Seismic fault detection is indispensable for exploring reservoirs of hydrocarbons, and a considerable amount of research has thus been devoted to it. With the rapid development of deep learning in recent years, researchers have begun using convolutional neural networks (CNNs) to identify seismic faults. However, it remains challenging for geophysical interpreters to quickly train an efficient model on a limited number of samples for fault interpretation. In this letter, we propose a workflow that uses a CNN-based method of semantic segmentation to interpret faults by using a small training set. It requires only the selection of some 2-D seismic sections from seismic volume data for interpretation and labeling to be trained to predict faults in the entire area considered. We simplified the VGG16 model to reduce training time and modified it to improve its performance. We used convolution layers instead of fully connected layers at the end of the network to implement the end-to-end classification of seismic images, and adopted dilation convolution to increase the receptive field and hybrid dilation convolution to avoid problems in it. We also applied the atrous spatial pyramid pooling (ASPP) module to further enhance the effects of segmentation. The results were subjected to postprocessing for refinement. The promising performance of the proposed method was verified on a set of real seismic data.

Ferromanganese (Fe-Mn) crusts are potential marine deposits for many high-tech metals and are exciting proxies for recording the oceanic paleoenvironment. During their growth, phosphatization generally occurs, causing the remobilization and reorganization of the elements and minerals in Fe-Mn crusts. Rare earth elements plus yttrium (REY), well-known critical metals for many new and emerging technologies, as well as valuable geological proxies, are the important critical metals in Fe-Mn crusts. The REY occurrence is closely influenced by the phosphatization processes, which still remain discursive. In this study, the textures, structures, and REY geochemistry of the growth of an Fe-Mn crust sample (MP2D32A) from the Line Islands archipelago were analyzed using multiple microanalysis methods. The analyzed Fe-Mn crust is mainly characterized by the presence of laminated and concentric colloforms. Massive fine particles and some veins of carbonate-rich fluorapatite (CFA) were observed in the old part of MP2D32A, demonstrating that this sample underwent phosphatization. The phosphatized and non-phosphatized layers, as well as the CFA veins, display distinctly different PAAS-normalized REY patterns. Higher REY contents in the phosphatized layer than those in the non-phosphatized layer suggest the positive role of phosphatization in REY enrichment. Moreover, the phosphatized layer contains higher REY contents than the CFA, implying that the REY enrichment in the phosphatized layer is not only influenced by CFA and Fe-Mn (oxyhydr)oxides but also other factors, such as the probable PO₄³⁻ complexation induced by Fe oxyhydroxides. The synergistical sorption of REY(III) and HPO₄²⁻ ions on Fe oxyhydroxides should facilitate REY enrichment during the phosphatization processes. These fundamental results provide novel insights into the influence of phosphatization in REY geochemical behaviors in the Fe-Mn crust.

Investigating post-earthquake surface ruptures is important for understanding the tectonics of seismogenic faults. The use of unmanned aerial vehicle (UAV) images to identify post-earthquake surface ruptures has the advantages of low cost, fast data acquisition, and high data processing efficiency. With the rapid development of deep learning in recent years, researchers have begun using it for image crack detection. However, due to the complex background and diverse characteristics of the surface ruptures, it remains challenging to quickly train an effective automatic earthquake surface rupture recognition model on a limited number of samples. This study proposes a workflow that applies an image segmentation algorithm based on convolutional neural networks (CNNs) to extract cracks from post-earthquake UAV images. We selected the 16-layer visual geometry group (VGG16) network as the primary network architecture. Then, we improved the VGG16 network and deleted several convolutional layers to reduce computation and memory consumption. Moreover, we added dilated convolution and atrous spatial pyramid pooling (ASPP) to make the network perform well in the surface crack identification of post-earthquake UAV images. We trained the proposed method using the data of the MS 7.4 Maduo earthquake and obtained a model that could automatically identify and draw small and irregular surface ruptures from high-resolution UAV images.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Purpose: During extracorporeal circulation in heart surgery, blood is exposed to non-physiological conditions, such as high shear stress, foreign surfaces, turbulence, and hypothermia. These factors cause damage to the red blood cells, which is manifested by immediate and delayed hemolysis or some changes in the mechanical properties of red blood cells, defined as sublethal trauma. Unfortunately, sublethal trauma is hard to detect, and there is not enough morphological evidence regarding red blood cell sublethal trauma. In this study, red blood cell sublethal trauma was observed after extracorporeal circulation by describing ultrastructural changes in red blood cell membranes using atomic force microscopy and scanning electron microscopy. Methods: Venous blood (2 mL) was collected into heparin tubes from preoperative, intraoperative and postoperative aortic dissection patients for comparison with blood from healthy patients. The red blood cell morphological study (malformations percentage, diameter, height, concavity, and roughness) was performed with scanning electron microscopy and atomic force microscopy. Results: Scanning electron microscopy and atomic force microscopy imaging analysis revealed that the red blood cell shape changed during extracorporeal circulation and that the red blood cell malformation percentage in the postoperative group was higher than those in the preoperative and intraoperative groups. Most morphological parameters had no obvious changes, except roughness (Ra and Rq) in aortic dissection patients. Atomic force microscopy quantitative analysis indicated that the roughness of red blood cell membranes increased during extracorporeal circulation. Conclusions: This study demonstrates that ultrastructural morphological damage occurs to red blood cells membranes due to extracorporeal circulation in aortic dissection patients. In addition, we provided a new parameter (Ra and Rq) to evaluate red blood cell sublethal trauma.

Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.