The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain ...The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain oceanic internal wave stripes from synthetic aperture radar(SAR)images,a stripe segmentation algorithm is proposed based on the TransUNet framework,which is a combination of U-Net and Transformer,which is also optimized.Through adjusting the number of Transformer layer,multi-layer perceptron(MLP)channel,and Dropout parameters,the influence of over-fitting on accuracy is significantly weakened,which is more conducive to segmenting lightweight oceanic internal waves.The results show that the optimized algorithm can accurately segment oceanic internal wave stripes.Moreover,the optimized algorithm can be trained on a microcomputer,thus reducing the research threshold.The proposed algorithm can also change the complexity of the model to adapt it to different date scales.Therefore,TransUNet has immense potential for segmenting oceanic internal waves.展开更多
医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取...医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取不同尺度的特征,融合两个分支的特征,实现特征互补;针对在融合两个分支的特征时可能存在冗余信息问题,添加多核并行池化模块,在保留多尺度特征的同时去除冗余信息;在解码器设计多尺度融合模块(USF),融合来自编码器的三个尺度信息,有效弥补编码器与解码器之间的信息差距。在Synapse和ACDC数据集上进行了多次对比实验,Synapse数据集上平均DSC系数可达79.20%,较TransUNet模型提高3.34%,HD距离为25.24%,降低了11.67%;在ACDC数据集上平均DSC系数可达90.30%,提高1.67%。In the medical image segmentation task, in order to make full use of the advantages of TransUNet model which can effectively capture global and local features, the DouTransNet model is proposed based on it. The encoder part aims at the problems of single learning Angle and easy loss of detail features in single-branch Transformer module. Transformer is designed as a two-branch parallel structure to extract features of different scales and fuse features of two branches to achieve feature complementarity. To solve the problem of redundant information when fusing the features of two branches, a multi-core parallel pooling module is added to remove redundant information while retaining multi-scale features. In the decoder design multi-scale fusion module (USF), the information of three scales from the encoder is fused to effectively bridge the information gap between the encoder and the decoder. Several comparison experiments were conducted on Synapse and ACDC data sets. The average DSC coefficient on Synapse data set can reach 79.20%, which is 3.34% higher than TransUNet model, and the HD distance is 25.24%, which is 11.67% lower. The average DSC coefficient on ACDC dataset can reach 90.30%, an increase of 1.67%.展开更多
Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characte...Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention,often resulting in suboptimal performance when dealing with complex and noisy data.In this study,we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network.Our model integrates the ad-vantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously.We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam.The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data.The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the Tran-sUNet achieves the optimal balance in its architecture and inference speed.With relatively low inference time and network complexity,it operates effectively in high-precision microseismic phase pickings.This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reser-voir monitoring applications.展开更多
基金The National Natural Science Foundation of China under contract No.51679132the Science and Technology Commission of Shanghai Municipality under contract Nos.21ZR1427000 and 17040501600.
文摘The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain oceanic internal wave stripes from synthetic aperture radar(SAR)images,a stripe segmentation algorithm is proposed based on the TransUNet framework,which is a combination of U-Net and Transformer,which is also optimized.Through adjusting the number of Transformer layer,multi-layer perceptron(MLP)channel,and Dropout parameters,the influence of over-fitting on accuracy is significantly weakened,which is more conducive to segmenting lightweight oceanic internal waves.The results show that the optimized algorithm can accurately segment oceanic internal wave stripes.Moreover,the optimized algorithm can be trained on a microcomputer,thus reducing the research threshold.The proposed algorithm can also change the complexity of the model to adapt it to different date scales.Therefore,TransUNet has immense potential for segmenting oceanic internal waves.
文摘医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取不同尺度的特征,融合两个分支的特征,实现特征互补;针对在融合两个分支的特征时可能存在冗余信息问题,添加多核并行池化模块,在保留多尺度特征的同时去除冗余信息;在解码器设计多尺度融合模块(USF),融合来自编码器的三个尺度信息,有效弥补编码器与解码器之间的信息差距。在Synapse和ACDC数据集上进行了多次对比实验,Synapse数据集上平均DSC系数可达79.20%,较TransUNet模型提高3.34%,HD距离为25.24%,降低了11.67%;在ACDC数据集上平均DSC系数可达90.30%,提高1.67%。In the medical image segmentation task, in order to make full use of the advantages of TransUNet model which can effectively capture global and local features, the DouTransNet model is proposed based on it. The encoder part aims at the problems of single learning Angle and easy loss of detail features in single-branch Transformer module. Transformer is designed as a two-branch parallel structure to extract features of different scales and fuse features of two branches to achieve feature complementarity. To solve the problem of redundant information when fusing the features of two branches, a multi-core parallel pooling module is added to remove redundant information while retaining multi-scale features. In the decoder design multi-scale fusion module (USF), the information of three scales from the encoder is fused to effectively bridge the information gap between the encoder and the decoder. Several comparison experiments were conducted on Synapse and ACDC data sets. The average DSC coefficient on Synapse data set can reach 79.20%, which is 3.34% higher than TransUNet model, and the HD distance is 25.24%, which is 11.67% lower. The average DSC coefficient on ACDC dataset can reach 90.30%, an increase of 1.67%.
基金supported by a National Natural Science Foundation of China(Grant number 41974150 and 42174158)Natural Science Basic Research Program of Shaanxi(2023-JC-YB-220).
文摘Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention,often resulting in suboptimal performance when dealing with complex and noisy data.In this study,we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network.Our model integrates the ad-vantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously.We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam.The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data.The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the Tran-sUNet achieves the optimal balance in its architecture and inference speed.With relatively low inference time and network complexity,it operates effectively in high-precision microseismic phase pickings.This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reser-voir monitoring applications.
