Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method ...Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.展开更多
Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segme...Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segmentation using deep learning.However,few studies employ deep learning in fault instance segmentation.We introduce mask propagation neural network for fault instance segmentation.Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles.Our method refers to the reference-guided mask propagation network,which is firstly used in video object segmentation:taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method.As a multi-level convolutional neural network,the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data,which can facilitate the fault reconstruction workflow.Compared with the traditional deep learning method,the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection.展开更多
Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-c...Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.展开更多
Seismic facies analysis plays important roles in geological research,especially in sedimentary environment identification.Traditional method is mainly based on seismic waveform or attributes of a single seismic gather...Seismic facies analysis plays important roles in geological research,especially in sedimentary environment identification.Traditional method is mainly based on seismic waveform or attributes of a single seismic gather to classify the seismic facies.Ignoring the correlation between adjacent seismic gathers leads to poor lateral continuities in generated facies map,which cannot fit the sedimentary characteristics well.In fact,according to sedimentology theory,the horizontal continuities of the stratum can be utilized as priori information to provide more information for waveform classification.Therefore,we develop an unsupervised method for pre-stack seismic facies analysis,which is constrained by spatial continuity.The proposed method establishes a probabilistic model to characterize the correlation between neighboring reflection elements.Subsequently,this correlation is used as a regularization term to modify the objective function of the clustering algorithm,allowing the mode assignment of reflective elements to be influenced by the labels of their neighbors.Test on synthetic data confirms that,compared with traditional seismic facies analysis methods,the facies maps generated by the proposed method have more continuous and homogeneous textures,and less uncertainty on the boundary.The test on actual seismic data further confirms that the proposed method can describe more details of the distribution of lithological bodies of interest.The proposed method is an effective tool for pre-stack seismic facies analysis.展开更多
We propose to use a Few-Shot Learning(FSL)method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data.Recently,artificial neural network(ANN)demonstrate...We propose to use a Few-Shot Learning(FSL)method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data.Recently,artificial neural network(ANN)demonstrates great advantages for seismic inversion because of its powerful feature extraction and parameter learning ability.Hence,ANN method could provide a high resolution inversion result that are critical for reservoir characterization.However,the ANN approach requires plenty of labeled samples for training in order to obtain a satisfactory result.For the common problem of scarce samples in the ANN seismic inversion,we create a novel pre-stack seismic inversion method that takes advantage of the FSL.The results of conventional inversion are used as the auxiliary dataset for ANN based on FSL,while the well log is regarded the scarce training dataset.According to the characteristics of seismic inversion(large amount and high dimensional),we construct an arch network(A-Net)architecture to implement this method.An example shows that this method can improve the accuracy and resolution of inversion results.展开更多
基金Supported by National Natural Science Foundation of China(41804126,41604107).
文摘Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.
基金Supported by Natural Science Foundation of China(U1562218 and 41974147)the authors would like to thank X.M.Wu for his public seismic synthetic data set.
文摘Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segmentation using deep learning.However,few studies employ deep learning in fault instance segmentation.We introduce mask propagation neural network for fault instance segmentation.Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles.Our method refers to the reference-guided mask propagation network,which is firstly used in video object segmentation:taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method.As a multi-level convolutional neural network,the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data,which can facilitate the fault reconstruction workflow.Compared with the traditional deep learning method,the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection.
基金This work was supported in part by the National Natural Science Foundation of China(61471101)the National Natural Science Foundation of China(U1736205).
文摘Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.
文摘Seismic facies analysis plays important roles in geological research,especially in sedimentary environment identification.Traditional method is mainly based on seismic waveform or attributes of a single seismic gather to classify the seismic facies.Ignoring the correlation between adjacent seismic gathers leads to poor lateral continuities in generated facies map,which cannot fit the sedimentary characteristics well.In fact,according to sedimentology theory,the horizontal continuities of the stratum can be utilized as priori information to provide more information for waveform classification.Therefore,we develop an unsupervised method for pre-stack seismic facies analysis,which is constrained by spatial continuity.The proposed method establishes a probabilistic model to characterize the correlation between neighboring reflection elements.Subsequently,this correlation is used as a regularization term to modify the objective function of the clustering algorithm,allowing the mode assignment of reflective elements to be influenced by the labels of their neighbors.Test on synthetic data confirms that,compared with traditional seismic facies analysis methods,the facies maps generated by the proposed method have more continuous and homogeneous textures,and less uncertainty on the boundary.The test on actual seismic data further confirms that the proposed method can describe more details of the distribution of lithological bodies of interest.The proposed method is an effective tool for pre-stack seismic facies analysis.
基金Wupport from the National Natural Science Foundation of China(Grant No.42130812,42174151,and 41804126).
文摘We propose to use a Few-Shot Learning(FSL)method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data.Recently,artificial neural network(ANN)demonstrates great advantages for seismic inversion because of its powerful feature extraction and parameter learning ability.Hence,ANN method could provide a high resolution inversion result that are critical for reservoir characterization.However,the ANN approach requires plenty of labeled samples for training in order to obtain a satisfactory result.For the common problem of scarce samples in the ANN seismic inversion,we create a novel pre-stack seismic inversion method that takes advantage of the FSL.The results of conventional inversion are used as the auxiliary dataset for ANN based on FSL,while the well log is regarded the scarce training dataset.According to the characteristics of seismic inversion(large amount and high dimensional),we construct an arch network(A-Net)architecture to implement this method.An example shows that this method can improve the accuracy and resolution of inversion results.
