Search Results (2,852)

Traditional fermented foods are important reservoirs of wild microorganisms with technological, sensory, protective, and functional potential. However, the performance of these isolates in controlled or compositionally different food matrices remains fragmented across microbial groups and food systems. This systematic review synthesized evidence on using wild microbial isolates from traditional fermented foods and beverages as starters or potential probiotic cultures. The conducted a systematic search exclusively in Scopus, following PRISMA 2020, to include original research articles published between 2021 and 2026, yielding 68 eligible studies. The included studies were analyzed by geographical origin, isolation source, recipient matrix, microbial group, and key physicochemical, technological, sensory, microbiological, nutritional, and functional outcomes. The evidence was organized into wild yeasts and filamentous fungi, lactic acid bacteria (LAB), Bacillus isolates, and defined mixed microbial cultures. Across food matrices, microbial incorporation frequently accelerated acidification, shortened fermentation time, modified volatile compound profiles, and altered texture, color, enzymatic activity, or substrate utilization. Sensory responses improved aroma, flavor, texture, and acceptance, whereas others produced profiles that deviated from the characteristic product and reduced overall liking. Functional effects included increases in phenolic compounds, antioxidant activity, GABA, folate, peptides, and resistant starch, along with reductions in phytates, nitrites, biogenic amines, aflatoxins, and nondigestible oligosaccharides. Researchers also reported antimicrobial, antifungal, protective, and preliminary probiotic properties. Defined mixed microbial cultures often provided complementary metabolic effects, although true synergistic interactions were demonstrated only sporadically. Overall, wild isolates from traditional fermentations represent promising resources for food bioprocessing; however, their performance varies widely across strains, recipient matrices, experimental conditions, and outcomes evaluated. Consequently, their application requires strain–matrix validation, comprehensive sensory assessment, safety characterization, and evaluation under processing and storage conditions relevant to industrial production. Full article

Desert highways have long been subjected to aeolian sand hazards, and sand accumulation on the pavement significantly weakens the surface texture and deteriorates skid resistance, which has become one of the core contributing factors to traffic accidents on desert road sections. Current research predominantly focuses on the attenuation law of the macroscopic friction coefficient of sand-covered pavements; however, the quantitative correlation mechanism between three-dimensional micro-texture characteristics and skid resistance has not been sufficiently revealed, and there is a lack of high-precision skid resistance prediction methods under multi-condition coupling scenarios. To address the above research deficiencies, this paper takes the asphalt pavement in the Tengger Desert region as the research object. A handheld three-dimensional texture scanning system was employed to acquire the three-dimensional pavement morphology parameters under different sand coverages, and the sideway force coefficient (SFC) was synchronously measured under the corresponding conditions. Through Pearson correlation analysis and dual multiple comparison correction using the FDR-BH and Bonferroni methods, the core influencing indicators were identified. Subsequently, a skid resistance prediction model based on a BP neural network optimized by the particle swarm optimization (PSO) algorithm was constructed and horizontally compared and validated with LSTM and PSO-SVM models. The research results show the following: ① under dry conditions, the root mean square height ($S_q$), peak density ($S_{pd}$), arithmetic mean peak curvature ($S_{pc}$), valley void volume ($V_{vv}$), root mean square slope ($S_{dq}$), and developed interfacial area ratio ($S_{dr}$) are significantly linearly correlated with the SFC, among which $S_q$, $S_{pd}$, $S_{pc}$, and $V_{vv}$ are the core controlling indicators, with the absolute values of their correlation coefficients all exceeding 0.73, and ② the constructed PSO-BP prediction model achieved a coefficient of determination $R^2$ of 0.86093 on the test set, and its prediction accuracy and generalization ability are both superior to those of the LSTM and PSO-SVM models, enabling it to effectively characterize the nonlinear mapping relationship between multiple texture parameters and skid resistance. This study can provide theoretical support and a technical basis for skid resistance evaluation, sand accumulation disaster warning, and scientific maintenance decision-making for desert highways. Full article

