Title: A self-supervised scheme for ground roll suppression Show Chinese title

Title: 一种用于地面滚动压制的自监督方案

Abstract: In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage, an unobtainable requirement for seismic data. However, current self-supervised methods usually suppress simple noise types, such as random and trace-wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self-supervised procedure, namely, blind-fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self-supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll. Show Chinese abstract

Abstract: 近年来，自监督过程推动了地震噪声衰减领域的发展，这是因为它们在训练阶段不需要大量干净的标记数据，而这对地震数据来说是一个无法实现的要求。 然而，当前的自监督方法通常只抑制简单的噪声类型，如随机噪声和道级噪声，而不是复杂的混叠地面滚波。 在此，我们提出了一种自监督过程的适应方法，即盲扇形网络，用于在不需任何干净数据的情况下去除地震单炮数据中的混叠地面滚波。 通过设计一个具有预定义方向的噪声掩码，以避免地面滚波的相干性被网络在预测一个像素值时学习，从而实现了自监督去噪过程。 在合成和实际地震数据上的数值实验表明，我们的方法可以有效衰减混叠地面滚波。