台湾集集和华北大同地震序列的定量模型分析

介绍了传染型余震序列(ETAS)模型的基本原理和发展概况，并利用该模型分析了1999年开始的台湾集集地震序列(主震Ms7．6)和1989年开始的大同地震序列(主震为Ms5．7)。通过分析计算，得到了二个序列的ETAS参数向量，并结合各自的孕震环境和机制分析了二个地震序列的异同和序列衰减规律。     

1791年福建东山外海地震参数复核

本文依据校核后的历史资料,重新评定了1791年福建东山外海地震的影响场。再由影响场数据复核了该次地震的震中位置、震中烈度和震级值。得到的复核结果对修订已有的地震目录和合理确认该地区的发震构造将有所裨益。     

An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering

The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non-linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions).     

基于区间序列的GM(1.1)地下水水位预测

为探讨基于区间序列的GM（1.1）模型预测地下水水位的准确性和信息量,本文选取界点建模、转换建模和参数建模3种区间方法进行比较和分析,结果表明：界点的相对误差均值都小于0.01%,区间相对误差均值小于7%;区间序列的GM（1.1）模型具有一定的延迟效应和紊乱效应;实际水位值波动幅度较小时模型预测精度较高;参数建模的预测精度较优于界点建模和转换建模。与点预测相比,区间序列的GM（1.1）模型预测的地下水水位变化规律更有利于地下水资源管理与开发利用。     

Rock physics model of tight oil siltstone for seismic prediction of brittleness

Tight oil siltstones are rocks with complex structure at pore scale and are characterized by low porosity and low permeability at macroscale. The production of tight oil siltstone reservoirs can be increased by hydraulic fracturing. For optimal fracking results, it is desirable to map the ability to fracture based on seismic data prior to fracturing. Brittleness is currently thought to be a key parameter for evaluating the ability to fracture. To link seismic information to the brittleness distribution, a rock physics model is required. Currently, there exists no commonly accepted rock physics model for tight oil siltstones. Based on the observed correlation between porosity and mineral composition and known microstructure of tight oil siltstone in Daqing oilfield of Songliao basin, we develop a rock physics model by combining the Voigt–Reuss–Hill average, self-consistent approximation and differential effective medium theory. This rock physics model allows us to explore the dependence of the brittleness on porosity, mineral composition, microcrack volume fraction and microcrack aspect ratio. The results show that, as quartz content increases and feldspar content decreases, Young's modulus tends to increase and Poisson ratio decreases. This is taken as a signature of higher brittleness. Using well log data and seismic inversion results, we demonstrate the versatility of the rock physics template for brittleness prediction.     

The impact of surface‐wave attenuation on 3‐D seismic survey design

Three‐dimensional seismic survey design should provide an acquisition geometry that enables imaging and amplitude‐versus‐offset applications of target reflectors with sufficient data quality under given economical and operational constraints. However, in land or shallow‐water environments, surface waves are often dominant in the seismic data. The effectiveness of surface‐wave separation or attenuation significantly affects the quality of the final result. Therefore, the need for surface‐wave attenuation imposes additional constraints on the acquisition geometry. Recently, we have proposed a method for surface‐wave attenuation that can better deal with aliased seismic data than classic methods such as slowness/velocity‐based filtering. Here, we investigate how surface‐wave attenuation affects the selection of survey parameters and the resulting data quality. To quantify the latter, we introduce a measure that represents the estimated signal‐to‐noise ratio between the desired subsurface signal and the surface waves that are deemed to be noise. In a case study, we applied surface‐wave attenuation and signal‐to‐noise ratio estimation to several data sets with different survey parameters. The spatial sampling intervals of the basic subset are the survey parameters that affect the performance of surface‐wave attenuation methods the most. Finer spatial sampling will reduce aliasing and make surface‐wave attenuation easier, resulting in better data quality until no further improvement is obtained. We observed this behaviour as a main trend that levels off at increasingly denser sampling. With our method, this trend curve lies at a considerably higher signal‐to‐noise ratio than with a classic filtering method. This means that we can obtain a much better data quality for given survey effort or the same data quality as with a conventional method at a lower cost.     

