青藏高原东南缘PmP波数据集构建及识别模型训练

李澍辰; 孙安辉; 李天觉; 童平; 房立华; 安艳茹; 张莹莹; 赵盼盼; 杨峰

doi:10.3799/dqkx.2025.128

Volume 51 Issue 3

Mar. 2026

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Article Navigation > Earth Science > 2026 > 51(3): 1169-1181

Li Shuchen, Sun Anhui, Li Tianjue, Tong Ping, Fang Lihua, An Yanru, Zhang Yingying, Zhao Panpan, Yang Feng, 2026. Constructing and Training of a Deep Learning Dataset for PmP Waves in the Southeastern Tibetan Plateau. Earth Science, 51(3): 1169-1181. doi: 10.3799/dqkx.2025.128

Citation:

Li Shuchen, Sun Anhui, Li Tianjue, Tong Ping, Fang Lihua, An Yanru, Zhang Yingying, Zhao Panpan, Yang Feng, 2026. Constructing and Training of a Deep Learning Dataset for PmP Waves in the Southeastern Tibetan Plateau. Earth Science, 51(3): 1169-1181. doi: 10.3799/dqkx.2025.128

Citation:

Li Shuchen, Sun Anhui, Li Tianjue, Tong Ping, Fang Lihua, An Yanru, Zhang Yingying, Zhao Panpan, Yang Feng, 2026. Constructing and Training of a Deep Learning Dataset for PmP Waves in the Southeastern Tibetan Plateau. Earth Science, 51(3): 1169-1181. doi: 10.3799/dqkx.2025.128

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Constructing and Training of a Deep Learning Dataset for PmP Waves in the Southeastern Tibetan Plateau

doi: 10.3799/dqkx.2025.128

1.
Institute of Earthquake Forecasting, China Earthquake Administration / Key Laboratory of Earthquake Forecasting and Risk Assessment, Ministry of Emergency Management / Key Laboratory of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
2.
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
3.
China Earthquake Networks Center, Beijing 100045, China

Available Online: 2026-04-13
Publish Date: 2026-03-25

Abstract

Abstract

The Moho-reflecting PmP wave with a different ray path to Pg wave and Pn wave, whose propagation characteristics are closely related to the seismogenic tectonic environment, provides crucial information for studying the deep crustal structure and the discontinuity of the Moho discontinuity. The main challenge in PmP waves identification is their rarity, and the significant manpower required for manual picking. To address this issue, we firstly obtained 1 713 PmP waves through manual picking and amplitude-ratio validation, and applied a semi-automatic workflow (screening of seismic amplitude threshold and testing of particle motion) to pick 1 536 PmP waves from waveforms recorded by permanent (2009—2022) and temporary (2011—2013) stations in the southeastern (SE) Tibetan Plateau, and then we constructed a high-quality PmP dataset using these waves. We retrained PmPNet, a deep neural network-based algorithm, to construct two new models PmPNet-SET_V1.0 and PmP-traveltime-Net-SET_V1.0, among which PmPNet-SET_V1.0 achieved a high F1-score of 0.863 7, with a precision of 86.6% and a recall of 84.8%, and we tripled the number of the high-quality PmP database in the study region to 6 268. All PmP picking results underwent rigorous manual inspection and were compared with the theoretical travel time to ensure the reliability. The study shows several hyper-parameters play a key role in determining both the quantity and quality of the picks. Furthermore, based on the constructed PmP dataset, the study preliminarily obtained the regional Moho depth, which displayed a similar pattern to previous inversion findings, showing deeper depths in the northwest and shallower depths in the southeast.
- southeastern Tibetan Plateau,
- PmP wave,
- deep learning,
- seismic dataset,
- PmPNet,
- seismology

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References(39)

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