With the rapid development of 5G and IoT technologies, privacy concerns caused by passive eavesdropping attacks have become increasingly prominent. Firstly, an economic dispatch model for information-energy systems considering the Cloud-Edge-Devices architecture is established, with refined modeling of energy transmission characteristics and losses. A KL-divergence-based differential privacy mechanism is designed for the data acquisition stage. By introducing Gaussian noise with standard deviation correlated to the marginal cost, the privacy of terminal devices during data collection and transmission is effectively preserved. Secondly, a privacy-preserving distributed consensus economic dispatch algorithm (PP-DCED) is proposed. Local secret functions are embedded into the multi-energy management controllers at the edge layer. Combined with a disturbance gain feedback mechanism and a Gaussian decaying noise-based iterative algorithm, the proposed method ensures privacy protection during data exchange among edge devices. Finally, a hierarchical privacy protection scheme is achieved across the Cloud-Edge-Devices architecture. Hardware-in-the-loop simulation results demonstrate that compared with traditional centralized methods, the proposed approach reduces the privacy disclosure risk by 32.5% and improves communication efficiency by 24.8% while maintaining a high convergence accuracy of 0.001. This confirms that the proposed method can effectively balance the trade-off between privacy requirements and economic dispatch performance in complex energy ecosystems.

Ground-penetrating radar (GPR) is an essential tool for nondestructive subsurface exploration. However, electromagnetic wave propagation in underground environments is severely attenuated, leading to the loss of important geological information and limiting the resolution of underground imaging. To address this challenge, we propose an attention-enhanced U-Net (AEU-Net) model for GPR signal attenuation compensation. This model builds upon the 1-D U-Net architecture and integrates a feature fusion attention block (FFAB) to effectively capture both local and global features, thereby enhancing its capability to process complex datasets. In addition, to overcome dataset acquisition challenges, we use GprMax software to simulate realistic geological structures based on the relationship between conductivity and electromagnetic wave attenuation, thereby generating the training dataset. Experimental results with synthetic and field data demonstrate that the proposed method significantly improves noise robustness, restores fine subsurface details, and effectively compensates for GPR signal attenuation, thereby showing its potential for high-resolution underground imaging.

Time–frequency analysis (TFA) technology plays a significant role in seismic signal processing. The time–frequency representation (TFR) calculated using the TFA method is helpful for the localization of time-varying frequencies within the signal. Nevertheless, limited by the Heisenberg uncertainty principle, traditional linear TFA methods always provide blurred TFRs, which makes them difficult to distinguish details of time–frequency structures. Recently, the synchrosqueezing transform (SST) was designed to improve the concentration of the TFR. The SST can provide a much concentrated TFR for the weakly frequency-modulated (FM) signal, but it is not effective for the interpretation of strongly FM signals, such as the thin interbed in seismic exploration. In this work, we propose a new tool by introducing the multi-synchrosqueezing operator to the frame of wavelet transform (WT). Employing an iterative operator to correct the frequency estimation of the original SST step-by-step, it thus can calculate a TFR with better concentration and robustness. Synthetic signals and a field seismic data are employed to verify the performance of the proposed method for characterizing the time-varying frequency features.

We propose a novel seismic signal processing approach to efficiently and effectively attenuate seismic random noises. The proposed approach is a generalized seismic noise attenuation solution that can be applied to typical denoising operators. Our work has two main contributions. First, conventional filtering operators “regularize” the denoised results through the operator design. However, as seismic data have strong nonstationarity, it is inevitable to remove certain signal components. The resampling mechanism alleviates the signal loss. Second, the resampling operation does not require a lot of parameter tuning, which improves the denoising efficiency. Using the proposed approach, compared with existing denoising operators, the intrinsic seismic signal components are better recovered since random noise has been suppressed. Synthetic example and field data applications quantitatively and qualitatively demonstrate excellent performances of the proposed approach.

Seismic attenuation estimation is a significant task for characterizing reservoirs and improving the resolution of seismic data. The logarithmic spectral ratio (LSR) is one of the widely used tools for seismic attenuation estimation. However, how to select a suitable frequency bandwidth for the LSR is a difficult task, especially for field data. Moreover, field data are often contained kinds of noises, which makes seismic attenuation estimation difficult and unstable. We proposed an enhanced LSR (ELSR) workflow to estimate seismic attenuation. First, we built a sparse S-transform (SST) to obtain a sparse time-frequency (TF) spectrum of the analyzed seismic trace. Then, based on the SST spectrum, we introduced an ELSR workflow to estimate seismic attenuation. It should be noticed that we provided an adaptive frequency band selection for the LSR. To demonstrate the effectiveness of the proposed workflow, we apply it on both synthetic and field data. Compared with the results from several traditional attenuation estimation methods, the proposed ELSR provides a more stable and more accurate attenuation estimation result and offers the potentials in improving the resolution of post-stacked seismic data.

