School of Computer Science, Kyungpook National University, Daegu 41566, Korea.
National Disaster Management Research Institute, Ulsan 44538, Korea.
Sensors (Basel). 2020 Feb 1;20(3):800. doi: 10.3390/s20030800.
Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.
使用智能手机或物联网设备实时检测地震是一项艰巨而具有挑战性的任务，这不仅是因为它受到硬实时问题的限制，还因为地震信号与非地震信号（即噪声或其他活动）相似。此外，人类活动的多样性也使得智能手机用作地震检测传感器时更加困难。为此，在本文中，我们利用具有地震特征的机器学习技术，而不是传统的地震方法。首先，我们将检测任务分为两类，包括静态环境和动态环境。然后，我们实验评估了不同的特征，并为静态环境提出了最合适的机器学习模型和特征，以解决噪声成分的问题，并以较低的误报率实时检测地震。所提出模型的实验结果不仅在给定数据集上表现出了有前景的结果，而且在未见数据上也表现出了模型的泛化特性。最后，我们证明如果使用不同的数据集对模型进行训练，它也可以用于动态环境。
