Crowdsourcing Felt Reports using the MyShake smartphone ...

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Crowdsourcing Felt Reports using the MyShake smartphone app

Authors: Qingkai Kong, Richard M Allen, Steve Allen, Theron Bair, Akie Meja, Sarina Patel,

Jennifer Strauss, Stephen Thompson.

Corresponding author: Qingkai Kong

Address: 7000 East Ave, Livermore, CA 94550

Email: [email protected]/[email protected]

Abstract:

MyShake is a free citizen science smartphone app that provides a range of features related to

earthquakes. Features available globally include rapid post-earthquake notifications, live maps of

earthquake damage as reported by MyShake users, safety tips and various educational features.

The app also uses the accelerometer to detect earthquake shaking and to record and submit

waveforms to a central archive. In addition, MyShake delivers earthquake early warning alerts in

California, Oregon and Washington. In this study we compare the felt shaking reports provided by

MyShake users in California with the US Geological Survey’s “Did You Feel It?” intensity reports.

The MyShake app simply asks “What strength of shaking did you feel” and users report on a five-

level scale. When the reports are averaged in spatial bins, we find strong correlation with the

Modified Mercalli Intensity scale values reported by the USGS based on the much more complex

DYFI surveys. The MyShake felt reports can therefore also be used to generate shaking intensity

maps.

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Introduction

“How strong was the shaking?” is a question we usually ask after each earthquake. From the very

earliest written records in our human history, to modern seismological instrumentation, we have

tried to provide more quantitative answers to this question. More recently, with the wide adoption

of the internet, computers and smartphones, new crowdsourcing ways to answer this question have

been developed. These methods include surveys to provide felt reports (Wald et al. 2001; Wald

and Dewey 2005; Atkinson and Wald 2007; Bossu et al. 2018, 2015, 2012; Rochford et al. 2018;

Liang, Lee, and Hsiao 2019), using messages from twitter (Sakaki, Okazaki, and Matsuo 2010;

Earle et al. 2010; Earle 2010; Ruan et al. 2022), and smartphones or standalone low-cost sensors

(Steed et al. 2019; Minson et al. 2015; Wu 2015; Nof et al. 2019; Clayton et al. 2012, 2015;

Cochran et al. 2009; Hsieh et al. 2014; Jan et al. 2018; Kong et al. 2016). While the high-quality

research grade regional seismic and geodetic networks provide precise but sparse observations,

these crowdsourcing approaches provide a dense but noisier view of earthquake shaking. They

also provide information about people’s perception of shaking and observations of damage after

an earthquake.

Within these newly developed crowdsourcing approaches, MyShake is an application developed

for smartphones at the Berkeley Seismology Lab. It utilizes both the sensors inside smartphones

and user-uploaded felt reports after the earthquake to learn more about the distribution of shaking

and its impacts (Allen, Kong, and Martin-Short 2019; Strauss et al. 2020). To learn more about

how MyShake uses the sensors inside the phones to detect earthquakes for earthquake early

warning and the data quality, please refer to (Kong et al. 2016; Qingkai Kong, Martin-Short, and

Allen 2020a, 2020b; Kong, Patel, and Inbal 2019). In this paper, we will focus on the MyShake

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users’ felt reports, provided through a short series of questions that the users can complete to

evaluate the shaking after an earthquake. In particular, we compare these MyShake felt reports to

the US Geological Survey’s Did You Feel It (DYFI) observations representing the gold standard

for such observations. We show examples of intensity maps derived from the MyShake reports

and the distribution of responses, and show the strong correlation between the MyShake felt reports

and those from the DYFI system. Even though the MyShake questions are very simple compared

to the DYFI survey, with sufficient reports from a large group of the users in the earthquake region,

these measurements can be useful and strongly correlate to the known intensity scale with careful

calibration.

