Interpretation of transient electromagnetic soundings over three-dimensional structures for the central-loop configuration

Summary. An assessment is made of the bias of fitting constrained layered-earth models to transient electromagnetic data obtained over 3-D structures. In this assessment we use the central-loop configuration and show that accurate estimates of the depth of burial of 3-D structures can be obtained with layered-earth model fitting. However, layered-earth interpretations are not reliable for estimating depth extents and resistivities of 3-D structures. When layered earths are used for interpretation, it is advantageous in some cases to use data based on the magnetic field instead of the voltage. A magnetic-field definition of apparent resistivity, in contrast to a definition based on the voltage, eliminates apparent-resistivity overshoots and undershoots in the data. A resistivity undershoot in the data can produce an extraneous and misleading layer in an interpretation of a 3-D resistive structure. Due to 3-D effects, apparent-resistivity soundings (magnetic field and voltage) may rise so steeply at late times that it may not be possible to fit a sounding to a reasonable layered-earth model. Truncating such a sounding, over a buried conductor, allows for a reasonable layered-earth fit and an accurate estimate of the depth to the conductor. However, the resistivity of the conductor is overestimated.
Measurements of the horizontal field in the central-loop configuration can map 3-D structures, provided the sensor is located accurately at the centre of the transmitting loop. Horizontal-field calculations show that the transients peak on the flanks of a 3-D structure, but are depressed over the structure's centre. Weak transient responses flanked by two large transient responses, which are opposite in sign, locate the structure. The sign reversal is caused by a corresponding reversal in the currents that are channelled through or deflected away from conductive or resistive structures, respectively.     

High Precision Geoid Models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen, China

High precision geoid models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen, China, have been developed with a hybrid approach of so-called sequential processing, using high precision GPS/leveling data, land and sea gravity anomalies, and digital terrain models. These two local geoid models have the same 1-km resolution. The estimated accuracy (external accuracy) is better than 1.7 cm for HKGEOID-2000 and 1.4 cm for SZGEOID-2000. Some common areas are covered by HKGEOID-2000 and SZGEOID-2000. So these two geoid models, along with high quality GPS/leveling data collected on the overlapping areas, can be used to detect the systematic bias between HKGEOID-2000 and SZGEOID-2000, as well as the difference between Hong Kong Principal Datum and 1956 yellow sea height datum of China, yielding RMS errors of 1.011 m and 1,003 m, respectively. Moreover, HKGEOID-2000, along with GPS ellipsoidal heights, is employed to determine the errors of the “orthometric heights” from purely trigonometric heighting, yielding an RMS error of 0.102 m. The combination of SZGEOID-2000 and GPS ellipsoidal heights has been used to replace the traditional spirit leveling and mapping, called GPS mapping.     

Nonparametric Estimates of the Sea State Bias for the Jason-1 Radar Altimeter

The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved.     

GPS单频精密单点定位方法与实践

讨论了单频精密单点定位的解算方法与思路,编写了单频精密动态单点定位的程序,实现了事后动态单频精密单点定位.     

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.     

Effects of detailed soil spatial information on watershed modeling across different model scales

Hydro-ecological modelers often use spatial variation of soil information derived from conventional soil surveys in simulation of hydro-ecological processes over watersheds at mesoscale (10–100 km²). Conventional soil surveys are not designed to provide the same level of spatial detail as terrain and vegetation inputs derived from digital terrain analysis and remote sensing techniques. Soil property layers derived from conventional soil surveys are often incompatible with detailed terrain and remotely sensed data due to their difference in scales. The objective of this research is to examine the effect of scale incompatibility between soil information and the detailed digital terrain data and remotely sensed information by comparing simulations of watershed processes based on the conventional soil map and those simulations based on detailed soil information across different simulation scales. The detailed soil spatial information was derived using a GIS (geographical information system), expert knowledge, and fuzzy logic based predictive mapping approach (Soil Land Inference Model, SoLIM). The Regional Hydro-Ecological Simulation System (RHESSys) is used to simulate two watershed processes: net photosynthesis and stream flow. The difference between simulation based on the conventional soil map and that based on the detailed predictive soil map at a given simulation scale is perceived to be the effect of scale incompatibility between conventional soil data and the rest of the (more detailed) data layers at that scale. Two modeling approaches were taken in this study: the lumped parameter approach and the distributed parameter approach. The results over two small watersheds indicate that the effect does not necessarily always increase or decrease as the simulation scale becomes finer or coarser. For a given watershed there seems to be a fixed scale at which the effect is consistently low for the simulated processes with both the lumped parameter approach and the distributed parameter approach.     

