Search Results (521)

Bubbles in submarine cold-seep plumes cause strong scattering, attenuation, and poor continuity in water-column seismic responses, hindering continuous two-dimensional characterization of acoustic properties. Using multichannel seismic data from the Shenhu area, northern South China Sea, this study proposes a conductivity–temperature–depth (CTD)-constrained, layer-wise two-angle allied elastic impedance (AEI) inversion method to reconstruct water-column velocity. Based on the scattering-equivalent AEI theory, a two-angle equation is derived for layer-wise estimation of water-column velocity, thereby providing a theoretical basis for the subsequent inversion. A relative-amplitude-preserving workflow mitigates bubble-induced attenuation and random scattering, while pre-stack time migration produces common reflection point (CRP) angle gathers. Coherent scattered energy after imaging is parameterized by the local scattering half-angle. Small-angle and large-angle gathers are selected according to effective angular coverage for AEI inversion. CTD-derived seawater velocity and density provide AEI constraints, and the normalized two-angle AEI equation yields a continuous velocity section. Results show velocities of 1460–1560 m/s, with pronounced vertical stratification, relatively weak lateral variation, and agreement between inverted and CTD-derived velocity trends. Within the 0–1800 ms water-column time window, the root-mean-square difference between the inverted and CTD-derived velocities is 18.19 m/s. Combining the lateral continuity of multichannel seismic data with high-accuracy CTD-derived physical-property constraints enables continuous two-dimensional velocity reconstruction in bubble-scattering water columns and offers a new approach for observing cold-seep velocity structure and geophysically characterizing bubble-plume activity. Full article

Geographic information systems have traditionally focused on the representation of visual and geometric aspects of geographic phenomena, while auditory information has largely been ignored or reduced to simple numerical attributes. Although numerous spatio-temporal data models have been proposed to describe dynamic geographic processes, few of them provide explicit semantic constructs for representing sound and its evolution over space and time. This paper extends the Modeling of Application Data with Spatio-Temporal Features (MADS) conceptual model by introducing an explicitly defined acoustic semantic dimension. The extension separates source-intrinsic acoustic emission (soundfeature), source–receiver propagation and exposure (AFFECTS and associated EXPOSUREZONE instances), and receiver-centered contextual perception (PERCEPTIONASSESSMENT). These complementary constructs enable acoustic emission, propagation, temporal dynamics, spatial exposure, and perceptual assessment to be represented within a unified spatio-temporal semantic framework. The applicability of the proposed approach is illustrated through a proof-of-concept scenario and representative database queries, demonstrating how the proposed acoustic constructs can be mapped, stored, and queried using standard GIS database infrastructure. Full article

Slow earthquakes represent an important mode of fault slip transitional between stable creep and dynamic rupture. Their occurrence is jointly controlled by mineral composition, pore-fluid pressure, effective normal stress, system stiffness, and microstructural evolution. Because the internal state of natural faults cannot be directly observed, studies of slow slip, tectonic tremor, and low-frequency earthquakes have long been challenged by weak signals, complex noise, and discrepancies in observational scales. Building on the physical foundations of rock friction and slip stability, this review summarizes recent applications of deep learning to laboratory friction and acoustic data, natural seismic waveforms, Global Navigation Satellite System (GNSS) observations, and strain measurements, with particular emphasis on event detection, fault-state estimation, rate-and-state friction parameter inversion, and forecasting of slip evolution. Existing studies have progressed from event identification to the reconstruction of shear stress, estimation of frictional parameters, and prediction of future fault states. Nevertheless, applications to natural faults remain dominated by event detection and catalog construction, whereas parameter inversion and forecasting still rely largely on laboratory experiments or synthetic data. Physics-informed neural networks, transfer learning, reduced-order modeling, and data assimilation provide promising pathways for integrating laboratory experiments, numerical simulations, and natural observations; however, their reliability remains limited by constitutive-model dependence, parameter non-uniqueness, domain shift, and insufficient independent validation. Future work should strengthen multi-observation integration, cross-region validation, and uncertainty quantification, while developing a bidirectional framework linking laboratory experiments, numerical simulations, and natural fault observations. At present, deep learning is better suited to fault-state characterization and probabilistic assessment of slip trends than to deterministic prediction of the exact timing of slow earthquakes. Full article

