Automated and Efficient Surface wave Phase Velocity Estimation and Quality Control Ambient Noise Tomography in The Big Data Era

Ambient noise tomography (ANT) has evolved from pioneering laboratory experiments into a widely used method for imaging Earth's subsurface at various scales. A standard framework involves selecting concurrent data windows from two stations, removing trends, normalising in time and frequency domains, cross-correlating (CC) the normalised data, and stacking CCs to obtain the noise correlation function (NCF). Surface wave dispersion properties between station pairs are then extracted from the NCF. A common approach of extracting dispersion curves matches the zeros of the NCF spectrum with those of the first-kind Bessel function. However, this method does not account for missing or extra zeros in the spectrum, leading to discontinuities in dispersion data. Researchers have employed various ways to limit such encounters, via employing various smoothing operators or by manually checking the data. In this study, we introduce an empirical relationship between zero spacing in the spectrum and inter-station distance, termed the empirical zero-distance law (EZDL). We use EZDL to develop a fully automated workflow that can navigate through missing or extra zeros in the spectrum. Additionally, we propose a new quality control metric, the well-placed zero ratio (WZR), which outperforms traditional metrics such as signal-to-noise ratio (SNR) in tracking surface wave arrivals. We demonstrate how applying EZDL for dispersion curve tracking and WZR for quality control significantly enhances ANT imaging results for the AusArray deployment in Northern Territory, Australia. AusArray, initially funded through the Exploring for the Future program continues with an expanded deployment as part of the Resourcing Australia's Prosperity initiative.
Presented at the 2025 IAGA/IASPEI Joint Scientific Meeting