Simulation experiment on hydrocarbon generating potential of various source rocks on the northern margin of the Qaidam Basin,Qinghai, China

The northern margin of the Qaidam Basin is one of the main oil-gas exploration areas in western China, where source rocks are composed mostly of Middle and Lower Jurassic dark mud shale, carbargillite and coal. A large number of subsurface and outcrop samples differing in lithology with different types of organic matter were selected for resource evaluation, research and calculation. And among them, 13 samples were used for simulation experiment on hydrocarbon generating potential of various source rocks. At first, two kinds of heating modes were compared through simulation experiment, including single temperature-step heating and continual heating. Perhaps, the process of hydrocarbon generation and expulsion occurred naturally between a close system and an open system. In addition, the first heating mode was convenient, and all its reaction products were involved in the whole thermal evolution, and the final simulation experimental results were basically in consistency with the natural evolution trend Therefore, the first heating mode was adopted and the hydrocarbon yield of every sample was worked out. According to the type and lithology of organic matter and the hydrocarbon yield of samples for simulation experiment, hydrocarbon generation and expulsion mode with three kinds of lithology and five types of source rock has been established to provide the basis for hydrocarbon generation evaluation, research and resource calculation.     

<Superscript>40</Superscript>Ar-<Superscript>39</Superscript>Ar dating of lamprophyre dykes in the Baimazhai nickel deposit,Yunnan Province,China, and its geological significance

The Baimazhai nickel deposit, Yunnan Province, China, is located in the southern part of the Sanjiang （Tri-river） alkali-rich intrusive rock belt （Sanjiang ARIR）. In this paper was conducted ^40Ar-^39Ar dating of two phlogopites in lamprophyres which are, as dikes, widely distributed in the orefield, and two plateau ages were acquired, i.e., 32.46±0.62 Ma and 32.01±0.60 Ma, respectively （averaging 32.23±0.60 Ma）. The ages are obviously younger than those of the ore-hosted complex and mineralization of the Baimazhai nickel deposit. In combination with the characteristics, it is indicated that lamprophyres in the orefield and those in the Sanjiang ARIR are similar in tectonic setting, mineral assemblage and geochemistry. It is considered that lamprophyres in the orefield are the important component of the Sanjiang ARIR, and the lamprophyres and ore-hosted complex in the orefield represent the products of two times of different magmatic activity from different mantle sources. On the other hand, the age of lamprophyres in the orefield is older than that of the strike-slip shearing of the Ailaoshan-Honghe fault belt, suggesting that the strike-slip shearing of the Ailaoshan-Honghe fault belt is not the factor leading to magmatic activity of lamprophyres in the orefield, while it is more possible that magmatic activity of the Sanjiang ARIR promoted strike-slip shearing of the fault belt.     

Distribution Characteristics of Sulfur and the Main Harmful Trace Elements in China’s Coal

To promote the rational development and use of clean coal resources in China, data on the regional and age distribution of sulfur, arsenic and other harmful elements in Chinese coal was broadly collected, tested for content, and analyzed. Coal in northwestern China is characterized by low to extremely low levels of sulfur; the coal of the Taiyuan Formation in northern China mainly has high-sulfur content; that of the Shanxi Formation is mainly characterized by low sulfur coal; and the Late Permian coal in southern China has overall higher sulfur content; other regions have low sulfur coal. The average content of harmful trace elements in the bulk of China＇s coal is similar to the corresponding content in the coal of the North America and the rest of the world, whereas the content of various elements （Hg, Sb and Se） is different in magnitude to the corresponding percentage in the crust. The average content of the elements Cr, Se, Co, Be, U, Br in Late Permian coal in S China ranks first in the country whereas the average content of Hg and CI in the coals of Late Carboniferous to Early Permian age in N China are the highest. The average content of Mn in Early and Middle Jurassic coal is higher in NW China. The high content of harmful elements in some coal should cause particular concern both in the development and utilization of coal.     

A study on the polarimetric properties of various features using SIR-C data

Spaceborne Imaging Radar (SIR-C) data acquired over Gujarat, India in 1994 were processed and analysed using differnet techniques applicable to polarimetric SAR data such as polarization signatures, polarization index, decomposition of the signal and polarization phase difference and limited groundtruth data. It has been observed that multi-frequency polarimetric data enhances the potential of retrieving geo-physical parameters. The polarization signatures are found to vary with the nature of the target. Target decomposition of the returned signal will be useful for the classification of various features. Polarization Phase Difference (PPD) gives good information about the vegetation parameters.     

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.     

Geodetic intersection on the ellipsoid

Through each of two known points on the ellipsoid a geodesic is passing in a known azimuth. We solve the problem of intersection of the two geodesics. The solution for the latitude is obtained as a closed formula for the sphere plus a small correction, of the order of the eccentricity of the ellipsoid, which is determined by numerical integration. The solution is iterative. Once the latitude is obtained, the longitude is determined without iteration.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.