Reconstruction of Clipped Seismic Waveform

Abstract

Seismic waveforms are clipped when ground motions exceed the dynamic range of seis mometers. Discarding these clipped waveforms impedes high-resolution studies of Earth’s structure and ground motion analysis. In this study, we develop and evaluate four meth ods to reconstruct clipped waveforms: polynomial interpolation, projection onto convex sets (POCS), similar relative source time function (rSTF) method, and machine learning method. The polynomial interpolation method is simple and computationally efficient, providing satisfactory reconstructed waveforms. However, its low stability is a significant limita tion. The POCS method combines both time and frequency-domain constraints, yield ing robust results for moderate and slight clipping scenarios but fails to reconstruct large clipped zones. The innovative similar rSTF method does not yield reasonable reconstruc tions due to ill-posed issues. The machine learning method with the hybrid CNN+LSTM model shows the best accuracy across various clipping scenarios. The main challenge of this method is the underestimation bias in strongly clipped waveforms. After identifying machine learning as the optimal approach, we apply it to reconstruct the real clipped waveforms from the M6.6 Italy earthquake in 2017. The result shows that when both the unclipped waveform and the reconstructed clipped waveform are utilized, the Peak Ground Velocity (PGV) in the near field is lower compared to the PGV predicted using only the unclipped data. This research demonstrates the effectiveness of machine learning approaches in clipped waveform reconstruction. Future work should focus on optimizing loss functions, iden tifying more suitable training datasets, and developing new machine learning architec tures.