Search Results (283)

The identification of mixed monopole and dipole sound sources under highly randomized acoustic environments is of interest in many industrial applications. The DAMAS–MS method is one of the few methods that has been explicitly developed to address this problem. However, it suffers from a critical constraint in that it consistently exhibits limited accuracy in identifying monopole sources, which leads to their underestimation in the final results. To overcome this constraint, this paper proposed a novel identification framework that integrates vision transformer (ViT) with beamforming techniques. The framework leverages preliminary beamforming results to construct input features by extracting the real and imaginary components of the cross-spectral matrix at target frequencies and incorporating spatial position encodings derived from estimated source locations. To ensure adaptability to varying source densities, multiple ViT sub-models are trained on representative scenarios. This strategy enables effective generalization across the target range and supports multi-label identification of monopole and dipole sources with varied configurations. Furthermore, anechoic chamber experiments with synthesized monopole and dipole emitters validate the method’s stability under single-frequency excitation. Compared to the DAMAS–MS method, the proposed method achieves improved identification accuracy for monopole sources, while maintaining comparable performance in dipole source identification, underscoring its potential for practical applications. Full article

Numerous artificial sources of acoustic waves have been described in the literature, which are designed to replicate the process by which actual damage occurs in a given material. Knowledge of the velocity with which an acoustic wave propagates is important here, both in order to correctly locate the signal source and to determine the degree of material degradation or the location of damage that has already occurred in the medium. This work presents the results of laboratory tests comparing two sources of artificial waves in terms of determining their parameters: the Hsu–Nielsen source and a sensor with the Auto Sensor Test function. The AST function allows the sensors to send and receive an elastic wave and is used to calibrate the sensor before, during, or after the test. In this study, the impact of the positioning of the sensors on the element being tested, their spacing, and the distance of the wave source from the sensor on selected parameters of the recorded waves are analyzed: velocity, amplitude, energy, rise time, waveform shape, and wavelet maps. This work demonstrates that a sensor with the AST function can be an effective alternative for the Hsu–Nielsen source in diagnostic studies. Full article

Ocean-bottom seismic (OBS) surveys have been applied in marine oil and gas exploration. In the typical OBS observation geometry, the source and receiver are located on/near different datums, i.e., the sea surface and the seafloor. Besides the desired primary reflections, abundant water-layer-related multiples (WLRMs) are the dominant noises. The demultiple processing for OBS data is a long-standing challenging task. If these WLRMs are not properly suppressed, they will be projected into the subsurface domain by the pre-stack depth migration (PSDM) engine, forming crosstalk imaging artifacts. By combining a finite-difference-based wave simulator and an acoustic Kirchhoff PSDM engine, we propose to build up a numerical analysis workflow to address the influence of WLRMs on depth images. We make a classification of typical WLRMs. Through an integrated numerical investigation, we conduct a detailed analysis of basic migration responses, wave-mode crosstalk, and effective artifact suppression solutions. With a generalized mirror migration approach, we emphasize the potential application of turning WLRMs into effective signals. The built-up investigation method and the obtained understanding of multiples can further benefit in suppressing and utilizing multiples in OBS datasets. Full article

Acoustic emission (AE) is a passive non-destructive testing (NDT) method that allows for online monitoring of oil tank bottom corrosion without production shutdown. However, AE signals are susceptible to ambient noise interference, causing the AE inspection system to mistakenly identify noise as corrosion signals, which significantly reduces AE inspection performance. Therefore, it is important to distinguish between AE signals caused by corrosion and those caused by noise. To address this, an AE inspection platform for vertical atmospheric tank corrosion is established. Six common noise sources in field AE inspections, including mechanical vibration and friction, fluid and raining disturbance, external impacts, and oil leakage are simulated. The impacts of these noises on AE location events are analyzed. Variational mode decomposition (VMD) and dispersion entropy (DE) are used to extract multi-domain features of AE signals. An improved distance evaluation (IDE) algorithm is then introduced to obtain a highly correlated feature subset. A support vector machine (SVM) model optimized by the bald eagle search (BES) algorithm is proposed to identify different noise sources. Field experiments demonstrate that for mechanical friction, external impacts, and effective corrosion signals, the proposed method achieves identification accuracy of 92.95% and 94.00% in the training and test sets, respectively. This proves the reliability of the BES-SVM model, which uses multi-domain features for AE source identification in oil tank bottom corrosion inspections. Moreover, the impacts of the optimization algorithm, feature selection algorithm, and feature type on AE source identification are further investigated. Full article

