Today I will be talking about a fixed-network
smartphone-based approach to earthquake early warning.
I will report on an implementation of this in Costa Rica that we call ASTUTI.
I’d like to acknowledge a large group of collaborators and USAID for funding.
The motivation for our work is listed in these bullets.
The earliest projections of earthquake early warning system performance and
benefits to society have been modified and scaled back as the theoretical,
empirical, practical, and social limitations of EEW are elucidated.
Low-cost consumer-grade sensors in smartphones are now sensitive enough
to be effectively used in EEW systems and even in crowdsourced modes.
A one-size-fits-all approach to EEW, however, may not be appropriate.
Achieving drop, cover, and hold on, for instance, has different requirements
and costs than automatic mitigation measures for expensive infrastructure.
Here we examine the low-cost end of this EEW spectrum.
Specifically, we ask, can off-the-shelf smartphones deployed in a fixed
network provide EEW in a reliable and effective enough manner so that
significant populations receive warnings in time for drop, cover,
and hold on protective actions to be undertaken?
The symbology of these figures will be repeated through this talk.
The copper-color map shows a 1-kilometer population data set.
The capital city, San José, is denoted with an SJ and is the
region of densest Costa Rican population in the center of the country.
The white triangles are phones installed in the field.
From September to December of 2019, we constructed the
ASTUTI network in Costa Rica. Currently the network comprises
82 stations with plans for a future densification phase.
The colored circles show earthquake epicenters coded
according to magnitude. The colored squares show
Did You Feel It? reported Modified Mercalli Intensity values.
Costa Rica’s seismic hazard is due primarily to earthquakes generated
by oblique subduction at the Middle America Trench.
Since 1853, eight earthquakes greater than magnitude 7 have
occurred either on the northern Nicoya or southern Osa peninsulas.
As shown on the right, recently the magnitude 7.6 2012 Nicoya Peninsula
earthquake caused shaking throughout the entire country with PGA values
as high as 0.5 to 1.4 g and MMI 5 to 7 reported in San José.
It is generally accepted that magnitude 6 and above
earthquakes are felt country-wide.
Given the size of the tectonic framework, we designed ASTUTI
with these principles. First, our EEW efforts are focused on warning people,
and our targeted user response is drop, cover, and hold on.
Second, we prioritized detecting and alerting for
Middle America Trench earthquakes.
Third, we attempt to issue warnings at the earliest occurrence of detected
events of potential concern rather than waiting until they exceed some size.
Fourth, because of local ground motion variability
and because it is unlikely that earthquake ruptures are deterministic,
our detection alerting is entirely non-parametric.
Fifth, we evaluate the scenario where every alert will be issued
for the entire country. And finally, sixth, because of
the previous design priorities, there will likely be a number of
false alerts issued for smaller events when users will feel no shaking
although an event was correctly detected.
This will require rapid post-event messaging to remind users that
the system performed correctly even if they did not feel shaking.
We stress, however, that no public alerts have yet been issued, aside from those
sent to our small group of beta testers.
We leave thorough investigation of this topic to a future presentation.
Phones with Android operating systems were installed in the ground floor
of buildings to protect the boxes. Criteria for phone choice was
based on a combination of in-country availability and cost.
Our control and sending app on the phones is called QED
for Quick Event Detection. We also use a mobile management
platform to permit remote root access to the phones.
As shown in the diagram on the right, the key flow of information is from
the phones to a UDP receiver and an MQTT broker that then permits
distribution of the data to any number of processing and archiving clients.
This makes it easily scalable and trivial to add new networks.
From December 2019 to late August 2020, phones streamed
accelerometer data at 10 hertz.
Subsequently, we increased streaming rate to 100 hertz.
At 100 hertz, each site records roughly 70 to 100 megabytes per day.
Any EEW processing algorithm that operates on accelerometer data can
subscribe to the MQTT broker. Here we report on a new algorithm,
PGAN, for Peak Ground Acceleration with N vertices, that is an adaption
of the PLUM – Propagation of Local Undamped Motion – method
for a network of smartphones. PGAN is similar to PLUM in that it
is a non-parametric ground motion- based algorithm which does not attempt
to estimate anything about a detected earthquake source.
