Relative Attributes-Based Generative Adversarial Network for Desert Seismic Noise Suppression

Since seismic data will be interfered with by a host of complicated noise during the acquisition process, the quality of the acquired seismic data is usually poor. The overlap of signals and noise makes it difficult to extract effective signals from desert seismic records. Therefore, the suppression of seismic noise and the retention of seismic signals are key issues in seismic signal processing. In order to improve the quality of the data obtained, we propose an unsupervised relative attributes-based generative adversarial network (RAGAN), which includes a generator, a discriminator, and an attribute match-aware discriminator. By encoding the data of different attributes in seismic records, the denoising task can be regarded as the conversion process of the data corresponding to the attributes. The relative attributes obtained by the difference between the target attribute and the original attribute are used to control the attributes of the data generated by the generator, so as to achieve the purpose of noise suppression. Experimental results of both synthetic and field seismic records show that the proposed method performs better than part of conventional methods.