近、现代黄河三角洲地貌形态反演

1855年黄河改夺大清河河道后于山东利津县东北入海。至今黄河尾癌河道历经10次大的改道变迁，塑造了近、现代黄河三角洲，形成了7个主要的亚三洲堆积体。该文利用黄河尾癌河道改道资料，近、现代河口流路变迁图，近、现代黄河三角洲体系图以及1985－1986年的水文工程地质钻孔资料，在研究黄河三角洲沉积模式的基础上，对工程地质钻孔数据进行沉积层序分析，将今论古，反推近、现代黄河三角洲沉积地貌，研究结果近似展示了近、现代黄河三角洲二维平面的发育演变和其时其地的地貌形态。     

运用三维变密度潮汐效应模型确定滨海含水系统的海底等效边界——山东烟台夹河中下游地区为例

确定咸淡水界面的位置是滨海地区海水入侵研究的主要任务之一。对于天然条件承压含水层而言，含水层顶板向海底延伸的距离直接影响了咸淡水界面的位置，它可以通过承压含水层中地下水的潮汐效应信息来确定。考虑到咸淡水之间密度的差异，建立了山东省夹河中下游地区滨海含水系统地下水三维变密度潮汐效应模型。通过反复对比潮汐效应观测中的地下水水头波动与模型计算出的水头波动，确定了滨海承压含水系统的海底边界。同时，也初步估计出海区与近海陆区含水层的水文地质参数。     

Inversion of receiver function by wavelet transformation

Introduction Receiver function has been extensively applied in studying S wave velocity of crust and up-per mantle for about 20 years (Owens, et al, 1987; LIU, et al, 1996), which is a time series ob-tained by the deconvolution of vertical component from horizontal component for teleseismic P waveform. Receiver function represents the teleseismic P plane wave response of crust and upper mantle beneath seismic station, from which the source and propagation effects are removed. Receiver funct…     

Geostatistical inversion of coupled problems: dealing with computational burden and different types of data

Practical application of geostatistical inversion to coupled problems is hampered by a number of difficulties. In this paper, we address two of them: first, the computational cost of sensitivity (Jacobian) matrices and, second, the evaluation of the relative weights of different types of data. Regarding the first, we revise the adjoint state equations to propose a form whose cost is independent of the number of unknown parameters and only grows with the number of observation wells. Regarding the second, we derive expressions for the relative weights of different types of data. These expressions are based on minimizing the expected likelihood, rather than the likelihood itself. The efficiency of both improvements is tested on a synthetic example. The example analyzes a wide range of groundwater flow and solute transport conditions. Yet, the expected likelihood consistently yields the optimal weights. The proposed form of the adjoint state equations leads to one order of magnitude reduction in CPU time with respect to the conventional sensitivity equations.