PGAN requires multiple neighboring stations to experience anomalous
accelerations in order to trigger an alert. The network is divided into a polygonal
mesh with a configurable number of vertices greater than or equal to 3.
Here we report primarily on quadrilateral PGAN-4 configurations.
Once-per-second PGA values are measured at each station, and the
values are compared with the primary threshold, currently 0.6% g.
If any station is above the threshold, the polygon it is part of is marked as
potentially triggered. The PGA values at each of the
remaining stations in the polygon it is part of are compared with a
secondary threshold, currently 0.55% g. If all stations in a potentially triggered
polygon are above the thresholds, an alert is issued.
An example of this is the green triangle of polygons from the
earthquake – the red star in the figure. Once a polygon is triggered, an alert
is sent out using the Amazon Web Services Simple Notification system.
Here is an example of some of the raw data from the accelerometers.
We combined the raw acceleration data from the three orthogonal
MEMS accelerometers – V, H2, and H1 in the figure –
into a PGA data type sampled and sent at 1 hertz.
This is called the P, for PGA, message. The P message is the vector norm
of the individually de-meaned 100 hertz acceleration values and
has units of meters per second squared. An example of an earthquake and
two small aftershocks are shown on the left figure.
To mitigate the transient noise spikes common to many smartphones,
while permitting real seismic accelerations to pass, the P message
implements a simple filter that takes the 30% highest in a 1-second window.
An example of the noise mitigation is on the right.
We characterize site and network data quality by examining histograms of
PGA data in the figure on the left. Generally, the entire network exhibits
mean P values of 0.25% g – the dark gray histogram in the figure on the left.
Over periods of time varying from minutes to days,
individual stations may experience elevated deviations from this background
behavior, which would be the red-line histogram in the figure on the left.
The causes of these transient elevated periods of background noise are varied
and likely related to temporally changing site characteristics,
including anthropogenic noise. On the right, from six months of
continuously streamed data, we find that data latency varies
from 0.35 to 0.45 seconds, depending on the time of day.
For comparison, published data latencies for the Italian, Chinese,
and ShakeAlert earthquake early warning networks
are 0.9, 2, and 1 to 3 seconds, respectively.
We test the full operation of the ASTUTI system using the smartphone’s
programmable vibration feature. We used the M7.6 2012 Nicoya
earthquake as a scenario event and programmed S wave arrival time
for each site. Over a period of three days, we vibrated the phones in the
Nicoya Peninsula three times an hour, resulting in a total of 216 simulations.
An example of one simulation is on the left.
The magenta title shows detection time, and the magenta circle shows the
S wave position at the time of detection.
The green triangles show the triggered stations.
On the right, we show that triggered detection latency for the PGAN-4
method has a mean value of 12 to 13 seconds for all of the simulations.
We measured the alert latency by sending text message alerts to a set of
15 people with cell phones in and around the greater San José region.
We find that the mean value for alerting latency is on the order of 4 seconds.
Because this is from a small number of phones that were relatively close
together, it is not clear how representative this metric is of latencies
that would result from sending many thousands of alerts across
a wider geographic region. To the best of our knowledge,
none of the other smartphone EEW projects, such as MyShake,
or the Earthquake Network Project, have reported alerting latency data.
Because of COVID-19-related travel restricting the development of our
alerting messaging algorithm, the entire system was not operational
until August of 2020. Accordingly, here we evaluate
performance based on offline playback from December 2019 to June 2020.
During the period of assessment, ASTUTI, using the PGAN-4 algorithm,
detected 5 of the 13 events that were accompanied by Did You Feel It?
reports of shaking somewhere in Costa Rica.
The majority of these events – 9 of 13 – occurred outside
of the network, and they ranged in magnitude from 5.3 to 5.9.
Only one of the detected events, from March 7th of 2020, had an
epicenter entirely within the network. That is the example shown in the figure.
In addition to the associated Did You Feel It? data, we plot the
estimated position of the S wave front at the time that the alert was issued
with the solid magenta circle. In the plots, we add to the detection
time 5 seconds – the median value of the alert time – and 15 seconds
for total estimate of drop, cover, hold on available time.
