The PLUM Method Considering the Attenuation Law of Ground Motion
DOI:
https://doi.org/10.54097/b7crcq39Keywords:
PLUM, Attenuation relation.Abstract
The Propagation of Local Undamped Motion (PLUM) method is a seismic early warning approach that directly predicts ground motion based on observed ground shaking. It can achieve good early warning effects in simultaneous multiple earthquakes and earthquakes with severe rupture zone expansion. The traditional PLUM method provides a relatively short effective early warning time. To further increase the early warning duration of PLUM, this paper proposes a PLUM method that considers the attenuation law of ground motion. Unlike the traditional PLUM method, which can only predict ground shaking for sites within a fixed radius, the new method can predict real-time ground motion for sites at greater distances based on the attenuation law of seismic intensity and issue early warning information to users. Finally, a retrospective simulation of the January 1, 2024, earthquake in the Noto Peninsula, Japan, is conducted.
Downloads
References
[1] Zhu, W., Beroza, G.C. PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216: 261-273, 2019.
[2] SHANG X, LI X, WENG L. Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker. Journal of Seismology, 2018, 22 (1): 21-33.
[3] WOOLLAM J, RIETBROCK A, BUENO A, et al. Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network. Seismological Research Letters 2019; 90 (2A): 491–502.
[4] SCAFIDI D, SPALLAROSSA D, TURINO C, et al. AutomaticP‐ andS‐Wave Local Earthquake Tomography: Testing Performance of the Automatic Phase‐Picker Engine “RSNI‐Picker”. Bulletin of the Seismological Society of America, 2016, 106 (2): 526-536.
[5] PEROL T, GHARBI M, DENOLLE M. Convolutional neural network for earthquake detection and location. Science Advances, 2018, 4 (2): e1700578.
[6] BOSE M, WENZEL F, ERDIK M. PreSEIS: a neural network-based approach to earthquake early warning for finite faults. Bulletin of the Seismological Society of America, 2008, 98 (1): 366-382.
[7] KAIL R, BURNAEV E, ZAYTSEV A. Recurrent convolutional neural networks help to predict location of earthquakes. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.
[8] MOUSAVI S M, BEROZA G C. Bayesian-deep-learning estimation of earthquake location from single-station observations. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58 (11): 8211-8224.
[9] KANG K, WANG X. Fully convolutional neural networks for crowd segmentation. Computer Science, 2014, 49 (1): 25-30.
[10] KANG K, WANG X. Fully convolutional neural networks for crowd segmentation. Computer Science, 2014, 49 (1): 25-30.
[11] ALLN R M, KANAMORI H. The potential for earthquake early warning in southern California. Science, 2003, 300 (5620): 786-789.
[12] WU Y M, KANAMORI H. Experiment on an onsite early warning method for the Taiwan early warning system. Bulletin of the Seismological Society of America, 2005, 95 (1): 347-253.
[13] WU Y M, KANAMORI H. Experiment on an onsite early warning method for the Taiwan early warning system. Bulletin of the Seismological Society of America, 2005, 95 (1): 347-253.
[14] ZOLLO A, LANCIERI M, NIELSEN S. Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records. Geophysical Research Letters, 2006, 33 (23): L23312.
[15] SHUAI Xianghua, FENG Wei, DONG Xiang, et al. Rapid identification method of earthquake intensity influence field in divided-period after earthquake. Geomatics and Information Science of Wuhan University, 2020, 45 (8): 1195-1204. (In Chinese)
[16] SHUAI Xianghua, FENG Wei, DONG Xiang, et al. Rapid identification method of earthquake intensity influence field in divided-period after earthquake. Geomatics and Information Science of Wuhan University, 2020, 45 (8): 1195-1204. (In Chinese)
[17] Yuki Kodera, Naoki Hayashimoto, Ken Moriwaki, Keishi Noguchi, Jun Saito, Jun Akutagawa, Shimpei Adachi, Masahiko Morimoto, Kuninori Okamoto, Seiichiro Honda, Mitsuyuki Hoshiba; First‐Year Performance of a Nationwide Earthquake Early Warning System Using a Wavefield‐Based Ground‐Motion Prediction Algorithm in Japan. Seismological Research Letters 2020; 91 (2A): 826–834.
[18] Kodera, Y., Yamada, Y., Hirano, K., Tamaribuchi, K., Adachi, S., Hayashimoto, N., et al. The Propagation of Local Undamped Motion (PLUM) method: A simple and robust seismic wavefield estimation approach for earthquake early warning. Bulletin of the Seismological Society of America, 2018, 108 (2), 983–1003.
[19] Saunders J K, Cochran E S, Bunn J J, et al. Incorporating intensity distance attenuation into PLUM ground‐motion‐based earthquake early warning in the United States: The APPLES configuration. Earth's Future, 2024, 12 (2): e2023EF004126.
[20] MA Yun-yi, LU Jian-qi, LI Shan-you, HE Pei-yang. CHINESE INSTRUMENTAL INTENSITY ATTENUATION LAW BASED ON LINE SOURCE MODEL. INLAND EARTHQUAKE, 2020, 34 (04): 330-339.
[21] LU J Q, LI S Y, XIE Z N, et al. Energy-and predominant-period-dependent P-wave onset picker (EDP-Picker). Seismological Research Letters, 2020, 91 (4): 2355-2367.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
