基于多角度热红外遥感的混合像元组分温度演化反演方法

研究了多年来热红外多通道遥感反演陆面温度的成果后指出, 由于通道间信息高度相关, 以及不能直接反演混合像元组分温度, 所以它的反演精度及应用价值都受到极大的限制. 在建立非同温混合像元热辐射方向性模型基础上, 指出热红外多角度遥感提供了直接反演组分温度的可能性, 但这是一个多参数的同步反演问题. 通过数值模拟和实验验证表明, 演化算法是一个行之有效的多参数同步反演方法. 热红外多角度遥感与演化反演算法结合有望实现组分温度反演精度达到1 K以内的目的.

演化参数反演方法

给出了一类基于演化计算的演化参数反演方法 ,此类方法既可以给定参数的函数类 ,用遗传算法 (Ge neticAlgorithms)来反演参数的最优估计值 ,也可以不指定函数类形式 ,用遗传程序设计 (GeneticProgramming)的方法反演出最优的函数模型 ,使参数反演实现客观化、自动化 .由此建立反演系统后 ,在使用过程中可以根据最新获得的数据对模型中的物理参数作适时校准 ,一旦发现预报误差较大 ,就利用演化算法及时修正方程中的参数以改进预报 .运用该方法于椭圆边值问题的物理参数反演的数值模拟 ,证实了此方法的有效性 ,为物理模型参数的反演提供了一种崭新的实用方法 .

An Optimal Algorithm for the Retrieval of Chlorophyll,Suspended Sediments and Gelbstoff of Case Ⅱ Waters in the Pearl River Estuary

An optimal algorithm for the retrieval of chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary was established with the optical parameters derived from the in-situ data obtained in Jan. 2003 in the same area. And then, the chlorophyll, suspended sediments and gelbstoff of the SeaWiFS pixels on Jan. 29, 2003 corresponding to the in-situ sites of Jan. 25 and 26, 2003 were synchronously retrieved, with average relative errors of 14.9%, 12.1% and 13.6% for chlorophyll, suspended sediments and gelbstoff, respectively. The research results indicated that the optimal retrieval algorithm established here was relatively fit for the retrieval of the chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary, and had quite good retrieval accuracy.

Extrapolated Tikhonov method and inversion of 3D density images of gravity data

Tikhonov regularization（TR） method has played a very important role in the gravity data and magnetic data process. In this paper, the Tikhonov regularization method with respect to the inversion of gravity data is discussed. and the extrapolated TR method（EXTR） is introduced to improve the fitting error. Furthermore, the effect of the parameters in the EXTR method on the fitting error, number of iterations, and inversion results are discussed in details. The computation results using a synthetic model with the same and different densities indicated that. compared with the TR method, the EXTR method not only achieves the a priori fitting error level set by the interpreter but also increases the fitting precision, although it increases the computation time and number of iterations. And the EXTR inversion results are more compact than the TR inversion results, which are more divergent. The range of the inversion data is closer to the default range of the model parameters, and the model features and default model density distribution agree well.

Parameter optimization of gravity density inversion based on correlation searching and the golden section algorithm

For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.

Reflectivity forward modeling and a CSSI method seismic inversion study of igneous intrusive area,coked area,and gas-enriched area located within a coal seam

We applied the reflectivity method and the constrained sparse spike inverse modeling(CSSI) method to the interpretation of coal field lithologic seismic data.After introducing the principles of these two methods we discuss some parameters of a geological model involving possible gas enriched areas or intruded igneous rock.The geological model was constructed and a 60 Hz seismic response profile was obtained looking for igneous rock intrusion and coked areas of the coal seam using the reflectivity method.Starting from synthesized logging data from two wells and a synthesized seismic wavelet we calibrated the model to show accurate strata.Finally,we predicted the lithology within a 10 m igneous rock area,a 3 m coal seam area,and a coked area using the CSSI technique.The results show that the CSSI technique can identify hard to recognize lithologic features that normal profil-ing methods might miss.It can quantitatively analyze and evaluate the intrusive area,the coked area,and the gas-enriched area.

Three-dimensional tomography using high-power induced polarization with the similar central gradient array

Induced polarization （IP） 3D tomography with the similar central gradient array combines IP sounding and IP profiling to retrieve 3D resistivity and polarization data rapidly. The method is characterized by high spatial resolution and large probing depth. We discuss data acquisition and 3D IP imaging procedures using the central gradient array with variable electrode distances. A 3D geoelectric model was constructed and then numerically modeled. The data modeling results suggest that this method can capture the features of real geoelectric models. The method was applied to a polymetallic mine in Gansu Province. The results suggest that IP 3D tomography captures the distribution of resistivity and polarization of subsurface media, delineating the extension of abrupt interfaces, and identifies mineralization.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Statistical physics in QCD evolution towards high energies

The concepts and methods used for the study of disordered systems have proven useful in the analysis of the evolution equations of quantum chromodynamics in the high-energy regime: Indeed, parton branching in the semi-classical approximation relevant at high energies and at a fixed impact parameter is a peculiar branching-diffusion process, and parton branching supplemented by saturation effects(such as gluon recombination) is a reaction-diffusion process. In this review article, we first introduce the basic concepts in the context of simple toy models, we study the properties of the latter, and show how the results obtained for the simple models may be taken over to quantum chromodynamics.