Acoustic Imaging Methods Research Articles

Markov chain-based temporal correlation processing algorithm in reverberant environment by sparse Bayesian learning for acoustic imaging

Identifying sound sources is an important step in noise control, different sound source localization algorithms have been proposed and studied for almost a century. Most of the studies assume an ideal measurement environment, i.e., an anechoic or half-anechoic environment, but there are limited studies discussing localizing sound sources in a reverberation environment. This paper proposes an acoustic imaging method in a reverberation environment based on the sparse Bayesian learning framework. Initially, a sound propagation model was established by accounting for the multipath effects of the sound source in a reverberant environment. Based on the temporal correlation of a Markov chain, the algorithm is further improved in the sparse Bayesian learning framework. By leveraging the temporal correlation the algorithm provides, identifying the true source in a reverberant environment becomes more accurate. Numerical simulation results have shown that the proposed algorithm achieves real sound source localization and eliminates the influence of image sources in contour maps under realistic reverberant environments.

High-resolution acoustic imaging method based on block sparsity constraint

Acoustic imaging method is a critical task in various applications since it can locate the sound sources. However, the resolution of the method becomes low at low frequencies. This paper proposes a novel method to realize high-resolution acoustic imaging based on block sparsity constraint. By dividing the focusing area into blocks, the block sparse property of sound sources can be utilized to improve the resolution of the result. In this study, a block orthogonal matching pursuit method is introduced to deal with source localization problem with block sparsity constraint. To evaluate the performance of the proposed method, it is tested through numerical simulations and an experiment. The results of simulations prove that the proposed acoustic imaging method based on block sparsity constraint can localize sound sources accurately and performs better than that based on sparsity constraint. The results of the experiment further validate these findings.

Determining the health status of natural monument trees using acoustic tomography method

Areas with natural beauty and rich biodiversity have had a different significance for people since ancient times. With the latest developments in industry, technology, and urbanization, the demand for natural areas has increased considerably. Accordingly, the need to protect such private areas has become even more significant. In order to protect areas with such natural characteristics effectively, problems and threat factors must be correctly revealed. For this purpose; a study was conducted to determine the health status of some trees, which are natural monuments in the Eastern Black Sea Region of Türkiye, using the acoustic tomography method. The general appearance of six trees in the study area was examined, and measurements were made with the Arbor Sonic 3D Acoustic Tomography device. In the research, it was found that the trunk of one of the natural monument trees was hollow. The other five trees were found to be in good general condition. According to the results of the tomography, it was determined that one of the six trees measured was in good health, three of them had partial hollows and decay, and two of them were completely hollow. According to the data obtained, it has been revealed that the acoustic tomography method is an effective tool in determining the health status of nature monument trees.

Correlation Iteration Method of Acoustic Tomography with Incoherent Field Sources

Correlation Iteration Method of Acoustic Tomography with Incoherent Field Sources

High-resolution acoustic imaging method based on equivalent source method and reweighted l1 minimization

Sparse regularization has been successfully applied to equivalent source method (ESM) in order to improve the acoustic imaging resolution. However, the application is not always feasible, especially at low frequencies. To overcome the problem, this paper proposes a high-resolution acoustic imaging method. In this method, reweighted l1 minimization is introduced to ESM to deal with the ill-posed inverse problems. Then the obtained equivalent source strengths are used to locate the sound sources. Compared to the sparse regularization-based ESM, the proposed method can provide a low side lobe and higher spatial resolution of acoustic imaging. Meanwhile, by arranging equivalent sources in three-dimensional space, the proposed method can also realize the acoustic imaging in three-dimensional sound field with high resolution. The results of the simulation and experiment demonstrate the validations.

Correlation Iterative Method of Acoustic Tomography with Incoherent Field Sources

Correlation Iterative Method of Acoustic Tomography with Incoherent Field Sources

Acoustic Imaging Method for Gas Leak Detection and Localization Using Virtual Ultrasonic Sensor Array.

