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- W4253161935 abstract "Abstract In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training dataset for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning dataset for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region." @default.
- W4253161935 created "2022-05-12" @default.
- W4253161935 creator A5006847883 @default.
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- W4253161935 date "2021-03-16" @default.
- W4253161935 modified "2023-09-28" @default.
- W4253161935 title "Application of Deep Learning-Based Neural Networks Using Theoretical Seismograms as Training Data for Locating Earthquakes in the Hakone Volcanic Region, Japan" @default.
- W4253161935 doi "https://doi.org/10.21203/rs.3.rs-295787/v1" @default.
- W4253161935 hasPublicationYear "2021" @default.
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