A Glitch Detection and Removal Method for Three-Component Seismic Data From Mars Based On Deep Learning

The data recorded by the seismometer on the InSight are contaminated by interference signals called ‘glitches’, which have a specific duration and waveform. These glitches emerge very frequently with large amplitude differences and affect the subsequent processing of the data. Traditional methods for glitches removal rely on the threshold and cannot perfectly detect non-standard and composite glitches. We propose a deep learning based method for glitch detection and removal. A detection model based on the PhaseNet network is developed for three-component data. The ConvTasNet from the field of speech signal separation is introduced into the noise removal model to separate the glitches from the single-component data. The advantages of deep learning include the ability to autonomously extract features from the training set without requiring parameter adjustment and the ability to quickly process large amounts of data. The proposed method can detect and suppress non-standard glitches and provides a novel approach to removing them from Mars exploration records.