Fault Geometry and Source Mechanics of the Altotiberina Fault System from a High-Resolution Machine-Learning Earthquake Catalog

Abstract

ABSTRACT Recent advances in machine learning (ML)-based earthquake detection and location techniques combined with dense seismic networks have dramatically increased the quantity of low-magnitude earthquakes that can be detected and accurately located. We analyze the seismicity of the northern Apennines, Italy, using data from the Alto Tiberina Near-Fault Observatory (TABOO-NFO), an ideal site for applying modern detection techniques due to the presence of a dense seismic network, intense microseismic activity involving a complex fault system, and deep fluid circulation. We use an ML-based workflow tailored to the TABOO area from 2010 to 2023 to construct an earthquake catalog with more than 400,000 events relocated with double difference. We leverage available data in the TABOO-NFO using manually picked waveforms to train a new regional version of PhaseNet, a deep learning-phase picker designed for high-accuracy P- and S-wave phase identification. The catalog provides new insights into geological and structural features in the area. We evaluate its quality by comparing the distribution of the hypocenters with the known active faults of the seismogenic region at TABOO. Our catalog illuminates structures and fault geometries with great detail, allowing detailed characterization of the spatiotemporal evolution of both the microseismicity occurring along the Altotiberina fault and the shallow sequences. The focal mechanisms we obtain show more complex fault kinematics for deeper events compared to minor, shallower fault segments. We illuminate a correlation between earthquake source properties and host-rock lithology that includes the spatial pattern of the seismicity and the frequency–magnitude behavior.

Topics & Concepts