测试时间的增加和质量控制,以改善区域地震阶段采集.
Bingyao Han1, Lin Tang2, Li Ma3
1Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究通过使用深度学习和测试时间增强,提高了区域地震的地震相位选择精度. 过器银行增强显著改善了Pn相选,有助于地球结构和地震研究.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 机器学习 机器学习
背景情况:
- 区域地震阶段对于理解地球结构至关重要.
- 使用深度学习的自动地震阶段选择显示出希望,但与Pn.等遥远阶段作斗争.
- 由于采集的局限性,大量的地震数据仍然未得到充分利用.
研究的目的:
- 系统地评估测试时间增长策略,以提高Pn阶段选择精度.
- 在深度学习模型 (PickNet,PhaseNet) 上评估不同增强方法 (过器银行,转移,旋转) 的有效性.
- 提出质量控制措施,以确保可靠的阶段挑选,而没有地面真相.
主要方法:
- 使用Seis-PnSn数据集进行全球Pn阶段选择.
- 应用PickNet和PhaseNet模型,使用过器银行,转移和旋转测试时间增强.
- 基于增强结果的标准偏差实施质量控制.
主要成果:
- 过器银行增强性能优于移动和旋转,在±0.5/1.0s误差范围内增加选择.
- PickNet从48.98%/66.94%提高到53.87%/70.82%的基线;PhaseNet从46.32%/64.28%提高到48.45%/67.06%的基线;PickNet从48.98%/66.94%提高到53.87%/70.82%的基线;PhaseNet从46.32%/64.28%提高到48.45%/67.06%的基线.
- 质量控制进一步提高了PickNet的67.39%/78.53%和PhaseNet的57.99%/74.72%的使用率.
结论:
- 测试时间增长,特别是过器银行,显著提高了基于深度学习的Pn阶段选择.
- 建议的质量控制方法即使没有基本事实,也会产生可靠的结果.
- 这项研究提供了实用脚本,改善了区域地震相选精度和地理物理研究的可访问性.
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