By tracking and monitoring the profile configuration, topography, and hydrodynamic factors of an artificial cobble beach in Tianquan Bay, Xiamen, China over three consecutive years after its completion, we analyzed the evolution of its profile configuration and plane morphology, and its storm response characteristics. The evolution of the profile configuration of the artificial cobble beach in Tianquan Bay can be divided into four stages. The beach was unstable during the initial stage after the beach nourishment the profile configuration changed obviously, and an upper concave composite cobble beach formed gradually, which was characterized by a steep upper part and a gentle lower part. In the second stage, the cobble beach approached dynamic equilibrium with minor changes in the profile configuration. At the third stage the beach was in a high-energy state under the influence of Typhoon Meranti, and the response of the artificial cobble beach differed significantly from that of the low-tide terrace sandy beach. Within a short time, there was net onshore transport of cobbles in the cross-shore direction. The beach face was eroded, the beach berm was accumulated, and the slope of the beach was steepened considerably. In the alongshore direction, there was notable transport of cobbles on the beach from east to west along the shore, and the total volume of the beach decreased by 4.5×103 m~3, which accounted for 50% of the total amount of beach volume lost within three years. The fourth stage was the restoration stage after the typhoon, characterized by a little gentler profile slope and the increase in width and the decrease in height of beach berm. Because of the action of waves and the wave-driven longshore current caused by the specific terrain and landform conditions along the coast(e.g., coastal headlands, near-shore artificial structures, and reefs), the coastline of the artificial cobble beach gradually evolved from being essentially parallel to the artificial coast upon completion to a slightly curved parabolic shape, and three distinct erosion hotspots were formed on the west side of the cape and the artificial drainpipe, and the reefs. Generally, the adoption of cobbles for beach nourishment on this macro-tidal coast beach with severe erosion has yielded excellent stability and adaptability. 相似文献
Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.
