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.

In this paper, we propose a new class of message routing scheme for a distributed network which can scale up to the needs of a very large number of mobile users. We leverage the work in the GeoKad paper which uses the Distributed Geographic Table, allowing node retrieval from resources that are close to any given region. The paper addresses the problem regarding efficient message passing and peer misses. We propose a new routing scheme that addresses this kind of limitation. In fact, the problem can be solved in two ways, either by preserving the state of peer and further use a prediction algorithm or by better message routing. The proposed scheme is based on swarm intelligence (SI) and its attribute of exploit and explore. We leverage the statistical properties of time-varying network connectivity for opportunistic message forwarding. Considering the present 3G or Wi-Fi environment this approach can also help in optimizing energy as it can prevent query over flooding which is mostly encountered in such network scenarios.