基于非常快速模拟退火算法的地震谱反演方法

地震勘探中地层薄层特征的描述对提高地震分辨率十分重要。谱反演是建立在谱分解、时频分析基础上的一种反演方法。本文在已知子波的情况下,采用全局搜索的非常快速模拟退火算法(VFSA)对高信噪比的叠后地震资料进行谱反演。有效的识别了调谐厚度以下的薄层,得到近似反射系数的剖面,同时拓展了原始资料的频带宽度,提高了资料的分辨率,为解释资料的局部细微特征起到重要作用。     

Improving the Wishart Synthetic Aperture Radar image classifications through Deterministic Simulated Annealing

This paper proposes the use of Deterministic Simulated Annealing (DSA) for Synthetic Aperture Radar (SAR) image classification for cluster refinement. We use the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution that is then supplied to the DSA optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The improvement is verified by computing a cluster separability coefficient. During the DSA optimization process, for each iteration and for each pixel, two consistency coefficients are computed taking into account two kinds of relations between the pixel under consideration and its neighbors. Based on these coefficients and on the information coming from the pixel itself, it is re-classified. Several experiments are carried out to verify that the proposed approach outperforms the Wishart strategy. We try to improve the classification results by considering the spatial influences received by a pixel through its neighbors. Finally, a link about the contribution of DSA to thematic mapping is also established.     

大地测量反演中的量子退火法初探

本文第一次将量子退火法引入到大地测量反演中,介绍了基本原理,给出了算法流程图,并通过与蒙特卡罗法、模拟退火法的算例比较,表明其收敛速度快等优点,显示了在实际大地测量非线性反演中的应用潜力。     

基于模拟退火算法的随机模拟方法研究

随着油气田开发难度的加大和技术的进步,储层随机建模技术已经成为储层表征的主要手段.基于地质统计学理论的认识,并结合模拟退火算法,本文研究了退火随机模拟储层地质模型,其结果表明该方法是一种易理解、易实现、较灵活、适应性强、较实用的算法.应用该方法可以建立渗透率、孔隙度、含油饱和度,以及波阻抗等参数的空间分布模型,这些模型很好的保持了已知数据所反映的储层空间特征,并真实地展现了各种参数在储层内的分布,为油气田储层预测和开发提供依据.     

Bathymetry Data Correction Using Global Optimization Method

The bathymetry data of marine bodies have been collected over a century, and the collected data have a wide range of resolution and accuracy. Acquisition of bathymetry data is very costly and time-consuming. One can use the old, low-quality bathymetry data to fill the gap in high-quality, recently acquired bathymetry data after correcting the old data to improve its quality so that it is comparable to the high-quality data. The old data correction can be treated as a nonlinear inverse problem. Simulated annealing (SA) global optimization method was used here in solving this problem. The two sets of data that were used are project survey (PS) and Vietnamese Navy Chart (VNC) data. The PS data were collected in 2000 in an offshore survey from the Vietnam coast in the South China Sea (SCS). The VNC data were obtained by digitizing VNC that was published in 1981. Inverse distance weighted (IDW) interpolation method was used for forward modeling. Weperformed the SA algorithm run starting at a high "temperature," then lowering the "temperature" gradually up to the "critical temperature" and then staying there for the rest of the run. The best model chosen by the algorithm showed an improvement of 63% from the original model. We then constructed a digital bathymetry model (DBM) of the study area with the combined corrected VNC and the PS data.