Overview of the MyShake felt report

The MyShake felt report is a set of four questions for the users to answer providing information

about their experience during the earthquake and observations of damage around them after the

earthquake (Rochford et al. 2018). With both simplicity and usefulness in mind, the felt report

questionnaires are designed to minimize the effort for the user to complete a report after an

earthquake. The first question asks the user about their location during the quake. The second asks

the user which of 5 shaking levels they experienced (none, light, moderate, strong and severe) each

of which are described and accompanied by a pictorial representation, see Figure 1. The user only

needs to scroll through the images/descriptions to a level that matches his/her experience. The third

and fourth questions ask the user about damage levels to buildings and roads around them

respectively, using a similar 4-level pictorial scale with simple descriptions. The felt report

completed by a user is then uploaded to the MyShake server. Each report immediately becomes

part of an aggregate map of felt shaking intensity visible to all MyShake users in the app. The map

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provides an immediate visualization of the area and strength of shaking and a user can click on a

location to see the number of reports and the different levels of shaking and damage. See figure 3

in (Strauss et al. 2020) for an example of reported shaking maps with information for a recent

earthquake in Puerto Rico.

In this study, we focus on earthquakes that have felt reports within California between 2019-10-

15 to 2021-05-11. In each felt report, we get the timestamp when the report arrived at the server,

the location of the submission, and the shaking level, see table 1 for an example. The shaking level

is an integer number, with -1, 0, 1, 2, 3, representing none, light shaking, moderate shaking, strong

shaking, and severe shaking, respectively. In order to compare the observed and felt report from

MyShake with the USGS DYFI, we obtained the corresponding earthquake data from the USGS.

Specifically, we downloaded Intensity Vs. Distance, Responses Vs. Time, and the Intensity Map.

An example can be accessed at

https://earthquake.usgs.gov/earthquakes/eventpage/ci38695658/dyfi/intensity.

Comparison with Did You Feel It Intensity

Each USGS DYFI survey response is converted into an estimate of seismic intensity on the

Modified Mercalli Intensity (MMI) scale (Wald et al. 2011). Here, we will compare the MyShake

reported shaking intensity scale with 5 levels, to the 10 level DYFI MMI. Our goal is to determine

the degree to which the simplified MyShake survey provides intensity estimates similar to the

DYFI survey. If they do correlate, then we want to develop a scaling relation that will allow us to

convert the MyShake reports to MMI. The MyShake felt report system is still quite new, and for

most earthquakes provides far fewer felt reports than the USGS DYFI submissions. Still, a good

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number of events have more than a few thousands reports submitted, which enables us to take the

first step to understand what these reports can tell us.

Figure 2 shows a histogram of the number of reports collected for each event from the period 2019-

10-15 to 2021-05-11 in California. In total, there are 325 events that have at least 1 felt report, and

the majority of the events have 500 or less reports, with 29 events having more than 500 reports

and 15 events having more than 1000 reports for each earthquake. Table 2 lists these 15 events

with the number of felt reports. A relationship between the number of felt reports and the

population within 0.5 degrees of the event is shown in figure 3, with colors representing the

magnitude. The population data are extracted from the 2020 Gridded Population of the World V4

(see data resources section). We can see the general trend; the number of reports increases when

larger populations are nearby and for larger magnitude earthquakes.

We use the 15 events that have the most felt reports (table 2) to find the relation between the

MyShake felt report shaking levels (-1, 0, 1, 2, 3) and the Modified Mercalli Intensity scale (MMI).

We first bin the MyShake and DYFI intensity observations as a function of epicentral distance.

We then use the two sets of intensity data to tune a simple relationship between MyShake shaking

level to the MMI scale using the following linear equation:

𝑀𝑀𝐼𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 = 𝑎 + 𝑀𝑦𝑆ℎ𝑎𝑘𝑒𝑆ℎ𝑎𝑘𝑖𝑛𝑔𝐿𝑒𝑣𝑒𝑙 × 𝑏 (1)

We minimize the absolute error between the MyShake observations with the DYFI observations,

and found a = 2.4 and b = 2.4 yield the best results. This maps the -1 (None), 0 (light), 1 (moderate),

2 (strong), 3 (severe) of the MyShake felt report shaking level to 0, 2.4, 4.8, 7.2 and 9.6 on the

MMI scale.

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While the MyShake felt reports only 4 levels, it may appear to provide only more granular data

that the DYFI MMI data. However, once the data are binned and averages calculated, it provides

very similar information to the DYFI data. Figure 4 shows the four earthquakes with the largest

number of felt reports and compares the converted MMI shaking scale from the MyShake data

with the DYFI MMI shaking scale. Both datasets are averaged within each epicentral distance bin.

