Processing and detecting artifacts in multi-input multi-output phase-sensitive ice penetrating radar data

Surface crevasses impact ice sheet mass loss by initiating hydrofracturing and calving at the margins and transporting supraglacial meltwater to the subglacial drainage sys-tem. This process subsequently modulates basal sliding and glacier motion. However, the development of robust models for calving and hydrofracture has been limited by a lack of field observations of crevasse formation and geometry. In this paper, we analyze a two-year Multi-Input Multi-Output Autonomous Phase-Sensitive Radio-Echo Sounder (MIMO ApRES) dataset collected at Store Glacier in West Greenland, which documents the formation of a crevasse that opened under the instrument. We present methods for processing the data as well as identifying and removing artifacts, including clipping, radio frequency interference (RFI), receiver failure events such as elevated thermal noise, and signal leakage between channels. Specifically, we perform a mean squared error (MSE) analysis, clipping detection and quantification, and calculations of total power over time in the frequency domain and the time domain. After characterizing and min-imizing these artifacts, we find that the bottom of a crevasse can be detected in the processed images. Our results suggest that, with appropriate data processing, the MIMO ApRES is a promising geophysical system for investigating future crevasse evolution.