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Article

Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

1
Center for Data Science, Peking University, Beijing 100871, China
2
Lab of Interdisciplinary Spatial Analysis, University of Cambridge, Cambridge CB3 9EP, UK
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Institute of Industrial Sciences, University of Tokyo, Tokyo 153-8505, Japan
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College of Engineering, Peking University, Beijing 100871, China
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School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Fumio Yamazaki
Remote Sens. 2021, 13(17), 3426; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173426
Received: 4 August 2021 / Revised: 24 August 2021 / Accepted: 26 August 2021 / Published: 29 August 2021
The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data. View Full-Text
Keywords: GeoSOT spatiotemporal grid; data organization model; 3D group convolution; atmospheric anomaly; earthquake early warning GeoSOT spatiotemporal grid; data organization model; 3D group convolution; atmospheric anomaly; earthquake early warning
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MDPI and ACS Style

Zhu, D.; Yang, Y.; Ren, F.; Murai, S.; Cheng, C.; Huang, M. Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model. Remote Sens. 2021, 13, 3426. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173426

AMA Style

Zhu D, Yang Y, Ren F, Murai S, Cheng C, Huang M. Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model. Remote Sensing. 2021; 13(17):3426. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173426

Chicago/Turabian Style

Zhu, Daoye, Yi Yang, Fuhu Ren, Shunji Murai, Chengqi Cheng, and Min Huang. 2021. "Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model" Remote Sensing 13, no. 17: 3426. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173426

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