Yan Zhou

Borehole electromagnetic (EM) technology facilitates rapid and accurate underground geophysical prospecting. However, analytical solutions of the Helmholtz equation involving a layered cylindrical borehole model couple the spatial position information of sensors with the target information, which tends to distort the measurement signals obtained when multiple sensors are used to improve the signal-to-noise ratio (SNR). Moreover, applying Gauss-Legendre quadrature approximation to convert the spatially coupled sensor and target terms into a product between the target vector and an observation matrix containing the spatial position information of each sensor array element leads to an ill-posed problem, while applications of the compressive-sensing (CS) technique to address this ill-posed problem have failed to address equally serious problems associated with signal quality and the lack of signal processing to enhance the SNR. The present work addresses these issues by introducing a sparsity constraint based on the dimensionality reduction property of the CS technique to optimize the sparsity of the target vector containing the target information, which provides a solution to the ill-posed problem obtained when borehole EM measurement signals are spatially decoupled via Gauss-Legendre quadrature approximation, while simultaneously enhancing the SNR obtained from multiple sensors. The ability of the proposed CS optimization model to solve the ill-posed reconstruction problem is established based on the results of sparsity analysis for the target vector and correlation analysis for the observation matrix of a standard case as an example, and the effectiveness of the proposed approach is demonstrated based on the results of simulations and experiments.

As the final crucial line of defense against well blowout accidents, the implementation of the relief well scheme facilitates the swiftest possible rescue of the target well, thereby minimizing economic losses and enhancing production efficiency. Among them, the relative distance and orientation determination between the relief well and the accident well becomes the key to the rapid intersection and connection of the two wells. However, the inaccessibility of the accident well due to safety factors makes it challenging to accurately determine the relative position of the two wells. The current injection method has now been proved to be able to achieve relief well detection, and some signal processing methods are necessary for optimizing the detection performance. In the process of signal characterization, analytical methods show many advantages due to their direct relevance to the principles of electromagnetic theory. To provide an effective theoretical basis for the optimization of relief well detection methods, this paper proposes an analytical solution to the corresponding electromagnetic field distribution for current injection detection scheme. Based on the principle of the current injection detection, the signal model is constructed, and the electromagnetic distribution expression is deduced. Then, the adaptability of the analytical solution obtained under different physical parameters is investigated. Moreover, the reliability and validity of the proposed analytical solution are validated by comparing the results with those obtained from finite element numerical simulations, thus providing a theoretical basis for subsequent signal processing and method optimization.

Borehole pulsed eddy-current (PEC) systems based on uniform linear multicoil arrays (ULMAs) perform efficient nondestructive evaluations (NDEs) of metal casings. However, the limited physical space of the borehole restricts the degrees of freedom (DoFs) of ULMAs to be less than the number of constraints, which leads to the difficulty of compensating for the differences in signals acquired by different receivers with different transmitting-to-receiving distances (TRDs), and thus limits the effectiveness of the ULMA system. To solve this problem, this paper proposes sparse linear constraint minimum variance (S-LCMV) for NDEs of downhole casings employing ULMAs. By transforming and characterizing the original PEC signal, it was observed that the signal power dramatically decreased with increasing Legendre polynomial stage, confirming that the signal was sparsely distributed over the Gauss–Legendre stages. Using this property, the S-LCMV cost function with reduced constraints was constructed to provide enough DoFs to accurately calculate the weight coefficients, thus improving the detection performance. The effectiveness of the proposed method was verified through field experiments on an 8-element ULMA installed in a borehole PEC system for NDEs of oil-well casings. The results demonstrate that the proposed method could improve the weighting effect by reducing the number of constraints by 70% while ensuring the approximation accuracy, which effectively improved the signal-to-noise ratio of the measured signals and reduced the computational cost by about 87.9%.

In this article, we present a space–time pulsed eddy current (PEC) array for nondestructive testing (NDT) of wellbore casings using a multiple-input multiple-output (MIMO) technique. On the basis of a multilayered cylindrical wellbore model, an MIMO-PEC model is developed according to the phase shift characteristics of the PEC response corresponding to different transmitting–receiving (TR) distances. To achieve a more significant number of TR channels than in a traditional receiving array to reduce the complexity of the TR structure, a space–time transmission scheme is proposed to recover multiple TR channels. Moreover, a specific space–time MIMO array structure is designed to maximize the number of independent TR channels. The effectiveness of the proposed method is verified by applying the proposed MIMO array to a borehole PEC system for NDT of wellbore casings. The simulation and experimental results demonstrate that using the array structure and space–time transmission scheme proposed in this article, an MIMO structure consisting of ${M}$ transmitters and ${N}$ receivers can be equivalent to a transceiver system with a single transmitter and ${M}\,\,\times \,\,{N}$ receivers. Thus, MIMO technique can be effectively applied to PEC-NDT systems and drastically reduce the cost and complexity of the PEC system.

