PPSD GAN: PPSD-Informed Generative Model for Ambient Seismic Noise Synthesizing

Extensive research has been conducted in the domain of seismic noise to enhance the quality of seismic signals. However, despite these efforts, a notable gap exists in the literature concerning the physical properties of seismic noise with rigorous quantitative assessment methodologies for its characterization. Therefore, we suggest our data-driven generative model probabilistic power spectral density (PPSD) GAN, and unconditional Wasserstein GAN with gradient penalty (WGAN-GP) framework which is trained with the PPSD loss. We define a metric PPSD score for evaluation by leveraging the information contained in the PPSD histogram. We used two distinct datasets sampled from noisy and quiet areas in our study. Compared with previous approaches, PPSD GAN achieved 9.6%-24.3% higher PPSD scores compared to the existing models in both regions. The waveform generated by PPSD GAN is visually similar to the actual waveform. Also, the experimental result shows that our model succeeded in learning the regional characteristics.