Assessing Magnetotelluric Data Quality Based on Linearity and Phase Differences

Numerous parameters have been proposed to constrain the influence of noisy data during magnetotelluric (MT) impedance estimation. However, the relationship between these parameters and noise can vary significantly across different case studies. This study presents a novel method for evaluating MT data quality using phase differences (PD) between electric and magnetic fields and linearity metrics. First, it categorizes measured MT data into three high-quality types and two low-quality types based on phase difference patterns. Then, PD analysis and linearity metrics are combined to identify the data type of each event, revealing temporal variations in quality. Third, other parameters, e.g., the error between predicted and observed electric fields, magnetic polarization direction, and the diagonal element of the hat matrix, are plotted together to examine their relationship with the noise and thus assist in removing the noise. Finally, four MT time series from the USArray project are used to demonstrate the proposed technique in discriminating between high-quality signals and noise. This technique enhances the preselection strategy for impedance estimation. Understanding data quality characteristics aids in selecting appropriate parameters to mitigate noise influence and assess impedance reliability by verifying the existence of high-quality data.