Toward Global Earthquake Early Warning with the MyShake Smartphone Seismic Network, Part 1: Simulation Platform and Detection Algorithm

Abstract 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 article series, we present a simulation platform and a network detection algorithm to test earthquake scenarios at various locations around the world. The proposed network detection algorithm is built on the classic density-based spatial clustering of applications with noise 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 4 January 2018 M 4.4 Berkeley and the 10 June 2016 M 5.2 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 that 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.

Download Full-text

Toward Global Earthquake Early Warning with the MyShake Smartphone Seismic Network, Part 2: Understanding MyShake Performance around the World

Seismological Research Letters ◽

10.1785/0220190178 ◽

2020 ◽

Vol 91 (4) ◽

pp. 2218-2233

Author(s):

Qingkai Kong ◽

Robert Martin-Short ◽

Richard M. Allen

Keyword(s):

Early Warning ◽

Mobile Application ◽

Large Scale ◽

Detection Algorithm ◽

Seismic Network ◽

Earthquake Early Warning ◽

High Population ◽

Simulation Platform ◽

Origin Time ◽

The World

Abstract The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning (EEW) 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-article series, we report the first-order performance of MyShake’s EEW capability in various selected locations around the world. Because of 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 in each region has the MyShake mobile application installed on their smartphone and use historical earthquakes from the last 20 yr 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 s 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 M 5.5. The details of the simulation platform and the network detection algorithm are available in part 1 of this two-article series.

Download Full-text

An Advanced Seismic Network in the Southern Apennines (Italy) for Seismicity Investigations and Experimentation with Earthquake Early Warning

Seismological Research Letters ◽

10.1785/gssrl.78.6.622 ◽

2007 ◽

Vol 78 (6) ◽

pp. 622-634 ◽

Cited By ~ 37

Author(s):

E. Weber ◽

V. Convertito ◽

G. Iannaccone ◽

A. Zollo ◽

A. Bobbio ◽

...

Keyword(s):

Early Warning ◽

Seismic Network ◽

Earthquake Early Warning ◽

Southern Apennines

Download Full-text

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421500014 ◽

2020 ◽

pp. 2150001

Author(s):

Wentie Wu ◽

Shengchao Xu

Keyword(s):

Cloud Computing ◽

Intrusion Detection ◽

Differential Evolution ◽

Large Scale ◽

Clustering Algorithm ◽

Detection System ◽

Differential Evolution Algorithm ◽

Detection Algorithm ◽

Local Optimum ◽

Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

Download Full-text

An Examination of the Threshold-Based Earthquake Early Warning Approach Using a Low-Cost Seismic Network

Seismological Research Letters ◽

10.1785/0220150073 ◽

2015 ◽

Vol 86 (6) ◽

pp. 1664-1667 ◽

Cited By ~ 5

Author(s):

Chih-Yih Hsieh ◽

Wei-An Chao ◽

Yih-Min Wu

Keyword(s):

Early Warning ◽

Low Cost ◽

Seismic Network ◽

Earthquake Early Warning

Download Full-text

A High-Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors

Seismological Research Letters ◽

10.1785/0220130085 ◽

2013 ◽

Vol 84 (6) ◽

pp. 1048-1054 ◽

Cited By ~ 42

Author(s):

Y.-M. Wu ◽

D.-Y. Chen ◽

T.-L. Lin ◽

C.-Y. Hsieh ◽

T.-L. Chin ◽

...

Keyword(s):

Early Warning ◽

Low Cost ◽

High Density ◽

Seismic Network ◽

Earthquake Early Warning

Download Full-text

The “TRUAA” Seismic Network: Upgrading the Israel Seismic Network—Toward National Earthquake Early Warning System

Seismological Research Letters ◽

10.1785/0220200169 ◽

2020 ◽

Vol 91 (6) ◽

pp. 3236-3255 ◽

Cited By ~ 1

Author(s):

Ittai Kurzon ◽

Ran N. Nof ◽

Michael Laporte ◽

Hallel Lutzky ◽

Andrey Polozov ◽

...

Keyword(s):

Early Warning ◽

High Speed ◽

High Capacity ◽

Strong Motion ◽

Virtual Private Network ◽

Seismic Network ◽

Earthquake Early Warning ◽

Fault System ◽

Earthquake Early Warning System ◽

Private Network

Abstract Following the recommendations of an international committee (Allen et al., 2012), since October 2017, the Israeli Seismic Network has been undergoing significant upgrades, with 120 stations being added or upgraded throughout the country and the addition of two new datacenters. These enhancements are the backbone of the TRUAA project, assigned to the Geological Survey of Israel (GSI) by the Israeli Government, to provide earthquake early warning (EEW) capabilities for the state of Israel. The GSI contracted Nanometrics (NMX), supported by Motorola Solutions Israel, to deliver these upgrades through a turnkey project, including detailed design, equipment supply, and deployment of the network and two datacenters. The TRUAA network was designed and tailored by the GSI, in collaboration with the NMX project team, specifically to achieve efficient and robust EEW. Several significant features comprise the pillars of this network:Coverage: Station distribution has high density (5–10 km spacing) along the two main fault systems—the Dead Sea Fault and the Carmel Fault System;Instrumentation: High-quality strong-motion accelerometers and broadband seismometers with modern three-channel and six-channel dataloggers sampling at 200 samples per second;Low latency acquisition: Data are encapsulated in small packets (<1 s), with primary routing via high-speed, high-capacity telemetry links (<1 s latency);Robustness: High level of redundancy throughout the system design:Dual active-active redundant acquisition routes from each station, each utilizing multicast streaming over an IP security Virtual Private Network tunnel, via independent high-bandwidth telemetry systemsTwo active-active independent geographically separate datacentersDual active-active redundant independent automatic seismic processing tool chains within each datacenter, implemented in a high availability protected virtual environment. At this time, both datacenters and over 100 stations are operational. The system is currently being commissioned, with initial early warning operation targeted for early 2021.

Download Full-text

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190710183635 ◽

2020 ◽

Vol 13 (4) ◽

pp. 542-549

Author(s):

Smita Agrawal ◽

Atul Patel

Keyword(s):

Complex Network ◽

Community Detection ◽

Large Scale ◽

Clustering Algorithm ◽

Detection Algorithm ◽

Connected Subgraph ◽

Comprehensive Review ◽

Detection Algorithms ◽

Community Detection Algorithm ◽

Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

Download Full-text

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Mathematical Problems in Engineering ◽

10.1155/2021/7318216 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Lili Pei ◽

Zhaoyun Sun ◽

Yuxi Han ◽

Wei Li ◽

Huaixin Zhao

Keyword(s):

Clustering Algorithm ◽

Spatial Clustering ◽

Original Data ◽

Detection Algorithm ◽

Massive Data ◽

Highway Traffic ◽

Traffic Condition ◽

Original Algorithm ◽

Event Mining ◽

Complex Relationships

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

Download Full-text