双稀疏字典和FISTA的地震数据去噪          下载免费PDF全文

张良  韩立国  方金伟  张盼  刘争光 《地球物理学报》2019,62(7):2671-2683

地震数据的随机噪声去除是地震数据处理中的一项重要步骤，双稀疏字典提供了两层稀疏模型，比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示，然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm，FISTA）对初始字典系数进行更新，接着采用数据驱动紧标架（data-driven tight frame，DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数，最后通过DDTF字典和更新后的字典系数获得新的contourlet系数，并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比，该方法可以自适应地对地震数据进行稀疏表示，在地震数据较为复杂时得到更高的信噪比；与字典学习去噪方法相比，该方法不仅拥有较快的去噪速度，而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.  相似文献   

Forests and floods: Using field evidence to reconcile analysis methods     

James C. Bathurst  Barry Fahey  Andrés Iroumé  Julia Jones 《水文研究》2020,34(15):3295-3310

The extent to which forests, relative to shorter vegetation, mitigate flood peak discharges remains controversial and relatively poorly researched, with only a few significant field studies. Considering the effect purely of change of vegetation cover, peak flow magnitude comparisons for paired catchments have suggested that forests do not mitigate large floods, whereas flood frequency comparisons have shown that forests mitigate frequencies over all magnitudes of flood. This study investigates the apparent inconsistency using field-based evidence from four contrasting field programmes at scales of 0.34–3.1 km². Repeated patterns are identified that provide strong evidence of real effects with physical explanations. Magnitude and frequency comparisons are both relevant to the impact of forests on peak discharges but address different questions. Both can show a convergence of response between forested and grassland/logged states at the highest recorded flows but the associated return periods may be quite variable and are subject to estimation uncertainty. For low to moderate events, the forested catchments have a lower peak magnitude for a given frequency than the grassland/logged catchments. Depending on antecedent soil saturation, a given storm may nevertheless generate peak discharges of the same magnitude for both catchment states but these peaks will have different return periods. The effect purely of change in vegetation cover may be modified by additional forestry interventions, such as road networks and drainage ditches which, by effectively increasing the drainage density, may increase peak flows for all event magnitudes. For all the sites, forest cover substantially reduces annual runoff.  相似文献   

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献   

基于Remez迭代算法求解差分系数的双相介质自适应有限差分正演模拟          下载免费PDF全文

王文化  文晓涛  张波  张懿疆  王威 《地球物理学报》2020,63(6):2400-2414

利用传统有限差分方法对基于Biot理论的双相介质波动方程进行数值求解时,由于慢纵波的存在,数值频散效应较为明显,影响模拟精度.相对于声学近似方程及普通弹性波方程,Biot双相介质波动方程在同等数值求解算法和精度要求条件下,其地震波场正演模拟需要更多的计算时间.本文针对Biot一阶速度-应力方程组发展了一种变阶数优化有限差分数值模拟方法,旨在同时提高其正演模拟的精度和效率.首先结合交错网格差分格式推导Biot方程的数值频散关系式.然后基于Remez迭代算法求取一阶空间偏导数的优化差分系数,并用于Biot方程的交错网格有限差分数值模拟.在此基础上把三类波的平均频散误差参数限制在给定的频散误差阈值和频率范围内,此时优化有限差分算子的长度就能自适应非均匀双相介质模型中的不同速度区间.数值频散曲线分析表明:基于Remez迭代算法的优化有限差分方法相较传统泰勒级数展开方法在大波数范围对频散误差的压制效果更明显;可变阶数的优化有限差分方法能取得与固定阶数优化有限差分方法相近的模拟精度.在均匀介质和河道模型的数值模拟实验中将本文变阶数优化有限差分算法与传统泰勒展开算法、最小二乘优化算法进行比较,进一步证明其在复杂地下介质中的有效性和适用性.  相似文献   

Changes in hillslope hydrology in a perched,shallow soil system due to clearcutting and residual biomass removal     

Colin P. R. McCarter  Stephen D. Sebestyen  Susan L. Eggert  Randall K. Kolka  Carl P. J. Mitchell 《水文研究》2020,34(26):5354-5369

