  Missing/erroneous data is a major problem in today's world. Collected seismic
data sometimes contain gaps due to multitude of reasons like interference and
sensor malfunction. Gaps in seismic waveforms hamper further signal processing
to gain valuable information. Plethora of techniques are used for data
reconstruction in other domains like image, video, audio, but translation of
those methods to address seismic waveforms demands adapting them to lengthy
sequence inputs, which is practically complex. Even if that is accomplished,
high computational costs and inefficiency would still persist in these
predominantly convolution-based reconstruction models. In this paper, we
present a transformer-based deep learning model, Xi-Net, which utilizes
multi-faceted time and frequency domain inputs for accurate waveform
reconstruction. Xi-Net converts the input waveform to frequency domain, employs
separate encoders for time and frequency domains, and one decoder for getting
reconstructed output waveform from the fused features. 1D shifted-window
transformer blocks form the elementary units of all parts of the model. To the
best of our knowledge, this is the first transformer-based deep learning model
for seismic waveform reconstruction. We demonstrate this model's prowess by
filling 0.5-1s random gaps in 120s waveforms, resembling the original waveform
quite closely. The code, models can be found at:
https://github.com/Anshuman04/waveformReconstructor.
