Ground Roll Attenuation in Seismic Data Based on Enhanced Deep Learning Framework With Adaptive Frequency Modulation Loss

Ground roll, a common coherent noise in field seismic data, exhibits distinct characteristics, including low frequency, low velocity, and high amplitude. This interference significantly degrades the signal-to-noise ratio (SNR) of recorded seismic data, impacting the reliability of the results of subsequent data analyses such as inversion and interpretation. To address this challenge, numerous techniques have been developed to effectively attenuate or separate ground roll from seismic data. In recent years, deep learning-based approaches for ground roll attenuation have emerged. These methods often incorporate the U-Net with mean squared error (MSE) to train the network in learning to estimate clean signals. In this article, we present a novel approach that utilizes a U-Net framework enhanced by attention mechanisms and residual modules to effectively extract ground roll features from training data. Additionally, we propose a hybrid loss function by integrating the mean squared error (MSE) with an adaptive frequency modulation (AFM) loss. By leveraging Fourier transform principles, the frequency modulation loss guides the network to integrate diverse frequency information within the time-space domain, particularly in scenarios with distinct frequency characteristics between noise and signal contents. Moreover, the incorporation of an adaptive mechanism within the frequency modulation loss enables the network to effectively handle complex and overlapped frequency components. Therefore, with the integration of this adaptive mechanism into the frequency modulation loss, the network significantly enhances its performance in attenuating ground roll. In comparison to both the traditional method and the deep learning (DL) approach, the proposed method excels in its capacity to retain low-frequency useful signals and eliminate ground roll.