Slope inclinometers for landslides

Slope inclinometers/indicators are used to determine the magnitude, rate, direction, depth, and type of landslide movement. This information is usually vitally important for understanding the cause, behavior, and remediation of a landslide. However, many inclinometer measurements fail to achieve these intended aims because of lack of appreciation of the many factors that need to be correctly implemented during installation, monitoring, and data reduction to yield useful data. This paper presents some guidelines for understanding, installing, and interpreting slope inclinometers and presents three case histories that illustrate some of the pitfalls that can develop if these guidelines are not followed.     

An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County

This paper presents an application of rock engineering system (RES) in an attempt to reveal and assess the inherent instability potential of 388 sites where equivalent landslides have been manifested and recorded in the region of Karditsa County, Greece. The main objective has been defining the principal causative and triggering factors responsible for the manifestation of landslide phenomena, quantify their interactions, obtain their weighted coefficients, and calculate the instability index, which refers to the inherent potential instability of each natural slope of the examined region. From the statistical interpretation of the data reported in a well-documented database and concerning the examined failure sites, a clear correlation between the instability index and the area affected by a single landslide event has been revealed. Almost the entire failure sites, 98% of the examined slope sites, exhibit an instability index value over 55, a value which is thought to be a critical threshold for landslide manifestation concerning natural slopes in Karditsa County. It is argued that the presented RES methodology, engaging the selected set of parameters, could be considered as an effective expert's tool for ranking, in an objectively optimal and simple way, the instability potential of natural slopes in Karditsa County, and thus providing a tool for sound zoning landslide hazard.     

Sediment Organic Carbon in Todos Santos Bay, Baja California, Mexico

Sediment grain size and organic carbon (OC) data collected over the past 50 years, together with δ¹³C values of OC in recently collected samples, were analyzed to improve understanding of sediment OC distribution and abundance in Todos Santos Bay. Sediments in the submarine canyon at the mouth of the bay and in quiet-water locations along the shore are fine grained, high in OC, and have generally low δ¹³C values; sediments in high-energy environments are low in OC and have high δ¹³C values. A bivariate isotopic mixing model indicates that none of the sediments contain >50% terrigenous OC (average ~30%), and that the terrigenous OC content of the sediments is a small proportion of the OC content of local soils. Sediment OC composition is apparently controlled by energy-related sorting and deposition, oxidation of much of the original terrigenous OC, and replacement of some terrigenous OC by marine OC.     

中国数字地质调查系统的基本构架 及其核心技术的实现

数字地质调查系统是贯穿整个地质矿产资源调查全过程的软件,涵盖地质矿产调查、矿产资源勘查、矿体模拟、品住估计、资源量估算、矿山开采系统优化等内容。考虑数字地质调查系统的应用技术层面,从数据“层”模型、数据流“池”技术、不同阶段数据模型继承技术、数据互操作技术等几个方面讨论了核心技术及其实现,通过基于无缝一体化技术的数据采集、管理、综合处理与成果表达,在实体或矿块的矿床建模技术与品住估计、储量估算等方面展现了全地质调查过程的数字化,不但为地质人员应用高新技术降低了门坎,而且极大地提高了研究精度和效率,丰富了成果的表现形式和服务形式。随着数字地质调查系统的完善和应用水平的提高,数字地质调查系统将成为中国地质调查的主流软件体系。     

山西保德—静乐地区新近纪地层时代讨论

通过对山西保德-静乐地区新近纪地层的野外地质调查和实测剖面,对分布于该区的新近纪地层进行了详细的研究和划分,获得了新的ESR和古地磁测年数据,并结合生物地层序列确定了该区新近系地层的顺序为：新近系上新统静乐组（N2j）、新近系中新统上部保德组（N1^26）和中新统下部芦子沟组（N1^1l）。     