闽,赣地区小震群性质探讨

本文运用地震频度衰减系数h值,震群能量释放均匀度u值、震群归一化熵值K等地震活动参数的统计分析,研究了长汀震群以及闽赣地区其它小震群的殊征,认为长汀震群是一次无前兆性质的正常地震活动,而龙南震群是一次前兆性震群。     

Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model

A recent study by Van der Schalie et al. (2015) showed good results for applying the Land Parameter Retrieval Model (LPRM) on SMOS observations over southeast Australia and optimizing and evaluating the retrieved soil moisture (θ in m³ m⁻³) against ground measurements from the OzNet sites. In this study, the LPRM parameterization is globally updated for SMOS against modelled θ from MERRA-Land (MERRA) and ERA-Interim/Land (ERA) over the period of July 2010–December 2010, mainly focusing on two parameters: the single scattering albedo (ω) and the roughness (h). The Pearson's coefficient of correlation (r) increased rapidly when increasing the ω up to 0.12 and reached a steady state from thereon, no significant spatial pattern was found in the estimation of the single scattering albedo, which could be an artifact of the used parameter estimation procedure, and a single value of 0.12 was therefore used globally. The h was defined as a function of θ and varied slightly for the different angle bins, with maximum values of 1.1–1.3 as the angle changes from 42.5° to 57.5°.This resulted in an average r of 0.51 and 0.47, with a bias (m³ m⁻³) of −0.02 and −0.01 and an unbiased root mean square error (ubrmse in m³ m⁻³) of 0.054 and 0.056 against MERRA (ascending and descending). For ERA this resulted in an r of 0.61 and 0.53, with a bias of −0.03 and an ubrmse 0.055 and 0.059. The resulting parameterization was then used to run LPRM on SMOS observations over the period of July 2010–December 2013 and evaluated against SMOS Level 3 (L3) θ and available in situ measurements from the International Soil Moisture Network (ISMN). The comparison with L3 shows that the LPRM θ retrievals are very similar, with for the ascending set very high r of over 0.9 in large parts of the globe, with an overall average of 0.85 and the descending set performing less with an average of 0.74, mainly due to the negative r over the Sahara. The mean bias is 0.03, with an ubrmse of 0.038 and 0.044. In this study there are three major areas where the LPRM retrievals do not perform well: very dry sandy areas, densely forested areas and over high latitudes, which are all known limitations of LPRM. The comparison against in situ measurement from the ISMN give very similar results, with average r for LPRM of 0.65 and 0.61 (0.64 and 0.59 for L3) for the ascending and descending sets, while having a comparable bias and ubrmse over the different networks. This shows that LPRM used on SMOS observations produce θ retrievals with a similar quality as the SMOS L3 product.     

A New Data Processing Strategy for Huge GNSS Global Networks

In Global Positioning System (GPS) data analyses, large networks are usually divided into sub-networks to solve the conflict between increasing amounts of data and limited computer resources, although an integrated analysis would provide better results. This conflict becomes even more critical with the increasing number of stations, and low-Earth-orbiting satellites and the Galileo system coming into operation. The major reason is that a huge number of ambiguity parameters are kept in the normal equation for sequential integer ambiguity fixing. In this paper, the problem is solved by a special procedure of parameter elimination for both real-valued and ambiguity-fixed solutions, based on an adapted ambiguity-fixing approach where the covariance-matrix of ambiguity parameters is not required anymore. It is demonstrated that, with the new strategy, the required memory can be reduced to one-tenth and the computation time to at least one-third compared to the existing methods, and huge GPS networks with several hundred stations can be processed efficiently on a personal computer.     

主成分估计与附加条件的参数平差

证明了主成分估计实质上是附着条件的参数平差，并根据此性质结合算例分析了主成分估计的局限性，提出了改进方法。