The presence of mistletoe in pine stands has expanded in recent decades, currently threatening Mediterranean forests. Mistletoe outbreaks can make the host trees more vulnerable to intense droughts, which are expected to increase due to climate change. We use multispectral (MS) and LiDAR UAV-derived data to classify Viscum album L. ssp. austriacum infestation levels at individual tree level in Scots pine (Pinus sylvestris L.) forests. First, spectral and structural differences between three infestation levels were assessed employing Kruskal–Wallis test with Benjamini–Hochberg correction and post hoc Dunn’s test for individual tree crowns. Second, classification algorithms were applied to evaluate infestation levels at the individual tree scale by combining UAV-derived datasets. The outcomes revealed significant differences between infestation levels in canopy cover and height based on LiDAR-derived metrics. Significant changes in vegetation vigor were also found through spectral and textural metrics. The highest classification accuracy (0.87) was achieved by the spectral metrics CIRE and NDVI in an SVM model, outperforming models based on the combination of sensor metrics (0.82) or solely LiDAR variables (0.69, MLR). This approach demonstrates their potential for detecting and characterizing morphological changes in up to three levels of mistletoe infestation at individual trees in Mediterranean Scots pine forests, lending support to forest management monitoring. Full article

This paper comprehensively investigates the ultraviolet (UV) nanosecond laser fabrication of micro-groove textures on planar PEEK surfaces, as well as their surface performance in terms of wettability and frictional properties. Firstly, a three-dimensional finite element model, incorporating a moving Gaussian heat source, solid heat transfer and deformed geometry, was established to simulate the transient temperature field and ablation profile of PEEK during UV laser ablation. The predicted groove depth agreed with the experimental value with a low deviation of 11.19%. Based on the simulation and single-factor experiments, the optimized single-track laser parameters were determined as a laser power of 1.2 W, a scanning speed of 100 mm/s and a repetitive frequency of 100 kHz. Secondly, systematic single-factor and multi-pass laser ablation experiments of PEEK surfaces were conducted to fabricate micro-groove textures with precisely tailored geometric parameters. Furthermore, ablated surface characterization showed that the laser-textured surfaces exhibited increased roughness, apparent crystallinity up to 22.13%, and pronounced anisotropic wettability, with improved liquid spreading along the groove direction and restricted spreading across the grooves. Finally, fretting wear tests under simulated body fluid lubrication were carried out. The research findings reported in this paper provide a systematic theoretical and experimental basis for the application of UV nanosecond laser surface texturing in the fabrication of PEEK-based components. Full article

Permian sedimentary manganese carbonate deposits in the Qiling Basin exhibit substantial resource potential, yet their depositional environments and precipitation pathways remain insufficiently constrained. This study integrates petrographic, mineralogical, whole-rock geochemical, and carbonate C–O isotope data from the Shuibutou Mn deposit in southern Hunan. We compare this deposit with the coeval Dongxiangqiao deposit and other Permian marine sedimentary Mn deposits to constrain its depositional setting, Mn-carbonate precipitation mechanisms, and potential Mn sources. The Shuibutou ores contain 10.88–17.18 wt.% MnO and are characterized by spherulitic–oolitic textures, abundant bioclasts, and framboidal pyrite. Mo–U enrichment indicates anoxic bottom waters, whereas the near-marine δ¹³C_carb values of ore carbonates (−0.24‰ to 1.28‰) are consistent with precipitation within a marine carbonate system and suggest direct precipitation of Mn(II) carbonates from dissolved Mn²⁺ under anoxic conditions. In this setting, dissolved Mn²⁺ accumulated below the chemocline. Partial dissolution of platform-derived calcite grains near the chemocline increased local dissolved inorganic carbon and alkalinity, driving Mn-carbonate supersaturation and the authigenic precipitation of manganoan calcite, ultimately forming manganese carbonate ores. The low Al/(Al + Fe + Mn) ratios, high Fe/Ti ratios, and Co–Ni–Zn and REY geochemical characteristics are consistent with a possible contribution from Mn-rich deep fluids to the dissolved Mn²⁺ inventory of the basin waters. Similarities in mineralogy, geochemistry, and depositional setting between Shuibutou and Dongxiangqiao point to a possible regional role for the direct precipitation of Mn(II) carbonates under anoxic conditions in the Middle Permian Qiling Basin. Relatively deep intraplatform basins may therefore represent favorable targets for manganese carbonate exploration and provide a reference for exploration targeting of Permian sedimentary Mn deposits within the Qiling Basin. Full article

