Current EEW Research at Caltech

EEW research and development at Caltech spans a wide range of approaches and methods, some of which are part of the ShakeAlert EEW System while others are exploratory efforts related to EEW applications in general. Current research topics include:

• Improving the FinDer EEW algorithm
• Improving the PLUM EEW algorithm (the APPLES configuration)
• Improving ground-motion modeling in EEW
• Investigating using fiber-optic DAS in EEW applications
• Developing EEW performance visualization tools, performance evaluation methods, and preferred alerting strategies
• Investigating station and network improvements

Improving the FinDer EEW Algorithm

For an overview of the Finite Fault Rupture Detector (FinDer) algorithm, see our page: EEW Algorithms in Current ShakeAlert Operations.

While the FinDer EEW algorithm has been integrated into ShakeAlert operations since 2018, FinDer is continually undergoing assessments and improvements (as are the other aspects of ShakeAlert). Recent improvements to FinDer include adjustments that allow FinDer to handle multiple sets of ground-motion templates as well as the development of fault-specific templates. The primary template set in FinDer was created using simple fault geometry. Simple fault geometry makes these templates extremely versatile, as they can be applied to most earthquakes that occurwithin the ShakeAlert reporting region. However, if we know what faults are likely to host large earthquakes, such as the San Andreas fault and the Cascadia subduction zone, we can also create additional FinDer template sets that incorporate the location-specific geometry of these faults, which could make FinDer’s source estimates more accurate should a large earthquake occur on one of these faults. Fault-specific templates for the San Andreas fault and the Cascadia subduction zone are currently undergoing refinement and are slated for formal testing in the near future. Other FinDer developments include improving FinDer’s performance during complex earthquake sequences, testing potential updates to the ground-motion models used for FinDer’s templates, and investigating how site-specific ground-motion corrections can be incorporated into FinDer’s source estimation procedure.

References

• McGuire, J.J., C.W. Ulberg, A.I. Lux, M. Böse, J.R. Andrews, D. Smith, B. Crowell, J.R. Murray, I. Henson, R. Hartog, et al. (2025). ShakeAlert version 3: Expected performance in large earthquakes. Bulletin of the Seismological Society of America, XX, 1-29. https://doi.org/10.1785/0120240189
• Böse, M., S. Ceylan, J. Andrews, F. Massin, J. Clinton, J.K. Saunders, O. Tatar, M. Türkoglu (2024). Rapid Finite-Fault Models for the 2023 Mw 7.8 Kahramanmaras, Türkiye, Earthquake Sequence. Seismological Research Letters, 95(5), 2761-2778. https://doi.org/10.1785/0220230426
• Lux, A.I., D. Smith, M. Böse, J.J. McGuire, J.K. Saunders, M. Huynh, I. Stubailo, J. Andrews, G. Lotto, B. Crowell, et al. (2024). Status and performance of the ShakeAlert earthquake early warning system: 2019-2023. Bulletin of the Seismological Society of America, 114(6), 3041-3062. https://doi.org/10.1785/0120230259
• Böse, M., J. Andrews, R. Hartog, C. Felizardo (2023). Performance and Next‐Generation Development of the Finite‐Fault Rupture Detector (FinDer) within the United States West Coast ShakeAlert Warning System. Bulletin of the Seismological Society of America. https://doi.org/10.1785/0120220183
• Böse, M., J. Andrews, C. O’Rourke, D. Kilb, A. Lux, J. Bunn, J. McGuire (2023). Testing the ShakeAlert earthquake early warning system using synthesized earthquake sequences. Seismological Research Letters, 94(1), 243-259. https://doi.org/10.1785/0220220088

Improving the PLUM EEW Algorithm (the APPLES Configuration)

For an overview of the Propagation of Local Undamped Motion (PLUM) algorithm, see our page: EEW Algorithms in Development for Potential Future ShakeAlert Operations.

Ongoing research for the U.S. version of the PLUM algorithm includes investigating potential adjustments to PLUM’s ground-motion estimation methodology. In the original PLUM approach, the ground-motion observations at a given seismic station are forward-predicted to an area within a set radius of that station, and the maximum-predicted value at a location is used to determine the alert for that location. PLUM operations in Japan consider a relatively small prediction radius of 30 km, but because EEW alert regions in Japan (sub-prefectures) are larger than the alert grid used in the ShakeAlert grid product, a larger prediction radius of 60 km is needed to maintain the same alert performance for PLUM in the U.S.

While this larger prediction radius was found to be optimal for most alerting needs, it can overwarn for smaller earthquakes where the spatial footprint of significant ground motions is smaller than the PLUM prediction radius. Current research involves adding attenuation into PLUM’s ground-motion estimation procedure, where the estimated ground motion at a location varies according to the observed ground motion at the seismic station and the distance from the station to the location of interest; we call this new configuration Attenuated ProPagation of Local Earthquake Shaking (APPLES). This adjustment will help reduce overwarning during smaller magnitude earthquakes as well as potentially increase warning times in some areas during larger magnitude earthquakes, as available warning times would no longer be restricted by a fixed forward-prediction distance. Adding attenuation in PLUM’s ground-motion forward-prediction procedure will also produce ground-motion distributions that are more similar in style to ShakeAlert’s ground-motion distributions, and may make them easier to incorporate into ShakeAlert operations.

