Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,...Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,and improves data processing ef-ficiency.In this study,we propose an intelligent method for suppressing marine seismic multiples using deep learning approaches.The proposed method enables the intelligent suppression of free-surface-related multiples from seismic records.Initially,we construct a multi-category marine seismic multiple dataset through finite difference forward modeling under different boundary conditions.We use various models and data augmentation methods,including sample rotation,noise addition,and random channel omission.Then,we apply depthwise separable convolution to develop our deep learning Mobilenet-Unet model.The Mobilenet-Unet framework sig-nificantly reduces the number of operations required for multiple elimination without sacrificing model performance,ultimately reali-zing the optimal multiple suppression model.The trained Mobilenet-Unet is applied to the test set for verification.Moreover,to deter-mine its generalization ability,it is implemented to seismic records containing multiples generated by two marine geophysical models that were not included in the training process.The performance of Mobilenet-Unet is also compared with that of different network structures.The results indicate that,despite its small size,our proposed Mobilenet-Unet deep learning model can rapidly and effective-ly separate multiples in marine seismic data,possessing reasonable generalization ability.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuatio...Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological conditions.Conventional multiple attenuation methods rely on prior geological information and involve extensive computations.Using deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple suppression.This study proposes an improved U-net-based method for multiple attenuation.The conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic data.Emphasis is placed on distinguishing between seismic multiples and primaries.The improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as output.The eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity model.Transfer learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.展开更多
The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain.When there is the interference in mainlobe,it wi...The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain.When there is the interference in mainlobe,it will cause serious mainlobe distortion,that the target detection suffers from.To overcome this problem and make radar cope with the complex multiple interferences scenarios,we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar.Specifically,the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization(DoP),and a filtering criterion based on the proposed linear constraint.The signal preprocessing provides the accurate estimated parameters of the interference,which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe.The proposed method can reduce the mainlobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences.The effectiveness of the proposed method is verified by simulations.展开更多
P16 gene expression was measured by immnohistochemical method in poor differentiated serous cystadenocarcinoma cell line, xenograft of highly metastasizing human ovarian carcinoma in nude mice and paramn embedded tiss...P16 gene expression was measured by immnohistochemical method in poor differentiated serous cystadenocarcinoma cell line, xenograft of highly metastasizing human ovarian carcinoma in nude mice and paramn embedded tissues from 69 patients with ovarian carcinoma. The result showed that P16 gene was positive expression in HO-8910 cell of mother line,HO8910PM cell line and xenograft of highly mcatstasizing human ovarian carcinoma in nude mice. However, P16gene in the metastatic cell had a weaker expression. P16gene positive expression were also found in sl cases of 69cases (73.9%) in the ovarian epithelial carcinoma paramn embedded tissues. Comparative studies showed that the positive rate of P16 gene expression markedly reduced with the increase of pathologic grade and clinical stage,metastasis in the lymph node and decrease of 5-year survival (P<0.05, p<0.01).P16 gene is not only a controller of cytokerastic cycle, but also a key member of tumorigenic suppresser:its absence and expression degree are also correlated with the ovarian carcinoma genesis and development,especially with the metastasis of the ovarian cancer.展开更多
基金supported by the Key Laboratory of Ma-rine Mineral Resources,Ministry of Natural Resources,Guangzhou(No.KLMMR-2022-G09)the Guangzhou Ba-sic Research Program-Basic and Basic Applied Research Project(No.2023A04J0917)the PI Project of South-ern Marine Science and Engineering Guangdong Labora-tory(Guangzhou)(No.GML2020GD0802).
文摘Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,and improves data processing ef-ficiency.In this study,we propose an intelligent method for suppressing marine seismic multiples using deep learning approaches.The proposed method enables the intelligent suppression of free-surface-related multiples from seismic records.Initially,we construct a multi-category marine seismic multiple dataset through finite difference forward modeling under different boundary conditions.We use various models and data augmentation methods,including sample rotation,noise addition,and random channel omission.Then,we apply depthwise separable convolution to develop our deep learning Mobilenet-Unet model.The Mobilenet-Unet framework sig-nificantly reduces the number of operations required for multiple elimination without sacrificing model performance,ultimately reali-zing the optimal multiple suppression model.The trained Mobilenet-Unet is applied to the test set for verification.Moreover,to deter-mine its generalization ability,it is implemented to seismic records containing multiples generated by two marine geophysical models that were not included in the training process.The performance of Mobilenet-Unet is also compared with that of different network structures.The results indicate that,despite its small size,our proposed Mobilenet-Unet deep learning model can rapidly and effective-ly separate multiples in marine seismic data,possessing reasonable generalization ability.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
基金supported by the Open Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(PLN2022-51,PLN2021-21)the Open Fund of the Science and Technology Bureau of Nanchong City,Sichuan Province(23XNSYSX0089,SXQHJH046).
文摘Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological conditions.Conventional multiple attenuation methods rely on prior geological information and involve extensive computations.Using deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple suppression.This study proposes an improved U-net-based method for multiple attenuation.The conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic data.Emphasis is placed on distinguishing between seismic multiples and primaries.The improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as output.The eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity model.Transfer learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.
基金supported by the National Natural Science Foundation of China(6190149661871385)。
文摘The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain.When there is the interference in mainlobe,it will cause serious mainlobe distortion,that the target detection suffers from.To overcome this problem and make radar cope with the complex multiple interferences scenarios,we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar.Specifically,the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization(DoP),and a filtering criterion based on the proposed linear constraint.The signal preprocessing provides the accurate estimated parameters of the interference,which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe.The proposed method can reduce the mainlobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences.The effectiveness of the proposed method is verified by simulations.
文摘P16 gene expression was measured by immnohistochemical method in poor differentiated serous cystadenocarcinoma cell line, xenograft of highly metastasizing human ovarian carcinoma in nude mice and paramn embedded tissues from 69 patients with ovarian carcinoma. The result showed that P16 gene was positive expression in HO-8910 cell of mother line,HO8910PM cell line and xenograft of highly mcatstasizing human ovarian carcinoma in nude mice. However, P16gene in the metastatic cell had a weaker expression. P16gene positive expression were also found in sl cases of 69cases (73.9%) in the ovarian epithelial carcinoma paramn embedded tissues. Comparative studies showed that the positive rate of P16 gene expression markedly reduced with the increase of pathologic grade and clinical stage,metastasis in the lymph node and decrease of 5-year survival (P<0.05, p<0.01).P16 gene is not only a controller of cytokerastic cycle, but also a key member of tumorigenic suppresser:its absence and expression degree are also correlated with the ovarian carcinoma genesis and development,especially with the metastasis of the ovarian cancer.
