1962—2017年辽宁省设施农业生长季寡照灾害时空特征及影响因素

利用辽宁省28个气象站日照时数观测数据,采用Fortran语言自动识别技术和统计诊断分析方法,以设施黄瓜和茄子为例,分析了1962-2017年辽宁省设施农业生长季寡照灾害发生频数的时空变化特征。结果表明：气候态下,辽宁省设施黄瓜和茄子轻度寡照灾害发生频数占总发生频数的百分比高达96.1%,中度寡照灾害发生频数仅占总发生频数的3.9%,且近56 a没有发生重度寡照灾害。轻度寡照灾害发生频数呈经向型分布,辽宁西部地区最少,辽宁北部和辽宁东部地区最多;11月发生频数最多,2月和3月最少。轻度寡照灾害发生频数空间上阜新东部和朝阳东部增加趋势最大,时间上气候倾向率为0.68次·（10 a）^-1,并且在1999年前后发生了显著的年代际突变。轻度寡照灾害发生频数与同期风速呈显著负相关,与相对湿度和降水日数呈显著正相关。风速和降水日数对辽宁大部地区寡照灾害发生频数的线性增加影响显著,相对湿度则对辽宁局部地区的线性增加影响显著。1962年以来,辽宁省设施黄瓜和茄子最易发生轻度寡照灾害,且发生频数在时间和空间上均呈显著增加趋势。     

基于Contourlet域自适应Wiener阈值的同时震源波场分离

同时震源数据包含了多炮之间的串扰噪声,不能直接用于常规数据处理流程.因此,需要对混叠的波场进行分离得到常规采集的单炮记录.本文基于稀疏迭代反演分离,提出了一种具有尺度与空间自适应的Wiener阈值选取方法.该阈值选取方法能够根据不同迭代环境计算不同尺度下串扰噪声的方差和不同空间位置有效信号的方差,从而自适应调整阈值大小,最终通过对变换域系数进行收缩来达到去除串扰噪声的目的.理论模型数据和实际数据测试结果表明,本文方法能够快速有效地压制串扰噪声和保护弱有效信号,取得了比Contourlet域子带一致Wiener阈值方法和Curvelet域指数衰减阈值方法更好的分离效果.     

Compactly supported radial covariance functions

The Least-squares collocation (LSC) method is commonly used in geodesy, but generally associated with globally supported covariance functions, i.e. with dense covariance matrices. We consider locally supported radial covariance functions, which yield sparse covariance matrices. Having many zero entries in the covariance matrice can both greatly reduce computer storage requirements and the number of floating point operations needed in computation. This paper reviews some of the most well-known compactly supported radial covariance functions (CSRCFs) that can be easily substituted to the usually used covariance functions. Numerical experiments reveals that these finite covariance functions can give good approximations of the Gaussian, second- and third-order Markov models. Then, interpolation of KMS02 free-air gravity anomalies in Azores Islands shows that dense covariance matrices associated with Gaussian model can be replaced by sparse matrices from CSRCFs resulting in memory savings of one-fortieth and with 90% of the solution error less than 0.5 mGal. This article is dedicated to Cerbère.     

利用CHRIS/PROBA数据定量反演草地LAI方法研究

以内蒙古锡林河流域典型草地为研究样区,基于新一代微卫星CHRIS/PROBA高光谱遥感数据,利用双层冠层反射率模型(A two - layer Canopy Reflectance Model,ACRM)定量反演叶面积指数(LAI).首先对高光谱数据进行预处理和统计分析,并结合反演结果对角度信息的敏感性进行分析,确定适...     

