Wireless Channel-based Secret Key Generation (WC-SKG) offers a promising alternative for wireless communication security, yet suffers from an extremely low key generation rate (KGR) in quasi-static environments (e.g., indoor Internet of Things), where key refresh cycles can extend for hours, creating a security vulnerability. Reconfigurable Intelligent Surfaces (RIS) can boost KGR by introducing artificial randomness, yet existing schemes only randomize the RIS phase shifts without using environmental priors, failing to maximize the KGR and meet the communication signal-to-noise ratio (SNR) requirement, causing service outages. To overcome these limitations, we propose the RIS-assisted Integrated Communication and WC-SKG (RICK) scheme. First, we treat the RIS-adjusted channel as a designable random variable and derive its optimal probability density function (PDF) using a novel geometric-algebraic framework to maximize the KGR under the communication SNR constraint. Second, we design a constrained-clustering-based quantization region division scheme tailored to this optimal non-uniform PDF, guaranteeing uniformly distributed secret keys. Simulation results show RICK achieves a KGR approximately 3.5-5 times higher than the state-of-the-art scheme while saving at least 20 dB of transmit power for the same KGR, confirming its effectiveness in quasi-static scenarios.

Deep-learning-based methods have been successfully applied to seismic data random noise attenuation. Among them, the supervised deep-learning-based methods dominate the unsupervised ones. The supervised methods need accurate noise-free data as training labels. However, the field seismic data cannot meet this requirement. To circumvent it, some researchers utilized realistic-looking synthetic data or denoised results via conventional methods as labels. The former ones encounter the problem of weak generalization ability because it requires the same distribution of test and training data. The latter ones encounter the issue of insufficient denoising ability because its denoising ability is difficult to significantly exceed the conventional methods which were used to generate labels. To avoid preparing noise-free labels, we propose a novel deep learning framework for attenuating random noise of prestack seismic data in an unsupervised manner. The prestack seismic data, such as common-reflection-point (CRP) gathers and common-midpoint (CMP) gathers after normal moveout (NMO) correction, have high self-similarity. It is because their events are coherent in the time–space domain and approximately horizontal from shallow to deep layers. The generator convolutional neural network (GCN) first learns self-similar features before any learning. The useful signals are more self-similar than random noise, which is incoherent and randomly distributed. Therefore, the GCN extracts features of useful signals before random noise. We select the specific training iteration and adopt the early stopping strategy to suppress random noise. Both synthetic and field prestack seismic data examples demonstrate the validity of our methods.