To see the examples of the rest of the events, please refer to the supplementary material. The

majority of the events show good agreement between the two independent shaking estimates. This

indicates that the MyShake simple 5 shaking levels can be converted into the standard MMI with

calibration.

Figure 5 shows measures of fit between the calibrated MyShake felt report intensity and DYFI for

all the 15 events that have 1000 or more reports. The Mean Absolute Error (MAE) calculated by

taking the absolute value of the errors in each distance bin, and then taking the average. Pearson

Correlation R (PR) is also calculated. There are clear trends that with more felt reports collected

by MyShake, the agreement with the DYFI intensity gets better, both for the MAE and PR. Figure

6 shows the spatial distribution of these 15 events with the MAE, and we can see the ones outside

the densely populated areas generally have larger errors due to the smaller number of felt reports

collected.

Not only can we derive the intensity Vs. distance using MyShake felt reports, a map of intensity

variations (similar to a ShakeMap) can also be generated. Using felt reports from the September

9, 2020, M4.5 earthquake in Los Angeles (ci38695658), Figure 7 shows the averaged shaking

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intensity within 0.01 degree (roughly about 1.1 km) bins. Compared with the DYFI intensity map

(see Figure S17), they have very similar patterns. They both show strong shaking about intensity

IV to V around the epicenter, with more shaking on the west side of the earthquake. Figure 8 plots

the submission time of the MyShake and DYFI reports versus time after the origin of this M4.5

earthquake. We can see the general patterns are similar, but in this case, MyShake submissions are

slightly slower in terms of the percentage of the total submission. Intensity maps and submission

histories for other events are included in the supplemental information.

Discussion

MyShake's felt report system is new and it will take some time for users to adapt and get used to

it, both to report damage and also to use the live map in the app to see where damage has occured

in an earthquake. It is important for the success of citizen science projects to provide interactive

features that show the utility of a users’ engagement. In the case of MyShake, showing the users a

community derived shaking map and the number of people who felt the earthquakes near them

provides a sense of participation and community. So far, with the current density of MyShake

users, for M3.5-5.5 earthquakes, we have collected hundreds to a few thousands felt reports. To

minimize the required effort by the users, the felt reports were kept very simple, but we did not

know if this data could still be used to estimate shaking intensity in the same way that the more

complex DYFI reports do. This paper suggests that the 5 shaking-level scale in MyShake can

indeed be converted to the MMI scale using a simple linear equation. This relation was developed

using 15 events that have more than 1000 felt reports collected by MyShake. While it appears to

be doing a good job at this point, it will need to be verified and improved in the future when more

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events are recorded. In particular, we note that the current dataset does not include examples of

the strongest shaking intensities, i.e. greater than intensity 5.

Figure 9 shows two events that have unusually large discrepancies between the MyShake and

DYFI reports. The M5.5 event that occurred in the Ridgecrest region (Figure 9a) has the largest

MEA in the entire dataset. For this event, most of the reports are from distances greater than 50km.

The largest difference between MyShake and DYFI is for the single MyShake report in the distance

bin at ~17km, where there is only one user report from MyShake which has a moderate shaking

report (converted to MMI intensity 4.8), but DYFI reports average above MMI 8. The second

largest difference is also a distance bin that has a very small number of MyShake felt reports

submitted. One potential solution in the future is to add a quality control factor by removing the

bins with a very small number of reports. Figure 9b shows the M4.7 event near Trukee for which

we can see the MyShake converted intensities are systematically higher than that from DYFI, by

about 0.5 - 0.7 units. Most of the distance bins (two exceptions) have 50 or more felt reports. We

do not have an explanation for this single event with systematically different intensities. One

possibility might be the location of the event in eastern California while most of the 15 events used

for the calibration were in western California. However, it is not obvious why the reports from

MyShake users and DYFI users would be different in different locations.