An acoustic imaging method for detecting and locating gas leaks based on a virtual ultrasonic sensor array is proposed and experimentally demonstrated. A scanning sensor array of only two sensors is used to collect the acoustic signals generated by the leakage hole. The matrix of the leakage signal is processed by the cross-power spectrum method to achieve time consistency, afterward, the location of the leakage source can be calculated by the virtual beamforming method. The influence of the number of sensors and the distance between adjacent sensors on the effect of the proposed method are compared and discussed. To verify the effectiveness and operability of the detection and localization method, several experiments were carried out. Furthermore, a series of experiments were conducted to assess the accuracy and stability of this method. The experimental results demonstrate that the proposed method based on a virtual sensor array can achieve highly accurate localization of gas leaks and performs well regarding stability.

Non-Synchronous Measurements Acoustic Imaging Method Based on Capped Nuclear Norm Minimization

In this work, the difference convex function based on capped nuclear norm minimization (CNNM-DC) method, a cross-spectral matrix (CSM) completion iterative algorithm with excellent noise immunity, is proposed to realize acoustic imaging of non-synchronous measurements (NSM) under the condition of low signal-to-noise ratio (SNR). Compared with the CSM completion algorithm based on the nuclear norm minimization (NNM) model, the truncated nuclear norm regularization (TNNR) model, and the weighted nuclear norm minimization (WNNM) model, the proposed method can obtain the acoustic image with narrow main lobes and no sidelobe under the condition of low SNR. The simulation results show that, under conditions of low SNR, the proposed method effectively reduces the width of the main lobe, suppresses the side lobes, and achieves more accurate imaging results than the previous method. Finally, experiments were conducted to verify the feasibility of CNNM-DC. The experimental results show that the proposed method is an accurate acoustic imaging algorithm for NSM under low SNR, which lays a foundation for acoustic imaging in industrial occasions with strong background noise interference.

Performance study of ray-based ocean acoustic tomography methods for estimating submesoscale variability in the upper ocean.

Ocean acoustic tomography (OAT) methods aim at estimating variations of sound speed profiles (SSP) based on acoustic measurements between multiple source-receiver pairs (e.g., eigenray travel times). This study investigates the estimation of range-dependent SSPs in the upper ocean over short ranges (<5 km) using the classical ray-based OAT formulation as well as iterative or adaptive OAT formulations (i.e., when the sources and receivers configuration can evolve across successive iterations of this inverse problem). A regional ocean circulation model for the DeSoto Canyon in the Gulf of Mexico is used to simulate three-dimensional sound speed variations spanning a month-long period, which exhibits significant submesoscale variability of variable intensity. OAT performance is investigated in this simulated environment in terms of (1) the selected source-receivers configuration and effective ray coverage, (2) the selected OAT estimator formulations, linearized forward model accuracy, and the parameterization of the expected SSP variability in terms of empirical orthogonal functions, and (3) the duration over which the OAT inversion is performed. Practical implications for the design of future OAT experiments for monitoring submesoscale variability in the upper ocean with moving autonomous platforms are discussed.

3D Reverse-Time Migration Imaging for Multiple Cross-Hole Research and Multiple Sensor Settings of Cross-Hole Seismic Exploration

The two-dimensional (2D) cross-hole seismic computed tomography (CT) imaging acquisition method has the potential to characterize the target zone optimally compared to surface seismic surveys. It has wide applications in oil and gas exploration, engineering geology, etc. Limited to 2D hole velocity profiling, this method cannot acquire three-dimensional (3D) information on lateral geological structures outside the profile. Additionally, the sensor data received by cross-hole seismic exploration constitute responses from geological bodies in 3D space and are potentially affected by objects outside the well profiles, distorting the imaging results and geological interpretation. This paper proposes a 3D cross-hole acoustic wave reverse-time migration imaging method to capture 3D cross-hole geological structures using sensor settings in multi-cross-hole seismic research. Based on the analysis of resulting 3D cross-hole images under varying sensor settings, optimizing the observation system can aid in the cost-efficient obtainment of the 3D underground structure distribution. To verify this method’s effectiveness on 3D cross-hole structure imaging, numerical simulations were conducted on four typical geological models regarding layers, local high-velocity zones, large dip angles, and faults. The results verify the model’s superiority in providing more reliable and accurate 3D geological information for cross-hole seismic exploration, presenting a theoretical basis for processing and interpreting cross-hole data.