A common challenge in traditional three-dimensional grid-free localization is the struggle to balance computational efficiency with localization accuracy. To address this trade-off, a Bayesian grid-free framework with global optimization (BGG) for three-dimensional acoustic source imaging is proposed. In this method, a Bayesian inference model is established based on equivalent source theory, where the negative log-posterior of the equivalent source positions serves as the fitness function. This function is minimized using a global optimization algorithm to estimate the source locations. Subsequently, the source strengths and noise variances are inferred via fixed-point iteration and projection-based estimation. Through both simulations and experiments with spatially distributed sources, a superior balance of computational efficiency and localization accuracy is demonstrated by the proposed BGG algorithm when compared to other state-of-the-art grid-free approaches. Full article

Eucalyptus nitens (H. Deane & Maiden) Maiden is a widely planted hardwood species in Australia, particularly in Tasmania, where it occupies approximately 168,000 ha. Although primarily managed for pulp production, the species is attracting interest for sawn and engineered wood applications. Previous evaluations of its properties have relied on destructive testing; however, non-destructive evaluation (NDE) techniques provide a viable alternative for industrial applications. This study examined the use of acoustic-based NDE to assess plantation-grown E. nitens logs sourced from two Tasmanian harvesting sites, focusing on the relationship between dynamic modulus of elasticity (DMOE), static MOE, and modulus of rupture (MOR) across radial positions from pith to bark. The results indicated that core samples exhibited stronger correlations with static MOE and MOR compared with middle and outer samples. DMOE consistently overestimated static MOE by 10.81% and 24.66% at the two sites, with variation evident across radial positions. These findings demonstrate the effectiveness of acoustic NDE for evaluating wood stiffness, highlighting the importance of sample location within logs. Full article

As the main load-bearing components of engineering structures, regular health assessment of reinforced concrete (RC) columns is crucial for improving the service life and overall performance of the structures. This study focuses on the health detection problem of in-service RC columns. By combining deep learning algorithms and acoustic emission (AE) technology, the AE sources of in-service RC columns are located, and the optimal sensor layout form for the health monitoring of in-service RC columns is determined. The results show that the data cleaning method based on the k-means clustering algorithm and the voting selection concept can significantly improve the data quality. By comparing the localization performance of the Back Propagation (BP), Radial Basis Function (RBF) and Support Vector Regression (SVR) models, it is found that compared with the RBF and SVR models, the MAE of the BP model is reduced by 7.513 mm and 6.326 mm, the RMSE is reduced by 9.225 mm and 8.781 mm, and the R² is increased by 0.059 and 0.056, respectively. The BP model has achieved good results in AE source localization of in-service RC columns. By comparing different sensor layout schemes, it is found that the linear arrangement scheme is more effective for the damage location of shallow concrete matrix, while the hybrid linear-volumetric arrangement scheme is better for the damage location of deep concrete matrix. The hybrid linear-volumetric arrangement scheme can simultaneously detect damage signals from both shallow and deep concrete matrix, which has certain application value for the health monitoring of in-service RC columns. Full article

Distributed Acoustic Sensing (DAS) is a new oceanographic measurement technology that exploits the physical sensitivities of fiber-optic communication cables to changes in pressure, allowing time series measurements of pressure at meter-scale spacing for ranges up to 150 km. The along-cable measurement locations, called channels, are evenly distributed, but the specific locations of each are initially unknown. In terrestrial applications, channel locations are often found by the “tap test” where acoustic transients are created at surveyed locations along the cable. For submarine installations, tap tests are inconvenient or logistically impossible. Here we describe a new method for submarine channel geolocation by comparing DAS signals to ambient ocean wave time series using a variety of cross-spectral methods. Ground truth data were derived from two remote sensing sources: marine radar (X-band) and shore-based cameras. The methods were developed and tested at two coastal locations and showed an ability to geolocate DAS channels to within 10 m at ranges of up to 3 km (radar) or within 1.0 m at ranges up to 600 m (optical). Full article