Using surface multiples to estimate primaries by sparse inversion from blended data

For data acquired with conventional acquisition techniques, surface multiples are usually considered as noise events that obscure the primaries. However, in this paper we demonstrate that for the situation of blended acquisition, meaning that different sources are shooting in a time‐overlapping fashion, multiples can be used to ‘deblend’ the seismic measurements. We utilize the recently introduced estimation of primaries by sparse inversion (EPSI) methodology, in which the primary impulse responses are considered to be the unknowns in a large‐scale inversion process. With some modifications the estimation of primaries by sparse inversion method can be used for blended seismic data. As output this process gives unblended primary impulse responses with point sources and receivers at the surface, which can be used directly in traditional imaging schemes. It turns out that extra information is needed to improve on the deblending of events that do not have much associated multiple energy in the data, such as steep events at large offsets. We demonstrate that this information can be brought in during acquisition and during processing. The methodology is illustrated on 2D synthetic data.     

利用IRS-P5影像制作1∶50000 3D产品的试验研究

讨论了利用IRS-P5卫星遥感影像立体模型进行1∶50000 DEM,DOM,DLG等地理信息产品生产的相关问题,并进行了精度检测;同时与传统的航空摄影测量方法在原始资料获取、生产效率、工作量、数据成本等方面进行了对比分析。     

地震数据的稀疏高斯束分解方法

本文研究了地震数据的稀疏分解问题.提出了一种用高斯束稀疏分解表示地震数据的方法.这是一个拟0范数约束优化问题.在求解拟0范数极小化问题的过程中,通过扫描同相轴的方法实现高斯束稀疏分解,数值实现上提出了使用一种快速单调下降的梯度优化方法.本文提出的稀疏优化方法同时具有去噪的功能,数据模拟试验表明了本方法的可行性和可靠性.     

地震数据压缩重构的正则化与零范数稀疏最优化方法

地震数据重构问题是一个病态的反演问题. 本文基于地震数据在curvelet域的稀疏性, 将地震数据重构变为一个稀疏优化问题, 构造0范数的逼近函数作为目标函数, 提出了一种投影梯度求解算法. 本文还运用最近提出的分段随机采样方式进行采样, 该采样方式能够有效地控制采样间隔并且保持采样的随机性. 地震数值模拟表明, 基于0范数逼近的投影梯度法计算效率有明显的提高; 分段随机采样方式比随机欠采样有更加稳定的重构结果.     

Dimensionality‐reduced estimation of primaries by sparse inversion

Wave‐equation based methods, such as the estimation of primaries by sparse inversion, have been successful in the mitigation of the adverse effects of surface‐related multiples on seismic imaging and migration‐velocity analysis. However, the reliance of these methods on multidimensional convolutions with fully sampled data exposes the ‘curse of dimensionality’, which leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensionality‐reduction technique where the ‘data matrix’ is approximated adaptively by a randomized low‐rank factorization. Compared to conventional methods, which need for each iteration passage through all data possibly requiring on‐the‐fly interpolation, our randomized approach has the advantage that the total number of passes is reduced to only one to three. In addition, the low‐rank matrix factorization leads to considerable reductions in storage and computational costs of the matrix multiplies required by the sparse inversion. Application of the proposed method to two‐dimensional synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational up‐front cost required by the low‐rank factorization.     

A model to predict ordinal suitability using sparse and uncertain data

We describe the development of the algorithms that comprise the Spatial Decision Support System (SDSS) CaNaSTA (Crop Niche Selection in Tropical Agriculture). The system was designed to assist farmers and agricultural advisors in the tropics to make crop suitability decisions. These decisions are frequently made in highly diverse biophysical and socioeconomic environments and must often rely on sparse datasets.The field trial datasets that provide a knowledge base for SDSS such as this are characterised by ordinal response variables. Our approach has been to apply Bayes’ formula as a prediction model.This paper does not describe the entire CaNaSTA system, but rather concentrates on the algorithm of the central prediction model. The algorithm is tested using a simulated dataset to compare results with ordinal regression, and to test the stability of the model with increasingly sparse calibration data. For all but the richest input datasets it outperforms ordinal regression, as determined using Cohen’s weighted kappa. The model also performs well with sparse datasets. Whilst this is not as conclusive as testing with real world data, the results are encouraging.