《地空学术讲座 第491期》Qinya Liu 教授：基于深度学习的微震源表征：应用于加拿大西部沉积盆地水力压裂诱发地震

以下内容根据公开信息整理，并经大模型处理生成，可能存在疏漏或误差，请以实际信息为准。

  • 题目: 基于深度学习的微震源表征：应用于加拿大西部沉积盆地水力压裂诱发地震
  • 主讲人：Qinya Liu 教授 @ University of Toronto
  • 时间：2026年7月30日 15:00-16:30
  • 地点：理学院E3152会议室

主讲人简介

Qinya Liu 博士是多伦多大学地球物理学教授兼勘探地球物理 Teck 讲席教授。她于2000年获中国科学技术大学地球与空间科学系及地球化学系（SCGY）地球物理学理学学士学位，2006年获加州理工学院地球物理学博士学位。刘教授曾任加州理工学院（Caltech）助理科学家（2006-2007），后在美国加州大学圣地亚哥分校斯克里普斯海洋研究所担任博士后研究员（2007-2008），随后于2008年赴多伦多工作。她的研究重点在于利用密集阵列数据对地壳结构进行全波形反演，以及先进的地震震源表征。刘教授已发表90余篇同行评审期刊论文，包括《Science》、《Earth and Planetary Science Letters》、《Geophysical Research Letters》、《Journal of Geophysical Research: Solid Earth》等。她目前担任《Journal of Geophysical Research: Solid Earth》副编辑。

讲座简介

Microseismic monitoring is of critical importance for characterizing induced earthquakes and understanding fault activation processes resulting from anthropogenic subsurface operations such as hydraulic fracturing. However, processing these specialized microseismic datasets with low-magnitude events and low signal-to-noise ratio (SNR) waveforms can be challenging with traditional methods. In this study, we present advanced machine-learning-based workflows for source characterization of microseismic datasets. Specifically, we apply deep-learning (DL) phase pickers to detect weak microseismic P and S arrivals through data-optimization strategies, and fine-tune a region-specific DL picker by integrating multi-model consensus and noise augmentation based on the public Tony Creek Dual Microseismic Experiment (ToC2ME) data. This targeted model adaptation drastically reduces the catalog magnitude of completeness and maximizes event detection. The DL-strategy is also applied to a surface-array recording of induced earthquakes in northern Montney play in the Western Canada Sedimentary Basin, enabling enhanced catalogs and high-resolution source-parameter evaluation. Using the open-source Moment Tensor Uncertainty Quantification (MTUQ) framework, full moment tensors were inverted for 567 microseismic events near an induced Mw 4.6 earthquake sequence. This unified open-source workflow provides an efficient and robust microseismic monitoring solution for both industrial operations and regulatory agencies.

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