Bayesian analysis of individual and systematic multiplicative errors for estimating ages with stratigraphic constraints in optically stimulated luminescence dating

Many dating techniques include significant error terms which are not independent between samples to date. This is typically the case in Optically Stimulated Luminescence (OSL) dating where the conversion from characteristic equivalent doses to the corresponding ages using the annual dosimetry data includes error terms that are common to all produced datings. Dealing with these errors is essential to estimate ages from a set of datings whose chronological ordering is known. In this work, we propose and we study a Bayesian model to address this problem. For this purpose, we first consider a multivariate model with multiplicative Gaussian errors in a Bayesian framework. This model relates a set of characteristic equivalent doses to the corresponding ages while taking into account for the systematic and non-systematic errors associated to the dosimetry. It thus offers the opportunity to deal properly with stratigraphic constraints within OSL datings, but also with other datings possessing errors which are independent from systematic errors of OSL (e.g. radiocarbon). Then, we use this model to extend an existing Bayesian model for the assessment of characteristic equivalent doses from Single Aliquot and Regenerative (SAR) dose measurements. The overall Bayesian model leads to the joint estimation of all the variables (which include all the dose–response functions and characteristic equivalent doses) of a sequence of, possibly heterogeneous, datings. We also consider a more generic solution consisting in using directly the age model from a set of characteristic equivalent dose estimates and their associated standard errors. We finally give an example of application on a set of five OSL datings with stratigraphic constraints and observe a good adequacy between the two approaches.     

利用微分进化法确定海洋磁场向下延拓中的最优参数

位场的解析延拓是实现不同高度海洋地磁场相互转换的主要途径,是构建海洋三维磁空间背景场模型的关键技术.针对位场向下延拓迭代法中最优正则化参数及最佳迭代次数难以确定问题,尝试引入微分进化法,以正则化参数及迭代次数为种群变量,以延拓结果的熵值为目标函数,以目标函数最小化为搜索准则,实现两种参数的并行全局寻优.采用实测数据对微分进化法在几种常用的迭代法中最优正则化参数及最佳迭代次数的确定进行了分析,与传统L-曲线准则确定的最优正则化参数及多次试验确定的最佳迭代次数进行对比,结果表明：微分进化法确定的最优参数能使三种迭代法取得最佳迭代效果,延拓结果与真实地磁场最为接近,并且该法计算稳定、自适应强,建议在海洋磁场数据向下延拓中应用.     

Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method

Rapid developments in SQUID-based technology make it possible for geophysical exploration to direct measure, inverse and interpret magnetic gradient tensor data. This contribution introduces a novel three-dimensional hybrid regularization method for inversion of magnetic gradient tensor data, which is based on the minimum support functional and total variation functional. Compared to the existing stabilizers, for example, the minimum support stabilizer, the minimum gradient support stabilizer or the total variation stabilizer, our proposed hybrid stabilizer, in association with boundary penalization, improves the revision result greatly, including higher spatial and depth resolution, more clear boundaries, more highlighted images and more evident structure depiction. Moreover, suitable selection of model parameter λ will further improve the image quality of the recovered model. We verify our proposed hybrid method with various synthetic magnetic models. Experiment results prove that this method gives more accurate results, exhibiting advantages of less computational costs even when less prior information of magnetic sources are provided. Comparison of results with different types of magnetic data with and without remanence indicates that our inversion algorithm can obtain more detailed information on the source structure based on rational estimation of total magnetization direction. Finally, we present a case study for inverting SQUID-based magnetic tensor data acquired at Da Hinggan Mountains area, inner Mongolia, China. The result also certifies that the method is reliable and efficient for real cases.     

Sparse Bayesian linearized amplitude-versus-angle inversion

The technique of seismic amplitude-versus-angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude-versus-angle inversion problem is intrinsically ill-posed and generally stabilized by the use of L2-norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude-versus-angle inversion problem to preserve the sharp geological interfaces. In this regard, a priori constraint term with two regularization functions is presented: the sparse constraint regularization and the low-frequency model information. In addition, to obtain high-resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude-versus-angle inversion.     