Mg-Gd-Y-Zr alloys, with strong age-hardening and thermal stability, are ideal for lightweight load-bearing components, yet forming large complex parts is limited by high sensitivity to hot deformation parameters. This work investigates the hot deformation behavior and microstructure evolution of a Mg-9Gd-4Y-0.5Zr (wt.%) alloy via hot compression at 400 to 510 °C and strain rates of 0.001 to 10 s⁻¹. An Arrhenius constitutive equation with an activation energy Q of 158.63 kJ/mol was established, and a hot processing map was constructed. EBSD characterization revealed the dynamic recrystallization, grain size evolution, and texture transition. The results show that flow stress depends strongly on temperature and strain rate. At strain rates of 0.001~1 s⁻¹, a dynamic balance between work hardening and dynamic softening is achieved, and the post-peak flow stress gradually stabilizes. At a high strain rate of 10 s⁻¹, the flow stress continues to decrease because the competition between softening from dynamic recrystallization and work hardening is disrupted by deformation-induced heating. Low strain rates (≤0.01 s⁻¹) and high temperatures (≥470 °C) promote dynamic recrystallization and significant grain refinement. Two optimal processing windows were determined: 400 to 430 °C at 0.001 to 0.01 s⁻¹, giving fully recrystallized fine equiaxed grains, and 440 to 460 °C at 0.01 to 0.1 s⁻¹ with a power dissipation efficiency η of 0.43 to 0.51, balancing processing efficiency and microstructural uniformity. This work provides systematic theoretical and data support for optimizing hot forming parameters of large Mg-Gd-Y-Zr load-bearing components and offers guidance for applying high-strength magnesium alloys in high-end equipment. Full article

The search and discovery of underwater shipwreck sites represent the most arduous and critical phases of underwater archaeology. Wooden shipwrecks, in particular, are characterized by low acoustic impedance contrast and weak magnetic anomalies, coupled with their limited physical dimensions. Consequently, they predominantly exist as shallow-buried, discontinuous small targets scattered within confined areas, making their detection exceptionally challenging. Furthermore, the complexity of the submarine environment—including rugged topography, turbid water columns, and strong currents—poses formidable obstacles to the effective detection of these archaeological remains. Single geophysical methods are often limited by insufficient imaging resolution, interpretation ambiguity, and geological noise, making precise localization and characterization difficult. Focusing on the Nan’ao I Ming Dynasty shipwreck located in waters approximately 24 m deep off the coast of Nan’ao, Guangdong Province, China, this study proposes and validates an “acoustic-magnetic” multi-source data integration detection method. This approach systematically integrates high-resolution multibeam echo sounding (MBES), side-scan sonar (SSS), sub-bottom profiling (SBP), and marine magnetic data to establish a comprehensive framework for identification and integration analysis. The results indicate that the MBES bathymetric data reveal a regular, elongated structure oriented north–south (approximately 34 m × 12 m), closely matching the main hull and deck configuration. The SSS imagery exhibited high backscatter intensity and parallel linear textures, effectively delineating the hard shipwreck structure and the associated rigid protective frame employed for in situ preservation. SBP data confirmed the semi-buried state of the shipwreck (burial depth of approximately 0.6 m). Spatial variations in sediment thickness around the site suggested ongoing modification by strong hydrodynamic processes. Marine magnetic surveys identified localized negative anomalies (−210 nT relative to the ambient magnetic field), contrasting sharply with the positive anomalies of the surrounding natural reefs, thereby indicating an artificial ferromagnetic source. The spatial registration and feature superposition of multi-source data facilitated the characterization of the shipwreck, demonstrating its potential to mitigate environmental interference and enhance detection reliability in this complex environment. Using the Nan’ao I shipwreck site as a case study, this study provides a detailed characterization of the site’s 3D morphology, burial state, and physical properties. The proposed methodology offers a practical and robust technical solution for underwater shipwreck archaeology in complex nearshore environments, providing significant implications for proactive discovery, efficient investigation, and protection of underwater cultural heritage (UCH). Full article

