Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency respo...Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion inthe pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore,one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.展开更多
Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for mult...Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for multi-scale morphology and the spectrum simulation method. These methods are applied in seismic energy compensation. First of all, the seismic data is decomposed into multiple scales and the effective frequency bandwidth is selectively broadened for some scales by using a spectrum simulation method. In this process, according to the amplitude spectrum of each scale, the best simulation range is selected to simulate the middle and low frequency components to ensure the authenticity of the simulation curve which is calculated by the median method, and the high frequency component is broadened. Finally, these scales are reconstructed with reasonable coefficients, and the compensated seismic data can be obtained. Examples are shown to illustrate the feasibility of the energy compensation method.展开更多
[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征...[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征的尺度分割思想与基于物候学的DESTIN(delineation by fusing spatial and temporal information)分割算法,提出了基于多尺度及DESTIN约束的高分遥感影像农田田块语义分割方法。[结果]多尺度与DESTIN约束下基于深度模型的田块语义分割有效改善模型出现的区域不封闭、边缘不贴合、噪点和块状模糊等问题,一定程度修正了深度模型语义分割的错误识别,IoU指标在2个测试集上分别达到94.08%和90.79%,相较深度模型的遥感影像田块语义分割分别提高1.65%和2.32%,对研究区域的田块提取区域更完整、精度更高。[结论]多尺度及DESTIN约束进一步改善了田块语义分割问题,有助于提高高分遥感影像的田块识别精度。展开更多
基金the sponsorship of National Natural Science Foundation Project(U1562215,41604101)National Grand Project for Science and Technology(2016ZX05024-004,2017ZX05032-003)+2 种基金the Post-graduate Innovation Program of China University of Petroleum(YCX2017005)Science Foundation from SINOPEC Key Laboratory of Geophysics(wtyjy-wx2016-04-10)the Fundamental Research Funds for the Central Universities
文摘Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion inthe pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore,one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.
文摘Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for multi-scale morphology and the spectrum simulation method. These methods are applied in seismic energy compensation. First of all, the seismic data is decomposed into multiple scales and the effective frequency bandwidth is selectively broadened for some scales by using a spectrum simulation method. In this process, according to the amplitude spectrum of each scale, the best simulation range is selected to simulate the middle and low frequency components to ensure the authenticity of the simulation curve which is calculated by the median method, and the high frequency component is broadened. Finally, these scales are reconstructed with reasonable coefficients, and the compensated seismic data can be obtained. Examples are shown to illustrate the feasibility of the energy compensation method.
文摘为了解决现有遥感图像超分辨率重建模型对长期特征相似性和多尺度特征相关性关注不足的问题,提出了一种基于跨尺度混合注意力机制的遥感图像超分辨率重建算法.首先提出了一个全局层注意力机制(global layer attention,GLA),利用层注意力机制加权融合不同层级的全局特征,建模低分辨率与高分辨率图像特征间的长期依赖关系.同时,设计了跨尺度局部注意力机制(cross-scale local attention,CSLA),在多尺度的低分辨率特征图中寻找与高分辨率图像匹配的局部信息补丁,并融合不同尺度的补丁特征,以优化模型对图像细节信息的恢复能力.最后,提出一种局部信息感知损失函数来指导图像的重建过程,进一步提高了重建图像的视觉质量和细节保留能力.在UC-Merced数据集上的实验结果表明,本文方法在3种放大倍数下的平均PSNR/SSIM优于大多数主流方法,并在视觉效果方面展现出更高的质量和更好的细节保留能力.
文摘[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征的尺度分割思想与基于物候学的DESTIN(delineation by fusing spatial and temporal information)分割算法,提出了基于多尺度及DESTIN约束的高分遥感影像农田田块语义分割方法。[结果]多尺度与DESTIN约束下基于深度模型的田块语义分割有效改善模型出现的区域不封闭、边缘不贴合、噪点和块状模糊等问题,一定程度修正了深度模型语义分割的错误识别,IoU指标在2个测试集上分别达到94.08%和90.79%,相较深度模型的遥感影像田块语义分割分别提高1.65%和2.32%,对研究区域的田块提取区域更完整、精度更高。[结论]多尺度及DESTIN约束进一步改善了田块语义分割问题,有助于提高高分遥感影像的田块识别精度。