Sustainable fuels legislation and volatility in energy prices have put additional pressures on the forestry sector to intensify the harvesting of biomass for “advanced biofuel” production. To better understand how residual biomass removal after harvest affects forest hydrology in relatively low slope terrain, a Before-After-Control-Impact (BACI) study was conducted in the USDA Forest Service's Marcell Experimental Forest, Minnesota, USA. Hydrological measurements were made from 2010–2013 on a forested hillslope that was divided into three treatment blocks, where one block was harvested and residual biomass removed (Biomass Removed), the second was harvested and residual biomass left (Biomass Left), and the last block was left as an Unharvested Control. The pre-harvest period (2 years) was 2010–11 and post-harvest (2 years) was 2012–13. Water table elevation at the upslope and downslope position, subsurface runoff, and soil moisture were measured between May–November. Mixed effect statistical models were used to compare both the before-after and “control” treatment ratios (ratios between harvested hillslopes and the Unharvested Control hillslope). Subsurface runoff significantly increased (p < .05) at both harvested hillslopes but to a greater degree on the Biomass Left hillslope. Greater subsurface runoff volumes at both harvested hillslopes were driven by substantial increases during fall, with additional significant increases during summer on the Biomass Left hillslope. The hydrological connectivity, inferred from event runoff ratios, increased due to harvesting at both hillslopes but only significantly on the Biomass Left hillslope. The winter harvest minimized soil disturbance, resulting in no change to the effective hydraulic conductivity distribution with depth. Thus, the observed hydrological changes were driven by increased effective precipitation and decreased evapotranspiration, increasing the duration that both harvested hillslopes were hydrologically active. The harvesting of residual biomass appears to lessen hydrological connectivity relative to leaving residual biomass on the hillslope, potentially decreasing downstream hydrological impacts of similar forestry operations.  相似文献   

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing     

《International Journal of Applied Earth Observation and Geoinformation》2020

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.  相似文献   

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning     

《International Journal of Applied Earth Observation and Geoinformation》2020

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.  相似文献   

结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测     

聂倩  赵艳福 《测绘通报》2020,(6):57-62

不同时相遥感影像变化检测已成为土地利用变更调查、城市扩张分析、自然灾害分析及其他环境问题必不可少的技术手段之一。本文提出了一种结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测方法。该方法通过伪不变特征法完成两期影像的相对辐射校正，有效改善影像间的配准误差，并利用IR-MAD算法对校正后的影像进行迭代运算，采用均值漂移算法对迭代后的影像进行分割，同时运用形态学方法处理分割后的影像，最终提取变化图斑。试验结果表明，该方法可以有效检测出变化区域，可应用于城市地表覆盖的变化检测。  相似文献   

不同测站点云数据的配准算法     

高益忠 《北京测绘》2020,(2):180-184

随着三维激光扫描技术的发展,利用三维激光扫描仪采集信息,构建三维模型成为了热门的课题。由于受到观测环境、观测方向等影响,无法一次性地获得物体的所有的点云数据。因此,不同视角下点云数据的配准成为了三维建模中的关键技术,直接影响了最终的重合结果以及模型精度。本文着重研究主方向贴合法和最近点迭代算法(ICP算法),基于matlab平台编写算法,并对算法进行研究,得出配准结果以及配准精度。  相似文献   

基于CLEAN算法的GPS监测数据处理方法     

张巨林 《北京测绘》2020,(3):412-416

对建筑物进行高精度的变形监测是后期信息提取,预防安全隐患的基础。针对GPS变形监测中存在的多径误差问题,本文提出一种基于CLEAN算法的GPS多径提取方法,首先对多径信号理论模型进行分析,结果表明多径信号可以作为直达信号的谐波分量,在此基础上利用CLEAN算法在频域对多径信号进行提取,基于仿真和实测数据的实验结果表明,所提方法相对于小波方法和经验模态分解(Empirical Mode Decomposition,EMD)方法具有更高的提取精度和更好的多径抑制性能。  相似文献