References

• Saunders, J.K., E.S. Cochran, J.J. Bunn, A.S. Baltay, S.E. Minson, C.T. O’Rourke (2024). Incorporating intensity distance attenuation into PLUM ground-motion-based earthquake early warning in the United States: the APPLES configuration. Earth’s Future, 12(2), e2023EF004126. https://doi.org/10.1029/2023EF004126
• Saunders, J.K., S.E. Minson, A.S. Baltay, J.J. Bunn, E.S. Cochran, D.L. Kilb, C.T. O’Rourke, M. Hoshiba, Y. Kodera (2022). Real‐time earthquake detection and alerting behavior of PLUM ground‐motion‐based early warning in the United States. Bulletin of the Seismological Society of America, 112(5), 2668-2688. https://doi.org/10.1785/0120220022
• Cochran, E.S., J.K. Saunders, S.E. Minson, J. Bunn, A. Baltay, D. Kilb, C. O’Rourke, M. Hoshiba, Y. Kodera (2022). Alert optimization of the PLUM earthquake early warning algorithm for the western United States. Bulletin of the Seismological Society of America, 112(2), 803-819. https://doi.org/10.1785/0120210259

Improving Ground-Motion Modeling in EEW

Researchers at Caltech are using shaking intensity observations from the USGS “Did You Feel It?” community science product to help re-evaluate the combination of ground-motion prediction equations (GMPEs) and ground-motion-to-intensity conversion equations (GMICEs) used in ShakeAlert EEW applications. Ground-motion model development, especially GMICEs, has traditionally focused on accurately modeling damaging ground-motion levels, as these ground motions are of greatest concern for seismic hazard analysis applications.

Non-damaging shaking intensity levels are more difficult to model, as these are defined primarily by peoples’ experiences of the earthquake and do not easily relate to specific peak ground acceleration and velocity observations. Accurately modeling the extents of non-damaging shaking is important for ShakeAlert, as the EEW system uses alert thresholds that correspond to these lower intensity levels. Understanding the transition between likely-felt and likely-not-felt shaking is also important for assessing EEW alert performance and for understanding public perceptions of EEW performance.

References

• Saunders, J.K., A.S. Baltay, S.E. Minson, and M. Böse (2024). Uncertainty in Ground-Motion-to-Intensity Conversions Significantly Affects Earthquake Early Warning Alert Regions. The Seismic Record, 4(2), 121-130. https://doi.org/10.1785/0320240004

Investigating Using Fiber-Optic DAS for EEW Applications

Fiber-optic distributed acoustic sensing (DAS) arrays provide a wealth of strain observations that could help EEW applications in multiple ways. DAS arrays could provide ground-motion observations in locations that are challenging for traditional seismic instruments, such as offshore environments where seafloor seismic instruments typically require fiber-optic cables for real-time data transmission. If DAS strain observations can be converted to other ground-motion metrics used by the seismic-based EEW algorithms (velocities or accelerations), DAS data could augment the traditional seismic data sent to existing EEW algorithms and densify the existing earthquake monitoring network.

Alternatively, strain-based EEW algorithms that use DAS data exclusively could provide independent earthquake source parameter estimates from those that use traditional seismic instruments, which could improve EEW system resiliency. Using DAS for EEW is still in early exploration stages, and solutions to many technical challenges are being investigated by researchers at Caltech/SCSN. Recent research highlights include the development of a strain-based magnitude scaling relationship specifically for DAS observations, which could be used for a future dedicated strain-based EEW algorithm, as well as applying machine learning methods for automatic P- and S-wave detections along a DAS array.

References

• Zhu, W., E. Biondi, J. Li, J. Yin, Z.E. Ross, Z. Zhan (2023). Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning. Nature Communications, 14, 8192. https://doi.org/10.1038/s41467-023-43355-3
• Yin, J., M.A. Soto, J. Ramírez, V. Kamalov, W. Zhu, A. Husker, Z. Zhan (2023). Real‐Data Testing of Distributed Acoustic Sensing for Offshore Earthquake Early Warning. The Seismic Record, 3(4), 269-277. https://doi.org/10.1785/0320230018
• Yin, J., W. Zhu, J. Li, E. Biondi, Y. Miao, Z.J. Spica, L. Viens, M. Shinohara, S. Ide, K. Mochizuki, A.L. Husker, Z. Zhan (2023). Earthquake magnitude with DAS: a transferable data-based scaling relation. Geophysical Research Letters, 50(10), e2023GL103045. https://doi.org/10.1029/2023GL103045
• Farghal, N.S., J.K. Saunders, G.A. Parker (2022). The potential of using fiber optic distributed acoustic sensing (DAS) in earthquake early warning applications. Bulletin of the Seismological Society of America, 112(3), 1416-1435. https://doi.org/10.1785/0120210214

