2008年Mw7 .9汶川地震余震区震相检测与拾取的深度学习
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake
摘要：
长期连续监测的地震资料量不断增加，促使基于卷积神经网络（CNN）的算法的发展，以实现更快、更可靠的相位检测和拾取。然而，许多较少研究的区域缺乏传统CNN方法所需的大量标记事件。在本文中，我们提出了一种基于CNN的相位识别分类器（CPIC），旨在对中小型训练数据集进行相位检测和拾取。当对30，146个标记的阶段进行训练并应用于2008年中国四川汶川7.9兆瓦地震余震序列期间为期一个月的连续记录时，CPIC检测到标准目录中97.5%的手动拾取阶段，并预测其到达时间，比ObsPy AR拾取器提高了五倍。此外，与其他基于CNN的方法需要数百万个训练样本不同，当CPIC的离线训练集大小减少到只有几千个训练样本时，准确率保持在95%以上。CPIC的在线实施只需要不到12小时就可以在14个车站的31天记录中挑选到达。除了分析师手动选择的目录阶段外，CPIC还为目录中遗漏的现有事件和新事件找到更多阶段。在这些额外的检测，一些匹配滤波器的方法确认，而其他人需要进一步的调查。最后，当在来自不同地区（美国俄克拉荷马州）的小数据集上进行测试时，CPIC仅对模型的全连接层进行微调后就达到了97%的准确率。这一结果表明，在这项研究中开发的CPIC可以用来识别和挑选在其他地区的P/S波到没有或最小的标记相位。
英文摘要：
The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase- Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 MW 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The online implementation of CPIC takes less than 12 hours to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
