9:45 AM - 10:00 AM
[SEM12-04] Assessing Magnetotelluric Data Quality Based on Linearity and Phase Differences
Keywords:Magnetotelluric, Data Quality Assess, Linearity, 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 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 is combined with linearity metrics 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 (Error_E), magnetic polarization direction (MPD), and the diagonal element of the hat matrix, are plotted together to examine the relationship with the noise and thus assist in removing the noise. Finally, the proposed technique was applied to evaluate the quality of MT time series at approximately 500 sites in the USArray project, and four case studies were used to demonstrate its effectiveness in discriminating between high-quality signals and noise. This technique facilitates impedance estimation by extracting high signal-to-noise ratio data when noise is intermittent. Understanding the data quality characteristics enables selecting appropriate processing methods and evaluating the impedance's reliability. It further supports adaptive quality control during MT surveys. Field observations can be halted once adequate high-quality data are collected for unbiased impedance estimation, which can improve fieldwork efficiency by avoiding unnecessary observation and invalid measurements.