  Faced with the scarcity of clean label data in real scenarios, seismic
denoising methods based on supervised learning (SL) often encounter performance
limitations. Specifically, when a model trained on synthetic data is directly
applied to field data, its performance would drastically decline due to
significant differences in feature distributions between the two. To address
this challenge, we develop an effective self-supervised strategy. This
strategy, while relying on a single denoising network model, adeptly attenuates
various types of seismic noise. The strategy comprises two main phases: 1. The
warm-up phase. By using prior knowledge or extracting information from real
data, we introduce additional noise to the original noisy data, constructing a
noisier data with intensified noise. This data serves as the input, with the
original noisy data acting as pseudo-labels. This facilitates rapid
pre-training of the network to capture a certain noise characteristics and
boosts network stability, setting the stage for the subsequent phase. 2.
Iterative data refinement (IDR) phase. During this phase, we use the
predictions of the original noisy data from the network trained in the previous
epoch as the pseudo-labels. We continue to add noise to the predictions,
creating a new noisier-noisy dataset for the current epoch of network training.
Through this iterative process, we progressively reduce the discrepancy between
the original noisy data and the desired clean data. Ultimately, the network's
predictions on the original noisy data become our denoised results. Validations
under scenarios with random noise, backscattered noise, and blending noise
reveal that our method not only matches the traditional SL techniques on
synthetic data but significantly outperforms them on field data.

Geophysics