在地震资料反演中,反褶积是一种重要的压缩地震子波、提高薄层纵向分辨率的地震数据处理方法。由于地层为层状结构,反射系数可视作稀疏的脉冲序列,因此地震反褶积可以描述为稀疏求解问题。然而,反褶积问题通常是病态的,需要引入正则化...在地震资料反演中,反褶积是一种重要的压缩地震子波、提高薄层纵向分辨率的地震数据处理方法。由于地层为层状结构,反射系数可视作稀疏的脉冲序列,因此地震反褶积可以描述为稀疏求解问题。然而,反褶积问题通常是病态的,需要引入正则化约束以获得稳定和准确的解。本研究介绍了几种不同的正则化方法,包括L1正则化、L2正则化、Cauchy正则化以及结合L1和L2正则化的方法,给出了它们的数学模型,并着重比较了Cauchy正则化与结合L1和L2正则化的方法。通过简单的一维模型和复杂的Marmousi2 (二维)模型的实验,我们评估了这些正则化方法在稀疏脉冲反褶积中的性能表现。结果表明,结合L1和L2正则化的联合方法在噪声抑制和分辨率提升方面表现优异,能够更准确地恢复地下结构的真实反射特性。本文的研究为选择适当的正则化策略以优化地震数据的反褶积处理提供了理论支持和实用指导。In seismic data inversion, deconvolution is an important seismic data processing method that compresses seismic wavelets and improves the vertical resolution of thin layers. Due to the layered structure of the strata, the reflection coefficient can be regarded as a sparse pulse sequence, so seismic deconvolution can be described as a sparse solution problem. However, deconvolution problems are often pathological and require the introduction of regularization constraints to obtain stable and accurate solutions. This study introduces several different regularization methods, including L1 regularization, L2 regularization, Cauchy regularization, and methods combining L1 and L2 regularization. Their mathematical models are given, and the comparison between Cauchy regularization and methods combining L1 and L2 regularization is emphasized. We evaluated the performance of these regularization methods in sparse pulse deconvolution through experiments using a simple one-dimensional model and a complex Marmousi2 (two-dimensional) model. The results show that the joint method combining L1 and L2 regularization performs well in noise suppression and resolution improvement, and can more accurately restore the true reflection characteristics of underground structures. This study provides theoretical support and practical guidance for selecting appropriate regularization strategies to optimize the deconvolution processing of seismic data.展开更多
随着全球工业化进程的加速,能源消耗持续增加带来CO_(2)的过度排放,引发了严重的气候与环境问题。在众多应对措施中,以具有独特孔隙特征的MOF材料为代表的物理吸附技术成为该领域的研究热点。基于PtS型MOF材料,设计构建并优化了一系列硝...随着全球工业化进程的加速,能源消耗持续增加带来CO_(2)的过度排放,引发了严重的气候与环境问题。在众多应对措施中,以具有独特孔隙特征的MOF材料为代表的物理吸附技术成为该领域的研究热点。基于PtS型MOF材料,设计构建并优化了一系列硝基(-NO_(2))和三嗪修饰的MOF结构,通过巨正则蒙特卡洛(GCMC)理论模拟研究了不同-NO_(2)数量对MOF材料的CO_(2)气体吸附分离性能的影响。结果表明:在298K和1.0bar条件下,引入-NO_(2)后,材料对CO_(2)的捕获量从PtS的1.92mmol/g提升到了PtS-2NO_(2)的2.52mmol/g,增加-NO_(2)数量后PtS-4NO_(2)的CO_(2)吸附量提升到了5.02mmol/g,而同时修饰三嗪结构后CO_(2)吸附量达到5.93mmol/g。计算不同结构的等温吸附热、van der Waals/Coulomb相互作用和选择性等揭示了MOF结构中引入-NO_(2)对其CO_(2)选择性吸附能力的促进作用和机制,对高性能MOF基CO_(2)吸附剂材料的开发具有重要的指导意义。展开更多
文摘在地震资料反演中,反褶积是一种重要的压缩地震子波、提高薄层纵向分辨率的地震数据处理方法。由于地层为层状结构,反射系数可视作稀疏的脉冲序列,因此地震反褶积可以描述为稀疏求解问题。然而,反褶积问题通常是病态的,需要引入正则化约束以获得稳定和准确的解。本研究介绍了几种不同的正则化方法,包括L1正则化、L2正则化、Cauchy正则化以及结合L1和L2正则化的方法,给出了它们的数学模型,并着重比较了Cauchy正则化与结合L1和L2正则化的方法。通过简单的一维模型和复杂的Marmousi2 (二维)模型的实验,我们评估了这些正则化方法在稀疏脉冲反褶积中的性能表现。结果表明,结合L1和L2正则化的联合方法在噪声抑制和分辨率提升方面表现优异,能够更准确地恢复地下结构的真实反射特性。本文的研究为选择适当的正则化策略以优化地震数据的反褶积处理提供了理论支持和实用指导。In seismic data inversion, deconvolution is an important seismic data processing method that compresses seismic wavelets and improves the vertical resolution of thin layers. Due to the layered structure of the strata, the reflection coefficient can be regarded as a sparse pulse sequence, so seismic deconvolution can be described as a sparse solution problem. However, deconvolution problems are often pathological and require the introduction of regularization constraints to obtain stable and accurate solutions. This study introduces several different regularization methods, including L1 regularization, L2 regularization, Cauchy regularization, and methods combining L1 and L2 regularization. Their mathematical models are given, and the comparison between Cauchy regularization and methods combining L1 and L2 regularization is emphasized. We evaluated the performance of these regularization methods in sparse pulse deconvolution through experiments using a simple one-dimensional model and a complex Marmousi2 (two-dimensional) model. The results show that the joint method combining L1 and L2 regularization performs well in noise suppression and resolution improvement, and can more accurately restore the true reflection characteristics of underground structures. This study provides theoretical support and practical guidance for selecting appropriate regularization strategies to optimize the deconvolution processing of seismic data.
文摘随着全球工业化进程的加速,能源消耗持续增加带来CO_(2)的过度排放,引发了严重的气候与环境问题。在众多应对措施中,以具有独特孔隙特征的MOF材料为代表的物理吸附技术成为该领域的研究热点。基于PtS型MOF材料,设计构建并优化了一系列硝基(-NO_(2))和三嗪修饰的MOF结构,通过巨正则蒙特卡洛(GCMC)理论模拟研究了不同-NO_(2)数量对MOF材料的CO_(2)气体吸附分离性能的影响。结果表明:在298K和1.0bar条件下,引入-NO_(2)后,材料对CO_(2)的捕获量从PtS的1.92mmol/g提升到了PtS-2NO_(2)的2.52mmol/g,增加-NO_(2)数量后PtS-4NO_(2)的CO_(2)吸附量提升到了5.02mmol/g,而同时修饰三嗪结构后CO_(2)吸附量达到5.93mmol/g。计算不同结构的等温吸附热、van der Waals/Coulomb相互作用和选择性等揭示了MOF结构中引入-NO_(2)对其CO_(2)选择性吸附能力的促进作用和机制,对高性能MOF基CO_(2)吸附剂材料的开发具有重要的指导意义。
