Smartphone-based networks for earthquake detection

Kong, Qingkai; Kwony, Young-Woo; Schreierz, Louis; Allen, Steven W.; Allen, R. M.; Strauss, J. A.

doi:10.1109/i4cs.2015.7294490

Cited by 31 publications

(16 citation statements)

References 8 publications

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“…Ground motions recorded around the trigger time are also stored in the database for further analysis. The system architecture is described in Kong et al (2015). Four existing ML algorithms are running in the MyShake system shown in Figure 1, which are summarized in the following two subsections.…”

Section: Current Applicationsmentioning

confidence: 99%

Machine Learning Aspects of the MyShake Global Smartphone Seismic Network

Kong

Inbal

Allen

et al. 2018

Seismological Research Letters

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This article gives an overview of machine learning (ML) applications in MyShake-a crowdsourcing global smartphone seismic network. Algorithms from classification, regression, and clustering are used in the MyShake system to address various problems, such as artificial neural network (ANN) and convolutional neural network (CNN) to distinguish earthquake motions, spatial-temporal clustering using density-based spatial clustering of applications with noise (DBSCAN) to detect earthquakes from phone aggregated information, and random forest regression to learn from existing physics-based relationships. Beyond existing efforts, this article also presents a vision of the role of ML in some new directions and challenges. Using MyShake as an example, this article demonstrates the promising combination of ML and seismology.

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Section: Current Applicationsmentioning

confidence: 99%

Machine Learning Aspects of the MyShake Global Smartphone Seismic Network

Kong

Inbal

Allen

et al. 2018

Seismological Research Letters

Self Cite

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“…Once in this "steady state, " the ANN classifies subsequent motion into earthquakelike or human-like types and any earthquake-type parameters are sent to the backend cloud server for aggregation with readings from other nearby devices using spatial and clustering algorithm (Kong et al, 2018c,d). In addition, MyShake uploads 5 min of acceleration time-series data from phones in the area of a detected quake, which are archived for research purposes (Kong et al, 2015).…”

Section: Citizen Science Data and Hardwarementioning

confidence: 99%

MyShake: Using Human-Centered Design Methods to Promote Engagement in a Smartphone-Based Global Seismic Network

et al. 2018

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MyShake is a global seismic platform that uses private citizens' smartphones to detect earthquakes and record both ground shaking and users' experiences. The goal is to reduce earthquake risk and provide users with a resource for earthquake science and information. It is powered by the participation of users, therefore, its success as a global network and its utility for the users themselves is reliant on their engagement and continued involvement. This paper discusses the citizen scientist participation that enables MyShake, with specific attention to the human-centered design process that was used to overhaul the mobile application's user interface. After the successful initial launch of the application in February of 2016, we had the opportunity to revisit the user interface based on user feedback and needs. The process began with an assessment of the user and geographic distribution of the original user base through surveys and Google Play Store analytics. Subsequently, through systematic examination of the motivations and needs of community members in the San Francisco Bay Area and iterative evaluations of design decisions, MyShake was redesigned to appeal as a resource to a wider range of users in earthquake-prone regions. The new user interface was then evaluated through interviews, surveys, and meetups with potential users. We highlight the human-centered methodology we employed, as well as the roadblocks we faced, in the hopes that our experience will be valuable to other citizen science projects in the future.

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“…Environmental monitoring becomes increasingly sensor dense and real time, supported through advances in technology and a variety of inexpensive (geo)sensors. Geosensor networks are deployed in various environments, such as urban observation (Mead et al, 2013;Murty et al, 2008;Resch, Mittlboeck, Girardin, Britter, & Ratti, 2009;Sanchez et al, 2011;Xiao et al, 2017), smart forests (Zhong, Kealy, Sharon, & Duckham, 2015), precision agriculture (Agrible, Inc., 2016), earthquake monitoring (Faulkner et al, 2011;Hudnut, Bock, Galetzka, Webb, & Young, 2002;Kong et al, 2015), or radiation monitoring (Safecast, 2016). The geosensors of a network, mobile or stationary, sample concurrently and often at high temporal frequency; geosensor networks in smart cities, emergency monitoring or precision agriculture, can reach up to millions of concurrently sampling sensors.…”

Section: Introductionmentioning

confidence: 99%

Real‐time inverse distance weighting interpolation for streaming sensor data

Liang

Nittel

Whittier

et al. 2018

Transactions in GIS

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With advances in technology and an increasing variety of inexpensive geosensors, environmental monitoring has become increasingly sensor dense and real time. Using sensor data streams enables real‐time applications such as environmental hazard detection, or earthquake, wildfire, or radiation monitoring. In‐depth analysis of such spatial fields is often based on a continuous representation. With very large numbers of concurrent observation streams, novel algorithms are necessary that integrate streams into rasters, or other continuous representations, continuously in real time. In this article, we present an approach leveraging data stream engines (DSEs) to achieve scalable, high‐throughput inverse distance weighting (IDW). In detail, we designed and implemented a novel stream query operator framework that extends general‐purpose DSEs. The proposed framework includes a two‐panel, spatio‐temporal grid‐based index and several algorithms, namely the Shell and k‐Shell algorithms, to estimate individual grid cells efficiently and adaptively for different sampling scenarios. For our performance experiments, we generated several different spatio‐temporal stream data sets based on the radiation deposits in the Fukushima region after the nuclear accident of 2011 in Japan. Our results showed that the k‐Shell algorithm of the proposed framework produces a raster based on 250k observation streams in under 0.5 s using a state‐of‐the‐art workstation.

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Smartphone-based networks for earthquake detection

Cited by 31 publications

References 8 publications

Machine Learning Aspects of the MyShake Global Smartphone Seismic Network

Machine Learning Aspects of the MyShake Global Smartphone Seismic Network

MyShake: Using Human-Centered Design Methods to Promote Engagement in a Smartphone-Based Global Seismic Network

Real‐time inverse distance weighting interpolation for streaming sensor data

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