Improved lithospheric seismic velocity and density model of the Korean Peninsula from ambient seismic noise data using machine learning

Abstract

To accurately estimate physical properties and tectonic behavior in the near-surface, a reliable seismic property model is needed for realistic seismic modeling and earthquake location estimation. Recording ambient seismic noise (ASN) data and producing interferometric reflection images traditionally provides subsurface structure observation without an active source. However, due to low signal-to-noise ratio (SNR) and vertical resolution, interpreting upper mantle structures and inverting seismic models from noise data is difficult. To address this, machine learning (ML) techniques are applied to enhance vertical resolution and interpret geologically meaningful boundaries. Spectral enhancement with convolutional U-net generates higher resolution data by preserving temporal continuity. Unsupervised ML interprets lithospheric boundaries more robustly and objectively than manual horizon picking, and model-based seismic inversion integrates improved seismic data with prior full-waveform inversion (FWI) models. ML-based results improve inverted models, displaying more detailed geological structures and seismic property changes, surpassing seismic data limitations.