摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-调查人员讨论人工智能的新发现。根据NewsRx记者从哥伦比亚发回的新闻报道，研究表明，“世界各地的地震网络被设计用来监测地震地震动。”我们的新闻编辑从国立大学Ombia上校的研究中获得了一句话：“这个过程包括识别信号中的地震事件，挑选和关联地震相位，确定事件的位置，并计算其震级。尽管机器学习(ML)方法在这些步骤中单独显示出了显著的改进，但在其他阶段，传统的非ML算法优于ML方法。我们介绍了SeisMonit或，一个用于监测地震活动的Python开源软件包，它使用Ready-Mad E ML方法进行事件检测、相位选择和关联，以及其他众所周知的方法来完成其余步骤。我们在位于科隆比亚地区的三个地震网络中，在几乎7年（2016-2022年）的时间里，将这些步骤应用于完全自动化的过程中。哥伦比亚地震台网和南美洲北部的两个地方和临时网络：Magdalena山谷中部和Caribbean-M Erida安第斯地震台阵。结果证明了该方法在创建自动地震目录、展示地震探测能力和与标准目录相似的定位精度方面的可靠性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Colombia, United Stat es, by NewsRx correspondents, research stated, “Seismic networks worldwide are d esigned to monitor seismic ground motion.” Our news editors obtained a quote from the research from National University Col ombia: “This process includes identifying seismic events in the signals, picking and associating seismic phases, determining the event’s location, and calculati ng its magnitude. Although machine-learning (ML) methods have shown significant improvements in some of these steps individually, there are other stages in whic h traditional non- ML algorithms outperform ML approaches. We introduce SeisMonit or, a Python open-source package to monitor seismic activity that uses ready-mad e ML methods for event detection, phase picking and association, and other well- known methods for the rest of the steps. We apply these steps in a totally autom ated process for almost 7 yr (2016-2022) in three seismic networks located in Co lombian territory, the Colombian seismic network and two local and temporary net works in northern South America: the Middle Magdalena Valley and the Caribbean-M erida Andes seismic arrays. The results demonstrate the reliability of this meth od in creating automated seismic catalogs, showcasing earthquake detection capab ilities and location accuracy similar to standard catalogs.”
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