Overlapped peaks resolution for linear sweep polarography using Gaussian-like distribution

A resolution method based on Gaussian-like distribution for overlapped linear sweep polarographic peaks was proposed to simultaneously detect the polymetallic components, such as Zn（Ⅱ） and Co（Ⅱ）, coexisting in the leaching solution of zinc hydrometallurgy. A Gaussian-like distribution was constructed as the sub-model of overlapped peaks by analyzing the characteristics of linear sweep polarographic curve. Then, the abscissas of each peak and trough were pinpointed through multi-resolution wavelet decomposition, the curve and its derivative curves were fitted by using nonlinear weighted least squares （NWLS）. Finally, overlapped peaks were resolved into independent sub-peaks based on fitted reconstruction parameters. The experimental results show that the relative error of half-wave potential pinpointed by multi-resolution wavelet decomposition is less than 1% and the accuracy of Ip fitted by NWLS is higher than 96%. The proposed resolution method is effective for overlapped linear sweep polarographic peaks of Zn（Ⅱ） and Co（Ⅱ）.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

A method for absorption compensation based on adaptive molecular decomposition

In this paper,we present a new method for seismic stratigraphic absorption compensation based on the adaptive molecular decomposition.Using this method,we can remove most of the effects resulting from wavelets truncation and interference which usually exist in the common time-frequency absorption compensation method.Based on the assumption that the amplitude spectrum of the source wavelet is smooth,we first construct a set of adaptive Gabor frames based on the time-variant properties of the seismic signal to transform the signal into the time-frequency domain and then extract the slowly varying component（the wavelet＇s time-varying amplitude spectrum） in each window in the timefrequency domain.Then we invert the absorption compensation filter parameters with an objective function defined using the correlation coefficients in each window to get the corresponding compensation filters.Finally,we use these filters to compensate the timefrequency spectrum in each window and then transform the time-frequency spectrum to the time domain to obtain the absorption-compensated signal.By using adaptive molecular decomposition,this method can adapt to isolated and overlapped seismic signals from the complex layers in the inhomogeneous viscoelastic medium.The viability of the method is verified by synthetic and real data sets.

Inverse spectral decomposition using an I_p-norm constraint for the detection of close geological anomalies

An important application of spectral decomposition(SD)is to identify subsurface geological anomalies such as channels and karst caves,which may be buried in full-band seismic data.However,the classical SD methods including the wavelet transform(WT)are often limited by relatively low time-frequency resolution,which is responsible for false high horizonassociated space resolution probably indicating more geological structures,especially when close geological anomalies exist.To address this issue,we impose a constraint of minimizing an lp(0<p<1)norm of time-frequency spectral coefficients on the misfit derived by using the inverse WT and apply the generalized iterated shrinkage algorithm to invert for the optimal coefficients.Compared with the WT and inverse SD(ISD)using a typical l1-norm constraint,the modified ISD(MISD)using an lp-norm constraint can yield a more compact spectrum contributing to detect the distributions of close geological features.We design a 3 D synthetic dataset involving frequency-close thin geological anomalies and the other3 D non-stationary dataset involving time-close anomalies to demonstrate the effectiveness of MISD.The application of 4 D spectrum on a 3 D real dataset with an area of approximately 230 km2 illustrates its potential for detecting deep channels and the karst slope fracture zone.

Method for obtaining high-resolution velocity spectrum based on weighted similarity

Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.

A Tunable Resolution MUSIC Algorithm for Interharmonics Analysis

The harmonic and interharmonic analysis recommendations are contained in the latest IEC standards on power quality. Measurement and analysis experiences have shown that great difficulties arise in the interharmonic detection and measurement with acceptable levels of accuracy. In order to improve the resolution of spectrum analysis, the traditional method (e.g. discrete Fourier transform) is to take more sampling cycles, e.g. 10 sampling cycles corresponding to the spectrum interval of 5 Hz while the fundamental frequency is 50 Hz. However, this method is not suitable to the interharmonic measurement, because the frequencies of interharmonic components are non-integer multiples of the fundamental frequency, which makes the measurement additionally difficult. In this paper, the tunable resolution multiple signal classification (TRMUSIC) algorithm is presented, which the spectrum can be tuned to exhibit high resolution in targeted regions. Some simulation examples show that the resolution for two adjacent frequency components is usually sufficient to measure interharmonics in power systems with acceptable computation time. The proposed method is also suited to analyze interharmonics when there exists an undesirable asynchronous deviation and additive white noise.

