Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data

The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based on the interferometric synthetic aperture radar (InSAR) technique using Sentinel-1 images. A targeted processing chain is first used to obtain reliable interferometric phase measurements under the circumstance of rapid ice flow. Unfortunately, only the descending data are available, which disallows the corresponding 2-D velocity field to be directly obtained from such measurements. A new approach is thus proposed to estimate the interferometric phase-based 2-D velocity field with the assistance of speckle tracking offsets. This approach establishes an implicit relationship between range and azimuth displacements based on speckle tracking observations. By taking advantage of such a relationship, the equivalent interferometric signals in the azimuth direction are estimated, thereby recovering the interferometric phase-based 2-D ice velocity field of the LCIS. To further investigate the state of the LCIS, the recovered 2-D velocity field is utilized to invert the ice rigidity. The shallow-shelf approximation (SSA) is the core of the reverse model, which is closely dependent on boundary conditions, including kinematic and dynamic conditions. The experimental results demonstrate that the spatial distribution of the rigidity varies approximately from 70 MPa·s1/3 to 300 MPa·s1/3. This rigidity distribution can reproduce a similar ice flow pattern to the observations

Retrieving the grounding lines of the Riiser-Larsen Ice Shelf using Sentinel-1 SAR images

Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change. However, it is challenging to find grounding lines since they are subglacial features. In this study, Sentinel-1 synthetic aperture radar (SAR) data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf. A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed. It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms, thereby providing grounding line proxies. As it fully exploits coherent signals in both spatial and temporal domains, the maximum possible number of grounding zone pixels can be obtained. Moreover, due to the introduction of the concept of the temporal consistency, the method can cope with short term grounding line fluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements. The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product. The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades

Deep Learning Based Protective Equipment Detection on Offshore Drilling Platform

There is a symmetrical relationship between safety management and production efficiency of an offshore drilling platform. The development of artificial intelligence makes people pay more attention to intelligent security management. It is extremely important to reinforce workplace safety management by monitoring protective equipment wearing using artificial intelligence, such as safety helmets and workwear uniforms. The working environment of the offshore drilling platforms is particularly complex due to small-scale subjects, flexible human postures, oil and gas pipeline occlusions, etc. To automatically monitor and report misconduct that violates safety measures, this paper proposes a personal protective equipment detection method based on deep learning. On the basis of improving YOLOv3, the proposed method detects on-site workers and obtains the bounding box of personnel. The result of candidate detection is used as the input of gesture recognition to detect human body key points. Based on the detected key points, the area of interest (head area and workwear uniform area) is located based on the spatial relations among the human body key points. The safety helmets are recognized using the deep transfer learning based on improved ResNet50, according to the symmetry between the helmets and the workwear uniforms, the same method is used to recognize the workwear uniforms to realize the identification of protective equipment. Experiments show that the proposed method achieves a higher accuracy in the protective equipment detection on offshore drilling platforms compared with other deep learning models. The detection accuracies of the proposed method for helmets and workwear uniforms are 94.8% and 95.4%, respectively

Human Activity Recognition and Location Based on Temporal Analysis

Current methods of human activity recognition face many challenges, such as the need for multiple sensors, poor implementation, unreliable real-time performance, and lack of temporal location. In this research, we developed a method for recognizing and locating human activities based on temporal action recognition. For this work, we used a multilayer convolutional neural network (CNN) to extract features. In addition, we used refined actionness grouping to generate precise region proposals. Then, we classified the candidate regions by employing an activity classifier based on a structured segmented network and a cascade design for end-to-end training. Compared with previous methods of action classification, the proposed method adds the time boundary and effectively improves the detection accuracy. To test this method empirically, we conducted experiments utilizing surveillance video of an offshore oil production plant. Three activities were recognized and located in the untrimmed long video: standing, walking, and falling. The accuracy of the results proved the effectiveness and real-time performance of the proposed method, demonstrating that this approach has great potential for practical application

Special issue on green networking, computing, and software systems

International audienceThe development of Information and Communication Technologies (ICT) enables the anywhere, anytime, and anything access, and has enhanced the quality of our life to a large extent. However, the other side is ever-increasing carbon emissions and severe environmental problems caused by the operational and computational cost of the fast-growing number of electronic devices. In this setting, there is a critical need of cooperating the green considerations into the solution design, development, and operation of hardware, network, and software systems. In fact, greenness is an urgent need for reducing carbon dioxide generation and optimizing energy consumption nowadays. Greenness covers a wide range of green topics, including green communication networks, green computing, and green software systems. This special issue dedicates to the research challenges and issues in novel greenness-aware engineering principles, methodologies, and tools about green networking, computing, and software system

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology

Fault Identification of U-Net Based on Enhanced Feature Fusion and Attention Mechanism

Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on enhanced feature fusion for automated end-to-end fault interpretation of 3D seismic data. EAResU-net uses an enhanced feature fusion mechanism to reduce the semantic gap between the encoder and decoder and improve the representation of fault features in combination with residual structures. In addition, EAResU-net introduces an attention mechanism, which effectively suppresses seismic data noise and improves model accuracy. The experimental results on synthetic and field data demonstrate that, compared with traditional deep learning methods for fault detection, our EAResU-net can achieve more accurate and continuous fault recognition results

Human elbow flexion behaviour recognition based on posture estimation in complex scenes

Abstract Aiming at the difficulty of recognising the smoking and making phone calls behaviours of people in the complex background of construction sites, a method of recognising human elbow flexion behaviour based on posture estimation is proposed. The human upper body key points needed are retrained based on AlphaPose to achieve human object localization and key points detection. Then, a mathematical model for human elbow flexion behaviour discrimination (HEFBD model) is proposed based on human key points, as well as locating the region of interest for small object detection and reducing the interference of complex background. A super‐resolution image reconstruction method is used for pre‐processing some blurred images. In addition, YOLOv5s is improved by adding a small object detection layer and integrating a convolutional block attention model to improve the detection performance. The detection precision of this method is improved by 5.6%, and the false detection rate caused by complex background is reduced by 13%, which outperforms other state‐of‐the‐art detection methods and meets the requirement of real‐time performance

Interferometric Phase Reconstruction Based on Probability Generative Model: Toward Efficient Analysis of High-Dimensional SAR Stacks

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed