The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ...The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.展开更多
We study the Cauchy problem of the Kolmogorov-Fokker-Planck equations and show that the solution enjoys an analytic smoothing effect with L?initial datum for positive time.
Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models.Existing defense mechanisms often suffer drawbacks,such as the need for mode...Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models.Existing defense mechanisms often suffer drawbacks,such as the need for model retraining,significant inference time overhead,and limited effectiveness against specific attack types.Achieving perfect defense against adversarial attacks remains elusive,emphasizing the importance of mitigation strategies.In this study,we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks.First,the image was randomly cropped to vary its dimensions and then placed at the center of a fixed 299299 space,with the remaining areas filled with zero padding.Subsequently,Gaussian×filtering with a 77 kernel and a standard deviation of two was applied using a convolution operation.Finally,the×smoothed image was fed into the classification model.The proposed defense method consistently appeared in the upperright region across all attack scenarios,demonstrating its ability to preserve classification performance on clean images while significantly mitigating adversarial attacks.This visualization confirms that the proposed method is effective and reliable for defending against adversarial perturbations.Moreover,the proposed method incurs minimal computational overhead,making it suitable for real-time applications.Furthermore,owing to its model-agnostic nature,the proposed method can be easily incorporated into various neural network architectures,serving as a fundamental module for adversarial defense strategies.展开更多
Combining TT* argument and bilinear interpolation,this paper obtains the Strichartz and smoothing estimates of dispersive semigroup e^(-itP(D)) in weighted L^(2) spaces.Among other things,we recover the results in[1]....Combining TT* argument and bilinear interpolation,this paper obtains the Strichartz and smoothing estimates of dispersive semigroup e^(-itP(D)) in weighted L^(2) spaces.Among other things,we recover the results in[1].Moreover,the application of these results to the well-posedness of some equations are shown in the last section.展开更多
We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination unif...We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.展开更多
叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹...叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹配参考地震道与其他地震道的相似性来校正同相轴的一致性,但当各地震道之间的幅值差异较大,或者地震数据信噪比较低时,该方法易出现错误匹配的情况,进而导致波形发生畸变,因而影响了其在道集校正中的应用效果。针对上述问题,提出了一种基于迭代光滑动态时间规整算法(iterative smooth dynamic time warping,ISDTW)的共成像点道集叠加方法,该方法引入平滑约束和迭代策略对规整路径进行优化,提高了道集的校正精度和同相轴的一致性。特别是在复杂地质条件下,ISDTW算法取得了较好的成像效果。合成数据和实际数据的测试结果验证了ISDTW算法在减少局部匹配误差、提升地震成像质量方面的显著优势。展开更多
基金supported by the National Key R&D Program of China(No.2022YFC3003502).
文摘The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.
基金Supported by NSFC (No.12031006)Fundamental Research Funds for the Central Universities of China。
文摘We study the Cauchy problem of the Kolmogorov-Fokker-Planck equations and show that the solution enjoys an analytic smoothing effect with L?initial datum for positive time.
基金supported by the Glocal University 30 Project Fund of Gyeongsang National University in 2025.
文摘Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models.Existing defense mechanisms often suffer drawbacks,such as the need for model retraining,significant inference time overhead,and limited effectiveness against specific attack types.Achieving perfect defense against adversarial attacks remains elusive,emphasizing the importance of mitigation strategies.In this study,we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks.First,the image was randomly cropped to vary its dimensions and then placed at the center of a fixed 299299 space,with the remaining areas filled with zero padding.Subsequently,Gaussian×filtering with a 77 kernel and a standard deviation of two was applied using a convolution operation.Finally,the×smoothed image was fed into the classification model.The proposed defense method consistently appeared in the upperright region across all attack scenarios,demonstrating its ability to preserve classification performance on clean images while significantly mitigating adversarial attacks.This visualization confirms that the proposed method is effective and reliable for defending against adversarial perturbations.Moreover,the proposed method incurs minimal computational overhead,making it suitable for real-time applications.Furthermore,owing to its model-agnostic nature,the proposed method can be easily incorporated into various neural network architectures,serving as a fundamental module for adversarial defense strategies.
基金supported by the NSFC(12071437)the National Key R&D Program of China(2022YFA1005700).
文摘Combining TT* argument and bilinear interpolation,this paper obtains the Strichartz and smoothing estimates of dispersive semigroup e^(-itP(D)) in weighted L^(2) spaces.Among other things,we recover the results in[1].Moreover,the application of these results to the well-posedness of some equations are shown in the last section.
基金supported by the National Natural Science Foundation of China(Grant No.62275235).
文摘We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.
文摘叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹配参考地震道与其他地震道的相似性来校正同相轴的一致性,但当各地震道之间的幅值差异较大,或者地震数据信噪比较低时,该方法易出现错误匹配的情况,进而导致波形发生畸变,因而影响了其在道集校正中的应用效果。针对上述问题,提出了一种基于迭代光滑动态时间规整算法(iterative smooth dynamic time warping,ISDTW)的共成像点道集叠加方法,该方法引入平滑约束和迭代策略对规整路径进行优化,提高了道集的校正精度和同相轴的一致性。特别是在复杂地质条件下,ISDTW算法取得了较好的成像效果。合成数据和实际数据的测试结果验证了ISDTW算法在减少局部匹配误差、提升地震成像质量方面的显著优势。
