Sheng Y, Mordret A, Brenguier F, Boué P, Vernon F, Takeda T, Aoki Y, Taira T, Ben-Zion Y
University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France.
Institute of Geophysics and Planetary Physics University of California San Diego San Diego CA USA.
J Geophys Res Solid Earth. 2023 Jan;128(1):e2022JB024725. doi: 10.1029/2022JB024725. Epub 2022 Dec 30.
Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high-frequency (>1 Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high-frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.
岩石中的地震波速度对永久变形和流体含量的变化高度敏感。地震准备阶段地震波速度的时间变化在实验室中已有充分记录,但在自然环境中很少被观测到。最近发现,一些人为的高频(>1赫兹)地震源强大到足以产生下行至几公里的体波,可用于监测震源深度处的断层带。人为地震源通常具有固定的空间分布,为速度监测提供了新的视角。在这项工作中,我们提出了一种系统的工作流程,以快速、直接的方式寻找这种强大的地震源。我们从统计学角度解决这个问题,认为持续、强大的地震源会在地震传感器对之间产生高度相干的相关函数(CFs)。该算法在加利福尼亚和日本进行了测试。靠近断层带的多个地点显示高频CFs在较长时间内保持稳定。这些发现对于监测断层带有很大潜力,包括圣哈辛托断层和南加州的里奇克莱斯特地区、北加州的纳帕以及日本中部的断层。然而,需要额外的步骤,如波束形成或极化分析,来确定主要地震源并研究源特征,这对于解释速度监测结果至关重要。通过本方法识别出的列车震颤在先前的研究中已成功用于圣哈辛托断层的地震波速度监测。