| 摘要(英) |
There are thousands of earthquakes take places in Taiwan within a single month, but there are not enough researchers to process the data. Nowadays, researchers can leverage the power of artificial intelligence to accomplish repetitive tasks. In 2018, after the Hualien earthquake, our temporal station network detected over 4,000 aftershocks within 12 days. Although there are numerous methods have been proposed to tackle the problem, we need a real-world solution for our routine workflow.
In this research, we design our toolbox based on PhaseNet which proposed by Zhu et al. in 2018. The main concept is labeling the phase picking time with a Gaussian distribution mask to represent the picking error. Furthermore, we swap the architecture from the well-known U-Net to its successor UNet ++, which redesigns the skip pathways to help the model converge faster. We develop our package SeisNN and open the source code to the public, using Obspy for data I/O, reading SEISAN s-file for generate training data and using Tensorflow 2.0 for the main deep learning framework. Besides, we provide Docker image and Dockerfile for fast deployment and uniform environment.
In our scenario, most of the data we recover from the field are only contained Z component, on the other hand, because we only care the P arrivals for further processing, so we shrink down the network output from P, S, Noise to P arrivals only. We test our model on three different dataset: Meinong dataset with 13,357 training sets, Hualien 2017 dataset with 30,852 training sets and Hualien 2018 dataset with 56,223 training sets. After training, the according F1 score is 0.729, 0.918 and 0.925.
In summary, this research utilize the historical data in our lab, successfully simplify the cumbersome picking process with artificial intelligence. After fully develop the toolbox, it can be integrated into our routine workflow. |
| 參考文獻 |
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Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3-11). Springer, Cham.
Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211. |
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