Simultaneous suppression of seismic random and erratic noise using PINN with high-frequency preservation

Abstract Random and erratic noise are common in seismic data. Traditional denoising methods that combine noise suppression for both types will lead to error accumulation. Deep learning methods often require large amounts of labeled data and complex network architectures to tackle this problem. To overcome these limitations, we utilize a physics-informed neural network (PINN) to remove both types of noise in this research. This method does not require a large amount of labeled data. Because it uses slope attribute, which is based on a local plane-wave partial differential equation. We apply velocity-dependent (VD) slope estimation to get more accurate slope values. This helps the PINN make better predictions. To enhance its ability of preservation in high-frequency signal, we use Fourier feature embedding and a periodic activation function from sinusoidal representation networks. These techniques effectively preserve high-frequency signals in seismic data and accelerates convergence. We refer to our proposed method as VD-PINN with high-frequency preservation. Its application to synthetic and field data shows effectiveness in simultaneously suppressing both noise compared to other methods.