为提高管道环焊缝超声衍射时差法(time of flight diffraction,TOFD)扫描图谱在背景信号干扰、样本量不均衡等情况下的缺陷识别效果,提出了一种改进的YOLOv5s网络模型.针对管道环焊缝TOFD图谱中缺陷形态不规则的特点,通过引入可变形卷积...为提高管道环焊缝超声衍射时差法(time of flight diffraction,TOFD)扫描图谱在背景信号干扰、样本量不均衡等情况下的缺陷识别效果,提出了一种改进的YOLOv5s网络模型.针对管道环焊缝TOFD图谱中缺陷形态不规则的特点,通过引入可变形卷积,使得网络自适应缺陷自身的形状特点,提高TOFD图谱中不规则缺陷的特征提取能力;针对TOFD扫描图谱中直通波和底面波等干扰波形对缺陷识别的影响,通过在网络不同深度分别添加自注意力机制,引导网络关注缺陷细微特征的同时抑制界面波对缺陷识别的影响;针对实际样本中各类缺陷不均衡的情况,采用SlideLoss损失函数代替原损失函数,提高网络对样本量较少的裂纹类缺陷的识别精度.对比试验结果表明,改进后的网络能够抑制TOFD图谱复杂背景干扰,提高样本不均衡条件下的识别率.相比原网络,整体平均识别率均值(mean Average Precision,mAP)和裂纹类缺陷的平均识别率(Average Precision,AP)分别提高了8.2%和7.3%.展开更多
We present a theoretical scheme to realize two-dimensional(2D)asymmetric diffraction grating in a five-level inverted Y-type asymmetric double semiconductor quantum wells(SQWs)structure with resonant tunneling.The SQW...We present a theoretical scheme to realize two-dimensional(2D)asymmetric diffraction grating in a five-level inverted Y-type asymmetric double semiconductor quantum wells(SQWs)structure with resonant tunneling.The SQW structure interacts with a weak probe laser field,a spatially independent 2D standing-wave(SW)field,and a Laguerre–Gaussian(LG)vortex field,respectively.The results indicate that the diffraction patterns are highly sensitive to amplitude modulation and phase modulation.Because of the existence of vortex light,it is possible to realize asymmetric high-order diffraction in the SQW structure,and then a 2D asymmetric grating is established.By adjusting the detunings of the probe field,vortex field,and SW field,as well as the interaction length,diffraction intensity,and direction of the 2D asymmetric electromagnetically induced grating(EIG)can be controlled effectively.In addition,the number of orbital angular momenta(OAM)and beam waist parameter can be used to modulate the diffraction intensity and energy transfer of the probe light in different regions.High-order diffraction intensity is enhanced and high-efficiency 2D asymmetric diffraction grating with different diffraction patterns is obtained in the scheme.Such 2D asymmetric diffraction grating may be beneficial to the research of optical communication and innovative semiconductor quantum devices.展开更多
In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and g...In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and gas.Due to these differences,the classification and identification of karst cavities and faults are of great significance for reservoir development.Traditional seismic attributes and diffraction imaging techniques can effectively identify discontinuities in seismic images,but these techniques do not distinguish whether these discontinuities are karst cavities,faults,or other structures.It poses a challenge for seismic interpretation to accurately locate and classify karst cavities or faults within the seismic attribute maps and diffraction imaging profiles.In seismic data,the scattering waves are associated with small-scale scatters like karst cavities,while diffracted waves are seismic responses from discontinuous structures such as faults,reflector edges and fractures.In order to achieve classification and identification of small-scale karst cavities and faults in seismic images,we propose a diffraction classification imaging method which classifies diffracted and scattered waves in the azimuth-dip angle image matrix using a modified DenseNet.We introduce a coordinate attention module into DenseNet,enabling more precise extraction of dynamic and azimuthal features of diffracted and scattered waves in the azimuth-dip angle image matrix.Leveraging these extracted features,the modified DenseNet can produce reliable probabilities for diffracted/scattered waves,achieving high-accuracy automatic classification of cavities and faults based on diffraction imaging.The proposed method achieves 96%classification accuracy on the synthetic dataset.The field data experiment demonstrates that the proposed method can accurately classify small-scale faults and scatterers,further enhancing the resolution of diffraction imaging in complex geologic structures,and contributing to the localization of karstic fracture-cavern reservoirs.展开更多