In recent years, sparse arrays based on the fourth‐order difference coarray (FODCA) have received increased research attention due to their small number of array elements and large consecutive virtual arrays. The expanding and shift structure based on the nested array (NA) and coprime array (CPA) (EAS‐NA‐CPA) is the representative one. The EAS‐NA‐CPA comprises two sparse subarrays, wherein the NA is expanded and shifted according to the second‐order difference coarray of the CPA. Although the number of consecutive elements is large, there are many redundant elements in the FODCA of the EAS‐NA‐CPA. To decrease the number of redundant elements and increase the number of consecutive elements in the FODCA, the authors proposed the enhanced EAS‐NA‐CPA (EEAS‐NA‐CPA). Compared with the original EAS‐NA‐CPA, the EEAS‐NA‐CPA increases the adjacent physical sensor spacing further by using more fourth‐order cross‐correlation coarrays in the FODCA, which are not considered in the original EAS‐NA‐CPA. By performing a theoretical analysis of the FODCA provided by the proposed EEAS‐NA‐CPA, the sensor spacing in the expanded NA was further increased to obtain a larger number of consecutive elements compared with the original EAS‐NA‐CPA. Furthermore, the relationship between the number of physical sensors and corresponding number of consecutive elements provided by the FODCA was explicitly expressed. Experimental simulations were conducted to verify the effectiveness of the proposed sparse array for the fourth‐order cumulant‐based direction of arrival estimation.

Target detection technology has been greatly improved for the synthetic aperture radar (SAR) images recently, due to the advancement in deep learning (DL) domain. However, because of the existence of clutter in the SAR images, it's still a challenge to detect small targets with high accuracy and low computational complexity. To solve this problem, a detection algorithm based on feature fusion and cross-layer connection (FFCLC) network is proposed in this paper. Firstly, the attention feature fusion (AFF) is applied to improve the feature fusion ability for the small targets through allocating weights to various feature maps adaptively. Meanwhile, the depthwise separable convolution (DW-Conv) is used to reduce the computational complexity caused by the increasement of network layers. Then, a cross-layer connection (Cross-Connect) submodule is proposed to fuse shallow features with deep features further. Finally, a multi-scale target detection (Multi-Detect) submodule is designed to improve the detection ability for the small targets. We compare the proposed algorithm with the other representative methods on the SAR-Ship-Dataset and SSDD, quantitative evaluations show that our proposed algorithm's can reach the highest computational efficiency. Therefore, because of the superior performance in terms of accuracy and efficiency, the algorithm proposed in this paper is more suitable to detect small targets for the SAR images.

The inspection of corrosion under insulation (CUI) has been identified as a significant challenge in the petroleum and chemical process industries. As some of the most effective strategies, pulsed eddy current (PEC) techniques have proved effective for the measurement of the CUI of pipelines. In this paper, we propose a circular-arc array (CAA) to improve the measurement efficiency for the PEC inspection of thermally insulated pipelines. Based on the PEC system model for inspecting the CUI of pipelines, the magnetic field distribution of the CAA with multiple excitors was investigated. It is shown that the coverage of induced magnetic field gets much larger than that of the single excitor to realize high-efficiency measurements. Moreover, a sparsely distributed receiver array is designed to further improve the signal-to-noise ratio by eliminating the waviness effect due to multiple excitors. Finally, experiments were conducted, and the results demonstrated the effectiveness of the proposed method for the inspection of thermally insulated pipelines.

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3D HR image acquisition typically requests long scan time and, results in small spatial coverage and low SNR. Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach a scale-specific projection between LR and HR images, thus these methods can only deal with a fixed up-sampling rate. For achieving different up-sampling rates, multiple SR networks have to be built up respectively, which is very time-consuming and resource-intensive. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images. Then the SR task is converted to represent the implicit voxel function via deep neural networks from a set of paired HR-LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Due to the continuity of the learned function, a single ArSSR model can achieve arbitrary up-sampling rate reconstruction of HR images from any input LR image after training. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.