Search Results (53)

Vertical seismic profiling has garnered widespread attention in the industry as a supplement to seismic exploration due to its higher data quality compared to surface seismic data. However, its unique observation system in which geophones are only distributed within observation wells results in uneven coverage of subsurface structures. This can lead to significant noise when directly applying conventional reverse-time migration techniques used in surface seismic imaging. This study addresses the issue of noise suppression in reverse-time migration imaging associated with walk-away vertical seismic profiling and presents two main innovations. First, a common-receiver reverse-time migration imaging method is proposed, which uses the observation signals as excitation signals for the corresponding shots after reverse-time processing. Second, an excitation-time-constrained cross-correlation imaging condition is introduced to eliminate non-contributing portions of the wavefield, thereby modifying the traditional cross-correlation imaging condition to include an excitation time constraint. The combination of these methods enhances imaging quality by effectively suppressing noise, as demonstrated through theoretical analysis and numerical simulations with synthetic models. Full article

Structural damage identification has been one of the key applications in the field of Structural Health Monitoring (SHM). With the development of technology and the growth of demand, the method of identifying damage anomalies in plate structures is increasingly being developed in pursuit of accuracy and high efficiency. Principal Component Analysis (PCA) has always been effective in damage identification in SHM, but because of its sensitivity to outliers and low robustness, it does not work well for complex damage or data. The effect is not satisfactory. This paper introduces the Robust Principal Component Analysis (RPCA) model framework for the characteristics of PCA that are too sensitive to the outliers or noise in the data and combines it with Lamb to achieve the damage recognition of wavefield images, which greatly improves the robustness and reliability. To further improve the real-time monitoring efficiency and reduce the error, this paper proposes a non-convex approximate RPCA (NCA-RPCA) algorithm model. The algorithm uses a non-convex rank approximation function to approximate the rank of the matrix, a non-convex penalty function to approximate the norm to ensure the uniqueness of the sparse solution, and an alternating direction multiplier method to solve the problem, which is more efficient. Comparison and analysis with various algorithms through simulation and experiments show that the algorithm in this paper improves the real-time monitoring efficiency by about ten times, the error is also greatly reduced, and it can restore the original data at a lower rank level to achieve more effective damage identification in the field of SHM. Full article

Guided ultrasonic wavefield propagation imaging (GUPI) is useful for visualizing hidden flaws in aerospace thin-walled structures, but the need for subjective signal processing involving three-dimensional Fourier transformation to increase the visibility of subtle flaws hinders its wider acceptance. A high-resolution wavenumber bandpass filter capable of consolidating subtle flaw-relevant information from a wide frequency band using only two-dimensional Fourier transformation was proposed. The filter overturns the long-standing belief that modes must be separated based on narrow-band data acquisition or processing to achieve high flaw visibility. Its characteristics and advantages were experimentally demonstrated through enhanced visualization of hidden wall-thinning flaws of a plate specimen. Its strength was further demonstrated through the first GUPI visualization of a partially loosened bolt, with unprecedented clarity to discern bolt tightness levels. The results conclusively proved that the proposed filter significantly enhances the resolution of GUPI within a structured processing framework. Full article

In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available. Full article

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves’ energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth. Full article

The parabolic wave equation describes wave propagation in a preferable direction, which is usually horizontal in underwater acoustics and vertical in seismic applications. For dense receiver arrays (receiver spacing is less than signal wavelength), this equation can be used for propagating the recorded wavefield back into the medium for imaging sources and scattering objects. Similarly, for multiple source and receiver array acquisition, typical for seismic exploration and potentially beneficial for ocean acoustics, one can model data in one run using an extension of the parabolic equation—the pseudo-differential Double Square Root (DSR) equation. This extended equation allows for the modeling and imaging of multi-source data but operates in higher-dimensional space (source, receiver coordinates, and time), which makes its numerical computation time-consuming. In this paper, we apply a faster ray method for solving the DSR equation. We develop algorithms for both kinematic and dynamic ray tracing applicable to either data modeling or true-amplitude recovery. Our results can be used per se or as a basis for the future development of more elaborated asymptotic techniques that provide accurate and computationally feasible results. Full article

