Cascading multi-segment rupture process of the 2023 Turkish earthquake doublet on a complex fault system revealed by teleseismic P wave back projection method

In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

Interseismic slip distribution and locking characteristics of the mid-southern segment of the Tanlu fault zone

We employ the block negative dislocation model to invert the distribution of fault coupling and slip rate deficit on the different segments of the Tanlu(Tancheng-Lujiang) fault zone, according to the GPS horizontal velocity field from 1991 to 2007(the first phase) and 2013 to 2018(the second phase). By comparing the deformation characteristics results, we discuss the relationship between the deformation characteristics with the M earthquake in Japan. The results showed that the fault coupling rate of the northern section of Tancheng in the second phase reduced compared with that in the first phase. However, the results of the two phases showed that the northern section of Juxian still has a high coupling rate, a deep blocking depth, and a dextral compressive deficit, which is the enrapture section of the 1668 Tancheng earthquake. At the same time, the area strain results show that the strain rate of the central and eastern regions of the second phase is obviously enhanced compared with that of the first phase. The occurrence of the great earthquake in Japan has played a specific role in alleviating the strain accumulation in the middle and south sections of the Tanlu fault zone. The results of the maximum shear strain show that the shear strain in the middle section of the Tanlu fault zone in the second phase is weaker than that in the first phase, and the maximum shear strain in the southern section is stronger than that in the first phase. The fault coupling coefficient of the south Sihong to Jiashan section is high, and it is also the unruptured section of historical earthquakes. At the same time, small earthquakes in this area are not active and accumulate stress easily, so the future earthquake risk deserves attention.

An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation

In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) regularizer.The convergence of the ADMM algorithm is analyzed.In the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation result.Numerical experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or both.We compare the AITV method with its original convex TV and nonconvex TVP(O<p<1)counterparts,showcasing the qualitative and quantitative advantages of our proposed method.

New normalized nonlocal hybrid level set method for image segmentation

This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.

AN AUTOMATIC SEGMENTATION METHOD FOR FAST IMAGING IN PET

A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time. The result shows that this method gives only 3 min-scan time which is perfect for attenuation correction of the PET images instead of the original 15-30 min-scan time. This approach has been successfully tested both on phantom and clinical data.

A Coastal Zone Segmentation Variational Model and Its Accelerated ADMM Method

Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaption parameter and modifying noisy data term, the proposed variational model provides a good solution for the coastal zone SAR image with common characteristics of inherent speckle noise and complicated geometrical details. However, the proposed model is difficult to solve due to to its nonlinear, non-convex and non-smooth characteristics. Followed by curve evolution theory and operator splitting method, the minimization problem is reformulated as a constrained minimization problem. A fast alternating minimization iterative scheme is designed to implement coastal zone segmentation. Finally, various two-stage and multiphase experimental results illustrate the advantage of the proposed segmentation model, and indicate the high computation efficiency of designed numerical approximation algorithm.

ANALYSIS OF DYNAMICAL BUCKLING AND POST BUCKLING FOR BEAMS BY FINITE SEGMENT METHOD

Based on the multi-rigid body discretization model, namely, finite segment model,a chain multi-rigid-body-hinge-spring system model of a beam was presented, then a nonlinear parametrically exacted vibration equation of multi-degrees of freedom system was established using the coordination transformation method, and its resonance fields were derived by the restriction parameter method, that is, the dynamical buckling analysis of the beam. Because the deformation of a beam is not restricted by the discrete model and dynamic equation, the post buckling analysis can be done in above math model. The numerical solutions of a few examples were obtained by direct integrated method, which shows that the mechanical and math model gotten is correct.

Segmentation of Bacteria Image Based on Level Set Method

In biology ferment engineering,accurate statistics of the quantity of bacteria is one of the most important subjects. In this paper,the quantity of bacteria which was observed traditionally manuauy can be detected automatically. Image acquisition and processing system is designed to accomplish image preprocessing,image segmentation and statistics of the quantity of bacteria. Segmentation of bacteria images is successfully realized by means of a region-based level set method and then the quantity of bacteria is computed precisely,which plays an important role in optimizing the growth conditions of bacteria.

