Homogeneous and accurate hypocenter distribution along the Japan Trench is important to better understand the process of plate subduction and the occurrence mode of large earthquakes. The seafloor seismic network (S-net) deployed recently along the Japan Trench has revealed new seismic activity including shallow slow earthquakes. However, conventional microseismic observations, such as those reported in the Japan Meteorological Agency (JMA) unified earthquake catalog, have been limited to land-based stations. Thus, steady seismic activity occurring at the shallow plate boundary, which is far from land is not always recorded. In the present study, we construct an automatic earthquake catalog using the nationwide observation network, including S-net. Because false detections caused by noise account for about 5% of automatically determined hypocenters, we attempted to reduce false detections using ensemble learning methods such as random forest and AdaBoost. First, we created a training dataset of earthquakes and noise by visual inspection based on the data recorded in the automatically determined catalog over a 2-month period, and we trained the dataset using the hypocenter and phase data as input. As a result of the training, AdaBoost was able to reduce the noise to about one-fifth of the total false detections, which is equivalent to 1%, while keeping the number of hypocenters above 99%. This method will contribute to significantly improving the efficiency of seismic activity monitoring and cataloging. In addition, the automatic denoised catalog data revealed that from January to August 2020, the completeness magnitude was M 1.6 along the Japan Trench. These microearthquakes were concentrated at depths of 20–50 km around the upper surface of the subducting Pacific Plate and are complementary to the slow earthquakes occurring at 10–20-km depths. Exceptionally, microearthquakes were observed off Iwate and Ibaraki prefectures, which correspond in location to areas of clustered foreshock activity. This spatial heterogeneity in microseismic activity is similar to the spatial complementary between the coseismic slip and afterslip of the 2011 Tohoku earthquake, which may be related to differences in interplate frictional properties and stress changes in the surrounding crust.

为了更好地了解板块俯冲过程和大地震的发生方式，日本海沟沿岸的震源分布均匀且准确是很重要的。最近沿日本海沟部署的海底地震网络（S-net）揭示了新的地震活动，包括浅慢地震。但是，常规的微震观测（例如，日本气象厅（JMA）统一地震目录中报告的观测）仅限于陆基台站。因此，并非总是记录在远离陆地的浅盘边界处发生的稳定地震活动。在本研究中，我们使用包括S-net在内的全国性观测网络构建了一个自动地震目录。由于由噪声引起的错误检测大约占自动确定的震源的5％，因此我们尝试使用诸如随机森林和AdaBoost之类的整体学习方法来减少错误检测。首先，我们根据目视检查记录在两个月内自动确定的目录中的数据，通过目视检查创建了地震和噪声训练数据集，并使用震源和相位数据作为输入来训练数据集。训练的结果是，AdaBoost能够将噪声降低到总错误检测量的大约五分之一，相当于1％，而震源数量保持在99％以上。这种方法将有助于显着提高地震活动监测和分类的效率。此外，日本海沟沿线的M 1.6。这些微地震集中在俯冲太平洋板块上表面约20–50 km的深度处，是对10–20 km深度处发生的缓慢地震的补充。在岩手县和茨城县附近观察到微地震，它们的位置与成群的前震活动区域相对应。微地震活动的这种空间异质性类似于2011年东北地震的同震滑动和后滑之间的空间互补，这可能与板间摩擦特性的差异以及周围地壳的应力变化有关。