Grain boundaries affect the mechanical and functional properties of crystalline materials by influencing interfacial energy, mobility, segregation, and the accumulation of damage. Among the grain boundaries in the grain boundary network, coincidence site lattice boundaries form a particular type of special grain boundary that is characterized by a higher degree of lattice-site coincidence. The present study examined the mechanisms involved in the formation of grain boundaries in a laser-powder-bed-fused AlSi10Mg alloy which had been subjected to two-pass twist equal-channel angular pressing (twist-ECAP). The microstructural evolution, deformation texture, and local orientation gradients were investigated using electron backscatter diffraction (EBSD). Moreover, atomistic simulations were carried out in order to assess the effect of geometrically necessary boundary (GNB)-like dislocation walls on the retention of planar faults. The EBSD results indicated that twist-ECAP considerably refined the microstructure and produced a strong fiber texture. Most of the Σ3 grain boundary segments detected were found in areas dominated by the ⟨110⟩||ED component, showing that their appearance is strongly dependent on the texture. Also, the atomistic modelling showed that the presence of a GNB-like wall led to the retention of planar-fault configurations and thus resulted in the highest number of atomic environments related to faults and extended dislocation-line lengths. These results show that the formation of Σ3 grain boundary segments in severely deformed LPBF AlSi10Mg is a coupled process which is mainly controlled by macroscopic texture selection and is locally assisted by deformation-boundary evolution. Full article

In dairy production, the fermentation process is a complex biochemical system that plays a significant role in determining the quality criteria of the final product. Traditional methods for controlling fermentation rely on limited and non-standard process parameters. In recent years, omics technologies have come to the forefront, enabling the monitoring of fermentation dynamics at the molecular level with their current, efficient, and reliable approaches. Thanks to omics approaches such as metabolomics, metagenomics, proteomics, and lipidomics, starter culture behavior, metabolite formation, aroma–texture formation, and microbial interactions in the fermentation process can be characterized more comprehensively. On the other hand, evaluating or calculating high-dimensional omics data using traditional statistical methods presents a challenge. Artificial intelligence applications are overcoming this challenge, offering significant opportunities for the accurate and reliable evaluation of data. Artificial intelligence-powered models hold promise in areas such as predicting fermentation kinetics, process control, optimizing quality criteria, and revealing the therapeutic potential of products through metabolites. This compilation aims to comprehensively address current approaches to modeling the fermentation process and quality parameters of dairy products using multi-omics technologies and artificial intelligence applications. In this respect, it will provide current and important perspectives for industrial applications and future studies. Full article

Process mineralogy provides valuable information for understanding the mineralogical and textural characteristics of volcanogenic massive sulfide (VMS) deposits and their influence on mineral liberation. This study investigates the relationship between ore texture and chalcopyrite liberation in massive and stockwork/disseminated copper ore samples from a Kuroko-type VMS deposit in the Eastern Black Sea Region of Türkiye. Whole-rock mineralogy, textures, and mineral intergrowth relationships were characterized using X-ray diffraction (XRD), reflected-light ore microscopy, and field-emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (FE-SEM/EDS) mapping. Both ore types contain chalcopyrite, pyrite, sphalerite, and galena as the main valuable minerals, with quartz as the predominant gangue mineral. However, they show distinct textural characteristics. In the massive ore, chalcopyrite occurs as fine-grained aggregates filling fractures in cataclastic pyrite and commonly forms complex intergrowths with adjacent sulfides. In contrast, the stockwork/disseminated ore exhibits a more dispersed sulfide distribution within a quartz-rich matrix. Liberation analyses conducted across six particle size fractions, ranging from −600 + 500 to −38 µm, demonstrate that chalcopyrite liberation increases as particle size decreases. The stockwork/disseminated ore consistently exhibits higher liberation than the massive ore across all size fractions. The highest liberation values were achieved in the −38 µm fraction, reaching 98% for the stockwork/disseminated ore and 90% for the massive ore. FE-SEM/EDS mapping confirms that micron-scale sulfide intergrowths in the massive ore limit complete liberation and contribute to its lower liberation efficiency. These findings highlight the influence of ore texture on chalcopyrite liberation and provide useful geometallurgical information for optimizing the grinding and beneficiation of Kuroko-type VMS ores. Full article

