Investigating the effect of data quality on time domain electromagnetic discrimination

Using field data and numerical simulations we investigate the effect of data quality on time domain electromagnetic discrimination. Data quality decreases when measurements contain responses not accounted for by our mathematical modelling. This can include instrument noise, inaccurately reported position and orientation information, geologic contributions to the signal, and loss of validity of the forward modelling. Survey design is critical to data quality in order to have sufficient sampling of data anomalies, and also to ensure that each target is illuminated such that both the axial and transverse components of the polarization can be excited and measured. For dipole model based discrimination algorithms, success is contingent upon the accuracy with which the components of the polarization tensor can be estimated. Field data from different survey modes are analysed to identify noise sources and provide quantitative estimates of the noise in each survey. Inversion results show that increased noise levels lead to greater spread in recovered parameters. Monte Carlo simulations are performed in order to investigate the importance of other data quality factors. Analysis of inversion results from the simulations show that anomaly size, signal to noise ratio, positioning error, line spacing and station spacing all play a role in the spread of recovered parameters. Through the analysis of our simulation results we propose a figure of merit as a means of quantifying different data quality factors with a single number and relate this number to the accuracy with which parameters can be estimated.