KVP: a multiscale kurtosis approach for seismic phase picking
Year: 2025
Type: article
Abstract: SUMMARY Automatic event detection and phase picking are critical for processing the large volumes of data produced by modern seismological instrumentation. Accurate picking is especially challenging in Distributed Acoustic Sensing (DAS) recordings, where data quality can significantly vary along segments of the fibre due to localized environmental noise and coupling issues, reducing signal-to-noise ratios (SNR). Similarly, Ocean Bottom Seismometer (OBS) data quality also suffers from these issues. To improve accuracy under diverse conditions, we developed a novel multiband kurtosis-based picking algorithm, Kurtosis-Value-Picker (KVP), that enhances phase picking for both impulsive and emergent seismic signals. Our approach uses characteristic functions (CFs) calculated with sliding windows across multiple frequency bands. Triggers are identified based on localized kurtosis jumps over a few samples, providing greater sensitivity to emergent signals than traditional finite-difference methods. Each individual CF has its own set of triggers, adding flexibility to phase picking and retaining spectral information. We validate the KVP algorithm using earthquake data recorded with DAS on two land and submarine fibre-optic cables, as well as OBS data. We also compare its performance with a widely cited, kurtosis-based algorithm, the widely used FilterPicker algorithm and the well-known PhaseNet model, using impulsive signals on nearby DAS channels as a ground truth for emergent arrivals. Our results demonstrate that KVP provides accurate picks and is suitable for complex seismic data sets.
DOI: 10.1093/gji/ggaf136
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,416
OpenAlex ID: W4409320245