Study of the segmentation of active fault by the boundary element method──analysis of the Xianshuihe fault zone

On the basis of the recent geological surveys and the relevant studies of the Xianshuihe fault zone, this paper analyzes the seismogenic mechanism of some faults and characteristic morphology on the fault zone by the boundary element method and discusses the fault segmentation and the related distribution of the earthquake ruptures. The main conclusions are: For the first order segmentation, the Xianshuihe fault zone can be divided into three major segments (the northwestern Luhuo-Qianning segment, the middle linking fracture region and the southeastern Kangding segment). Among them, the differences are not only in geometry and structure, but also in mechanical property and dynamics. Some of the characteristic morphologies on the Xianshuihe fault zone reflect the effects in cumulative deformation due to long-term fault movement, and reveal the fault segmentation in different orders. Such morphologies control, to some extent, the developments and the distributions of the earthquake ruptures on the fault zone.

Improved C-V Level Set Algorithm and its Application in Video Segmentation

Image segmentation method based on level set model has wide potential application for its excellent seg-mentation result. However its complex computing restricts its application in video segmentation. In order to improve the speed of image segmentation, this paper presents a new level set initialization method based on Chan-Vese level set model. After a simple iterative, we can separate out the outline of objects. Experiments show that the method is simple and efficient, with good separation effects. The improved Chan-Vese method can be applied in video segmentation.

A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

FLEXIBLE MULTIBODY SYSTEM DYNAMICS-FINITE SEGMENT METHOD

The principle and method of flexible multibody system dynamics is presented. The dynamic equation have been developed by means of Huston's method based on Kane's equation. In which the flexible members with general cross-section characters were divided into finite segment models under the assumption of small strain. In order to decrease the degrees of freedom of the system and increase the efficiency of numerical calculation. the mode transformation has been introduced. A typical example is presented. and the foregoing method has been perfectly verified.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

改进DeepLabV3+算法提取无作物田垄导航线

机器视觉导航是智慧农业的重要部分,无作物田垄的导航线检测是旱地移栽导航的关键。针对无作物田垄颜色信息相近、纹理差距小,传统图像处理方法适用性差、准确率低,语义分割算法检测速度慢、实时性差的问题,该研究提出一种基于改进DeepLabV3+的田垄分割模型。首先对传统DeepLabV3+网络进行轻量化设计,用MobileNetV2网络代替主干网络Xception,以提高算法的检测速度和实时性;接着引入CBAM(convolutional block attention module,CBAM)注意力机制,使模型能够更好地处理垄面边界信息;然后利用垄面边界信息获得导航特征点,对于断垄情况,导航特征点会出现偏差,因此利用四分位数对导航特征点异常值进行筛选,并采用最小二乘法进行导航线拟合。模型评估结果显示,改进后模型的平均像素精确度和平均交并比分别为96.27%和93.18%,平均检测帧率为84.21帧/s,优于PSPNet、U-Net、HRNet、Segformer以及DeepLabV3+网络。在不同田垄环境下,最大角度误差为1.2°,最大像素误差为9,能够有效从不同场景中获取导航线。研究结果可为农业机器人的无作物田垄导航提供参考。

基于改进Canny边缘检测的堆叠货箱分割定位方法

为了提高集装箱卸货的自动化水平,针对集装箱内堆叠货箱难分割定位的问题,提出了一种基于改进Canny边缘检测的堆叠货箱分割定位方法。通过阈值分割和形态学处理进行图像预处理,去除背景干扰,提取堆叠货箱区域图像,基于改进Canny算法对堆叠货箱进行边缘检测,根据堆叠货箱边缘特征进行筛选并基于最小二乘法进行直线拟合,解决边缘线条不连续和虚假边缘问题,对边缘进行区域化处理,以此将堆叠货箱分割成独立的货箱区域,提取每个独立货箱的最小外接矩形,得到货箱中心点的位置信息。实验结果表明,该方法对堆叠货箱有很好的分割效果,定位精度小于5 mm,满足定位精度要求。