文摘为提高管道环焊缝超声衍射时差法(time of flight diffraction,TOFD)扫描图谱在背景信号干扰、样本量不均衡等情况下的缺陷识别效果,提出了一种改进的YOLOv5s网络模型.针对管道环焊缝TOFD图谱中缺陷形态不规则的特点,通过引入可变形卷积,使得网络自适应缺陷自身的形状特点,提高TOFD图谱中不规则缺陷的特征提取能力;针对TOFD扫描图谱中直通波和底面波等干扰波形对缺陷识别的影响,通过在网络不同深度分别添加自注意力机制,引导网络关注缺陷细微特征的同时抑制界面波对缺陷识别的影响;针对实际样本中各类缺陷不均衡的情况,采用SlideLoss损失函数代替原损失函数,提高网络对样本量较少的裂纹类缺陷的识别精度.对比试验结果表明,改进后的网络能够抑制TOFD图谱复杂背景干扰,提高样本不均衡条件下的识别率.相比原网络,整体平均识别率均值(mean Average Precision,mAP)和裂纹类缺陷的平均识别率(Average Precision,AP)分别提高了8.2%和7.3%.
基金supported by the National Natural Science Foundation of China(Grant No.12105210)the Knowledge Innovation Program of Wuhan-Basi Research(Grant No.2023010201010149)。
文摘We present a theoretical scheme to realize two-dimensional(2D)asymmetric diffraction grating in a five-level inverted Y-type asymmetric double semiconductor quantum wells(SQWs)structure with resonant tunneling.The SQW structure interacts with a weak probe laser field,a spatially independent 2D standing-wave(SW)field,and a Laguerre–Gaussian(LG)vortex field,respectively.The results indicate that the diffraction patterns are highly sensitive to amplitude modulation and phase modulation.Because of the existence of vortex light,it is possible to realize asymmetric high-order diffraction in the SQW structure,and then a 2D asymmetric grating is established.By adjusting the detunings of the probe field,vortex field,and SW field,as well as the interaction length,diffraction intensity,and direction of the 2D asymmetric electromagnetically induced grating(EIG)can be controlled effectively.In addition,the number of orbital angular momenta(OAM)and beam waist parameter can be used to modulate the diffraction intensity and energy transfer of the probe light in different regions.High-order diffraction intensity is enhanced and high-efficiency 2D asymmetric diffraction grating with different diffraction patterns is obtained in the scheme.Such 2D asymmetric diffraction grating may be beneficial to the research of optical communication and innovative semiconductor quantum devices.
基金supported by Science Fund for Creative Research Groups of the National Natural Science Foundation of China,No.42321002。
文摘In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and gas.Due to these differences,the classification and identification of karst cavities and faults are of great significance for reservoir development.Traditional seismic attributes and diffraction imaging techniques can effectively identify discontinuities in seismic images,but these techniques do not distinguish whether these discontinuities are karst cavities,faults,or other structures.It poses a challenge for seismic interpretation to accurately locate and classify karst cavities or faults within the seismic attribute maps and diffraction imaging profiles.In seismic data,the scattering waves are associated with small-scale scatters like karst cavities,while diffracted waves are seismic responses from discontinuous structures such as faults,reflector edges and fractures.In order to achieve classification and identification of small-scale karst cavities and faults in seismic images,we propose a diffraction classification imaging method which classifies diffracted and scattered waves in the azimuth-dip angle image matrix using a modified DenseNet.We introduce a coordinate attention module into DenseNet,enabling more precise extraction of dynamic and azimuthal features of diffracted and scattered waves in the azimuth-dip angle image matrix.Leveraging these extracted features,the modified DenseNet can produce reliable probabilities for diffracted/scattered waves,achieving high-accuracy automatic classification of cavities and faults based on diffraction imaging.The proposed method achieves 96%classification accuracy on the synthetic dataset.The field data experiment demonstrates that the proposed method can accurately classify small-scale faults and scatterers,further enhancing the resolution of diffraction imaging in complex geologic structures,and contributing to the localization of karstic fracture-cavern reservoirs.