Chromite is a strategically critical mineral resource in short supply in China, and detecting the spatial distribution of concealed chromitite orebodies in plateau regions remains challenging. The Yarlung Zangbo Suture Zone in Tibet has a surface environment characterized by high elevations, deeply dissected terrain, and thick Quaternary cover. Remote sensing exploration in this setting is constrained by the spatial–spectral resolution trade-off, while Quaternary cover further obscures surface spectral and structural information. Accordingly, taking the Kubinongyue–Mendangga’ermu area in Zhongba County as the study area, we propose a geologically constrained intelligent exploration framework based on high-resolution hyperspectral (HR-HSI) data that integrates super-resolution reconstruction, deep visual interpretation, and ensemble machine learning. The HySure algorithm is introduced to fuse high-resolution multispectral (HR-MSI) and low-resolution hyperspectral (LR-HSI) data, reconstructing a data cube that preserves both fine spatial topological details and continuous hyperspectral signatures. To overcome the severe class imbalance resulting from extremely sparse lineaments and the interference of topographic artifacts, we developed a Trans-CBAM UNet model—integrating the Convolutional Block Attention Module (CBAM) and Transformer architectures—to extract ore-controlling physical boundaries with high connectivity. On this basis, ensemble learning models such as XGBoost and Random Forest were jointly employed to conduct high-dimensional nonlinear classification of subtle metallogenic indicators, including dunite and serpentinization. Quantitative evaluation demonstrates that the reconstructed data significantly enhance the spatial texture representation of hyperspectral imagery while largely preserving overall spectral fidelity, thereby improving the accuracy of lithological unit classification. The Trans-CBAM UNet model achieved an Area Under the Curve (AUC) exceeding 0.90 for fault prediction, providing robust physical constraints for subsequent intelligent extraction. Using the reconstructed HR-HSI data as the primary input and the extracted fault network as a geological constraint, high- and moderate-prospectivity zones for concealed chromitite were delineated. By combining HR-HSI reconstruction, deep-learning-based structural extraction, and ensemble lithological classification, this study establishes a geologically constrained remote sensing workflow for delineating concealed chromitite prospectivity zones. Full article

Complex surfaces exhibit hierarchical morphological organizations that cannot be fully described by conventional roughness parameters alone. In this study, a fractal–statistical framework is proposed to elucidate the substrate-controlled morphological evolution of 2,6-diphenyl anthracene (DPA) thin films deposited on chemically modified dielectric substrates, including hexamethyldisilazane (HMDS), octyltrimethoxysilane (OTMS), octadecyltrichlorosilane (OTS), and bare silicon dioxide (SiO₂). A multidimensional morphological descriptor vector (M_DPA) is introduced by integrating ISO 25178 areal surface parameters (H_ISO), fractal dimension (D_f), texture direction parameters (T_d), power spectral density (PSD), and scale-sensitive fractal analysis (SSFA) descriptors to quantify amplitude-based, spatial-frequency, and scale-dependent morphological information. Atomic force microscopy (AFM) topographies of 5 nm and 50 nm thick films were analyzed using complementary approaches, including ISO 25178 areal surface parameters, texture direction analysis, peak statistics, morphological envelope fractal analysis, two-dimensional Fourier analysis, power spectral density (PSD), and scale-sensitive fractal analysis (SSFA). The results demonstrate that substrate chemistry governs not only the amplitude of surface roughness but also the lateral organization, spatial frequency distribution, and scale-dependent fractal complexity of DPA morphologies. The fractal dimension analysis revealed substrate-dependent variations in surface complexity, with values ranging from 2.11 to 2.45 for 5 nm films and from 2.19 to 2.52 for 50 nm films. PSD analysis identified distinct substrate-induced modifications in spectral organization, while SSFA revealed significant changes in smooth–rough crossover scales, maximum complexity scales, and fractal surface complexity during film growth. In particular, OTMS promoted the strongest hierarchical organization for thicker films, exhibiting the highest scale-sensitive fractal complexity, whereas OTS generated highly developed but less hierarchically correlated rough structures. The integrated fractal–spectral methodology establishes quantitative relationships between substrate functionalization and multiscale surface evolution, demonstrating that morphological complexity cannot be described solely by conventional height parameters. This framework provides a robust approach for characterizing hierarchical thin-film architectures and can be extended to other organic semiconductor systems where substrate-driven morphological control is critical. Full article

