The temporal variation in subsurface structure of a landslide area was monitored using spatial autocorrelation (SPAC) as a simple and robust seismic observational method. The SPAC method is often used in civil engineering to estimate one-dimensional (1D) subsurface structures. However, in this study, we monitor the temporal variation in the SPAC coefficient deviation to evaluate environmental change effects and changes in the subsurface velocity structure. We used a seismic array comprising 10 seismometers in a landslide-prone area in Morimachi Town, western Shizuoka Prefecture, Japan, and continuous observations were performed from October 2020 – May 2022. We obtained a 1D velocity structural model as a reference using the multi-mode SPAC (MMSPAC) method with the averaged SPAC coefficients at different distances for all observation periods. We calculated the daily variation in SPAC coefficient relative to the average for all observation periods. We then applied cluster analysis to the SPAC coefficient deviation, which revealed weekly changes likely due to human activity and the effect of stream surges on nearby streams. After removing stream surge clusters and correcting for the weekly change, we reapplied cluster analysis to identify two major clusters. The differences between the two clusters can be attributed to structural changes in the very near surface (∼5 m) and deeper parts (∼20 m), likely influenced by shallow groundwater due to rainfall. By investigating the location of the landslide mass near the observation site, we suggest that structural changes around 20 m deep may correspond to the depth of potential slip surfaces.

中文翻译：

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使用环境噪声进行表面波监测，以检测滑坡地区地下结构的时间变化


使用空间自相关（SPAC）作为一种简单而稳健的地震观测方法来监测滑坡区域地下结构的时间变化。 SPAC 方法通常在土木工程中用于估计一维 (1D) 地下结构。然而，在本研究中，我们监测 SPAC 系数偏差的时间变化，以评估环境变化影响和地下速度结构的变化。我们在日本静冈县西部森町镇的滑坡多发区使用了由 10 个地震仪组成的地震台阵，并在 2020 年 10 月至 2022 年 5 月期间进行了连续观测。我们使用多维速度结构模型获得了一维速度结构模型作为参考。模式 SPAC (MMSPAC) 方法，具有所有观测周期不同距离处的平均 SPAC 系数。我们计算了 SPAC 系数相对于所有观察期平均值的每日变化。然后，我们对 SPAC 系数偏差进行了聚类分析，揭示了可能由于人类活动以及溪流涌动对附近溪流的影响而导致的每周变化。在删除水流涌动集群并校正每周变化后，我们重新应用集群分析来识别两个主要集群。两个簇之间的差异可归因于非常近地表（∼5 m）和较深部分（∼20 m）的结构变化，可能受到降雨造成的浅层地下水的影响。通过调查观测地点附近滑坡体的位置，我们认为 20 m 深左右的结构变化可能与潜在滑动面的深度相对应。