Xi-Net: Transformer Based Seismic Waveform Reconstructor

TL;DR

Xi-Net is a novel transformer-based deep learning model that effectively reconstructs missing seismic waveform data by leveraging multi-domain inputs, offering a more efficient alternative to traditional convolution-based methods.

Contribution

This paper introduces the first transformer-based seismic waveform reconstruction model, combining time and frequency domain features for improved accuracy and efficiency.

Findings

Successfully reconstructs 0.5-1s gaps in 120s waveforms

Outperforms traditional convolution-based models in accuracy

Operates efficiently with transformer architecture

Abstract

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.