GIS支持下的煤层气目标区模糊综合评价模型

地理信息系统(GIS)以空间数据库技术为核心,将空间要素与统计数据有机地结合在一起,具有直观灵活、分析功能强大、制表制图方便等优点。根据中国煤层气(CBM)目标区的特点,建立了4种原则下用于不同情形的煤层气目标区评价模型:第1种模型为全面考虑各个因素原则,第2种模型为只考虑重点因素原则,第3种模型为着重考虑重点因素原则,第4种模型为既全面考虑又兼顾重点因素原则。根据这4种模型,在常用GIS软件MapGIS基础上进行了二次开发并实现模型,以华北聚气区煤层气为例进行综合评价,目标区评价结果与实际情况基本相符。     

华北安鹤煤田煤储层特征与煤层气有利区分布

通过对安鹤煤田采集煤样的煤质、显微组分、煤相、显微裂隙分析,等温吸附、低温氮比表面及孔隙结构和压汞孔隙结构测试,研究了该区煤层气赋存的地质条件、煤层气生气地质特征和煤储层物性特征。并采用基于G IS的多层次模糊数学评价方法计算了该区的煤层气资源量,预测了煤层气有利区分布。研究结果表明,该区煤层气总资源量为1 115.73×108m3,煤层气资源丰度平均为1.18×108m3/km2,具有很好的煤层气资源开发潜力。在煤田中部的四矿到八矿之间的地区以及北部的水冶镇附近地区,煤层累计有效厚度大、煤层气资源丰度高、煤层埋深适中、煤储层孔裂隙系统发育、渗透性高,是该区煤层气勘探开发的最有利目标区。     

松辽盆地葡萄花油田北部鞍部地区成藏控制因素分析

在系统描述葡萄花油田北部鞍部地区构造特征和断裂体系的基础上,分析了烃源岩大量排烃期与构造形成时期的匹配关系、砂体分布、地层压力和断层封闭性对研究区成藏的控制作用。研究结果表明:烃源岩大量排烃期和构造形成时期良好匹配,地层压力低势区和在成藏时期断块边界断层侧向开启是成藏的有利条件,构造和砂体分布则是成藏的主要控制因素。研究区成藏条件较好,可作为有利扩边潜力区。     

粤西云浮镇安-阳春罗阳地区 地球化学特征与找矿方向

分析云浮镇安—阳春罗阳地区元素的分布、分配特征和区域地质背景后,指出该地区有找矿潜力,提出Au、Ag、Cu、Pb、Zn是重要的找矿元素,找构造蚀变岩型金矿、火山岩型金矿、岩浆热液型银—多金属矿前景好,主要的找矿标志是断裂和蚀变。     

Benthic nutrient fluxes in the intertidal flat within the Changjiang （Yangtze River） Estuary

In an annual cycle from March 2005 to February 2006, benthic nutrient fluxes were measured monthly in the Dongtan intertidal flat within the Changjiang （Yangtze River） Estuary. Except for NH4^＋, there always showed high fluxes from overlying water into sediment for other four nutrients. Sediments in the high and middle marshes, covered with halophyte and consisting of macrofauna, demonstrated more capabilities of assimilating nutrients from overlying water than the low marsh. Sampling seasons and nutrient concentrations in the overlying water could both exert significant effects on these fluxes. Additionally, according to the model provided by previous study, denitrification rates, that utilizing NO3- transported from overlying water （Dw） in Dongtan sediments, were estimated to be from -16 to 193 μmol·h^-1·m^-2 with an average value of 63 μmol·h^-1·m^-2 （n=18）. These estimated values are still underestimates of the in-situ rates owing to the lack of consideration of DN, i.e., denitrification supported by the local NO3^- production via nitrification.     

Environmental Investigation and Evaluation of Land Subsidence in the Datong Coalfield Based on InSAR Technology

Heavy mining of Jurassic and Carboniferous horizontal coal seams in the Datong coalfield has seriously affected the local geological environment, which is mainly manifested by such geohazards as soil avalanches, landslides, mudflows, surface subsidence, surface cracks, surface solid waste accumulation and surface deformation. More seriously, coal mining causes groundwater to leak. Overpumping of groundwater has resulted in substantial land subsidence of the urban area in Datong City. Based on the previous geo-environmental investigation in the work area, the authors used radar remote sensing techniques such as InSAR （synthetic aperture radar interferometry） and D-InSAR （differential synthetic aperture radar interferometry）, supplemented by the optical remote sensing method, for geo-environmental investigation to ascertain the geo-environmental background of the Datong Jurassic and Carboniferous-Permian coalfield and evaluate the effects of the geohazards, thus providing a basis for the geo-environmental protection, geohazard control and prevention, land improvement and optimization of the human environment. In this study 8 cog-nominal ERS-1/2 SAR data frames during 1992 to 2003 were used, but the following processing was made： （1） the multitemporal SAR magnitude images were used to interpret the geological structure, vegetation, microgeomorphology and drainage system; （2） the multi-temporal InSAR coherent images were used to make a classification of surface features and evaluate the coherence change due to coal mining; and （3） the multi-temporal cog-nominal SAR images were used to complete D-InSAR processing to remove the information of differential deformation areas （sites）. In the end, a ten-year time series of differential interferograms were obtained using the multi-temporal cog-nominal SAR images. In the tests, 84 deformed areas （sites） were obtained, belonging to those in 1993-1996, 1996-1997, 1997-1998, 1998-2001, 1998-2002 and 2001-2003 respectively. Of the 84 areas, 44 are m     

GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals

This paper presents a general modeling strategy for ambiguity resolution (AR) and position estimation (PE) using three or more phase-based ranging signals from a global navigation satellite system (GNSS). The proposed strategy will identify three best “virtual” signals to allow for more reliable AR under certain observational conditions characterized by ionospheric and tropospheric delay variability, level of phase noise and orbit accuracy. The selected virtual signals suffer from minimal or relatively low ionospheric effects, and thus are known as ionosphere-reduced virtual signals. As a result, the ionospheric parameters in the geometry-based observational models can be eliminated for long baselines, typically those of length tens to hundreds of kilometres. The proposed modeling comprises three major steps. Step 1 is the geometry-free determination of the extra-widelane (EWL) formed between the two closest L-band carrier measurements, directly from the two corresponding code measurements. Step 2 forms the second EWL signal and resolves the integer ambiguity with a geometry-based estimator alone or together with the first EWL. This is followed by a procedure to correct for the first-order ionospheric delay using the two ambiguity-fixed widelane (WL) signals derived from the integer-fixed EWL signals. Step 3 finds an independent narrow-lane (NL) signal, which is used together with a refined WL to resolve NL ambiguity with geometry-based integer estimation and search algorithms. As a result, the above two AR processes performed with WL/NL and EWL/WL signals respectively, either in sequence or in parallel, can support real time kinematic (RTK) positioning over baselines of tens to hundreds of kilometres, thus enabling centimetre-to-decimentre positioning at the local, regional and even global scales in the future.     

Ocean tide loading (OTL) displacements from global and local grids: comparisons to GPS estimates over the shelf of Brittany, France

In this paper we examine OTL displacements detected by GPS stations of a dedicated campaign and validate ocean tide models. Our area of study is the continental shelf of Brittany and Cotentin in France. Brittany is one of the few places in the world where tides provoke loading displacements of ∼10–12 cm vertically and a few cm horizontally. Ocean tide models suffer from important discrepancies in this region. Seven global and regional ocean tide models were tested: FES2004 corrected for K2, TPXO.7.0, TPXO.6.2, GOT00.2, CSR4.0, NAO.99b and the most recent regional grids of the North East Atlantic (NEA2004). These gridded amplitudes and phases of ocean tides were convolved in order to get the predicted OTL displacements using two different algorithms. Data over a period of 3.5 months of 8 GPS campaign stations located on the north coast of Brittany are used, in order to evaluate the geographical distribution of the OTL effect. We have modified and implemented new algorithms in our GPS software, GINS 7.1. GPS OTL constituents are estimated based on 1-day batch solutions. We compare the observed GPS OTL constituents of M₂, S₂, N₂ and K₁ waves with the selected ocean tide models on global and regional grids. Large phase-lag and amplitude discrepancies over 20° and 1.5 cm in the vertical direction in the semi-diurnal band of M₂ between predictions and GPS/models are detected in the Bay of Mont St-Michel. From a least squares spectral analysis of the GPS time-series, significant harmonic peaks in the integer multiples of the orbital periods of the GPS satellites are observed, indicating the existence of multipath effects in the GPS OTL constituents. The GPS OTL observations agree best with FES2004, NEA2004, GOT00.2 and CSR4.0 tide models.     

The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS

A new method for modeling the ionospheric delay using global positioning system (GPS) data is proposed, called the ionospheric eclipse factor method (IEFM). It is based on establishing a concept referred to as the ionospheric eclipse factor (IEF) λ of the ionospheric pierce point (IPP) and the IEF’s influence factor (IFF) . The IEF can be used to make a relatively precise distinction between ionospheric daytime and nighttime, whereas the IFF is advantageous for describing the IEF’s variations with day, month, season and year, associated with seasonal variations of total electron content (TEC) of the ionosphere. By combining λ and with the local time t of IPP, the IEFM has the ability to precisely distinguish between ionospheric daytime and nighttime, as well as efficiently combine them during different seasons or months over a year at the IPP. The IEFM-based ionospheric delay estimates are validated by combining an absolute positioning mode with several ionospheric delay correction models or algorithms, using GPS data at an international Global Navigation Satellite System (GNSS) service (IGS) station (WTZR). Our results indicate that the IEFM may further improve ionospheric delay modeling using GPS data.     

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.