Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network

One of the difficulties in desert seismic data processing is the large spectral overlap between noise and reflected signals. Existing denoising algorithms usually have a negative impact on the resolution and fidelity of seismic data when denoising, which is not conducive to the acquisition of underground structures and lithology related information. Aiming at this problem, we combine traditional method with deep learning, and propose a new feature extraction and denoising strategy based on a convolutional neural network, namely VMDCNN. In addition, we also build a training set using field seismic data and synthetic seismic data to optimize network parameters. The processing results of synthetic seismic records and field seismic records show that the proposed method can effectively suppress the noise that shares the same frequency band with the reflected signals, and the reflected signals have almost no energy loss. The processing results meet the requirements of high signal-to-noise ratio, high resolution and high fidelity for seismic data processing.