Study on feasibility and location method of remote detection acoustic reflection imaging for open hole

Study on feasibility and location method of remote detection acoustic reflection imaging for open hole

Discontinuity characterization of rock strata using borehole wall imagery and scanline

Rock discontinuity mapping techniques, such as scanlines on slopes or exposed vertical surfaces, can collect data on discontinuity characteristics required to assess the rock’s stability. When slope conditions are highly weathered, or it is challenging to identify discontinuity characteristics using a scanline mapping survey, other methods must be used to obtain this information. One of them employs borehole wall acoustic imaging. This method is sophisticated, fast, and accurate for getting discontinuity characteristic data, but it has a disadvantage in that it requires preliminary drilling work. This issue can be solved if it is done in conjunction with the work of exploration or geotechnical drilling. This study aims to compare the discontinuity characterization data recorded using borehole wall acoustic imaging and scanline methods by considering the advantages and disadvantages of both approaches. The characterization data is essential for assessing rock mass quality. When these two methods are combined in mining activities, they will provide added value by utilizing the right technology and ensuring high accuracy while following good mining practices.

Numerical and experimental study on acoustic tomographic reconstruction of flow field

This paper reports the acoustic vector tomography (AVT) method to reconstruct the airflow as a vector field. In order to increase the efficiency of reconstruction process, proposed radial basis functions approximate the flow velocity components. Tikhonov-Gaussian regularization combined with Generalized Singular Value Decomposition method and modified L-curve are introduced to solve the ill-posed problem and select the optimal regularization parameters. The feasibility and validity of the method are verified by numerical simulation and experiment. The numerical results confirmed the effectiveness of the proposed method with higher accuracy, better anti-noise capability and shorter consuming time compared with other reconstruction methods. Besides, a physical experiment is performed for further testing. We measured the incident flow and vortex field using the proposed acoustic method. The results show that the tendency and data of the acoustical reconstructed flow field are consistent with the strip display and anemometer results. The relative errors of most measurement points are under 15% and our proposed acoustic method is effective in practical velocity field measurement.

Dose Images Reconstruction Based on X-ray-Induced Acoustic Computed Tomography

The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging methods suffer from several limitations, including high signal-to-noise ratio, poor imaging quality and massive loss of structural information. To address these limitations, we propose a dose image reconstruction method based on tensor sparse dictionary learning. Specifically, we combine tensor coding with compressed sensing data, extend two-dimensional dictionary learning to three-dimensional by using tensor product, and then utilize the spatial information of X-ray acoustic signal more efficiently. To reduce the artifacts of reconstruction images caused by spare sampling, we design the alternate iterative solution of the tensor sparse coefficient and tensor dictionary. In addition, we build the X-ray-induced acoustic dose images reconstruction system, simulate the X-ray acoustic signals based on patients’ information from Sichuan Cancer Hospital, and then create the simulated datasets. Compared to some typical state-of-the art imaging methods, the experimental results demonstrate that our method can significantly improve the quality of reconstructed images and the accuracy of dose distribution.

Modified algebraic reconstruction technique based on circular scanning geometry to improve processing time in photoacoustic tomography

AbstractPhotoacoustic imaging is low‐risk, and noninvasive tool for imaging biological tissues that acoustically respond to absorbed irradiating nonionized short laser pulse by tissue, then the image of light energy absorption distribution in the tissue has been reconstructed by image reconstruction algorithms. Indeed, photoacoustic tomography is a hybrid imaging modality that combines pure optical and acoustic imaging methods to take advantage of them, therefore, it contains high optical contrast with good ultrasonic resolution. However, improving its main parts is challenging. One of the most important parts of photoacoustic imaging is image reconstruction using appropriate algorithms. There are various image reconstruction methods. Image reconstruction based on an algebraic algorithm can reconstruct a clear image without artifact, but the algorithm is typically time‐consuming. This paper deals with this drawback of the algorithm. Simulations demonstrate significant improvement in the processing time of reconstructed images achieved by our proposed algorithm as compared to the phase‐controlled algorithm. Based on the image reconstruction algorithm in circular scanning geometry presented in this article, the processing time required to create a 2D tomographic image is reduced from 60 to 3.89 s compared to our previous work, which was based on a phase‐controlled algorithm. This improvement was based on inspiration from the experiences of our past works in dealing with various image reconstruction algorithms, which ultimately led to improvement in the calculation time.

