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The construction of fine-scale three-dimensional models of deep structures at a regional scale remains challenging. These models can be significantly benefitted by focusing on the binary lithological architecture (i.e. different lithologies in the hanging wall and the footwall), which exists in several types of structures, such as detachment faults, domes and intrusion contact zones. Along these lines, in this work, a novel approach to the three-dimensional modelling of deep structures was proposed by using the level set method and transforming the geometric construction of deep structures into the lithological distribution interpreted from the gravity data. With this method, first, an initial model was constructed that provides a general model frame. Subsequently, the initial model was optimized to generate a refined model that represents fine-scale features of deep structures. The initial model was established based on the high-density geological data and extrapolation of trends. The refined model was reconstructed from the stepwise addition of the geological–geophysical data to the initial model, including gravity data, seismic profiles and geological knowledge. This was achieved through the parametrization of the initial model as the level set function to convert the data to a series of weighted misfit functions, which were solved by the gradient descent algorithm. In addition, the proposed approach was applied to the modelling of the Zhaoping detachment fault in the Jiaodong Peninsula, Eastern China. Interestingly, the lithologies of the hanging wall and the footwall are Precambrian metamorphic rocks and Mesozoic granite, indicating a binary feature. An initial model and two stepwise refined models were also constructed. From the extracted modelling results, it was demonstrated that the errors of location and morphology of the refined model were 1% and 30%, which were lower than those of the initial model (8% and 100%), demonstrating the effectiveness of the developed refined modelling approach. This work provides a new and powerful three-dimensional modelling approach for geological structures by effectively integrating both geological–geophysical data and knowledge. Owing to the unique flexibility of manipulating surfaces and incorporating data, the proposed modelling approach has the potential to be easily extended to the modelling of other types of deep structures, as well as application to other geophysical data. [ABSTRACT FROM AUTHOR]

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance. [ABSTRACT FROM AUTHOR]

Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020. [ABSTRACT FROM AUTHOR]