基于贝叶斯正则化BP神经网络的DEM趋势面逼近

趋势面从宏观上揭示了研究对象的特性,在各领域发挥着重要作用。BP神经网络可以对复杂系统进行无限逼近,进而进行预测。建立了基于贝叶斯正则化BP神经网络的数字高程模型趋势面,与二次多项式建立的数字高程模型趋势面进行比较分析,证明了该方法的可行性和有效性。     

关于最小二乘问题近似解误差估计的进一步分析

Brezinski等人给出了最小二乘问题近似解的一族误差估计。该误差估计含有1个实参数。本文的贡献有3个方面:其一是给出了1种参数选择策略;其二是对该族误差估计的性质做出进一步分析;其三是分析了应用于Tikhonov正则化时应注意的一些问题。     

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基于谱反演方法的叠后纵波阻抗反演

提出一种基于谱反演方法的叠后地震数据纵波阻抗反演算法,用于提高地震反演精度。谱反演在地震高分辨率和反射系数反演中应用广泛,其基于反射系数的奇偶分解,能降低薄层之间的调谐效应,使反演数据体的分辨率得以提高,而由反射系数计算纵波阻抗的过程不适定,分步进行纵波阻抗反演会引入较大的累积误差。本研究提出基于谱反演方法的叠后纵波阻抗反演算法,引入TV正则化约束目标方程,通过迭代求解,可直接得到相对阻抗,然后同预先建立的低频模型进行频率域融合得到绝对阻抗。模型和实际数据说明,相比基于稀疏脉冲反褶积的阻抗反演,本文提出的方法反演分辨率较高,更有利于后续储层预测等研究的开展。     

Adaptive Multiscale Permeability Estimation

With multiscale permeability estimation one does not select parameterization prior to the estimation. Instead, one performs a hierarchical search for the right parameterization while solving a sequence of estimation problems with an increasing parameterization dimension. In some previous works on the subject, the same refinement is applied all over the porous medium. This may lead to over-parameterization, and subsequently, to unrealistic permeability estimates and excessive computational work. With adaptive multiscale permeability estimation, the new parameterization at an arbitrary stage in the estimation sequence is such that new degrees of freedom are not necessarily introduced all over the porous medium. The aim is to introduce new degrees of freedom only where it is warranted by the data. In this paper, we introduce a novel adaptive multiscale estimation. The approach is used to estimate absolute permeability from two-phase pressure data in several numerical examples.     

非饱和土中镉离子传输模型参数反演

使用自行设计的的真空实验装置,采用一维实验室土柱实验方法在不同实验条件下进行了15组实验,并基于实验所得的穿透曲线,使用梯度正则化方法反演得到了镉离子在8种葡萄牙土样和1种比利时土样中相应的传输模型参数。同时通过数值模拟实验验证了梯度正则化方法的有效性和可靠性,进而证明所得结果是有效的。     

地基沉降预测模型的正则化算法

通过分析地基沉降预测模型,指出最小二乘的病态性会导致模型参数求解失败。应用正则化理论,基于矩阵求逆理论,提出了一种沉降预测模型参数的正则化无偏估计算法,说明了新算法的无偏性和方差最小性。在一定条件下,证明了新算法中正则参数的存在性,并给出了正则参数的计算公式。结合文献和工程实例进行的分析表明,新算法降低了矩阵条件数,减轻矩阵病态程度,能有效求得地基沉降预测模型参数。     

Solution of an unstable axisymmetric Cauchy–Poisson problem of dispersive water waves for a spectrum with compact support

 It has been known that the axisymmetric Cauchy–Poisson problem for dispersive water waves is well posed in the sense of stability. Thereby time evolution solutions of wave propagation depend continuously on initial conditions. However, in this paper, it is demonstrated that the axisymmetric Cauchy–Poisson problem is ill posed in the sense of stability for a certain class of initial conditions, so that the propagating solutions do not depend continuously on the initial conditions. In order to overcome the difficulty of the discontinuity, Landweber–Fridman's regularization, famous and well known in applied mathematics, are introduced and investigated to learn whether it is applicable to the present axisymmetric wave propagation problem. From the numerical experiments, it is shown that stable and accurate solutions are realized by the regularization, so that it can be applicable to the determination of the ill-posed Cauchy–Poisson problem.     

添加正则化项的黏声逆时偏移成像方法研究

在实际地球介质中传播的地震波会产生衰减和频散现象,因此其更接近于黏弹性介质,在地震处理中补偿这些黏性影响是十分必要的。基于波动方程的叠前深度偏移中进行吸收衰减补偿更准确,也更有物理意义,直接求解双程波动方程的逆时偏移(RTM)能够成像大倾角复杂构造,具有诸多优势。然而当考虑吸收衰减补偿时通常会产生不稳定现象,大部分研究都是在逆时偏移的波场延拓中进行波数域的低通滤波来解决这个问题。本文采用广义标准线性固体的黏声波动方程进行吸收衰减补偿的Q--RTM方法,通过添加正则化项的方式来稳定延拓过程。添加正则化项本质上是低通滤波,滤波窗口是指数形式,在时空域有明确的形式,可以阻止发生高频不稳定。与直接滤波相比,正则化参数可以是空变的,因此比较适合剧烈变化的区域,我们还发现震源归一化互相关成像条件更适合Q--RTM方法。     

Determination of gravity anomaly at sea level from inversion of satellite gravity gradiometric data

Gravity gradients can be used to determine the local gravity field of the Earth. This paper investigates downward continuation of all elements of the disturbing gravitational tensor at satellite level using the second-order partial derivatives of the extended Stokes formula in the local-north oriented frame to determine the gravity anomaly at sea level. It considers the inversion of each gradient separately as well as their joint inversion. Numerical studies show that the gradients T_zz, T_xx, T_yy and T_xz have similar capability of being continued downward to sea level in the presence of white noise, while the gradient T_yz is considerably worse than the others. The bias-corrected joint inversion process shows the possibility of recovering the gravity anomaly with 1 mGal accuracy. Variance component estimation is also tested to update the observation weights in the joint inversion.