Akua M. McLeod1, Sean T. Peters1, Dustin M. Schroeder1,2, Nicole Bienert1, Tun Jan Young3, Poul Christoffersen3
1: Stanford University, Department of Electrical Engineering, Stanford, CA United, States
2: Stanford University, Department of Geophysics, Stanford, CA United, States
3: University of Cambridge, Scott Polar Research Institute, Cambridge, United Kingdom
Recent work has demonstrated how large variations in surface meltwater created clipping artifacts in the processed autonomous phase-sensitive radio-echo sounder (ApRES) signal. These artifacts can obscure real signals, significantly impact the interpretation of the measurement, and thus reduce the accuracy of the results. To expand on this existing work, we present methods for automating the identification of these artifacts and several other data anomalies while analyzing and processing multi-input multi-output (MIMO) ApRES data. In this paper, we analyze MIMO ApRES data gathered in the ablation zone of Store Glacier, West Greenland, from July 2017 to July 2019. Our analysis highlights several artifacts identified in the data, including radio frequency interference (RFI) issues, receiver failure events such as elevated thermal noise, and signal leakage between channels. We present methods for automating identification of each of these anomalies and processed ApRES signal artifacts while working with large datasets. Specifically, we leverage mean squared error (MSE) analysis, clipping detection and quantification, and calculation of total power over time in the frequency domain and the time domain. We then evaluate the efficacy of these methods for processing data artifacts. Additionally, we demonstrate that by highlighting artifacts caused by clipping, instrumentation error, and ambient noise, our analysis provides an effective approach to avoid misinterpretation of one’s data. We find these methods to be particularly useful for glaciologists seeking to mitigate the impact of large variations in the environment on their ApRES signal. Ultimately, by characterizing and minimizing signal disruption, our methods increase the accuracy of ice-penetrating radar data analysis and interpretation.
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