Silica aerogels are among the most extraordinary porous materials produced through sol–gel chemistry, distinguished by ultralow density, exceptionally high porosity, large specific surface area, and extremely low thermal conductivity. Despite these characteristics, widespread application of conventional silica aerogels has been constrained by inherent brittleness, poor mechanical strength, and moisture sensitivity. Significant research has therefore focused on silica aerogel composites, in which reinforcing or functional phases—fibers, polymers, carbon nanomaterials, metal oxides, and biopolymers—are integrated into the silica network to enhance mechanical robustness, flexibility, hydrothermal stability, electrical conductivity, catalytic activity, and multifunctionality while largely preserving the parent aerogel’s desirable properties. We review the synthesis, characterization, properties, and applications of silica aerogel composites. Sol–gel processing and drying technologies are discussed, followed by composite-formation strategies and the advanced techniques used to evaluate structural, mechanical, thermal, surface, and functional properties. The effects of reinforcing phases on mechanical performance, thermal conductivity, and hydrothermal stability are analyzed, and current and emerging applications in thermal insulation, environmental remediation, catalysis, acoustic damping, aerospace systems, biomedical engineering, and energy storage are highlighted. Finally, key challenges and future directions involving multifunctional materials, green synthesis, and data-driven materials design are discussed. Full article

Bearings are core components of rotating machinery, and the development of precise and efficient fault diagnosis technologies is of paramount importance for realizing early warning and accurate localization of faults. This paper briefly analyzes bearing fault mechanisms and provides a comprehensive review and critical commentary on the research progress of bearing fault diagnosis methods, outlining future development trends. Specifically, this study begins by introducing common bearing fault types and reviewing the advancements in fault signal acquisition techniques. Subsequently, it categorizes fault diagnosis methods based on vibration signals and critically evaluates their respective research methodologies. Furthermore, focusing on non-contact acoustic signal diagnosis, the paper summarizes mainstream technical pathways for acoustic signal denoising and highlights innovative applications of deep-learning models tailored to acoustic characteristics. Finally, addressing the urgent demands for industrial deployment, future research directions are projected from three perspectives: the deep integration of physics-driven and data-driven multi-modal fusion, interpretable diagnosis assisted by Large Language Models (LLMs), and lightweight engineering deployment. This work aims to provide a reference for constructing an all-scenario intelligent monitoring system. Full article

As cutting-edge semiconductor devices become smaller and more densely packed (i.e., ultra large-scale integration, ULSI), there is an increased risk of parasitic capacitance preventing proper device operation. There is consequently growing interest in the development of low-permittivity dielectric materials to serve as intermetal/interlayer coatings that would minimize this effect. Highly porous aerogels exhibit extremely low dielectric permittivity; silica-based aerogel films, in particular, are candidates for this application. The present report focuses on the synthesis, structure and dielectric permittivity of hydrophobic, tetramethyl orthosilicate (TMOS)-based silica aerogels prepared in disc-form. Using our 3D printed polymer sample holder with few metallic components, we are now able to determine the dielectric permittivity of highly porous, hydrophobic silica aerogels in the very low frequency (VLF) range, 10–27 kHz. Commercial systems that operate in the VLF range include circuits for biological signal processing, characterized by low amplitude and frequency, and circuits that operate in marine environments, where the useful frequency range is limited by acoustic signal deterioration. During impedance measurements, aerogel samples were confined in pure, dry oxygen atmosphere. Low mass density (10–250 mg/cm³) TMOS-based aerogel discs present structural characteristics and relative dielectric permittivity values in the VLF range that are not readily comparable with analogous data reported to date in the relevant literature. Full article

Speech is a fundamental component of human communication and has become increasingly important with the widespread adoption of voice-based technologies, text messaging systems, Chatbots, and Large Language Models (LLMs). While AI systems are going through exponential breakthroughs, they often prioritize the dominant variant of a language. Using the TEDx Talks Brazilian Accents (TTBAcc) dataset, this research provides an acoustic analysis of two Brazilian Portuguese accents: Nordestino and Paulistano and an unsupervised accent clustering to find distinctive characteristics and investigate factors that can potentially support the monitoring of emerging and endangered accents. The acoustic analysis compares rhotics and vowels produced by speakers of both accents. The analysis of rhotics provided limited evidence of meaningful difference between accents. In contrast, the vowels exhibit significant differences in the fundamental frequency ($f_0$), the first and second formant frequencies ($F_1$ and $F_2$) and intensity, suggesting that these acoustic features may be more effective to distinguish between the investigated accents. For accent clustering, $f_0$, length, volume, and formant frequencies ($F_1$ and $F_2$) are used as inputs. Among the evaluated approaches, K-Means, with $K=38$ achieves the best performance, obtaining a Rand Score of 0.775 ± 0.028 and 0.524 ± 0.032 homogeneity, the best performing model. The optimal number of cluster exceeds the number of classified accents, which may indicate either an over-segmentation of data or a finer-grained phonetic variation captured in acoustic features. These findings highlight the potential of using acoustic features for investigating the variation in regional accents, and provides a basis for further research in monitoring emerging and endangered accents. Full article

