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App Earthquake Detection and Automatic Mapping of Felt Area

机译：应用地震检测和毛毡区域的自动映射

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摘要

Rapid identification of felt earthquakes is essential for determining public earthquake information. We present a new method to detect such earthquakes that hinges on the ubiquity of smartphones and the accurate geolocation that they offer. More precisely, the method is based on launches by its users of the LastQuake app, the European-Mediterranean Seismological Centre (EMSC) app providing rapid global earthquake information. Similar to two other existing methods, one based on the analysis of earthquake information website traffic and the other based on the publication on Twitter of earthquake related messages, it exploits the online reaction of eyewitnesses following ground shaking. Its time performance is shown to depend on the number of app users in the epicentral region and whether the earthquake happens during day or night. Over the 16-month study period, the observed time difference between the arrival of the P waves and the app launch times is typically 10 s longer at night than during the day. These reaction times can significantly decrease during a sequence of earthquakes affecting the same region, leading in the best cases to earthquake detection times as fast as 20 s from earthquake origin time. Eyewitnesses' locations determined from app launches also map the felt area. In turn, in some cases, the surface of the felt area could offer a first-order magnitude estimate within a few tens of seconds of their occurrence for small-magnitude earthquakes and in a few minutes for larger ones. The analysis of online reaction of eyewitnesses not only offers seismological information that complements that derived from seismological networks (e.g., rapid identification of felt earthquakes, mapping of the felt area) but also provide insights into eyewitnesses' behaviors and expectations during and immediately after a tremor.
机译：对毛毡地震的快速识别对于确定公共地震信息至关重要。我们提出了一种探测这种地震的新方法，即涉及智能手机的无处不经的地震以及它们提供的准确地理位置。更确切地说，该方法基于其后所示应用的用户的推出，欧洲地中海地震学中心（EMSC）应用程序提供了快速的全球地震信息。类似于另外两种现有方法，一个基于地震信息网站流量的分析，基于对地震相关信息的推特上的出版物，它利用了在地面摇动后目镜的在线反应。其时间绩效显示依赖于震中地区的应用用户数以及地震在白天或夜晚发生。在16个月的研究期间，P波的到达与应用程序发射时间之间观察到的时间差通常在晚上的时间越长，而不是白天。这些反应时间在影响相同区域的一系列地震期间可以显着降低，在最佳情况下导致地震检测时间从地震原因时间快到20秒。目击者从App Mailnes确定的位置也映射了毛毡区域。反过来，在某些情况下，毛毡区域的表面可以在其发生的小幅地震的发生之内，在几十几秒钟内提供一阶幅度估计，并且在几分钟内进行较大的速度。目击者的在线反应分析不仅提供了从地震网络的补充（例如，毛毡地震的快速识别，毛毡区域的绘图）的互动信息，而且还提供对目击者的行为和期望的洞察力，并且在震颤之后立即提供洞察力。

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  • 来源
    《Seismological research letters》 |2019年第1期|共8页
  • 作者

    Bossu Remy; Steed Robert; Roussel Frederic; Landes Matthieu; Fuenzalida Amaya; Matrullo Emanuela; Dupont Aultlien; Roch Julien; Fallou Laure;

  • 作者单位

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

    CEA European Mediterranean Seismol Ctr F-91297 Arpajon France;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地震学;
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