Cholesteric liquid crystals (CLCs) possess the unique ability to selectively reflect incident circularly polarized light, exhibiting tremendous potential in diverse optical applications. In this study, a trilayer composite architecture of polymer-stabilized cholesteric liquid crystals (PSCLCs) was successfully fabricated. Introducing poly(vinyl alcohol) (PVA) as intervening barrier layers enabled the formation of independent and mutually non-interfering broadband reflection bands within each respective layer. Initially, a single-layer system was evaluated to identify the effects of component concentrations and polymerization conditions on the reflection bandwidth. Under optimal conditions, a maximum reflection bandwidth of 890 nm was achieved. Building upon these parameters, the effective concatenation of two independent reflection bands was accomplished by precisely regulating the concentration of the chiral dopant R5011 in the adjacent layers. Subsequently, the trilayer PSCLC film was constructed, ultimately broadening the total reflection bandwidth to 1650 nm. Characterization via polarized optical microscopy (POM) confirmed that the liquid crystal molecules consistently maintained a well-defined planar texture throughout the fabrication process of the multilayer films. Additionally, the film shows good infrared shielding performance. Its ability to regulate ambient light makes it highly promising as an optical filter and thermal management component in LC smart windows and emerging displays. Full article

The sustainable valorization of marine shell waste as functional additives for geopolymer materials represents a promising strategy for developing multifunctional materials for environmental remediation. In this study, metakaolin-based geopolymers were modified with calcium-rich particles obtained by calcination of five marine shell species (Chamelea gallina, Mya arenaria, Mytilus edulis, Pecten maximus, and Rapana venosa) under identical synthesis conditions to evaluate the influence of shell mineralogy on the structural, textural, adsorption, and antibacterial properties of the resulting composites. The materials were comprehensively characterized by Fourier transform infrared spectroscopy in attenuated total reflectance (ATR-FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and nitrogen adsorption–desorption (BET/BJH) analyses. Functional performance was assessed through methylene blue (MB) adsorption experiments, adsorption kinetic modeling, and antibacterial tests against Escherichia coli (E. coli). ATR-FTIR and XRD analyses confirmed the formation of a stable amorphous geopolymer network containing residual crystalline phases together with shell-derived calcium carbonate, predominantly as calcite or aragonite depending on shell origin. The incorporation of shell-derived particles modified the pore architecture of the geopolymers. GP-SJ exhibited the highest BET specific surface area (94.30 m² g⁻¹) and the most developed mesoporous structure. Among the investigated formulations, GP-RP showed the most favorable overall combination of functional properties under the investigated conditions, exhibiting the highest methylene blue removal efficiency (63.97%) and experimental adsorption capacity at 160 min (9.60 mg g⁻¹), together with a comparatively high reduction in recoverable E. coli colonies during preliminary antibacterial screening. The combined structural and functional analyses demonstrate that the environmental performance of shell-modified geopolymers cannot be predicted from a single parameter such as BET surface area or calcium content alone, but results from the synergistic interaction between mineralogical composition, particle dispersion, pore accessibility, and matrix compactness. Under the investigated conditions, these findings provide evidence for proposed structure–property correlations under the investigated conditions and suggests that shell-derived calcium particles act as microstructural regulators of geopolymer matrices, providing a basis for the further development of sustainable multifunctional materials for simultaneous dye removal and bacterial reduction in wastewater treatment. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article