Developing EEW Visualization Tools, Performance Evaluation Methods, and Preferred Alerting Strategies

For several years, researchers at Caltech have been involved in the development of alert visualizations and alert performance analysis methods for applications to the ShakeAlert project as well as for EEW more generally. Caltech created and maintains the EEW Display software, which provides map visualizations of ShakeAlert alert regions and shows how alerts can update in time during an earthquake as more observations become available. EEW Display is used internally by ShakeAlert project team members as well as some ShakeAlert technical users to help monitor the real-time alert system. It can also run earthquake simulations and past event playbacks to demonstrate how the EEW system works.

Station-based alert quality analysis methods, which compare the predicted ground motions and warning times relative to the ground-motion observations at the seismic stations used to determine the alert regions, were originally developed at Caltech and adopted by ShakeAlert into the official system testing protocols. Current research efforts involve additional development and refinement of alert quality analysis methods that consider EEW performance in terms of the overall area alerted (i.e., expanding beyond seismic station locations) as well as in terms of population. There are also efforts to compare these alert performance calculations to reported alert receipt and warning time information from the USGS “Did You Feel It?” survey, the results of which will be used to develop alert quality visualization tools to help with communications about EEW alert performance.

Another active area of research is applying these alert quality evaluation methods in cost-benefit analyses to find optimal alerting strategies for specific EEW performance needs. An optimal EEW alerting strategy is one that balances the need to reduce missed alerts to locations that experience ground-motion levels of interest for EEW while also reducing overwarning to locations that ultimately experience lower ground motions that are not of interest for EEW. Because of ground-motion variabilities that are difficult to model in real time, there will always be locations that experience higher-than-expected ground motions compared to the EEW information (potentially leading to missed alerts) as well as locations that experience lower-than-expected ground motions (potentially leading to overwarning). Thus, missed alerts cannot be minimized without also increasing overwarning. Some EEW system users may be less tolerant of overwarning compared to other users, and if these two sets of users have the same target ground-motion thresholds for EEW alerts, they likely could have different preferred alerting strategies. For the ShakeAlert system, different alerting strategies usually means selecting different magnitude and ground-motion alert thresholds.

References

• McGuire, J.J., C.W. Ulberg, A.I. Lux, M. Böse, J.R. Andrews, D. Smith, B. Crowell, J.R. Murray, I. Henson, R. Hartog, et al. (2025). ShakeAlert version 3: Expected performance in large earthquakes. Bulletin of the Seismological Society of America, XX, 1-29. https://doi.org/10.1785/0120240189
• Goltz, J.D., D.J. Wald, S.K. McBride, E. Reddy, V. Quitoriano, J.K. Saunders (2024). The Ojai California Earthquake of 20 August 2023: Earthquake Early Warning Performance and Alert Recipient Response in the Mw5.1 Event. Seismological Research Letters, 95(5), 2745-2760. https://doi.org/10.1785/0220240023
• Minson, S.E., E.S. Cochran, J.K. Saunders, S.K. McBride, S. Wu, A.S. Baltay, K.R. Milner (2022). What to expect when you are expecting earthquake early warning. Geophysical Journal International, 231(2), 1386-1403. https://doi.org/10.1093/gji/ggac246
• Saunders, J.K., S.E. Minson, A.S. Baltay (2022). How low should we alert? Quantifying intensity threshold alerting strategies for earthquake early warning in the United States. Earth’s Future, 10(3), e2021EF002515. https://doi.org/10.1029/2021EF002515
• Cochran, E.S., J.K. Saunders, S.E. Minson, J. Bunn, A. Baltay, D. Kilb, C. O’Rourke, M. Hoshiba, Y. Kodera (2022). Alert optimization of the PLUM earthquake early warning algorithm for the western United States. Bulletin of the Seismological Society of America, 112(2), 803-819. https://doi.org/10.1785/0120210259

Investigating Station and Network Improvements

The seismic networks that contribute real-time data to the ShakeAlert system are in the final stages of adding new real-time stations to meet the station-spacing goals for the early warning system. At the SCSN, most of the new stations that are slated to be added to ShakeAlert data streams are existing SCSN seismic stations that are receiving upgraded data telemetry equipment. All new stations to the ShakeAlert data streams go through a station acceptance process, where the real-time seismic data quality is assessed for an interim period before the station is allowed to send data to the ShakeAlert production system. Caltech/SCSN researchers assist with this process for stations from all ShakeAlert-contributing seismic networks, and have been integral in the development of the EEW algorithm trigger monitoring tools and the station telemetry latency monitoring tools necessary for this analysis.