高分辨率厘米级局部大地水准面的典型应用

介绍了香港大地水准面HKGEOID_2 0 0 0和深圳市高分辨率、高精度似大地水准面SZGEOID_2 0 0 0。利用HKGEOID_2 0 0 0和GPS椭球高求得的正常高与香港地区由三角高程测量得到的“正常高 (或本地高 )”进行比较 ,结果表明 ,其差值的均方根为 0 .1 0 2m ,标准差 (STD)为± 3 .4cm。结合HKGEOID_2 0 0 0、SZGEOID_2 0 0 0和这两个大地水准面模型重复覆盖地区的高精度GPS水准数据 ,探测这两个大地水准面模型之间的差异和香港主要高程基准面 (HKPD)与我国 1 95 6黄海高程基准面之间的系统偏差。     

Global scale annual and semi-annual variations of daytime NmF2 in the high solar activity years

The annual and semi-annual variations of the ionosphere are investigated in the present paper by using the daytime F2 layer peak electron concentration (NmF2) observed at a global ionosonde network with 104 stations. The main features are outlined as follows. (1) The annual variations are most pronounced at magnetic latitudes of 40–60° in both hemispheres, and usually manifest as winter anomalies; Below magnetic latitude of 40° as well as in the tropical region they are much weaker and winter anomalies that are not obvious. (2) The semi-annual variations, which are usually peak in March or April in most regions, are generally weak in the near-pole regions and strong in the far-pole regions of both hemispheres. (3) Compared with their annual components, the semi-annual variations in the tropical region are more significant.In order to explain the above results, we particularly analyze the global atomic/molecular ratio of [O/N2] at the F2 layer peak height by the MSIS90 model. The results show that the annual variation of [O/N2] is closely related with that of NmF2 prevailing in mid-latitudes and [O/N2] annual variation usually may lead to the winter anomalies of NmF2 occurring in the near-pole region. Moreover, NmF2 semi-annual variations appearing in the tropical region also have a close relationship with the variation of [O/N2]. On the other hand, the semi-annual variations of NmF2 in the far-pole region cannot be simply explained by that of [O/N2], but the variation of the solar zenith angle may also have a significant contribution.     

Fitting the Linear Model of Coregionalization by Generalized Least Squares

In geostatistical studies, the fitting of the linear model of coregionalization (LMC) to direct and cross experimental semivariograms is usually performed with a weighted least-squares (WLS) procedure based on the number of pairs of observations at each lag. So far, no study has investigated the efficiency of other least-squares procedures, such as ordinary least squares (OLS), generalized least squares (GLS), and WLS with other weighing functions, in the context of the LMC. In this article, we compare the statistical properties of the sill estimators obtained with eight least-squares procedures for fitting the LMC: OLS, four WLS, and three GLS. The WLS procedures are based on approximations of the variance of semivariogram estimates at each distance lag. The GLS procedures use a variance–covariance matrix of semivariogram estimates that is (i) estimated using the fourth-order moments with sill estimates (GLS₁), (ii) calculated using the fourth-order moments with the theoretical sills (GLS₂), and (iii) based on an approximation using the correlation between semivariogram estimates in the case of spatial independence of the observations (GLS₃). The current algorithm for fitting the LMC by WLS while ensuring the positive semidefiniteness of sill matrix estimates is modified to include any least-squares procedure. A Monte Carlo study is performed for 16 scenarios corresponding to different combinations of the number of variables, number of spatial structures, values of ranges, and scale dependence of the correlations among variables. Simulation results show that the mean square error is accounted for mostly by the variance of the sill estimators instead of their squared bias. Overall, the estimated GLS₁ and theoretical GLS₂ are the most efficient, followed by the WLS procedure that is based on the number of pairs of observations and the average distance at each lag. On that basis, GLS₁ can be recommended for future studies using the LMC.