Highly efficient and stable electrooxidation of methanol and ethanol on 3D Pt catalyst by thermal decomposition of In2O3 nanoshells

In this paper In2O3nanoshells have been synthesized via a facile hydrothermal approach. The nanoshells can be completely cracked into pony-size nanocubes by annealing, which are then used as a support of Pt catalyst for methanol and ethanol electrocatalytic oxidation. The prepared In2O3and supported Pt catalysts (Pt/In2O3) were characterized by X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), field effect scanning electron microscopy (FESEM), and transmission electron microscopy (TEM). Cyclic voltammetry (CV), linear sweep voltammetry (LSV), chronoamperometry and electrochemical impedance spectroscopy (EIS) were carried out, indicating the excellent catalytic performance for alcohol electrooxidation can be achieved on Pt/In2O3nanocatalysts due to the multiple active sites, high conductivity and a mass of microchannels and micropores for reactant diffusions arising from 3D frame structures compared with that on the Pt/C catalysts. © 2016 Science Press

Image Super Resolution Reconstruction Based MCA and PCA Dimension Reduction

Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.

走滑断裂带三维地震特征增强处理与描述研究

走滑断裂带由于纵向断距小,超深层地震信号弱,常规叠前深度偏移地震资料难以满足超深层断裂带精细描述需求。为提高断裂带成像精度,指导走滑断裂带解释描述和评价部署,以顺北地区走滑断裂带发育区三维地震资料为例,建立了一套以提高地震资料品质的保真保幅优化处理、频谱恢复提高分辨率处理、频谱分解处理、频率域多尺度断裂检测等技术为主的走滑断裂带地震特征增强处理与描述技术,该技术组合有效拓宽了地震数据频带,提高了地震数据分辨率,使超深走滑断裂带成像精度更高,为超深走滑断裂带的精细解释、描述评价、三维雕刻提供了高品质资料基础。结合顺北地区前人研究成果,综合利用频谱恢复提高分辨率处理、频谱分解处理、频率域断裂检测数据,不同尺度断裂带特征及断储关系预测效果更好,为进一步评价断裂带和部署井位提供了技术支撑。

多分辨分解下扩频通信信号重叠变换干扰仿真

非理想条件下的干扰会对扩频通信信号的传输质量产生负面影响,为提高扩频通信的安全性和稳定性,提出一种多分辨重叠变换下扩频通信信号抗干扰方法。利用多陪集采样技术采集扩频通信信号,通过对扩频通信信号展开多分辨分解获得多尺度下的扩频通信信号特征。利用重叠变换对扩频通信信号特征展开分离,获得干扰信号的特征,并对其展开抑制,再利用重叠逆变换恢复扩频通信信号中的有效信号,实现扩频通信信号抗干扰。仿真结果表明,上述方法的信号分离准确性高,抗干扰性能强。

基于变分模态分解的谱蓝化方法在叠前地震资料高分辨处理中的研究及应用

叠前地震资料的分辨率会影响叠前反演的精度以及AVO分析的结果,所以提高叠前道集的分辨率就显得尤为重要。谱蓝化高分辨方法结合了测井信息,使处理后的地震资料更加接近实际情况。但其在计算过程中,使用各道叠加的平均振幅谱计算谱蓝化算子,导致部分频谱信息未参与计算。因此笔者提出基于VMD(变分模态分解方法)的谱蓝化方法,通过VMD将地震数据分解为多个IMF(本征模态分量),计算其谱蓝化算子,以提高各IMF的分辨率,并重构得到高分辨地震数据结果。在合成道集和实际地震道集数据处理中运用该方法,结果表明:基于VMD的谱蓝化方法进一步提高频谱信息以及叠前道集分辨率,与实际测井资料正演道集相吻合。

