Classification of Small Seismic Signals Associated with the Oak Ridge Earthflow in California Using a Combination of Machine Learning and Array Signal Processing

The mechanics of slow frictional creep in landslides remains debated and only a few detailed seismic studies have been conducted in landslide-prone areas. To illuminate basal slip processes for a slow moving landslide, we deployed a dense 80 node seismic array at Oak Ridge Earthflow in California’s Diablo Range for one to two months during the rainy seasons of 2023 and 2024, both winters where decimeters of landslide displacement occurred at Oak Ridge. Simultaneously, GNSS receivers, strain meters, and piezometers were deployed at the same site. During our deployments, various types of very small signals were recorded by the seismometers. These events were local, detected only by nearby stations sited within about 100 m of each other. The cause of these events remains unclear, whether due to shear slip at the base of the earthflow or other sources, such as water movement or animal activity. To investigate the cause of these signals, and evaluate the role of stick-slip motion and shear localization, we automatically detected the events and analyzed their spatiotemporal distribution. We used quakephase (Shi et al., 2024) to identify the phases of the very small signals. The primary challenge with automatic picking in our dataset is the long processing time due to high sampling rates. To address this issue, we applied array signal processing, covseisnet (Seydoux et al., 2016), to extract signal candidates based on the coherence of dominant frequencies across the seismic array, followed by automatic picking. This approach successfully and efficiently identified specific signals we believe are associated with earthflow motion. These signals are not continuously observed but concentrate within specific time periods. We focused on events in these time periods, utilizing scattering networks and matched-filter techniques for more detailed classification. By combining our results with other temporal data, such as pore fluid pressure, precipitation, temperature, and displacement, we will discuss the causes of these signals to better understand the mechanism of the earthflow motion.