Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research.

In recent years, a low-cost micro-electro-mechanical systems (MEMS) acceleration sensor has been widely used for earthquake early warning (EEW). In our previous work, we introduced a networked earthquake detection system, CrowdQuake with three-hundred smartphones’ acceleration sensors and a deep-learning based earthquake detection model. For one year’s operation, CrowdQuake detected a series of earthquakes and collected various earthquake and non-earthquake data. Based on the successful operation of CrowdQuake, in this paper, we discuss how it can be expanded across the country by addressing the following challenges: (1) sensor deployments for highly dense network, (2) earthquake detection performance using a deep learning model, and (3) high performance and scalable system design for big data processing. The improved system is CrowdQuake+ which can deal with acceleration data sent from 8,000 IoT sensors and detect an earthquake in few seconds using a newly proposed detection model. Moreover, CrowdQuake+ stores all acceleration data sent from sensors and assesses their qualities by calculating noise levels. Then, the collected data are used for deep learning model training, so that its detection performance becomes more accurate.

Recently, low-cost acceleration sensors have been widely used to detect earthquakes due to the significant development of MEMS technologies. It, however, still requires a high-density network to fully harness the low-cost sensors, especially for real-time earthquake detection. The design of a high-performance and scalable networked system thus becomes essential to be able to process a large amount of sensor data from hundreds to thousands of the sensors. An efficient and accurate earthquake-detection algorithm is also necessary to distinguish earthquake waveforms from various kinds of non-earthquake ones within the huge data in real time. In this paper, we present CrowdQuake, a networked system based on low-cost acceleration sensors, which monitors ground motions and detects earthquakes, by developing a convolutional-recurrent neural network model. This model ensures high detection performance while maintaining false alarms at a negligible level. We also provide detailed case studies on two of a few small earthquakes that have been detected by CrowdQuake during its last one-year operation.