proximity of prior large earthquakes.
Now, UCERF3-ETAS is kind of a new model that extends both of those.
It merges kind of a typical spaciotemporal clustering model –
ETAS from Ogata – with the UCERF3 finite fault model.
So the inputs to the model could include a historical catalog or
scenario events or kind of trigger ruptures that we put into the model,
either on or between UCERF3 faults.
Now, the outputs of this model are synthetic catalogs –
suites of many of them. And, from those synthetic catalogs,
we compute things like triggering statistics – X percent probability
of triggering a magnitude Y – or even fault-specific ones,
so X percent probability of triggering a magnitude Y on fault Z.
And also realistic scenario aftershock catalogs.
So examples of typical and extreme sequences.
This is an example for a Parkfield magnitude 6 as a main shock.
Kind of typically, there will be some small aftershocks.
But, every once in a while, when one of those happens, you might get a really
large sequence where, here, the southern San Andreas was triggered.
Now, we call this operational-ish earthquake forecasting
because it’s still somewhat of a manual process.
UCERF3-ETAS requires high-performance computing.
You need many synthetic catalogs for stable results.
So, for the Ridgecrest results I’m going to show, we ran 100,000
catalogs for each Ridgecrest forecast, and each took about seven hours
on 36 compute nodes – a total of about 2,500 CPU hours for forecasts.
So it’s a big model.
It’s run on demand. So what that meant is, when I felt
shaking on July 4th in the Ridgecrest 6.4, I got to work.
I had simulations running about 33 minutes after the – after the
main shock on kind of our local cluster at USC.
And the first results from kind of a partial simulation were posted
to response.scec.org at 11:39, so just about an hour after the –
after the magnitude 6.4. Now, those first results gave
a 2.8% chance of another 6.4 or larger in the next week and
just a 0.4% chance of a magnitude 7 or larger.
Which is less than what you would get from a Gutenberg-Richter
extrapolation. The 6.4 occurred in an area between mapped UCERF3 faults.
And, in this areas, UCERF3 is anti-characteristic, and that ends up
being something that’s an important consequence of the UCERF3
framework for this type of – type of model.
Now, the USGS forecasted a 2.3% chance of a magnitude 7 the next week.
And that was using the Reasenberg and Jones model as updated in
Page et al. 2016, which assumes a Gutenberg-Richter magnitude
frequency distribution. So this was one of our kind of
first lesson learned, where UCERF3 is anti-characteristic in off-modeled
fault areas, which leads to lower probabilities than generic forecasts
if a sequence is happening in one of those off-fault areas.
But, if we knew about those structures a priori, that might not be
anti-characteristic in the model. So I think this is a place where
we need to rethink things in the next – in the next model.
We could re-evaluate fault characteristicness in UCERF4,
both on- and off-fault.
And potentially, we could add a logic tree branch which forced
off-fault areas to be Gutenberg-Richter or even an inversion constraint that
could encourage Gutenberg-Richter scaling on faults, where possible,
where the data did not require characteristic distribution.
Another potential thing that could change the characteristicness
on faults is more connectivity. So, if you have more magnitudes –
if a fault is able to participate in earthquakes that rupture more faults,
it can – some of it’s kind of – slip rate can be accommodated
by larger ruptures, which would result in a less-characteristic
distribution on an individual fault.
So, when the 7.1 occurred, the primary developer, which is me,
was at a Dodger game. You can see the players
didn’t quite notice it, but we were excited up in the stands.
And since this is still a somewhat manual process, it requires someone
to submit the jobs. I couldn’t at that moment, so I messaged
my collaborators – Bill Savran, who had attended a prior training
session, and he submitted the simulations for me.
We were able to get results to the California Earthquake Prediction
Evaluation Council – CEPEC – via Tom Jordan just in time
for their 10:00 a.m. call the next – the next morning.
The results are shown here on the bottom right.
It’s kind of an anti – still anti-characteristic 1% probability
of another 7.1 or larger in the next week. And 0.6% chance of
triggering a 7 or larger on the Garlock, which was a key concern for CEPEC.
And that was using just a point-source representation of the –
of the earthquake. We updated the results later on that – on that day
with a finite fault that my collaborator Ned Field drew.