This work is also a step towards the development of a citizen science platform that can utilize

multiple data sources to study the earthquakes and their impact. Previous studies with MyShake

data have focused on the smartphone accelerometers (Kong, Allen, and Schreier 2016; Kong,

Patel, and Inbal 2019; Inbal et al. 2019). The MyShake felt reports are a relatively new feature in

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the MyShake app with the aim of collecting users’ observations about the earthquake after it

occurred. This data is complementary to the accelerometer data. The data collected from the felt

reports are more subjective and depend on the sensitivity level of each person, but we show here

that by aggregating large numbers of these felt reports in a region, the averaged ground shaking

reports are consistent with the more detailed DYFI surveys and reports. The “wisdom of the

crowd” states that by aggregating opinions from a large crowd of people, we can yield good results.

A classic point estimation from a seminal paper published in Nature (Galton 1907) described how

the median of all the entries in a guess the cattle weight competition was amazingly accurate. This

is based on the fact that independent noise in each individual’s estimation cancels out. The

MyShake felt reports are another successful example of the “wisdom of the crowd”. Also, even

though the MyShake reports are based on a small number of categorical levels, i.e. none, light,

moderate, strong, severe shaking, once averaged in spatial bins, the averaged values provide a

more granular estimate of shaking intensity than the available report levels.

Conclusion

This paper shows our initial efforts to evaluate and link the simple MyShake felt reports to MMI

shaking as reported by the USGS DYFI product. We find good correlation between the two when

they are both averages in spatial bins and compared as a function of epicentral distance. The

correlation improves with the increasing number of MyShake felt reports. We need several

thousand reports in order to provide meaningful shaking estimates. The spatially averaged felt

reports can also be plotted as a map to provide a shaking intensity map. Through this established

link between MyShake felt reports and MMI, the crowdsourced MyShake felt reports can be used

by the scientific community to provide another independent shaking intensity dataset.

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Data and Resources

MyShake data are currently archived at Berkeley Seismology Laboratory and use is constrained

by the privacy policy of MyShake (see http://myshake.berkeley.edu/privacy-policy/index.html),

but data for the research purposes can be requested from the authors. The data for the Gridded

Population of the World can be accessed at

https://sedac.ciesin.columbia.edu/data/collection/gpw-v4. USGS DYFI data can be accessed at

https://earthquake.usgs.gov/data/dyfi/.

Acknowledgements

The Gordon and Betty Moore Foundation funded this analysis through grant GBMF5230 to UC

Berkeley. The California Governor’s Office of Emergency Services (Cal OES) funds the operation

of MyShake through grant 6142-2018 to Berkeley Seismology Lab. We thank the previous and

current MyShake team members: Roman Baumgaertner, Garner Lee, Arno Puder, Louis Schreier,

Stephen Allen, Stephen Thompson, Jennifer Strauss, Kaylin Rochford, Akie Mejia, Doug

Neuhauser, Stephane Zuzlewski, Asaf Inbal, Sarina Patel and Jennifer Taggart. All the analysis of

this project is done in Python. We thank all the MyShake user citizen scientists for their data

contributions. We also thank the USGS DYFI project for enabling this study. Qingkai Kong's work

was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore

National Laboratory under Contract Number DE-AC52-07NA27344. Any opinions, findings,

conclusions, or recommendations expressed in this publication are those of the authors and do not

necessarily reflect those of the supporting agencies. This is LLNL Contribution Number LLNL-

JRNL-834409.

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Tables, with captions above each table

Table 1. An example of the felt report data used in the study.

Type Example

Time of the submission Integer, Unix timestamp 1621007178230

Location of the report Float Latitude and Longitude pair

(37.23, -122.34)

Shaking level Integer: -1, 0, 1, 2, 3 2

Table 2. List of events have more than 1000 felt reports submitted.

Earthquake id Magnitude # of reports

ci38695658 4.5 8872 nc73512355 4.2 4810 nc73291880 4.5 4432 ci39838928 4 4357 ci39400304 3.7 3723 ci39126079 4.9 3050 ci39462536 5.5 2789 ci39277736 3.6 2787 ci39493944 5.8 2494 ci38905415 3.5 2211

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ci39322287 4.2 1808 nc73559265 4.7 1671 nc73322626 3.9 1354 nc73505175 3.3 1207 nc73510910 3.6 1187

List of figure captions

Figures, with captions below each figure

Figure 1. The 5 different shaking levels that users can select within MyShake felt report

questionnaire (none, light, moderate, strong and severe). Users scroll to a page that matches his/her

experience to select.