Geologic structure characterization is a key step for seismic structure interpretation, such as fluvial channels, faults, and fractures. The coherence attribute is a widely used tool for describing seismic discontinuities, which is usually calculated based on the similarity and dissimilarity of the adjacent seismic traces. However, accurately extracting coherence attribute is a difficult task in field data applications because seismic signal is one of the typical nonstationary, non-Gaussian, and wideband signals. To describe seismic discontinuities at different scales, we propose a workflow to extract the multiscale coherence (MSC) attribute. We first decompose seismic data into several band-limited intrinsic mode functions (IMFs) with different dominant frequencies via the multichannel variational mode decomposition (MVMD). Afterward, we develop a Cauchy kernel correlation-based coherence algorithm to extract the coherence attributes at different scales based on the decomposed IMFs. Finally, we can compute the MSC attribute by utilizing the calculated coherence attributes. Field data applications demonstrate that the proposed MSC attribute characterizes seismic discontinuities, such as faults and fluvial channels, more accurately and more clearly than the traditional coherence attribute and the 1-D variational mode decomposition (VMD)-based coherence attribute.

Random noise elimination acts as an important role in the seismic data processing. Moreover, protecting and recovering useful subsurface structure information are also significant. In this study, the S-mean that can obtain the geometric mean of the seismic traces on the symmetric positive definite (SPD) matrix manifold is adopted as a nonlinear filter for seismic denoising. Furthermore, S-mean has the best correlation with other elements based on the S-divergence due to the optimization of finding the S-mean on the SPD manifold. Therefore, the broken correlation features in noisy seismic data are compensated and maintained well, which can be conducive to describe the subsurface structures. Synthetic examples and field data applications qualitatively and quantitatively demonstrate the validity and effectiveness of the proposed workflow.

In this letter, we concern the acoustic impedance (AI) inversion from the known reflectivity based on the increasingly mature deconvolution techniques. For seismic data with the complicated geological structures, we first construct the regularization model for the AI inversion with the proposed regularizer consisting of the total variation (TV) seminorm and the Frobenius norm of the Hessian. Second, we develop the split Bregman (SB) iterative algorithm in the frequency domain for solving the proposed model. Finally, we verify the effectiveness of our proposed method via synthetic and field data. Experimental results demonstrate that our proposed method can not only preserve the lateral continuity and the impedance interfaces of the inverted AI section well, but also provide a higher resolution impedance section than the other related methods.

In the 6G network, lots of edge devices facilitate the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in the 6G network, this article fuses a self-similarity-based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of the context layer for each video frame. First, we extract the patch centered at each pixel and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of the correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of the absent frame by using the bidirectional context match and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have satisfying objective and subjective qualities.

Motion‐Compensated Frame Interpolation (MCFI) is one of the common temporal‐domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata‐based MVS (CA‐MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA‐based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.

In[1], the grant number in the first footnote for the National Postdoctoral Program for Innovative Talents should be BX20190279.

Traditional coherence algorithms are mostly based on the assumption that seismic traces are Gaussian and linear correlative. However, for nonGaussian seismic traces, the linear correlation analysis cannot accurately describe the similarity between adjacent seismic traces. To solve this issue, we adopt the kernel correlation instead of the linear correlation to improve coherence-based algorithms. Note that the kernel correlation is a generalized correlation with various kernel functions. It provides a framework for expanding the coherence algorithm based on linear correlation. Moreover, we discuss how to choose an appropriate kernel function in detail. To testify the validity of the kernel correlation, we apply it to field data using different kernels. The results demonstrate the effectiveness of the proposed algorithm to describe geological discontinuity and heterogeneity, such as fluvial channels and faults.

By building an adaptive filter bank, the empirical wavelet transform (EWT) decomposes an analyzed signal into several intrinsic mode functions (IMFs). Although some applications have certified the effectiveness of the EWT, the effectiveness of the EWT is affected obviously when analyzing nonstationary signals (e.g., seismic data). In this letter, we propose an improved EWT (IEWT) to decompose a nonstationary seismic signal into several IMFs and describe its frequency features. After computing the Fourier spectrum of the seismic signal, the scale-space representation (SSR) is used to extract the slowly varying component of the Fourier spectrum. Then, the frequency components contained in the seismic signal and the boundaries can be obtained using the central frequency information. Finally, we obtain an adaptive spectrum segmentation using detected boundaries based on the SSR. Afterward, the proposed algorithm obtains accurate and stable IMFs in decomposing the nonstationary seismic signal. To demonstrate the effectiveness of the proposed IEWT, we apply it to synthetic seismic signal and field data.