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Contourite drifts of alternating sand and mud, shaped by the Labrador Current, formed during the late Quaternary in Flemish Pass seaward of the Grand Banks of Newfoundland, Canada. The drifts preserve a record of Labrador Current flow variations through the last glacial maximum. A high-resolution seismic profile and a transect of four cores were collected across Beothuk drift on the southeast side of Flemish Pass. Downcore and lateral trends in grain size and sedimentation rate provide evidence that, between 16 and 13 ka, sediment was partitioned across Beothuk drift and the adjacent Flemish Pass floor by a strong current flow but, from 29 to 16 ka, sedimentation was more of a blanketing style, represented by draped reflections interpreted as being due to a weaker current. The data poorly resolve the low sedimentation rates since 13 ka, but the modern Labrador Current in Flemish Pass is the strongest it has been in at least the past 29 ka. Pre-29 ka current flow is interpreted based on reflection architecture in seismic profiles. A prominent drift on the southwestern side of Flemish Pass formed above a mid-Miocene erosion surface, but was buried by a mass-transport deposit after the penultimate glacial maximum and after drift deposition switched to eastern Flemish Pass. These findings illustrate the temporal complexity of drift sedimentation and provide the first detailed proxy for Labrador Current flow since the last glacial maximum.  相似文献   
2.
This paper introduces an extension of the traditional stationary linear coregionalization model to handle the lack of stationarity. Under the proposed model, coregionalization matrices are spatially dependent, and basic univariate spatial dependence structures are non-stationary. A parameter estimation procedure of the proposed non-stationary linear coregionalization model is developed under the local stationarity framework. The proposed estimation procedure is based on the method of moments and involves a matrix-valued local stationary variogram kernel estimator, a weighted local least squares method in combination with a kernel smoothing technique. Local parameter estimates are knitted together for prediction and simulation purposes. The proposed non-stationary multivariate spatial modeling approach is illustrated using two real bivariate data examples. Prediction performance comparison is carried out with the classical stationary multivariate spatial modeling approach. According to several criteria, the prediction performance of the proposed non-stationary multivariate spatial modeling approach appears to be significantly better.  相似文献   
3.
This paper is concerned with the problem of predicting the surface elevation of the Braden breccia pipe at the El Teniente mine in Chile. This mine is one of the world’s largest and most complex porphyry-copper ore systems. As the pipe surface constitutes the limit of the deposit and the mining operation, predicting it accurately is important. The problem is tackled by applying a geostatistical approach based on closed-form non-stationary covariance functions with locally varying anisotropy. This approach relies on the mild assumption of local stationarity and involves a kernel-based experimental local variogram a weighted local least-squares method for the inference of local covariance parameters and a kernel smoothing technique for knitting the local covariance parameters together for kriging purpose. According to the results, this non-stationary geostatistical method outperforms the traditional stationary geostatistical method in terms of prediction and prediction uncertainty accuracies.  相似文献   
4.
Source‐to‐sink studies and numerical modelling software are increasingly used to better understand sedimentary basins, and to predict sediment distributions. However, predictive modelling remains problematic in basins dominated by salt tectonics. The Lower Cretaceous delta system of the Scotian Basin is well suited for source‐to‐sink studies and provides an opportunity to apply this approach to a region experiencing active salt tectonism. This study uses forward stratigraphic modelling software and statistical analysis software to produce predictive stratigraphic models of the central Scotian Basin, test their sensitivity to different input parameters, assess proposed provenance pathways, and determine the distribution of sand and factors that control sedimentation in the basin. Models have been calibrated against reference wells and seismic surfaces, and implement a multidisciplinary approach to define simulation parameters. Simulation results show that previously proposed provenance pathways for the Early Cretaceous can be used to generate predictive stratigraphic models, which simulate the overall sediment distribution for the central Scotian Basin. Modelling confirms that the shaly nature of the Naskapi Member is the result of tectonic diversion of the Sable and Banquereau rivers and suggests additional episodic diversion during the deposition of the Cree Member. Sand is dominantly trapped on the shelf in all units, with transport into the basin along salt corridors and as a result of turbidity current flows occurring in the Upper Missisauga Formation and Cree Member. This led to sand accumulation in minibasins with a large deposit seawards of the Tantallon M‐41 well. Sand also appears to bypass the basin via salt corridors which lead to the down‐slope edge of the study area. Sensitivity analysis suggests that the grain size of source sediments to the system is the controlling factor of sand distribution. The methodology applied to this basin has applications to other regions complicated by salt tectonics, and where sediment distribution and transport from source‐to‐sink remain unclear.  相似文献   
5.
This article addresses the problem of the prediction of the breccia pipe elevation named Braden at the El Teniente mine in Chile. This mine is one of the world’s largest known porphyry-copper ore bodies. Knowing the exact location of the pipe surface is important, as it constitutes the internal limit of the deposit. The problem is tackled by applying a non-stationary geostatistical method based on space deformation, which involves transforming the study domain into a new domain where a standard stationary geostatistical approach is more appropriate. Data from the study domain is mapped into the deformed domain, and classical stationary geostatistical techniques for prediction can then be applied. The predicted results are then mapped back into the original domain. According to the results, this non-stationary geostatistical method outperforms the conventional stationary one in terms of prediction accuracy and conveys a more informative uncertainty model of the predictions.  相似文献   
6.
A fundamental decision to make during the analysis of geostatistical data is the modeling of the spatial dependence structure as stationary or non-stationary. Although second-order stationary modeling approaches have been successfully applied in geostatistical applications for decades, there is a growing interest in second-order non-stationary modeling approaches. This paper provides a review of modeling approaches allowing to take into account the second-order non-stationarity in univariate geostatistical data. One broad distinction between these modeling approaches relies on the way that the second-order non-stationarity is captured. It seems unlikely to prove that there would be the best second-order non-stationary modeling approach for all geostatistical applications. However, some of them are distinguished by their simplicity, interpretability, and flexibility.  相似文献   
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