Rock-glacier dams in High Asia

Rock glaciers in semiarid mountains contain large amounts of ice and might be important water stores aside from glaciers, lakes, and rivers. Yet whether and how rock glaciers interact with river channels in mountain valleys remains largely unresolved. We examine the potential for rock glaciers to block or disrupt river channels, using a new inventory of more than 2000 intact rock glaciers that we mapped from remotely sensed imagery in the Karakoram (KR), Tien Shan (TS), and Altai (ALT) mountains. We find that between 5% and 14% of the rock glaciers partly buried, blocked, diverted or constricted at least 95 km of mountain rivers in the entire study area. We use a Bayesian robust logistic regression with multiple topographic and climatic inputs to discern those rock glaciers disrupting mountain rivers from those with no obvious impacts. We identify elevation and potential incoming solar radiation (PISR), together with the size of feeder basins, as dominant predictors, so that lower-lying and larger rock glaciers from larger basins are more likely to disrupt river channels. Given that elevation and PISR are key inputs for modelling the regional distribution of mountain permafrost from the positions of rock-glacier toes, we infer that river-blocking rock glaciers may be diagnostic of non-equilibrated permafrost. Principal component analysis adds temperature evenness and wet-season precipitation to the controls that characterise rock glaciers impacting on rivers. Depending on the choice of predictors, the accuracy of our classification is moderate to good with median posterior area-under-the-curve values of 0.71–0.89. Clarifying whether rapidly advancing rock glaciers can physically impound rivers, or fortify existing dams instead, deserves future field investigation. We suspect that rock-glacier dams are conspicuous features that have a polygenetic history and encourage more research on the geomorphic coupling between permafrost lobes, river channels, and the sediment cascades of semiarid mountain belts. © 2018 John Wiley & Sons, Ltd.     

Adaptive hybrid diffusion model using variational mode decomposition for edge preserving noise attenuation

Preserving the structural and stratigraphic discontinuities or edges is essential in seismic data processing and interpretation. According to several numerical experiments, it is obvious that random noise has a constant spectral density, whereas the structural features vary significantly within different frequency bands, which means that the ratio between the densities of noise and structural features varies significantly in different frequency bands. Therefore, we propose a method called adaptive hybrid diffusion to attenuate random noise, which utilizes a novel adaptive frequency-based parameter. First, the adaptive hybrid diffusion method decomposes the seismic sections into several band-limited portions using variational mode decomposition. These portions are called intrinsic mode functions, in which noise and structural energy have distinct differences. Subsequently, utilizing the adaptive frequency-based parameter, each intrinsic mode function is divided into several monotonous portions that represent the noise or structural area. Afterwards, the total variation and L2 minimization algorithms are utilized separately to suppress the noise in different band-limited monotonous areas. The algorithms are chosen dynamically, as the portion changes with the change in the adaptive parameter. Finally, these denoised portions are combined to obtain the denoised seismic section. Experimental results on synthetic and field seismic data showed that seismic noise is effectively suppressed by the adaptive hybrid diffusion method, with the edge details of seismic events well preserved.     

试谈地层数据库的基本结构及规范化问题

地层数据库的主要内容、对象是岩石地层剖面。分层描述的地层单元具有多种地层属性,它们均可统一在一个数据库系统之中。地层数据库总体上具层次结构。人们习惯于分地区或分时代阐述地层发育,但在地层数据库应用中以时代划分为基础的方法不太适用,而将地质时代视为地层单元的一个属性予以处理。本文提出的地层数据库基本结构方案适用于地层典或区域地层表。地层数据库的规范化一方面要求数据模型的规范化,另一方面要求数据使用的规范化或标准化。地层数据库应建立地层数据辞典,以作为名词术语标准的基础。     

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

ABSTRACT

A fractional vegetation cover (FVC) estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed, which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarse-resolution FVC pixel were all assumed to have the same vegetation growth model. However, this assumption does not hold over heterogeneous areas, meaning that the method cannot be applied to large regions. Therefore, this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite (GLASS) FVC product. The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data. Then, independent dynamic vegetation models were built for each finer-resolution pixel. Finally, the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale. Validation results indicated that the proposed method (R²?=?0.7757, RMSE?=?0.0881) performed better than either the previous method (R²?=?0.7038, RMSE?=?0.1125) or a commonly used method involving look-up table inversions of the PROSAIL model (R²?=?0.7457, RMSE?=?0.1249).     

A Bayesian belief network approach for mapping water conservation ecosystem service optimization region

Journal of Geographical Sciences - Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using...     

等式约束病态总体最小二乘模型的正则化解及其精度评定

利用平差参数间合理的先验信息能够显著提高解的精度。在病态总体最小二乘模型的基础上,引入等式约束条件,建立等式约束病态总体最小二乘模型,构建该模型的约束正则化准则,并根据拉格朗日极值法导出参数的迭代解及方差-协方差阵,最后以数值算例和病态测边网算例验证公式的正确性。结果表明,新方法通过正则化准则能改善法矩阵的病态性,且遵从EIV准则顾及系数阵的误差,同时还考虑参数间合理的先验信息,其解的精度得到显著提升。