MyShake Citizen Seismologists Help Launch Dual-Use Seismic Network in California

Strauss, J. A.; Kong, Qingkai; Pothan, Sharon; Thompson, Stephen L.; Mejia, Ramon Francisco Pacquiao; Allen, Steven W.; Patel, Sarina; Allen, R. M.

doi:10.3389/fcomm.2020.00032

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…Similar to their approach, researchers from the University of California implemented a smartphone-based EEWS called "MyShake" (Allen et al, 2020;Kong et al, 2015) where they proved that a robust EEWS could be implemented using a smartphone-based network with artificial intelligence to detect an earthquake from human activities. The network delivers alerts to the phones if the estimated magnitude of an earthquake is 4.5 or greater or Modified Mercalli Intensity (MMI) III or greater zone (Strauss et al, 2020). MyShake can detect an earthquake and its characteristics within ~5-7 s after the origin time, and the alert can be disseminated to smartphones within ~1-5 s (Kong et al, 2019a;Allen et al, 2020).…”

Section: Systems Implemented Using Smartphonesmentioning

confidence: 99%

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

et al. 2022

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Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motion-based EEW algorithms for generating EEW.

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Section: Systems Implemented Using Smartphonesmentioning

confidence: 99%

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

et al. 2022

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“…Second, MyShake offers additional user-centered features beyond its citizen science functions to encourage broad usership beyond users/researchers specifically motivated to participate in seismology or SHM efforts. Users can access real-time earthquake information, submit and view community damage reports, and obtain safety and preparedness information [20,21]. The app has gained significant traction in the US states of California, Oregon, and Washington, where it delivers ShakeAlert earthquake early warnings [22].…”

Section: Introductionmentioning

confidence: 99%

Toward Structural Health Monitoring with the MyShake Smartphone Network

Patel,

Günay,

Marcou

et al. 2023

Sensors

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The field of structural health monitoring (SHM) faces a fundamental challenge related to accessibility. While analytical and empirical models and laboratory tests can provide engineers with an estimate of a structure’s expected behavior under various loads, measurements of actual buildings require the installation and maintenance of sensors to collect observations. This is costly in terms of power and resources. MyShake, the free seismology smartphone app, aims to advance SHM by leveraging the presence of accelerometers in all smartphones and the wide usage of smartphones globally. MyShake records acceleration waveforms during earthquakes. Because phones are most typically located in buildings, a waveform recorded by MyShake contains response information from the structure in which the phone is located. This represents a free, potentially ubiquitous method of conducting critical structural measurements. In this work, we present preliminary findings that demonstrate the efficacy of smartphones for extracting the fundamental frequency of buildings, benchmarked against traditional accelerometers in a shake table test. Additionally, we present seven proof-of-concept examples of data collected by anonymous and privately owned smartphones running the MyShake app in real buildings, and assess the fundamental frequencies we measure. In all cases, the measured fundamental frequency is found to be reasonable and within an expected range in comparison with several commonly used empirical equations. For one irregularly shaped building, three separate measurements made over the course of four months fall within 7% of each other, validating the accuracy of MyShake measurements and illustrating how repeat observations can improve the robustness of the structural health catalog we aim to build.

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“…In this research, we use a transfer learning based approach to select images from social media platforms that contain damaged buildings, so they can be used in an application we developed in the earthquake science domain. The motivation of this work came from the earthquake crowdsourcing application MyShake 7 – 11 , which recently started to allow users to upload felt reports after an earthquake and display the felt region to the users 12 , 13 . These reports are short surveys that the users can complete with pre-defined options for the felt experience and observation of damaged structures.…”

Section: Introductionmentioning

confidence: 99%

Detecting damaged buildings using real-time crowdsourced images and transfer learning

Chachra

Kong

Huang

et al. 2022

Sci Rep

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After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

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MyShake Citizen Seismologists Help Launch Dual-Use Seismic Network in California

Cited by 13 publications

References 23 publications

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

Toward Structural Health Monitoring with the MyShake Smartphone Network

Detecting damaged buildings using real-time crowdsourced images and transfer learning

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