基于变分模态分解的弹性参数核密度估计方法

概率密度建模是地震随机模拟中至关重要的环节,而弹性参数高频成分的概率密度估计决定了高分辨率地震随机模拟结果的精度。针对常规方法中弹性参数高频成分提取精度不足、概率密度建模先验条件过度约束以及弹性参数的概率密度建模分层设计等问题,提出了一种基于变分模态分解(variational mode decomposition,VMD)的弹性参数核密度估计方法。该方法首先采用VMD对测井弹性参数数据进行模态分解,筛选出本征模态函数(intrinsic mode function,IMF)中的高频项叠加得到测井弹性参数的高频成分;然后使用核密度估计分层计算得到高频成分的概率密度模型,并通过该模型进行随机抽样生成随机高频成分叠加至井旁地震数据上以达到丰富地震弹性参数数据高频内容的目的。珠江口盆地34号井区的实验结果显示,VMD有效分离出了中心频率在70 Hz以上的测井弹性参数高频成分,分层设计的核密度估计方法凸显了高频成分的统计规律,叠加随机高频成分后地震弹性参数70 Hz以上的高频成分得到了明显补充。该方法为地震高分辨率随机模拟提供了新的思路。

一种奇异值分解与子空间加权联合的改进MUSIC算法

在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力。针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法。该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合。仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法。

基于匹配追踪算法的地震数据提高分辨率方法

提高地震数据的分辨率对于薄储层的识别与表征具有重要意义。常规的反褶积、反Q滤波等提高地震分辨率处理方法,存在假设条件多、信噪比较低、算法不稳定等不足,使得方法的应用效果和适用性受限。提出了一种新的基于匹配追踪算法的地震数据提高分辨率方法,可以在保持较高信噪比的同时提高地震数据分辨率。首先,根据地震信号特点,利用频率加权指数函数计算基准子波,并由此构建子波字典,频率加权指数函数可以很好地拟合不同形状的频谱,更好地适应不同的工区。然后,利用匹配追踪算法将原始地震数据分解为一系列子波的线性叠加,增加较高信噪比的高频匹配子波的振幅。最后对地震数据进行重构,获得高分辨率的地震数据。应用结果表明,该方法可有效提高地震分辨率,具有较高的信噪比,并且提高分辨率处理的结果与测井合成地震记录有很好的吻合性,证明方法是可靠的,且处理结果有利于后续薄储层识别与描述。

多尺度注意力特征融合的单图像超分辨率研究

高分辨率意味着图像具有高像素密度,可以提供更多的细节,这些细节往往在应用中起到关键作用。基于生成对抗网络的图像超分辨率由于具有生成丰富细节的潜力,近年来受到越来越多的关注。针对现有的网络模型忽略从特征中学习本质纹理特征和感受野有限的问题,基于Real-ESRGAN和多尺度注意力特征融合,对网络进行优化,将残差稠密块替换成大核分解和多尺度学习相结合模块与全局学习与下采样模块的双分支结构方法,提出一种多尺度注意力融合的单图像超分辨率重建算法,增强每个局部与全局令牌对之间的交互,从而形成更丰富和信息量更大的表示。对数据集进行2,3,4倍超分辨率重建实验,通过峰值信噪比(PSNR)、结构相似性(SSIM)对重建结果进行评价,与SRCNN、SRGAN、ACMF、MSRDN、WYD、LBW、YJX、Real-ESRGAN等方法进行对比。结果表明,该算法优于其他模型,且具有更好的直观视觉效果。