非均匀月壤介质的被动微波辐射传输模拟

基于非均匀月壤物理模型和辐射传输方程,模拟月壤介质中的微波辐射传输特性,探讨频率、月壤厚度等与月表亮温的关系。结果表明:在低频段,月壤微波辐射亮温的动态变化范围较大,可探测的月壤厚度大,3 GHz时的最大可探测月壤厚度达12.4 m;在高频段对应的可探测月壤厚度较小,特别是从50GHz往后的频率段内,最大可探测月壤厚度均小于2 m。不同频率的亮温-厚度变化曲线没有交叉点,且频率越高,所能探测的月壤厚度越小。根据模拟结果,建立了月壤厚度与亮温的查找表。基于查找表,利用单个波段的亮温数据即可得到月壤厚度信息。     

Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Multitemporal remote sensing provides a unique tool to track lake dynamics at the pan-Arctic scale but requires precise registration of thousands of satellite images. This is a challenging task owing to a dearth of stable features to be used as tie points [(TPs), i.e., control points] in the dynamic landscapes. This letter develops an automated method to precisely register images in the lake-rich Arctic. The core premise of the method is that the centers of lakes are generally stable even if their shorelines are not. The proposed procedures first extract lakes in multitemporal satellite images, derive lake centroids and match them between images, and then use the centroids of stable lakes as TPs for image registration. The results show that this approach can achieve subpixel registration accuracy, outcompeting the conventional manual methods in both efficiency and accuracy. The proposed method is fully automated and represents a feasible way to register images for lake change detection at the pan-Arctic scale.      

Using Sequential Self-Calibration Method to Identify Conductivity Distribution: Conditioning on Tracer Test data

An iterative inverse method, the sequential self-calibration method, is developed for mapping spatial distribution of a hydraulic conductivity field by conditioning on nonreactive tracer breakthrough curves. A streamline-based, semi-analytical simulator is adopted to simulate solute transport in a heterogeneous aquifer. The simulation is used as the forward modeling step. In this study, the hydraulic conductivity is assumed to be a deterministic or random variable. Within the framework of the streamline-based simulator, the efficient semi-analytical method is used to calculate sensitivity coefficients of the solute concentration with respect to the hydraulic conductivity variation. The calculated sensitivities account for spatial correlations between the solute concentration and parameters. The performance of the inverse method is assessed by two synthetic tracer tests conducted in an aquifer with a distinct spatial pattern of heterogeneity. The study results indicate that the developed iterative inverse method is able to identify and reproduce the large-scale heterogeneity pattern of the aquifer given appropriate observation wells in these synthetic cases.     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

The Tau Model for Data Redundancy and Information Combination in Earth Sciences: Theory and Application

Many decision-making processes in the Earth sciences require the combination of multiple data originating from diverse sources. These data are often indirect and uncertain, and their combination would call for a probabilistic approach. These data are also partially redundant with each other or with all others taken jointly. This overlap in information arises due to a variety of reasons—because the data arises from the same geology, because they originate from the same location or the same measurement device, etc. The proposed tau model combines partially redundant data, each taking the form of a prior probability for the event being assessed to occur given that single datum. The parameters of that tau model measure the additional contribution brought by any single datum over that of all previously considered data; they are data sequence-dependent and also data value-dependent. Data redundancy depends on the sequence in which the data is considered and also on the data values themselves. However, for a given sequence, averaging the tau model parameters over all possible data values leads to exact analytical expressions and corresponding approximations and inference avenues. Information on multiple-point connectivity of permeability arrives from core data, well-test data and seismic data which are defined over varying supports with complex redundancy between these information sources. In order to compute these tau weights for determining connectivity, one needs a model of data redundancy, here expressed as a vectorial training image (Ti) constructed using a prior conceptual knowledge of geology and the physics of data measurement. From such a vectorial Ti, the tau weights can be computed exactly. Neglecting data redundancy leads to an over-compounding of individual data information and the possible risk of making extreme decisions.