基于改进神经网络的弱光图像自动分割算法

弱光条件下图像的对比度低、细节模糊、噪声干扰大.为了使分割后的弱光图像边缘更加整齐,分割目标更加完整,提出一种基于改进神经网络的弱光图像自动分割算法.采用SLIC方法展开弱光图像超像素,根据弱光图像中所存在的像素相似性,对所生成的全部超像素块展开超像素块融合处理;并结合改进的神经网络,分割出弱光图像的目标图像与背景图像,实现弱光图像自动分割.实验结果表明,所提方法的弱光图像超像素融合效果好、分割精度高.

盾构隧道缓和曲线段管片组合轴线拟合计算方法

盾构隧道在缓和曲线部分存在管片拼装问题,因缓和曲线线型复杂,管片拼装点位多变,一直是施工过程中的一个难题。基于错缝拼装要求与楔形管片特点,提出一种在平面范围内,利用循环计算与误差控制的条件判定,综合比选标准环与楔形环组合,完成每环管片拼装施工,最终拟合出既满足规范与控制指标,又可保证线型较为均匀的管片拼装轴线的管片施工设计方法与验证方法。并根据实际工程需要,提出最大允许偏差控制方法和缓圆点附近的管片拼装优化措施,结合珠海市珠机线湾仔北站—湾仔站区间盾构隧道实例,证明了此方法的准确性与适用性,为缓和曲线上控制管片拼装质量提供理论依据。

基于四电极多频率扫描的生物阻抗法人体成分测试仪设计

生物阻抗包含丰富的人体病理和生理电信号,可以作为医务人员准确判断病人症状和治疗疾病的主要依据。基于此,提出一种基于Cole-Cole理论的人体多种参数测量系统。构建人体阻抗模型,通过碳纳米材料电极对人体阻抗进行采集分析,得到人体的各种成分参数。通过信号发生电路产生注入人体的低频信号,利用幅度检测电路检测流经人体电流产生的电压,采用分段检测法测得人体不同分段的阻抗信息;信号经放大电路输入RT1052微处理器,对数据和人体成分参数相关性进行分析,得到人体各种成分参数并显示测量结果。通过对系统进行测试分析,得出所设计系统能够准确地对人体蛋白质、脂肪等参数进行测量,误差范围可以控制在5%以内,具有很好的应用前景。

采用大直径灌浆套筒连接的预制桥墩抗震性能研究

为探究墩台处采用大直径灌浆套筒连接的预制装配式桥墩抗震性能,进行拟静力试验及有限元分析。设计、制作2个采用大直径灌浆套筒(主筋直径40 mm)连接的预制装配式桥墩(预制桥墩)及1个现浇桥墩开展拟静力试验,对2类试件的试验现象、破坏形态、滞回曲线和承载能力进行对比分析,并采用有限元法对预制桥墩混凝土、套筒及连接钢筋的受力性能进行研究。试验结果表明:在水平循环荷载作用下,预制桥墩与现浇桥墩一致表现为弯剪破坏,但墩底区域破坏形式不同,现浇桥墩主要表现为柱脚混凝土大面积压碎,而预制桥墩的破坏现象更为严重,集中表现为套筒四周混凝土成块压碎剥落;相较于现浇桥墩,预制桥墩的承载能力平均值偏小5.2%,位移延性系数平均值偏小2.7%,预制桥墩力学性能与现浇桥墩相当。有限元分析结果表明:预制桥墩会在套筒顶部和桥墩底部出现2个塑性区,且预制桥墩中大直径灌浆套筒的受力状态以轴向受力为主,按照轴向受力进行套筒设计可满足其在桥墩结构中的受力要求;预制桥墩在墩底混凝土破坏后,其主筋和灌浆料仍保持可靠粘结,采用大直径灌浆套筒连接的预制桥墩抗震性能良好。