Application of X-ray fluorescence core-scanning(XRF-CS) on both marine and lake sediments has achieved remarkable results. However, its application has not been widely extended to the research on speleothems. In this study, we measure the Sr abundance and the Sr/Ca ratios of three stalagmites(two aragonite stalagmites, one calcite stalagmite) using the state-of-the-art fourth-generation Avaatech high-resolution XRF core scanner. Through comparisons among different scan paths and among different scan resolutions, as well as comparisons with inductively coupled plasma optical emission spectrometer(ICP-OES), Itrax XRF, and Artax XRF results, we confirm that the Avaatech XRF core scanner could precisely, quickly, and nondestructively analyze the high-resolution Sr abundance of speleothems. Furthermore, we combine the stalagmite δ~(18)O records to explore the paleoclimatic significance of the measured stalagmite Sr/Ca. 相似文献
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
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.