Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity insp...Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.展开更多
为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪...为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。展开更多
基金supported by the National Natural Science Foundation of China(Nos.61403146 and 61603105)the Fundamental Research Funds for the Central Universities(No.2015ZM128)the Science and Technology Program of Guangzhou in China(Nos.201707010054 and 201704030072)
文摘Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.
文摘为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。
