Low-frequency reconstruction of seismic data using stacked-BiLSTM networks

Abstract Seismic data is often contaminated with various low-frequency noises such as swell noise, instrumental noise, and tow noise. Typically, in the processing of seismic data, a high-pass filter is used to eliminate these unwanted low-frequency components. However, this filtering not only removes noise but also eliminates meaningful low-frequency signals essential for broadband seismic imaging. Numerous studies have been conducted to reconstruct low-frequency components by using machine learning techniques. However, most studies use convolutional neural networks and transform the data into image form rather than raw time-series data directly. In this study, we propose a low-frequency reconstruction method based on a recurrent neural network tailored to the sequence nature of seismic data. To improve the accuracy of the low-frequency component reconstruction network, we augment the feature information by incorporating envelope data, which provides additional aspects to seismic traces. Synthetic data tests confirm that our proposed method can reconstruct low-frequency components accurately. Furthermore, the validated network also successfully reconstructs low-frequency components in real dataset from the Arctic Sea.