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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.

Microstructured flexible pressure sensors featured with good mechanical properties, boosting a variety of sophisticated application scenarios, including electronic skin (e‐skin), soft robotics, wearable electronics, etc. This review is very focusing on the recent research progress of microstructured flexible pressure sensors. For better understanding the corresponding devices, different mechanisms, materials, preparation methods are briefly introduced at the beginning. And with highlighting the significance of microstructure for device performance, the microstructures of different configurations (e.g., pyramid, pillar, hemisphere) are introduced and discussed in detail through analyzing the influence of configuration characteristics and material properties. Finally, according to the existing problems in the application, the research directions of flexible pressure sensor are prospected. Currently, catering to the explosive and ineluctable growth of this intelligent world, considerable microstructured flexible pressure sensors have emerged but their development is still at very first stage. In this review, some guidelines and tunable methods are suggested for the microstructured flexible pressure sensors of wide practical use in the future.

At present, the deterministic models which are widely adopted cannot bemeet the structural reliability design requirements of coastal engineering. In this paper, aiming to the uncertainty of four hydrodynamics parameters, i.e. wave height, wave period, incident direction, and water level, a probability design of wave model tests and its analytical method are proposed, which can be used for laboratory physical experiments and numerical model tests.