MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration
MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration
复制标题
MALMI：基于机器学习和波形迁移的自动地震检测和定位工作流程
DOI：
10.1785/0220220071
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发表时间：
2022
影响因子：
3.3
通讯作者：
S. Wiemer
中科院分区：
文献类型：
--
作者：
P. Shi;F. Grigoli;F. Lanza;G. Beroza;L. Scarabello;S. Wiemer
强大的自动事件检测和定位是实时地震监测的核心。随着计算能力和数据可用性的提高，利用机器学习（ML）技术的自动化工作流程变得越来越流行;然而，基于ML的经典工作流程在应用于微震数据分析时仍然面临挑战。这些地震序列通常具有短间隔时间和/或低信噪比（SNR）的特点。不依赖于相位拾取和关联的全波形方法适合于处理这样的数据集，但是计算成本高并且缺乏明确的事件识别标准，这对于实时处理不是理想的。为了充分利用这两种方法的优点，我们提出了一个新的工作流程-机器学习辅助地震迁移定位（MALMI），它集成了ML和波形偏移来执行自动事件检测和定位。新的工作流程使用预训练的ML模型来生成连续相位概率，然后将其反向投影和堆叠，以使用偏移来定位震源。我们将工作流程应用于在冰岛Hengill地热区收集的一个月的连续数据，以监测两个地热生产场地周围的诱发地震。通过使用地震的全球分布进行预训练的ML模型（EQ-Transformer），与使用SeisComP软件生成的参考目录相比，所提出的工作流程自动检测和定位250个额外的地震事件（占所获得目录中的36%事件）。新事件多为震级小于0.对新检测到的事件的波形的视觉检查表明，它们是低SNR的真实的地震事件，并且仅由阵列中的极少数台站可靠地记录。与传统的基于短期平均-长期平均的偏移方法相比，MALMI方法可以得到更清晰的叠加图像，具有更高的分辨率和可靠性，特别是对于低信噪比的同相轴。该工作流程在GitHub上免费提供，为连续地震数据的同时事件检测和定位提供了自动化工具。
Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-term average over long-term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.
影响因子：
3.5
作者：
Wessel, P.;Luis, J. F.;Tian, D.
通讯作者：
Tian, D.
影响因子：
2.8
作者：
Zhu, Weiqiang;Beroza, Gregory C.
通讯作者：
Beroza, Gregory C.
影响因子：
3
作者：
Ross, Zachary E.;Meier, Men-Andrin;Heaton, Thomas H.
通讯作者：
Heaton, Thomas H.