And we noticed the inclusion of the finite fault increased the
Garlock probability of – through a pretty large [inaudible],
up from 0.6 to 1.7% of triggering a Garlock magnitude 7 in one month.
And that’s because this fault surface here is standing
close to the Garlock Fault down here.
Now, later in that week, on Thursday, the 11th of July, I drew a new fault
surface kind of using the extents from some InSAR data that I had
seen and aftershock locations. And this new surface got even closer
to the Garlock, which increased the probability all the way up to 4.3%.
So it became apparent that there’s a sensitivity to the finite fault sources.
So here, showing three different models – point source, the first model,
the second model, and basically, as these surfaces got closer and closer
to the Garlock, those probability of any 7.1 and also triggering
the Garlock here, in blue, increased greatly.
So, with that apparent sensitivity, we sought a more authoritative
source than just Ned or myself drawing polygons.
So some options we have are ShakeMap and the teleseismic
inversions posted on ComCat. They both have finite fault surfaces.
We decided to use ShakeMap as our preferred source.
Here’s the latest version, Version 14, which is based on InSAR data,
and this has been our preferred model ever since.
We can fetch it directly from ComCat.
They had some crude finite fault surfaces available shortly after
the event, but it should be noted that this particular finite fault source
took four days to be posted in ShakeMap.
The teleseismic inversion source was available quickly –
about three hours, but was unsuitable for our –
for our uses. It’s enormous and cut across the Garlock Fault.
We thought about potentially tapping into finite fault estimates from
earthquake early warning for a more immediate estimate.
This is – this is the FinDer model, which estimates a finite fault source on the fly.
So that’s something we can potentially tap into to get
good early finite fault estimates.
So another lesson learned, that those fault trigger probabilities are really
sensitive to finite rupture sources. And, as we would expect,
higher for surfaces that extend closer to other known faults in this model.
And so it’s important to know that early results are likely to change as better
finite rupture models are published. And maybe we could think about
adding some sort of measure of source uncertainty to reduce this sensitivity.
We also spent a lot of time through this sequence improving model outputs.
A lot of automatic plot generation for different time periods.
And this is a magnitude probability plot.
It shows kind of order of magnitude increase of probability on the
Garlock Fault as a function of magnitude over the long term
time-dependent and time-independent models.
Include kind of examples of individual catalogs at various percentiles.
This is a median catalog. This is a 97.5th percentile catalog.
And this is the most extreme one, where it triggered a Garlock rupture,
which triggered a magnitude 8 on the southern San Andreas –
really extreme simulation, but a good way of kind of showing the range
of possibilities in these catalogs.
Could also be, then, beneficial to some potential users.
We also are now doing automatic comparisons with ComCat.
So we can kind of see how our forecast is doing against the data.
Also working within the Collaboratory for the Study of Earthquake
Predictability, CSEP, to kind of get more formal testing automated.
So there’s some other automatically generated plots.
You can see here, this is the spatial distribution.
This is another thing that shows the importance of using a good
finite fault model to forecast the spatial distribution.
If you use a point source, you get a very kind of circular forecasted
spatial distribution. But, using the ShakeMap sources,
we can – we estimated the – our outputted spatial distribution
matches the data, which is in cyan here, pretty well.
So that was another important lesson learned.
But I would say the main – the main takeaway from this was
that real events are really important to learn model sensitivities
and what tools are required to build and evaluate forecasts.
And I think one of the important ones for the next round of national maps
is going to be talking about and thinking carefully about the degree of
fault characteristicness in the models. Because they clearly have
a large impact on our short-term earthquake forecasts.
But, in general, I think that, despite these sensitivities,
we learned that UCERF3-ETAS provides useful information about
fault-triggering probabilities and can produce realistic synthetic
aftershock catalogs. And may be worth the investment to fully operationalize.
And, until then, we’ll continue to run it on demand.
If you’d like to learn more about this model and our experience with the
Ridgecrest earthquake, we published a paper in Seismological
Research Letters last year. Take a look, and if we have time for any questions,
I am available. Thank you very much for your – for your attention.
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