高分辨率地震勘探数据的噪声抑制算法研究

近年来,随着地震勘探技术的发展,高分辨率地震勘探数据成为地震勘探的重要数据源,这些数据提供了更细致、更清晰的地下结构信息,对于提高资源勘探的精度和效率具有重要意义。然而,在地震数据处理过程中,不可避免地存在噪声,严重影响了地震数据的分辨率和准确性。因此,为获得高分辨率地震数据,噪声抑制的重要性显而易见。基于此,详细讨论各类噪声的特性,探讨当前主要噪声抑制算法,比较这些算法的性能,对提高高分辨率地震勘探数据的质量,进一步揭示地下复杂结构信息,提高地震勘探和资源勘查准确性具有重要意义。

基于模态分解的低压串联电弧故障特征提取方法比较

低压交流系统串联电弧电流的非线性、非平稳和随机等特点给故障特征提取和检测带来极大困难,同时以包络线分析为基础的模态分解在非平稳信号分析中展现了良好效果。鉴于模态分解方法的优异效果以及串联电弧故障检测的实际困难,首先对目前较为成熟的经验模态分解(empirical mode decomposition,EMD)等6种模态分解方法进行了系统梳理,并深入分析了该系列方法在电弧故障信号分析和特征提取中的适用性和有效性。然后,通过实测电弧电流的分解实验和特征计算实验,从不同角度探讨了模态分解算法在电弧电流特征提取和故障检测中的优势与不足。最后,对未来可能的研究方向做了展望。

M-Cholesky分解法快速求解GNSS整周模糊度

为解决传统最小二乘模糊度去相关算法(least-square ambiguity decorrelation adjustment,LAMBDA)中LDL^(T)分解的对角矩阵D、下三角矩阵L及其转置矩阵L^(T)计算过程复杂、耗时较长等问题,提出M-Cholesky分解法。该方法利用四角规则法,逐步消元计算合成矩阵各元素,每次消元计算中最多只用到3个元素,可减少存储空间、提高计算效率。仿真与实测实验结果表明,相比于Cholesky分解法,M-Cholesky分解法求解GNSS整周模糊度的计算效率提高约15%。

融合迭代反馈与注意力机制的图像超分辨重建方法

针对图像超分辨重建过程中原始高清图片与低质量图像之间缺乏依赖关系、深度网络中特征图信息不分主次重构导致的图像高频信息高精度重构困难的问题,提出一种融合迭代反馈与注意力机制的单幅图像超分辨重建方法。首先使用频率分解模块分别提取图像中的高、低频信息,并将二者分别处理,使网络重点关注提取出的高频细节部分,增强方法在图像细节上的复原能力;其次通过通道注意力机制将重建的重点放在有效特征所在的特征通道上,增强网络提取特征图信息的能力;然后采用迭代反馈的思想,在反复重建和比对过程中增加图像的还原程度;最后通过重建模块生成输出图像。在Set5、Set14、BSD100、Urban100和Manga109基准数据集上的2倍、4倍和8倍放大实验中,与主流超分辨率方法相比,所提方法表现出更优越的性能。在Manga109数据集的8倍放大实验中,相较于传统插值方法和基于卷积神经网络的图像超分辨率算法(SRCNN),所提方法的峰值信噪比(PSNR)均值分别提升了约3.01 dB和2.32 dB。实验结果表明:所提方法能够降低重建过程中出现的误差,并有效重建出更精细的高分辨率图像。

一种基于极化分解特征和SVDD的扩展目标检测算法

多极化距离高分辨雷达是地面静止目标检测的重要手段,其回波中目标占据多个距离单元,成为扩展目标。传统基于回波能量的扩展目标检测方法的性能随信杂比的降低而下降。提出一种基于极化分解特征的扩展目标检测算法,利用目标和杂波之间的极化散射特性差异提升低信杂比下的检测性能。所提方法提取16种极化分解特征组成特征向量作为检测统计量,再使用支持向量数据描述(SVDD)算法估计判别门限。在判别门限的训练阶段,杂波数据的极化分解特征被提取用作训练数据。并且为保证虚警概率,在SVDD的目标函数中引入了两个惩罚参数。使用实测数据进行了实验验证,所提方法在戈壁背景、虚警概率为10-4、检测概率为90%的情况下,所需信杂比约为12.6 dB,相较于基于能量的对比方法降低约1.7 dB。