At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achievi...At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.展开更多
Physical properties of sea water,such as salinity,temperature,density and acoustic velocity,could be demarcated through degradation of energy caused by water absorption,attenuation and other factors.To overcome the ch...Physical properties of sea water,such as salinity,temperature,density and acoustic velocity,could be demarcated through degradation of energy caused by water absorption,attenuation and other factors.To overcome the challenging difficulties in the quick monitoring of these physical properties,we have explored the high resolution marine seismic survey to instantly characterize them.Based on the unique wavefield propagating in the sea water,we have developed a new approach to suppress the noise caused by the shallow sea water disturbance and obtain useful information for estimating the sea water structure.This approach improves seismic data with high signal-to-noise ratio and resolution.The seismic reflection imaging can map the sea water structure acoustically.Combined with the knowledge of local water body structure profile over years,the instant model for predicting the sea water properties could be built using the seismic data acquired from the specially designed high precision marine seismic acquisition.This model can also be updated with instant observation and the complete data processing system.The present study has the potential value to many applications,such as 3D sea water monitoring,engineering evaluation,geological disaster assessment and environmental assessment.展开更多
Using a bottom simulating reflector(BSR)on a seismic profile to identify marine gas hydrate is a traditional seismic exploration method.However,owing to the abundance differences between the gas hydrate and free gas i...Using a bottom simulating reflector(BSR)on a seismic profile to identify marine gas hydrate is a traditional seismic exploration method.However,owing to the abundance differences between the gas hydrate and free gas in different regions,the BSR may be unremarkable on the seismic profile and invisible in certain cases.With the improvement of exploration precision,difficulty arises in meeting the requirements of distinguishing the abundance differences in the gas hydrate based on BSR.Hence,we studied other sensitive attributes to ascertain the existence of gas hydrate and its abundance variations,eventually improving the success rate of drilling and productivity.In this paper,we analyzed the contradiction between the seismic profile data and drilling sampling data from the Blake Ridge.We extracted different attributes and performed multi-parameter constraint analysis based on the prestack elastic wave impedance inversion.Then,we compared the analysis results with the drilling sampling data.Eventually,we determined five sensitive attributes that can better indicate the existence of gas hydrate and its abundance variations.This method overcomes the limitations of recognizing the gas hydrate methods based on BSR or single inversion attribute.Moreover,the conclusions can notably improve the identification accuracy of marine gas hydrate and provide excellent reference significance for the recognition of marine gas hydrate.Notably,the different geological features of reservoirs feature different sensitivities to the prestacking attributes when using the prestack elastic inversion in different areas.展开更多
Ocean-Bottom Node(OBN)acquisitions provide both non-converted and converted reflection energy.There is a clear advantage to independently imaging both P-and S-waves,as they provide more information collectively than e...Ocean-Bottom Node(OBN)acquisitions provide both non-converted and converted reflection energy.There is a clear advantage to independently imaging both P-and S-waves,as they provide more information collectively than either does alone.In many conventional converted-wave pre-stack migration algorithms,density is treated as a constant,which is not the real-life case on earth.S-wave velocity and density information is crucial for hydrocarbon detection because it helps in the identification of porefilling fluids.In this paper,we focused on the effect of density on imaging,and developed a method of reverse-time migration(RTM)on converted s-waves of varying densities(VD-RTMCS).Phase correction was required prior to pre-stack migration to avoid constructive interference between data from adjacent sources.Synthetic data examples showed that when density variations were included,image profiles showed advantages in signal-to-noise ratio,vertical resolution and imaging of complex structures.展开更多
基金This study was supported by the National Natural Science Foundation of China under the project‘Research on the Dynamic Location of Receiver Points and Wave Field Separation Technology Based on Deep Learning in OBN Seismic Exploration’(No.42074140).
文摘At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.
基金the Natural Science Foundation of China(41176077)Subject of 973(2009CB219505)+2 种基金Natural Science Foundation of Shandong(ZR2010DM012)Basic Research Special Foundation of the Third Institute of Oceanography affiliated to the State Oceanic Administration(TIOSOA,2009004)the Science Research Project for the South China Sea of Ocean University of China for their financial support to this work
文摘Physical properties of sea water,such as salinity,temperature,density and acoustic velocity,could be demarcated through degradation of energy caused by water absorption,attenuation and other factors.To overcome the challenging difficulties in the quick monitoring of these physical properties,we have explored the high resolution marine seismic survey to instantly characterize them.Based on the unique wavefield propagating in the sea water,we have developed a new approach to suppress the noise caused by the shallow sea water disturbance and obtain useful information for estimating the sea water structure.This approach improves seismic data with high signal-to-noise ratio and resolution.The seismic reflection imaging can map the sea water structure acoustically.Combined with the knowledge of local water body structure profile over years,the instant model for predicting the sea water properties could be built using the seismic data acquired from the specially designed high precision marine seismic acquisition.This model can also be updated with instant observation and the complete data processing system.The present study has the potential value to many applications,such as 3D sea water monitoring,engineering evaluation,geological disaster assessment and environmental assessment.
基金supported by the National Natural Science Foundation of China (No. 41230318)
文摘Using a bottom simulating reflector(BSR)on a seismic profile to identify marine gas hydrate is a traditional seismic exploration method.However,owing to the abundance differences between the gas hydrate and free gas in different regions,the BSR may be unremarkable on the seismic profile and invisible in certain cases.With the improvement of exploration precision,difficulty arises in meeting the requirements of distinguishing the abundance differences in the gas hydrate based on BSR.Hence,we studied other sensitive attributes to ascertain the existence of gas hydrate and its abundance variations,eventually improving the success rate of drilling and productivity.In this paper,we analyzed the contradiction between the seismic profile data and drilling sampling data from the Blake Ridge.We extracted different attributes and performed multi-parameter constraint analysis based on the prestack elastic wave impedance inversion.Then,we compared the analysis results with the drilling sampling data.Eventually,we determined five sensitive attributes that can better indicate the existence of gas hydrate and its abundance variations.This method overcomes the limitations of recognizing the gas hydrate methods based on BSR or single inversion attribute.Moreover,the conclusions can notably improve the identification accuracy of marine gas hydrate and provide excellent reference significance for the recognition of marine gas hydrate.Notably,the different geological features of reservoirs feature different sensitivities to the prestacking attributes when using the prestack elastic inversion in different areas.
基金supported by the National Science and Technology Major Project (No. 2016ZX05027-002)the National Natural Science Foundation of China (No. 41230 318)Qingdao National Laboratory for Marine Science and Technology Innovation Project of Ao-Shan (No. 2015ASKJ03)
文摘Ocean-Bottom Node(OBN)acquisitions provide both non-converted and converted reflection energy.There is a clear advantage to independently imaging both P-and S-waves,as they provide more information collectively than either does alone.In many conventional converted-wave pre-stack migration algorithms,density is treated as a constant,which is not the real-life case on earth.S-wave velocity and density information is crucial for hydrocarbon detection because it helps in the identification of porefilling fluids.In this paper,we focused on the effect of density on imaging,and developed a method of reverse-time migration(RTM)on converted s-waves of varying densities(VD-RTMCS).Phase correction was required prior to pre-stack migration to avoid constructive interference between data from adjacent sources.Synthetic data examples showed that when density variations were included,image profiles showed advantages in signal-to-noise ratio,vertical resolution and imaging of complex structures.
