Narrow Results

This study aims to detect objects quickly and accurately and then start the necessary obstacle avoidance procedure when the uncrewed vessel is at sea. This study uses a multivariate convolutional neural network (CNN) to perform automatic object detection and direction prediction for uncrewed vessels. This study is divided into three parts for processing. The first part of this process uses camera calibration technology to correct the image. Discrete cosine transform (DCT) is then used to detect sea level. Finally, this study uses Kalman filtering and affine transformation to stabilize images taken by uncrewed vessels at sea. The second part of the processing system uses a CNN to detect sea objects automatically. The third part of the process uses the dual-lens camera installed on the vessel to detect the distance and direction of objects at sea. In the experiment, the detection rate can reach more than 91% in this study method. In the experiment on image stabilization, this study’s method can also effectively improve video instability. In the experiment involving the distance and direction of the object, the experimental results show that the distance and direction of the object obtained by this study method have a distance error value of less than 10%, and the prediction results have a good effect no matter whether the object is at a short or long distance. It is hoped that this paper’s method can be applied to the automatic obstacle avoidance of unmanned vessels.

On the Southern Marion carbonate platform, dolomitization is triggered by the circulation of normal or slightly modified seawater and is related to changes in sedimentation rate and sea level change. Dolomitization further modifies formation permeability and fluid flow patterns. Dolostone/calcareous dolostone with large vuggy or moldic porosity is formed by fabric-preserving dissolution and recrystallization, which increases the pore space and facilitates the fluid flow effectively, with permeability ranging from 1mD to 10,000mD. The frame flexibility factor ($\gamma$) is a rock physics parameter which is a proxy of pore structure. We find that at given porosity dolostone with larger pores, higher permeability and higher sonic velocity usually has lower values of frame flexibility factor than limestone. After strong compaction and cementation, the limestone frame occludes fluid flow, prevents dolomitization and has permeability as low as 0.02mD.

Acoustic impedance inversion confirms that the asymmetric geometry of the Southern Marion platform is shaped by the oceanographic currents, which are caused by the southward-flowing East Australian Current. Three layers of dolostone with large pores in the upper platform reveal strong fluid flow within the carbonate platform, leading to dolomitization and dissolution. These three strongly dolomitized zones follow the platform topography, indicating that the diagenetic fluid flow is driven by oceanographic currents. Three large-pore-formation dolomitization events match well with three highstands of sea level events, illustrating that the highstand of sea level induces the formation of dolomitization zones with large pores. This study demonstrates the positive feedback loop of dolomitization and ocean current circulation, as well as the relationship between dolomitization and sea level change, which could be applicable for better understanding subsurface fluid-rock interactions and dolomitization pore systems in other carbonate environments.