Carbon dioxide (CO₂) fracturing and acid treatment are currently considered promising approaches to overcome the challenge of excessively high initiation pressure during conventional hydraulic fracturing in tight sandstone gas reservoirs. However, the mechanisms of these methods weaken the reservoir rock’s mechanical properties, remain unclear. Using a machine learning approach, we elucidate the differences in initiation mechanisms between CO₂ fracturing and hydraulic fracturing under acid-treated conditions, thereby providing a mechanistic explanation for the lower initiation pressure observed in CO₂ fracturing compared to conventional hydraulic fracturing. The tensile fractures, shear fractures, and acid-modified fractures have been identified by a specially trained AI model, which achieved exceptional accuracy (95.4%). Acoustic emission source locations show that CO₂ fracturing mainly causes shear fracture along acid-weakened planes, which promotes the propagation of composite tensile-shear fractures in untreated reservoir areas. Due to the significantly lower diffusivity of water compared to CO₂, hydraulic fracturing predominantly induces non-acidic mixed-mode (tensile-shear) fractures. This fundamental difference in fracture patterns accounts for the higher initiation pressure observed in hydraulic fracturing compared to CO₂ fracturing. These findings offer crucial insights into pressurized fluid-driven fracturing mechanisms and propose an optimized technical pathway for enhancing hydrocarbon recovery in low-permeability sandstone formations. Full article

Acoustic Emission tomography (AET) has the potential to visualize damage in existing structures, contributing to structural health monitoring. Further, AET requires only the arrival times of elastic waves at sensors to identify velocity distributions, as source localization based on ray-tracing is integrated into its algorithm. Thus, AET offers the advantage of easy acquisition of measurement data. However, accurate source localization requires a large number of elastic wave source candidate points, and increasing these candidates significantly raises the computational resource demand. Lagrange Interpolation has the potential to reduce the number of candidate points, optimizing computational resources, and this potential has been validated numerically. In this study, AET incorporating Lagrange Interpolation is applied to identify the velocity distribution in a defective concrete plate, validating its effectiveness using measured wave data. The validation results show that the defect location in the concrete plate is successfully identified using only 36 source candidates, compared to the 121 candidates required in conventional AET. Furthermore, when using 36 source candidates, the percentage error in applying Lagrange Interpolation is 8.4%, which is significantly more accurate than the 25% error observed in conventional AET. Therefore, it is confirmed that AET with Lagrange Interpolation has the potential to identify velocity distributions in existing structures using optimized resources, thereby contributing to the structural health monitoring of concrete infrastructure. Full article

Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

This study investigates how different longitudinal steel rebars influence the flexural performance and cracking mechanisms of reinforced ultra-high-performance concrete (UHPC) beams, combining axial tensile tests using acoustic emission monitoring with standard four-point bending tests. A series of experimental assessments on the flexural behavior of UHPC beams reinforced with various types of longitudinal reinforcement was conducted. The types of longitudinal reinforcement mainly encompassed HRB 400, HRB 600, and NPR steel rebars. The test results revealed that the UHPC beams reinforced with the three types of longitudinal steel rebar exhibited distinctly different failure modes. Compared to the single dominant crack failure typical of UHPC beams reinforced with HRB 400 steel rebars, the beams using HRB 600 rebars exhibited a tendency under balanced failure conditions to develop fewer main cracks (typically two or three). Conversely, the UHPC beams incorporating NPR steel rebars exhibited significantly more cracking within the pure bending zone, characterized by six to eight uniformly distributed main cracks. Meanwhile, the HRB 600 and NPR steel rebars effectively upgraded the flexural load-bearing capacity and deformation ability compared to the HRB 400 steel rebars. By integrating the findings from the direct tensile tests on reinforced UHPC, aided by acoustic emission source location, this research specifically highlights the damage mechanisms associated with each rebar type. Full article

This paper provides a conceptual framework for soundscape appraisal as a key outcome of the hearing process. Sound appraisal involves auditory sense-making and produces the soundscape as the perceived and understood acoustic environment. The soundscape exists in the experiential domain and involves meaning-giving. Soundscape research has reached a consensus about the relevance of two experiential dimensions—pleasure and eventfulness—which give rise to four appraisal quadrants: calm, lively/vibrant, chaotic, and boring/monotonous. Requirements for and constraints on the hearing and appraisal processes follow from the demands of living in a complex world, the specific properties of source and transmission physics, and the need for auditory events and streams of single-source information. These lead to several core features and functions of the hearing process, such as prioritizing the auditory channel (loudness), forming auditory streams (audibility, primitive auditory scene analysis), prioritizing auditory streams (audible safety, noise sensitivity), and initial meaning-giving (auditory gist and perceptual layers). Combined, this leads to a model of soundscape appraisal yielding the ISO quadrant structure. Long-term aggregated appraisals lead to a sonic climate that allows for an insightful comparison of different locations. The resulting system needs additional validation and optimization to comply in detail with human appraisal and evaluation. Full article