Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended interference.T...Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended interference.Therefore,deblending algorithms have been developed to separate this interference.Recently,deep learning(DL)has been proved its great potential in suppressing the interference.The most popular DL method employs neural network as a filter to attenuate the blended noise in an iterative estimation and subtraction framework(IESF).However,there are still amplitude distortion and blended noise residual problems,especially when dealing with weak signal submerged in strong interference.To address these problems,we propose a hybrid WUDT-NAFnet,which contains two sub-networks.The first network is a wavelet based U-shape deblending transformer network(WUDTnet),incorporated into IESF as a robust regularization term to iteratively separate the blended interference.The second network is a nonlinear activate free network(NAFnet)designed to recover the event amplitude and further suppress the weak noise residual in IESF.With the hybrid network,the blended noise can be separated purposefully and accurately.Examples using synthetic and field seismic data demonstrate that the WUDTNAFnet outperforms traditional curvelet transform(CT)based method and the deblending transformer(DT)model in terms of deblending.Additionally,for field applications,the data augmentation method of bicubic interpolation is applied to mitigate the feature difference between synthetic and field data.Consequently,the trained network exhibits strong signal preservation ability in numerical field example without requiring additional training.展开更多
The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended dat...The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended data may introduce crosstalk noise and affect the imaging quality. To address this problem, we introduce a structure-oriented filtering operator as preconditioner into the multisource least-squares reverse-time migration （LSRTM）. The structure-oriented filtering operator is a nonstationary filter along structural trends that suppresses crosstalk noise while maintaining structural information. The proposed method uses the conjugate-gradient method to minimize the mismatch between predicted and observed data, while effectively attenuating the interference noise caused by exciting several sources simultaneously. Numerical experiments using synthetic data suggest that the proposed method can suppress the crosstalk noise and produce highly accurate images.展开更多
Considerable attempts have been made on removing the crosstalk noise in a simultaneous source data using the popular K-means Singular Value Decomposition algorithm(KSVD).Several hybrids of this method have been design...Considerable attempts have been made on removing the crosstalk noise in a simultaneous source data using the popular K-means Singular Value Decomposition algorithm(KSVD).Several hybrids of this method have been designed and successfully deployed,but the complex nature of blending noise makes it difficult to manipulate easily.One of the challenges of the K-means Singular Value Decomposition approach is the challenge to obtain an exact KSVD for each data patch which is believed to result in a better output.In this work,we propose a learnable architecture capable of data training while retaining the K-means Singular Value Decomposition essence to deblend simultaneous source data.展开更多
地震勘探中并行震源地震数据中的工频噪声普遍存在,其强度一般弱于浅层强反射,但明显淹没中浅层弱反射信号及深部信号,造成勘探深度受限。目前已有的工频噪声压制方法很少考虑工频频率不稳定以及信号频率和工频噪声频率部分重叠的特征,...地震勘探中并行震源地震数据中的工频噪声普遍存在,其强度一般弱于浅层强反射,但明显淹没中浅层弱反射信号及深部信号,造成勘探深度受限。目前已有的工频噪声压制方法很少考虑工频频率不稳定以及信号频率和工频噪声频率部分重叠的特征,因此压噪效果不理想、方法适用性受限。针对具体的工频噪声在并行震源地震勘探数据中的实际特征提出了一种基于主成分分析（PCA）最优阶数的并行震源工频噪声压制方法。首先对含工频干扰的各道信号构建Hankel矩阵,通过线性映射得到各个主成分;然后分别根据信噪比、信号失真度和噪声抑制率3个准则得出Hankel矩阵中每个工频频率对应两个低阶主成分这一规律,将反映工频信息的主成分置零得到重构矩阵;最后基于重构矩阵得到压噪后的并行震源地震信号。仿真信号压噪结果表明,PCA最优阶数技术压噪后信噪比提高了27.6 d B,最后通过实测数据结果进一步论证了所提方法的有效性和优越性。展开更多
基金partly supported by the National Natural Science Foundation of China(grant Nos.42004104,42030812,42204136)。
文摘Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended interference.Therefore,deblending algorithms have been developed to separate this interference.Recently,deep learning(DL)has been proved its great potential in suppressing the interference.The most popular DL method employs neural network as a filter to attenuate the blended noise in an iterative estimation and subtraction framework(IESF).However,there are still amplitude distortion and blended noise residual problems,especially when dealing with weak signal submerged in strong interference.To address these problems,we propose a hybrid WUDT-NAFnet,which contains two sub-networks.The first network is a wavelet based U-shape deblending transformer network(WUDTnet),incorporated into IESF as a robust regularization term to iteratively separate the blended interference.The second network is a nonlinear activate free network(NAFnet)designed to recover the event amplitude and further suppress the weak noise residual in IESF.With the hybrid network,the blended noise can be separated purposefully and accurately.Examples using synthetic and field seismic data demonstrate that the WUDTNAFnet outperforms traditional curvelet transform(CT)based method and the deblending transformer(DT)model in terms of deblending.Additionally,for field applications,the data augmentation method of bicubic interpolation is applied to mitigate the feature difference between synthetic and field data.Consequently,the trained network exhibits strong signal preservation ability in numerical field example without requiring additional training.
基金supported by the National Natural Science Foundation of China(Nos.41374122 and 41504100)
文摘The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended data may introduce crosstalk noise and affect the imaging quality. To address this problem, we introduce a structure-oriented filtering operator as preconditioner into the multisource least-squares reverse-time migration （LSRTM）. The structure-oriented filtering operator is a nonstationary filter along structural trends that suppresses crosstalk noise while maintaining structural information. The proposed method uses the conjugate-gradient method to minimize the mismatch between predicted and observed data, while effectively attenuating the interference noise caused by exciting several sources simultaneously. Numerical experiments using synthetic data suggest that the proposed method can suppress the crosstalk noise and produce highly accurate images.
基金Supported by State Key Research and Development Program of China(No.2018YFC0310104)National Natural Science Foundation of China(Nos.41974163,4213080)。
文摘Considerable attempts have been made on removing the crosstalk noise in a simultaneous source data using the popular K-means Singular Value Decomposition algorithm(KSVD).Several hybrids of this method have been designed and successfully deployed,but the complex nature of blending noise makes it difficult to manipulate easily.One of the challenges of the K-means Singular Value Decomposition approach is the challenge to obtain an exact KSVD for each data patch which is believed to result in a better output.In this work,we propose a learnable architecture capable of data training while retaining the K-means Singular Value Decomposition essence to deblend simultaneous source data.
文摘地震勘探中并行震源地震数据中的工频噪声普遍存在,其强度一般弱于浅层强反射,但明显淹没中浅层弱反射信号及深部信号,造成勘探深度受限。目前已有的工频噪声压制方法很少考虑工频频率不稳定以及信号频率和工频噪声频率部分重叠的特征,因此压噪效果不理想、方法适用性受限。针对具体的工频噪声在并行震源地震勘探数据中的实际特征提出了一种基于主成分分析（PCA）最优阶数的并行震源工频噪声压制方法。首先对含工频干扰的各道信号构建Hankel矩阵,通过线性映射得到各个主成分;然后分别根据信噪比、信号失真度和噪声抑制率3个准则得出Hankel矩阵中每个工频频率对应两个低阶主成分这一规律,将反映工频信息的主成分置零得到重构矩阵;最后基于重构矩阵得到压噪后的并行震源地震信号。仿真信号压噪结果表明,PCA最优阶数技术压噪后信噪比提高了27.6 d B,最后通过实测数据结果进一步论证了所提方法的有效性和优越性。
