The identification of seismic reflections in vibroseis data is crucial for evaluating subsurface structures. However, precisely localizing these reflections and determining their polarity is challenging. In this study, we propose a robust method for detecting weak reflections and accurately locating reflection spikes as well as their polarity using multiscale phase-only correlation (MPOC). By applying the generalized S-transform, we obtain local phase information from both the vibroseis data and the Klauder wavelet across multiple scales. We then compute the phase-only correlation between these two signals in the time–frequency domain at different scales, and stack all MPOC coefficients to quantify their local phase similarity. This approach achieves high precision in detecting weak reflections and identifying their polarity, even in the presence of noise. Numerical experiments with synthetic and real data confirm the effectiveness of the proposed method.

Graph neural networks (GNNs) are extensively employed in location-related scenarios, relying on aggregation to gather features from neighboring nodes based on edge weights. Features are closely bound to nodes’ locations and edge weights mirror distance correlations. In this sense, certain privacy concerns exist while providing location-based services if there is insufficient privacy protection. To this end, we propose a privacy-preserving and distance-aware data aggregation framework (PEDA) for GNNs. Specifically, PEDA achieves location privacy by combining circular-based positional coding with inner product functional encryption. Because of the masks in the codes, the decryption returns masked distances, preventing distance leakage. Following this, in order to protect feature privacy, we employ secret sharing. To preserve the collection strategy’s privacy, we implement an oblivious transfer for collecting the shared features. Additionally, we securely generate the adjacency matrix and aggregate features based on multi-party computation. Thorough security analysis and comprehensive evaluation demonstrate the privacy, feasibility and practicality of our approach. When compared to related works, PEDA offers four types of privacy, maintains distance awareness and feature utility, and allows for oblivious data collecting with little computational cost sacrifice.

The Martian north polar layered deposits (NPLD) are composed of layered ice and dust that record the changing climate over the last few million years. Shallow Radar (SHARAD) onboard the Mars Reconnaissance Orbiter is critical for studying the fine structures and compositions of the NPLD. The radar reflectivity correlates to the changing properties of real stratigraphy (e.g., dust content). Inversion of the reflectivity is commonly based on Fresnel equations, which is inefficient when coupling abundant subsurface reflectors such as those from NPLD. SHARAD data are considered as a convolution product of a known source wavelet and unknown sparse reflectivity series. Here, we propose a sparse deterministic deconvolution (SDD) method to obtain the NPLD reflectivity profile with enhanced resolution compared with raw radar data. We solve the reflectivity series as the ℓ2- ℓ1problem using a minimization technique by alternating direction method of multipliers. Incoherent summing can be used to reduce speckle noise prior to deconvolution, leading to results with less impact from speckle noise. Synthetic and real data examples illustrate that the SDD method efficiently estimates subsurface reflectivity series and enables the detection of laterally continuous, subtle, and deep reflectors with higher resolution and sparsity. The proposed method provides a robust and efficient approach for inverting the reflectivity of SHARAD data, which is promising for investigating the detailed structure and composition of the polar caps that is related to Mars paleoclimate.

The investigation of subsurface stratigraphy is crucial for understanding the evolution of planetary bodies. Successful missions have underscored the indispensable role of orbital radar sounders in exploring the Martian subsurface. The abundance of sounding radar data has spurred the development of automatic techniques for identifying subsurface reflectors, to reduce the reliance on manual picking. However, the prevailing detection algorithms face significant challenges in addressing the variability of radar data across different geological contexts or from various instruments, especially with strong noises. These obstacles complicate the accurate extraction of subsurface layer structures and echo power, thereby impeding our understanding of geological evolution. Here, we introduce a novel approach for automatically detecting subsurface reflectors from orbital radar data, leveraging a combination of phase congruency and the shearlet system for multiscale analysis. It employs the Shannon-Gabor (SG) wavelet as the principal component in its detection framework, which proves to be more effective than the traditionally used Mexican hat wavelet, as evidenced by synthetic data experiments. Tests on both shallow radar (SHARAD) and mars advanced radar for subsurface and ionosphere sounding (MARSIS) radargrams have shown that our method is able to detect weak, quasi-scattering, and highly undulating reflectors, while maintaining a desirable level of precision comparable to that of human analysis. The proposed method demonstrates versatility across various radar datasets and imaging scenes, showing great promise for revealing additional information about planetary subsurface structures and supporting data analysis for future spaceborne missions.

The ocean bottom nodes (OBNs) acquire seismic data at a challenging depth to explore the subsurface structures. The recorded body wave and Scholte wave are highly mixed and are difficult to be separated. The strong body wave would influence the high-order modes extraction using the Scholte wave, while the Scholte wave would degrade the imaging of sedimentary structures using body wave. The lacking of effective methods for separating both waves prevents their application. We developed a migration-based method to accurately separate the Scholte wave and body wave in the OBN data. First, we use high-pass filtering to divide the original OBN data into three parts: background noise, high-frequency body wave, and the mixture of Scholte wave and low-frequency body wave. Then, we separate the Scholte wave and low-frequency body wave using migration and demigration based on the fact that they have different limits of reversible-migration velocity. Finally, we generate the separated body wave by subtracting the Scholte wave from the denoised OBN data. For the off-line data, the local orthogonalization method is required to retrieve the weak leakage of Scholte wave around the apices. Theoretical analyses and numerical experiments show that the proposed method can accurately separate Scholte wave and body wave without any visible artifacts while retaining most of their inherent properties. The separated body wave provides a high-quality input for imaging sedimentary structures, and the separated Scholte wave enables the extraction of high-order modes of dispersion curve that are crucial for high-resolution surface-wave inversion.

Random noise attenuation plays an important role in the seismic data processing. The global coherency among different spectral segments is often neglected in the traditional denoising methods, even though the seismic data are naturally broadband in the frequency spectrum. We proposed a multispectral denoising framework (MDF) for seismic random noise attenuation. The MDF contains three key components. First, we use a series of narrow-band filters on the noisy data to construct the multispectral data. Second, we normalize the constructed multispectral data to mitigate the amplitude discrepancy between different spectral slices. Third, we apply the intrinsic tensor sparsity regularization method to denoise the multispectral data. Numerical experiments on synthetic and field data show that the proposed framework can reduce the random noise as well as the erratic noise, and it can achieve improved denoised results, compared with a traditional method (i.e., the$f$-$x$deconvolution method) and the single-spectral version of the proposed method (i.e., the weighted nuclear norm minimization (WNNM) method). The proposed framework can be applied to both prestack and stacked data and generally well preserve the amplitude of useful signals, compared with the other two methods.