Underground petrochemical pipelines face severe risks from unreported third-party damage during excavation. Existing monitoring systems based on supervised deep learning or distributed acoustic sensing often require expensive hardware and large volumes of labeled defect data, and they exhibit high false-alarm rates in noisy urban environments. To overcome these limitations, this study proposes a cost-effective, field-feasible early warning framework utilizing attached acceleration sensors. High-frequency transient vibrations from physical impacts are demodulated into low-frequency rhythms using a Hilbert transform-based envelope algorithm, and these rhythms are then converted into 2D spectrograms via the Short-Time Fourier Transform. A 2D Convolutional Neural Network-Variational Autoencoder (2D CNN-VAE) is employed for unsupervised anomaly detection, trained specifically on normal background data. Furthermore, a hierarchical alarm classification logic is implemented to evaluate the reconstruction error, incorporating impulse noise rejection and temporal continuity checks. In a field demonstration, the proposed framework effectively suppressed false alarms caused by severe continuous noise, such as ground compacting, while reliably detecting sustained asphalt-breaking threats. This methodology demonstrates practical feasibility for isolating genuine excavation activities from transient environmental noise, offering a promising predictive maintenance approach that reduces alarm fatigue without requiring labeled defect data. Full article

The Red beds of Hazlehurst (RbH), a deep saline formation underlying parts of Georgia and South Carolina, has been identified as a potential geologic reservoir for large-scale carbon dioxide (CO₂) storage. However, limited subsurface characterization has hindered a detailed assessment of reservoir quality and storage capacity. In this study, we integrated 2D seismic reflection data and well logs from two boreholes in southeastern Georgia to perform post-stack seismic inversion and develop acoustic impedance and porosity models of the RbH formation. The resulting models were used to identify potential storage intervals and evaluate reservoir properties relevant to CO₂ sequestration. The inverted acoustic impedance model reveals laterally continuous zones of relatively low impedance within the upper portion of the RbH, suggesting the presence of more porous reservoir rocks and representing the most favorable intervals for CO₂ injection. Porosity estimates derived from well log calibration range from approximately 8% to 10% within these intervals. Although these values indicate generally low reservoir quality and a shale-rich lithology, they remain within the range considered suitable for deep saline storage. Higher acoustic impedance values observed near the transition between the RbH and overlying Coastal Plain sediments are interpreted as lower porosity intervals that may contribute to vertical containment. Storage capacity calculations indicate an estimated CO₂ storage potential ranging from 2.6 to 19.4 megatons within the evaluated reservoir intervals. These results provide new constraints on the distribution of reservoir and seal facies within the Red beds of Hazlehurst and support ongoing regional carbon storage assessments conducted through the Southeast Regional Carbon Sequestration Partnership (SECARB). The study demonstrates the value of seismic inversion for reducing subsurface uncertainty and improving storage resource evaluation in undercharacterized saline formations of the southeastern United States. Full article

Noise mitigation on urban rail transit lines in service is often constrained by the need to maintain uninterrupted operation and avoid large-scale demolition or reconstruction. Using Kunming Metro Line 4 as an engineering case study, this study combined onboard field measurements with data-driven analysis to characterize passenger-compartment noise and identify its principal determinants. A green noise-mitigation framework integrating source control, transmission-path control, and green operational management was developed, and its benefits were quantified across four dimensions: acoustic-environment improvement, human-health protection, low-carbon energy savings, and enhanced operation and maintenance efficiency. Passenger-compartment noise levels predominantly ranged from 78 to 82 dB(A). SHapley Additive exPlanations (SHAP) analysis identified train operating speed and track gradient as the two dominant factors, with relative importance values of 35.03% and 27.56%, respectively. Engineering implementation of the framework reduced peak noise levels in curved sections by 3–8 dB(A). The results further showed that noise-pollution mitigation and carbon-emission reduction share common sources and intervention pathways. The scenario-based assessment indicated that, under the specified parameter assumptions, the relevant measures could collectively achieve an estimated annual carbon emission reduction of approximately 3353.20 tCO₂. These findings provide a practical and scientific basis for green noise mitigation and coordinated pollution and carbon reduction on urban rail transit lines in service. Full article