Precise surveys are indispensable in coastal engineering projects. The extensive presence of sand in the coastal area leads to significant attenuation of seismic waves within unsaturated loose sediments. As a result, it becomes challenging for seismic waves to penetrate the weathered zone and reach the desired depth with significant amount of energy. In this study, the application of three-dimensional (3D) cross-well elastic reverse time migration (RTM) imaging based on multi-wave and multi-component techniques in coastal engineering exploration is explored. Accurate decomposition of vector compressional (P) and shear (S) waves is achieved through two wavefield decoupling algorithms without any amplitude and phase distortion. Additionally, compressional wave pressure components are obtained, which facilitates subsequent independent imaging. This study discusses and analyzes the imaging results of four imaging strategies under cross-correlation imaging conditions in RTM imaging. The analysis leads to the conclusion that scalarizing vector wavefields imaging yields superior imaging of P- and S-waves. Furthermore, the imaging results obtained through this approach are of great physical significance. In order to validate the efficacy of this method in 3D geological structure imaging in coastal areas, RTM imaging experiments were performed on two representative models. The results indicate that the proposed 3D elastic wave imaging method effectively generates accurate 3D cross-well imaging of P- and S-waves. This method utilizes the multi-wave and multi-component elastic wave RTM imaging technique to effectively leverage the Earth’s elastic medium without increasing costs. It provides valuable information about the distribution of subsurface rock layers, interfaces, and other structures in coastal engineering projects. Importantly, this can be achieved without resorting to extensive excavation or drilling operations. This method addresses the limitations of current cross-well imaging techniques, thereby providing abundant and accurate geological and geophysical information for the analysis and interpretation of 3D geological structures in coastal engineering projects. It has important theoretical and practical significance in real-world production, as well as for the study of geological structures in coastal engineering. Full article

In seismic modeling and reverse time migration (RTM), incorporating anisotropy is crucial for accurate wavefield modeling and high-quality images. Due to the trade-off between computational cost and simulation accuracy, the pure quasi-P-wave equation has good accuracy to describe wave propagation in tilted transverse isotropic (TTI) media. However, it involves a fractional pseudo-differential operator that depends on the anisotropy parameters, making it unsuitable for resolution using conventional solvers for fractional operators. To address this issue, we propose a novel pure quasi-P-wave equation with a generalized fractional convolution operator in TTI media. First, we decompose the conventional pure quasi-P-wave equation into an elliptical anisotropy equation and a fractional pseudo-differential correction term. Then, we use a generalized fractional convolution stencil to approximate the spatial-domain pseudo-differential term through the solution of an inverse problem. The proposed approximation method is accurate, and the wavefield modeling method based on it also accurately describes quasi-P-wave propagation in TTI media. Moreover, it only increases the computational cost for calculating mixed partial derivatives compared to those in vertical transverse isotropic (VTI) media. Finally, the proposed wavefield modeling method is utilized in RTM to correct the anisotropic effects in seismic imaging. Numerical RTM experiments demonstrate the flexibility and viability of the proposed method. Full article

In marine seismic exploration, seismic signals comprise primaries that undergo first-order scattering, as well as multiples resulting from multi-order scattering events. Surface-related multiples involve multi-order scattering at the free surface interface between seawater and air and exhibit a smaller reflection angle and broader illumination compared to primaries. Internal multiples, originating from multi-order scattering among stratified layers, provide additional illumination compensation beneath the reflecting interface. However, in conventional primary migration, different-order wavefields may result in crosstalk artifacts. To address this issue, we developed a least-squares migration (LSM) method based on the multi-order wavefield global normalization condition. This methodology investigates the illumination effects and crosstalk artifacts associated with different-order surface-related and internal multiples, and then modifies the global normalization condition by incorporating an illumination compensation perspective. Virtual sources, represented by surface-related multiples and internal multiples, are integrated into the source compensation term, ultimately yielding a multi-order wavefield normalization condition. This normalization condition is subsequently combined with least-squares full-wavefield migration (LSFWM). Numerical experiments demonstrate that the normalization condition of multi-order wavefields can resolve the problem of weak deep imaging energy and promote the suppression of multiple crosstalk artifacts in the least-squares algorithm. Full article

The current reverse-time migration, which is based on wave equations for imaging wavefields, employs an imaging formula derived from Claerbout’s imaging principle. This imaging formula is only valid for plane waves with small incident angles on the perfectly flat reflecting surface. However, the complexity of seismic wave propagation may lead to situations that do not meet this requirement. Therefore, this paper divides the subsurface into local scattering and reflecting bodies. It proposes linear forward representations for scattering and reflection data based on perturbations in the physical parameters and wave impedance, respectively. To further describe the effect on the reflecting body boundary, the local reflection coefficient is defined and the linear forward representation for the reflection data based on it is obtained. After that, the proposed linear forward representations are used as the forward equations for the linear inverse of the seismic data, and the seismic data waveform imaging method is developed based on linear inversion theory. At the same time, the specific waveform imaging calculation formulas for scalar wave scattering data and scalar wave reflection data are provided and validated via numerical experiments. Compared with the current reverse-time migration, waveform migration not only has the correct phase and higher resolution in theory but also does not increase the computational complexity. To some extent, it improves the deficiencies of the current structural imaging and provides a basis for subsurface lithological imaging. Full article

Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging. However, stability is a prominent concern due to the exponential increase in high-frequency ambient noise during seismic wavefield propagation. The two primary strategies for mitigating instability in Q-RTM are regularization and low-pass filtering. Q-RTM instability can be addressed through regularization. However, determining the appropriate regularization parameters is often an experimental process, leading to challenges in accurately recovering the wavefield. Another approach to control instability is low-pass filtering. Nevertheless, selecting the cutoff frequency for different Q values is a complex task. In situations with low signal-to-noise ratios (SNRs) in seismic data, using low-pass filtering can make Q-RTM highly unstable. The need for a small cutoff frequency for stability can result in a significant loss of high-frequency signals. In this study, we propose a multi-task learning (MTL) framework that leverages data-driven concepts to address the issue of amplitude attenuation in seismic records, particularly when dealing with instability during the Q-RTM (reverse time migration with Q-attenuation) process. Our innovative framework is executed using a convolutional neural network. This network has the capability to both predict and compensate for the missing high-frequency components caused by Q-effects while simultaneously reconstructing the low-frequency information present in seismograms. This approach helps mitigate overwhelming instability phenomena and enhances the overall generalization capacity of the model. Numerical examples demonstrate that our Q-RTM results closely align with the reference images, indicating the effectiveness of our proposed MTL frequency-extension method. This method effectively compensates for the attenuation of high-frequency signals and mitigates the instability issues associated with the traditional Q-RTM process. Full article

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging. Full article

Vector decomposition of P- and S-wave modes from elastic seismic wavefields is a key step in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging accuracy. Most previously developed methods based on the apparent velocities or the polarization characteristics of different wave modes are unable to accurately achieve the vector decomposition of P- and S-wave modes. To effectively overcome the shortcomings of conventional methods, we develop a vector decomposition method of P- and S-wave modes using self-attention deep convolutional generative adversarial networks (SADCGANs) to effectively separate the horizontal and vertical components of P- and S-wave modes from elastic seismic wavefields and accurately preserve their amplitude and phase characteristics for isotropic elastic media. For an elastic model, we use many time slices for a given source position to train the neural network, and use other time slices not in this training dataset to test the neural network. Numerical examples of different models demonstrate the effectiveness and feasibility of our developed method and indicate that it provides an effective intelligent data-driven vector decomposition method of P- and S-wave modes. Full article

The viscosity of actual underground media can cause amplitude attenuation and phase distortion of seismic waves. When seismic images are processed assuming elastic media, the imaging accuracy for the deep reflective layer is often reduced. If this attenuation effect is compensated, the imaging quality of the seismic data can be significantly improved. Q-compensated Gaussian beam migration (Q-GBM) is an effective seismic imaging method for viscous media, and it has the advantages of both wave equation and ray-based Q-compensated imaging methods. This study develops a Q-GBM method in visco-acoustic media with an irregular surface. Initially, the basic principles of Gaussian beam in visco-acoustic media are introduced. Then, by correcting the complex-value time of the Gaussian beam in visco-acoustic media, energy compensation and phase correction are carried out for the forward continuation wavefield at the seismic source of the irregular surface and the reverse continuation wavefield at the beam center, which effectively compensates the absorption and attenuation effects of visco-acoustic media on the seismic wavefield. Further, a Q-GBM method under the irregular surface is proposed using cross-correlation imaging conditions. Through migration tests for three numerical models of visco-acoustic media with irregular surfaces, it is verified that our method is an effective depth domain imaging technique for seismic data in visco-acoustic media under the condition of irregular surfaces. Full article

Seismic oceanography can provide a two- or three-dimensional view of the water column thermocline structure at a vertical and horizontal resolution from the multi-channel seismic dataset. Several seismic imaging methods and techniques for seismic oceanography have been presented in previous research. In this study, we suggest a new formulation of the frequency-domain reverse-time migration method for seismic oceanography based on the analytic Green’s function. For imaging thermocline structures in the water column from the seismic data, our proposed seismic reverse-time migration method uses the analytic Green’s function for numerically calculating the forward- and backward-modeled wavefield rather than the wave propagation modeling in the conventional algorithm. The frequency-domain reverse-time migration with analytic Green’s function does not require significant computational memory, resources, or a multifrontal direct solver to calculate the migration seismic images as like conventional reverse-time migration. The analytic Green’s function in our reverse-time method makes it possible to provide a high-resolution seismic water column image with a meter-scale grid size, consisting of full-band frequency components for a modest cost and in a low-memory environment for computation. Our method was applied to multi-channel seismic data acquired in the Arctic Ocean and successfully constructed water column seismic images containing the oceanographic reflections caused by thermocline structures of the water mass. From the numerical test, we note that the oceanographic reflections of the migrated seismic images reflected the distribution of Arctic waters in a shallow depth and showed good correspondence with the anomalies of measured temperatures and calculated reflection coefficients from each XCDT profile. Our proposed method has been verified for field data application and accuracy of imaging performance. Full article