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Figure 2. Histogram showing the number of earthquakes for which a specified number of felt

reports were submitted between 2019-10-15 and 2021-05-11 in California.

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Figure 3. Number of felt reports versus population (in millions) with 0.5 degree of the earthquake. The color shows the magnitude of the earthquake. Note, we add 1 to the reported population for each earthquake in order to plot on a logarithmic scale.

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Figure 4. Shaking intensity (MMI scale) versus distance for four earthquakes. Each panel compares the estimated MMI shaking derived from the MyShake felt reports using equation (1) to the MMI estimates from DYFI. The MyShake data are shown as circles, and the DYFI data are shown as inverted triangles. The color represents the number of reports in each distance bins. The distance bins are set to be the same as reported by DYFI: 13.0, 17.3, 23.1, 30.8, 41.1, 54.8, 73., 97.4, 129.9, 173.2, and 200 km. Each figure title gives the event id, magnitude, place of the earthquake, number of felt reports Mean Absolute Error (MAE) and Pearson Correlation R (PR).

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Figure 5. Fitting metrics between MyShake calibrated intensity and the DYFI intensity in different distance bins for the 15 events with the most felt reports. Left: Mean Absolute Errors versus the number of felt reports. Right: the Pearson Correlation versus the number of felt reports.

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Figure 6. Spatial distribution of the 15 events with more than 1000 felt reports. The size of the star represents magnitude, and the color represents the MAE.

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Figure 7. Derived intensity map from MyShake felt reports after conversion to MMI The map uses the same intensity color scale as the one used in ShakeMap by the USGS. The intensities are averaged within each 0.01 by 0.01 bins. The red star is the location of the 2020-09-19 M4.5 earthquake. The corresponding DYFI intensity map is shown in figure S17.

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Figure 8. Reporting time history for the MySHake and DYFI felt reports. The percentage of the felt reports is shown versus time after the origin of the earthquake. The 1-hour, 6-hour and 12-hour lines are also plotted in the figure.

Figure 9. Two events with poor correlations between the MyShake and DYFI felt reports. Left: Event showing the largest discrepancy between the two datasets. Right: Event shows systematically higher intensity reports from MyShake users than from DYFI. Note that there are also substantially more MyShake reports than DYFI for this event (colors representing the number of reports for MyShake are clipped at 10).

Supplementary Material for Crowdsourcing Felt Reports using the MyShake smartphone app

Qingkai Kong, Richard M Allen, Steve Allen, Theron Bair, Akie Meja, Sarina Patel, Jennifer

Strauss, Stephen Thompson.

Figure S1 - The error of the grid search of the two parameters in equation (1) in the main paper. The best results achieved at a = 2.4 and b = 2.4.

Figure S2 - Shaking scale comparison between MyShake converted MMI with DYFI MMI for event ci38695658









Figure S11 - Shaking scale comparison between MyShake converted MMI with DYFI MMI for event nc73291880






Figure S17 - The 1km DYFI intensity map for 2020-09-19 M4.5 earthquake. The corresponding intensity map using MyShake felt reports is shown in figure 5 in the main text.

Figure S18 - Derived intensity map from MyShake converted MMI scale for 2021-01-17 M4.2 earthquake (nc73512355), we use the same color scale as the one used in ShakeMap from USGS.

Figure S19 - Corresponding MMI scale for 2021-01-17 M4.2 earthquake (nc73512355) from USGS DYFI.

Figure 20. The percentage of the felt reports and DYFI submissions versus time after the origin of the earthquake (nc73512355).

Figure S21 - Derived intensity map from MyShake converted MMI scale for 2021-05-07 M4.7 earthquake (nc73559265), we use the same color scale as the one used in ShakeMap from USGS.

Figure S22 - Corresponding MMI scale for 2021-05-07 M4.7 earthquake (nc73559265) from USGS DYFI.

Figure 23. The percentage of the felt reports and DYFI submissions versus time after the origin of the earthquake (nc73559265).

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