That’s shown as the dashed circle. In this example, detection was
at 11 seconds after origin time. At least 20 seconds passed until
that S wave would reach the San José dense population,
suggesting that drop, cover, and hold on could be achievable.
Generally, our detection times for PGAN-4 ranged from 11 to 30 seconds.
This range for subduction zone events compares well with systems such as
SASMEX and ShakeAlert. We classify alerting and felt shaking
outcomes for users as true positive shaking – TP-S;
TP-ns – true positive no shaking; NA – no alert – the system correctly
detects and event, but a user does not receive an alert prior to shaking;
FA – false alert – the system incorrectly detects an event when none occurred;
and MA – missed alert – a felt event occurs,
but the system does not issue an alert.
For each event, we plot P message records sections showing the expected
range of P and S wave velocities of 6 to 3 kilometers per second,
respectively, shown as the pink envelope. The figure on the right
is a zoom of the record section on the left showing only the triggering stations,
which are indicated in bold in the figure on the left.
The triggering time is shown as the vertical magenta line.
Clearly, the trigger occurs with the earliest PGA elevated values,
which correspond with predicted P wave arrival times.
We find that generally two of the five ASTUTI-detected
events triggered on the P wave.
Generally, the ASTUTI-detected events were accompanied by stronger shaking
that was felt by much larger percentages of the population.
The detected events had median and max MMI levels of 4.3 and 6,
with roughly 17 to 73% of the population experiencing felt shaking,
while the non-detected events had median and max MMI levels of 2.9
and 3.8, with zero to 19% of the population experiencing felt shaking.
The figure on the right shows all the non-detected events and the paucity of
Did You Feel It? reports indicates the low levels of shaking from these events.
For comparison, here is an offshore event that ASTUTI detected, but rather
than P, it triggered on the S wave. See the record sections on the right
for clear S triggering. The 29-second detection time for this event was more
than 2 times slower than other events for two related reasons.
The first is the event was offshore. And the second is it triggered
on the S wave. Despite this, more than 60% of the Costa Rican
population would have gotten the alert prior to S wave arrival.
But that would have only permitted a couple of seconds
for mitigating actions in San José.
The alerting outcomes for the five detected events are shown in the table.
For the PGAN-4 algorithm, the zero percent false alarm rate
is lower than all other EEW systems reported false alarm rates,
aside from retrospective testing of PLUM with West Coast U.S. data.
Detection times range from 11 to 30 seconds, depending on
offshore or onshore origins. The detection times offshore
are notably longer than from events closer to shore.
TP-S percentages vary from 6 to 63%. TP-ns percentages vary from 27 to 71%.
And the NA percentages are less than 10%.
As expected, a low detection threshold criteria, combined with a country-wide
alerting region, also leads to significant TP-ns outcomes.
It’s not clear what penalty there may be in terms of user engagement if
an EEW system provides alerts without felt shaking, especially if
users were to receive a rapid follow-on message stating that
an event had been correctly detected, although they did not feel shaking.
In general, recent social science research suggests that the boy who cried
wolf assumption may be overstated. From the little social science work done
in EEW, it appears that EEW users may tend to be tolerant of TP-ns outcomes.
For instance, Nakayachi et al. found that 75% of EEW users rated the Japanese
EEW system positively, even though a significant population
received TP-ns, what they term false alarm, outcomes.
Furthermore, users reported that these outcomes were not the
main reasons for negative perceptions of EEW.
Clearly, a high priority must be EEW pre- and post-event education
and messaging and research in this – on this topic.
So the take-home messages are operating in Costa Rica over
six months, the ASTUTI network detected five events that caused
widespread felt shaking and had zero false alarms.
ASTUTI detected the events that mattered.
Large percentages of the population experienced shaking for detected
events, while very small percentages of the population
experienced shaking for missed events.
The detections and alerts would have provided time for drop, cover,
and hold on protective actions to be taken before S wave arrival
for large percentages of the Costa Rican population.
Two of the five events were triggered by P waves on the phones, suggesting
that smartphone-based EEW could be more effective than previously thought.
And finally, fixed-network smartphone-based EEW can provide
performance on par with scientific-grade EEW,
but capital and annual costs for ASTUTI – around $20,000 each –
are two orders of magnitude greater than for ShakeAlert.