High resolution acoustic tomography based on backpropagation of waves

A method of high-resolution acoustic tomography of scattering inhomogeneities based on the method of reverse time migration is proposed. An inhomogeneous background medium with a known distribution of refractive inhomogeneities is considered, where it is necessary to detect scattering inhomogeneities. Probing is carried out by broadband arbitrary waveforms. The method of reverse time migration is supplemented with linear filtering of the direct propagation field, which makes it possible to increase the resolution of reconstructed images. The results of numerical modeling and experiment are presented. Sounding was carried out in water at frequencies from 20 to 350 kHz using a signal with linear frequency modulation. The source of the ultrasonic signal was fixed and irradiated the objects under study in the water. The scattered signal was measured in a plane near the surface in water on a rectangular area with a step of less than 2 mm. Signal processing by the proposed method made it possible to reconstruct a three-dimensional image of scattering inhomogeneities with a resolution of about 7 mm. Comparison of the proposed method and the reverse time migration method showed that the proposed method allows obtaining a higher resolution.

Performance study of ocean acoustic tomography methods in the upper-ocean environment using autonomous platforms

Accurate knowledge of the spatial and temporal evolution of the three-dimensional ocean sound speed profile (SSP) is crucial for underwater source localization. Ocean acoustic tomography (OAT) methods aim at reconstructing SSPs variations based on acoustic measurements between multiple source-receiver pairs (e.g., eigenrays arrival times). This study investigates the estimation of range-dependent SSPs using a classical model-based OAT method (i.e., ray-based ocean acoustic tomography), for various configurations of source and receiver configurations using autonomous platforms in a highly-dynamic upper ocean environment. A regional oceanographic simulation of the De Soto Canyon circulation in the Gulf of Mexico is used to construct 3D sound speed variations spanning a month long which exhibits significant sub-mesoscale variability. Two main aspects affecting OAT performance in the presence of high 3D SSPs variability are investigated: (1) The influence of the input acoustic data (i.e., source-receivers configuration and platform motion, arrival-times accuracy...) and (2) the actual implementation of the OAT scheme (i.e., selection of complexity reduction basis, linearized forward model assumptions as well as the use of iterative solvers) on SSPs estimations errors. Practical implications for the design of OAT experiments in the dynamic upper ocean will be discussed. [Work supported by the Office of Naval Research.]

High Resolution Backscattering Acoustic Tomography Method Based on Reverse Time Migration for Arbitrary Wideband Sounding Signal

Backscattered wave acoustic tomography using wideband probing signals makes it possible to obtain three-dimensional (3D) images of scattering inhomogeneities. Signal processing based on the reverse time migration (RTM) method allows one to take into account the influence of background refractive obstacles of the medium to minimize distortions of reconstructed tomographic images. We propose a noniterative method of acoustic tomography in an immersion medium based on RTM approach supplemented with linear signal preprocessing to enhance resolution of reconstructing tomography images. The visualization of scattering objects is based on wave inversion from the measurement area considering the probing wave field specially distorted to perform regularized back convolution. The applicability of the proposed method for visualizing scattering objects in water is shown analytically, numerically and experimentally. The proposed method is resistant to noise according to regularization. The results obtained show the agreement between the numerical and analytical solution. Using the example of sounding with linear frequency modulation signals, it is demonstrated that the proposed method allows increasing the resolution of tomographic images in comparison with conventional RTM. The novelty of the proposed method is the preliminary filtration of the forward propagation wave in the course of solving the inverse problem. This approach improves the resolution of tomographic images and allows considering the influence of obstacles.

Methods for high-performance jet aircraft noise source imaging and sound field reconstruction

Source characterization and sound field reconstruction are vital to understanding noise radiation from turbulent, heated, supersonic jets produced by high-performance engines. This paper describes the application of two acoustic imaging methods to full-scale tactical aircraft noise analyses: statistically optimized near-field acoustical holography and hybrid beamforming. These methods constitute equivalent wave and equivalent source models, respectively. A review of these methods is given and their respective features are discussed.

Machine learning approaches for ray-based ocean acoustic tomography.

Underwater sound propagation is primarily driven by a nonlinear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g., eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSP variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This article investigates the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution [i.e., the neural adjoint (NA) method], which combines deep learning of the forward model with a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.