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem. Full article

To address the need for low-power, real-time underwater acoustic signal processing and autonomous target detection on deep-sea unmanned mobile platforms, such as profiling acoustic buoys and underwater gliders, this study developed an embedded Linux-based signal processing system for buoy platforms. Conventional digital signal processing hardware platforms are often constrained by large size, high power consumption, and limited data communication capability. The proposed system adopts a compact, low-power architecture and a multithreaded processing framework based on the AM6254 heterogeneous multicore processor. It acquires four-channel vector-hydrophone signals together with attitude data from an inertial navigation module and performs band-pass filtering, fast Fourier transform (FFT), direction-of-arrival (DOA) estimation, and constant false alarm rate (CFAR) detection for autonomous target detection. The measured typical power consumption was approximately 2.3 W. Anechoic-tank and sea-trial results showed the lowest tested spectral level at which autonomous detection was achieved was 54 dB at 1 kHz, corresponding to an average in-band level of 46 dB. Under sea state 3, the system maintained continuous bearing tracking after target acquisition for a surface target traveling at 7 kn, up to a range of approximately 7 km, and provided unambiguous bearing estimation. These results demonstrate the target-detection capability and practical applicability of the system under representative operating conditions and indicate its potential for marine environmental monitoring and unmanned-platform observation and detection. Full article

The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km² of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments. Full article

Reliable full-line monitoring of belt-conveyor idlers remains challenging because large numbers of idlers operate under spatially varying structural stiffness and strong industrial vibration. This study develops an ultra-weak fiber Bragg grating distributed acoustic sensing (UWFBG-DAS) method combined with characteristic energy feature analysis for long-distance idler monitoring. The method makes three main contributions. First, a simplified finite-element model identifies the middle crossbeam as an effective vibration-transmission path and guides the deployment of the sensing array. Second, envelope demodulation and variational mode decomposition (VMD) are employed to isolate the fault-sensitive IMF2 component, whose energy is temporally accumulated and evaluated using a zone-specific self-referencing threshold derived from normal-operation data. Third, the method is validated through field deployment and fault-type classification. Approximately 1.2 km of a sensing cable was deployed in a coal-fired power plant, and identifiable characteristic-energy increases were observed in 9 of 10 idler-replacement tests. For three representative fault types, stratified five-fold cross-validation of 300 samples achieved an overall classification accuracy of 90.3%, with a 95% Wilson confidence interval of 86.5–93.2%. These results demonstrate the feasibility of UWFBG-DAS combined with zone-specific characteristic energy analysis for long-distance idler monitoring under spatially heterogeneous industrial conditions. Full article

With the gradual decline in conventional oil and gas production growth, unconventional natural gas has become a strategic alternative for hydrocarbon supply. Coal-rock gas (CRG) represents a deep unconventional gas resource with huge potential. Major exploration breakthroughs of CRG have been achieved in China, while systematic research targeting CRG as an independent gas reservoir is still lacking internationally. After effective commercial development, CRG serves as an important supplementary energy source for the domestic natural gas supply. Existing logging evaluation methods exhibit notable deficiencies, as porosity is typically estimated by fitting well logging data or proximate analysis data, resulting in limited accuracy. To address the lack of a dedicated logging volumetric model, ambiguous coal-matrix framework parameters, and substantial porosity calculation errors in deep CRG reservoirs, this study investigates the medium–high rank No. 8 coal seam of the Benxi Formation in the central-eastern Ordos Basin. From an oil and gas reservoir logging evaluation perspective, multi-scale experiments were conducted to systematically characterize the material composition and microscopic characteristics of the coal rock. From the perspective of oil and gas reservoir logging evaluation, a three-component logging volumetric model, consisting of a coal matrix, inorganic minerals, and pore fluids, was constructed, and the corresponding coal-matrix framework parameters were calibrated. The results demonstrate that coal rock is an organic–inorganic composite system, with organic macerals dominated by vitrinite (averaging 59.1%) and inertinite (27.1%). The sum of fixed carbon and volatiles exhibits strong correlations with total organic carbon (TOC) and micro-CT-derived coal-matrix content, yielding determination coefficients of 0.99 and 0.95, respectively, which validates the reliability of the multi-scale quantitative composition characterization. The coal-matrix framework parameters are non-constant: density ranges from 1.08 to 1.56 g·cm⁻³, acoustic slowness from 281 to 425 μs·m⁻¹, and compensated neutron from 39% to 79%. Borehole enlargement severely affects compensated density and neutron logs but has negligible interference with acoustic slowness. Notably, inertinite content shows a significant negative correlation with the acoustic-slowness framework response (R² = 0.80), indicating that structurally dense inertinite is a key intrinsic factor controlling the elastic response of the coal matrix. For porosity evaluation, a dual-variable framework model is proposed. The core novelty of this method is that it simultaneously incorporates variations in inorganic mineral content and differences in inertinite proportion within organic components as dynamic framework constraints, breaking through the limitation of the conventional constant-matrix assumption. The acoustic-slowness-based model achieves an average relative error of merely 7.1%, effectively resolving the large errors inherent in conventional fitting methods. The dedicated coal-rock logging evaluation system established in this study overcomes the limitations of fixed framework models, offers a scientific basis for fine-scale interpretation and resource assessment of deep CRG reservoirs, and provides a valuable reference for evaluating analogous reservoirs. Full article