arXiv subjects

Richard M. Allen

Publications and source records attributed to Richard M. Allen.

6 recordsLinked to original sources

arXiv2026

Performance and User Response of Android's Smartphone-Based Alerts in the 2025 Marmara Ereglisi Earthquake

This study presents a comprehensive evaluation of Googles Android Earthquake Alert (AEA) system during the Mw 6.2 Marmara Ereglisi, Turkiye earthquake. AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users. Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds. While near-source warning windows were shorter, the system achieved 90% true positives and 99% precision overall. The high density of the phone network enabled faster detection than traditional stations, even for this offshore epicenter. Feedback data shows AEA recipients were highly likely to take protective actions, such as drop, cover, and hold on, or warn others. Timely alerts substantially increased user engagement, perceived usefulness, and future trust. These results highlight how crowd-sourced technology and behavioral insights can effectively enhance seismic resilience on a massive scale.

physics.geo-ph↗

arXiv2026

Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings

Android's Earthquake Alert (AEA) system provided timely early warnings to millions during the Mw 6.2 Marmara Ereglisi, T\"urkiye earthquake on April 23, 2025. This event, the largest in the region in 25 years, served as a critical real-world test for smartphone-based Earthquake Early Warning (EEW) systems. The AEA system successfully delivered alerts to users with high precision, offering over a minute of warning before the strongest shaking reached urban areas. This study leveraged Large Language Models (LLMs) to analyze more than 500 public social media posts from the X platform, extracting 42 distinct attributes related to user experience and behavior. Statistical analyses revealed significant relationships, notably a strong correlation between user trust and alert timeliness. Our results indicate a distinction between engineering and the user-centric definition of system accuracy. We found that timeliness is accuracy in the user's mind. Overall, this study provides actionable insights for optimizing alert design, public education campaigns, and future behavioral research to improve the effectiveness of such systems in seismically active regions.

stat.AP↗

arXiv2023

Crowdsourcing Felt Reports using the MyShake smartphone app

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 correlations 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.

physics.geo-ph↗

arXiv2019

Towards Global Earthquake Early Warning with the MyShake Smartphone Seismic Network Part 1 -- Detection algorithm and simulation platform

The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning and other applications by leveraging the power of crowdsourcing. The MyShake mobile application first detects earthquake shaking on a single phone. The earthquake is then confirmed on the MyShake servers using a "network detection" algorithm that is activated by multiple single-phone detections. In this part one of the two paper series, we present a network detection algorithm and a simulation platform to test earthquake scenarios at various locations around the world. The proposed network detection algorithm is built on the DBSCAN classic spatial clustering algorithm, with modifications to take temporal characteristics into account and the association of new triggers. We test our network detection algorithm using real data recorded by MyShake users during the M4.4 January 4th, 2018, Berkeley and the M5.2 June 10th, 2016, Borrego Springs earthquakes to demonstrate the system's utility. In order to test the entire detection procedure and to understand the first order performance of MyShake in various locations around the world representing different population and tectonic characteristics, we then present a software platform which can simulate earthquake triggers in hypothetical MyShake networks. Part two of this paper series explores our MyShake early warning simulation performance in selected regions around the world.

physics.geo-ph↗

arXiv2019

Towards Global Earthquake Early Warning with the MyShake Smartphone Seismic Network Part 2 -- Understanding MyShake performance around the world

The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning and other applications by leveraging the power of crowdsourcing. The MyShake mobile application first detects earthquake shaking on a single phone. The earthquake is then confirmed on the MyShake servers using a "network detection" algorithm that is activated by multiple single-phone detections. In part two of this two paper series, we report the first order performance of MyShake's Earthquake Early Warning (EEW) capability in various selected locations around the world. Due to the present sparseness of the MyShake network in most parts of the world, we use our simulation platform to understand and evaluate the system's performance in various tectonic settings. We assume that 0.1% of the population has the MyShake mobile application installed on their smartphone, and use historical earthquakes from the last 20 years to simulate triggering scenarios with different network configurations in various regions. Then, we run the detection algorithm with these simulated triggers to understand the performance of the system. The system performs best in regions featuring high population densities and onshore, upper crustal earthquakes M<7.0. In these cases, alerts can be generated ~4-6 sec after the origin time, magnitude errors are within ~0.5 magnitude units, and epicenters are typically within 10 km of true locations. When the events are offshore or in sparsely populated regions, the alerts are slower and the uncertainties in magnitude and location increase. Furthermore, even with 0.01% of the population as the MyShake users, in regions of high population density, the system still performs well for earthquakes larger than M5.5. For details of the simulation platform and the network detection algorithm, please see part one of this two paper series.

physics.geo-ph↗

arXiv2019

Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.

eess.SY↗