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MyShake

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Journal Article
Published: 21 August 2019
Seismological Research Letters (2019) 90 (5): 1937-1949.
...Qingkai Kong; Sarina Patel; Asaf Inbal; Richard M. Allen Abstract MyShake harnesses private and personal smartphones to build a global seismic network. It uses the accelerometers embedded in all smartphones to record ground motions induced by earthquakes, returning recorded waveforms to a central...
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Journal Article
Published: 10 April 2019
Seismological Research Letters (2019) 90 (3): 1209-1218.
...Asaf Inbal; Qingkai Kong; William Savran; Richard M. Allen ABSTRACT MyShake is a growing smartphone‐based network for seismological research applications. We study how dense array analysis of the seismic wavefield recorded by smartphones may enhance microearthquake monitoring in urban environments...
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Journal Article
Published: 05 December 2018
Seismological Research Letters (2019) 90 (2A): 546-552.
...Qingkai Kong; Asaf Inbal; Richard M. Allen; Qin Lv; Arno Puder ABSTRACT 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...
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(a) Earthquakes with one or more useful waveform recordings from <b>MyShake</b> ph...
Published: 21 August 2019
Figure 1. (a) Earthquakes with one or more useful waveform recordings from MyShake phones in the first two years of operation (12 February 2016 to 12 February 2018). The size of the circle and colors represent magnitude and depth of the earthquake (both magnitudes and locations are from the U.S
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Epicentral distance of all <b>MyShake</b> earthquake waveform recordings as a func...
Published: 21 August 2019
Figure 2. Epicentral distance of all MyShake earthquake waveform recordings as a function of magnitude (blue dots). The red curve is equation  (1) , which approximates the maximum distance to which MyShake phones can trigger and detect earthquakes; the shaded lines are 1000 bootstrap
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Example three‐component acceleration waveforms from <b>MyShake</b> detections glob...
Published: 21 August 2019
Figure 3. Example three‐component acceleration waveforms from MyShake detections globally. The black line is the event origin time from the USGS catalog; green and red lines are estimated P and S arrival time using ak135 ( Kennett et al. , 1995 ). The zero time on each panel is the time when
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Accuracy of <b>MyShake</b> waveform and trigger timestamps. The histogram shows th...
Published: 21 August 2019
Figure 4. Accuracy of MyShake waveform and trigger timestamps. The histogram shows the change in phone timing offsets extracted from the network time protocol synchronization process. Data are from 26 random days between August 2016 to August 2017. The dashed lines mark the 50th, 75th, 85th, 90th
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The magnitude estimates from <b>MyShake</b> compared with those from the USGS ComC...
Published: 21 August 2019
Figure 7. The magnitude estimates from MyShake compared with those from the USGS ComCat. The mean error is 0.0, and the st. dev. is 0.2. The color of the circle shows the number of waveforms used. The solid red line is the 1‐to‐1 line, and two black dashed lines show an error of 1 magnitude unit
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The distance, origin time, and station coverage from <b>Myshake</b> network using ...
Published: 21 August 2019
Figure 10. The distance, origin time, and station coverage from Myshake network using all the 44 events that have four or more identifiable phases without any coverage filtering. The sizes of the circles indicate how many phase pickings were available for each event, which ranged from 4 to 66
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Sketch overview of the <b>MyShake</b> system and the machine learning (ML) algorit...
Published: 05 December 2018
Figure 1. Sketch overview of the MyShake system and the machine learning (ML) algorithms that are currently used or under testing in the system both in real time and offline modes. DBSCAN, density‐based spatial clustering of applications with noise; PGA, peak ground acceleration. The color
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Ratio of <b>MyShake</b> artificial neural network (ANN) triggers to short‐term ave...
Published: 05 December 2018
Figure 2. Ratio of MyShake artificial neural network (ANN) triggers to short‐term average/long‐term average (STA/LTA) triggers. Data used here are from 1 July 2017 to 1 July 2018 in the San Francisco Bay area of California, with a total of 4853 unique users with 3,498,239 STA/LTA triggers
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Spatial and temporal dynamics of the <b>MyShake</b> network. (a) The footprint of ...
Published: 05 December 2018
Figure 7. Spatial and temporal dynamics of the MyShake network. (a) The footprint of the MyShake users in the San Francisco Bay Area, California, U.S.A., and the dots are user locations reported in the heartbeat messages (modified from Kong, Inbal, et al. , 2018 ). (b) The percentage of phones
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The NN used in the <b>MyShake</b> earthquake early warning (EEW) phone application...
Published: 14 November 2018
Figure 5. The NN used in the MyShake earthquake early warning (EEW) phone application. (a) The workflow of the NN algorithm on the phone, including extraction of features from recorded phone motion and implementation of an NN classifier to distinguish between motions from humans and earthquakes
Journal Article
Published: 10 January 2018
Seismological Research Letters (2018) 89 (2A): 594-602.
... results demonstrate the ability to extract the fundamental translational and torsional frequencies, and absolute displacements from upper levels of buildings shaken by small local earthquakes. This work builds on the ongoing MyShake project—a global smartphone seismic network. The Millikan Library...
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Accuracy of smartphone location using Global Positioning System (GPS) point...
Published: 21 August 2019
Figure 5. Accuracy of smartphone location using Global Positioning System (GPS) points reported with MyShake triggers and seismic waveforms, both (a) horizontal and (b) vertical. Ten phones were placed on a second‐floor windowsill facing into a partially sheltered courtyard and periodically
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Earthquake source parameter estimation. (a) 10 June M 5.2 2016 Borrego Spri...
Published: 21 August 2019
Figure 8. Earthquake source parameter estimation. (a) 10 June M 5.2 2016 Borrego Springs event, with maximum azimuthal gap of 56°. (b) 4 January M 4.4 2018 Berkeley event, with maximum azimuthal gap of 17°. The magenta dots are phones that triggered during the earthquake. The estimates MyShake
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Earthquake source parameter estimation. (a) 3 September M5.8 2016 Pawnee, O...
Published: 21 August 2019
dots are phones that triggered during the earthquake. The estimates MyShake epicentral location is shown as a blue star and the USGS catalog location as a red star. Errors of the estimated source parameters with respect to the catalogs are shown in Table  3 . The color version of this figure
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(a) The  M  5.4 South Korea event on 12 September 2016 11:32:55.770 (UTC) a...
Published: 05 December 2018
of the P (green circle) and S (red circle) waves are the noise triggers based on the observations from the MyShake network. The blue magnitude on the right of each figure is the estimated magnitude by the random forest regressor. Warning times, estimated, and true modified Mercalli intensities (MMIs
Journal Article
Published: 13 February 2019
Seismological Research Letters (2019) 90 (2A): 477-480.
... neural network‐based framework for developing ground‐motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas , Seismol. Res. Lett. doi: 10.1785/0220180218 . Kong Q. Allen R. M. Schreier L. , and Kwon Y. W. 2016 . MyShake: A smartphone seismic network...
Journal Article
Published: 14 November 2018
Seismological Research Letters (2019) 90 (1): 3-14.
...Figure 5. The NN used in the MyShake earthquake early warning (EEW) phone application. (a) The workflow of the NN algorithm on the phone, including extraction of features from recorded phone motion and implementation of an NN classifier to distinguish between motions from humans and earthquakes...
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