地震是极具破坏性与不确定性的自然现象,在人们毫无察觉的情况下地震发生在人口稠密区时,将严重危害人们生命财产安全。人们不断努力了解地震的物理特征和物理危害与环境之间的相互作用,以便在地震发生前发出适当的警报。可靠的地震预...地震是极具破坏性与不确定性的自然现象,在人们毫无察觉的情况下地震发生在人口稠密区时,将严重危害人们生命财产安全。人们不断努力了解地震的物理特征和物理危害与环境之间的相互作用,以便在地震发生前发出适当的警报。可靠的地震预测应包含对地震信号的分析,但是这些信号在地震发生前不明显;因此使用数据驱动机器学习的方法来分析这些信号与地震的联系并预测地震。通过建立观测台网连续监测与地震发生相关的各种物理量或化学量,据此获取的地震前兆信息是地震预测的研究基础。地震发生前,地球物理场发生显著变化,伴随电磁和地声等多种前兆信号,其中电磁和地声信号具有临震特性,是开展地震临震观测预测研究的重要数据来源;因此对地下的电磁扰动和地声信号进行实时监测,获取长期观测数据用于数据驱动机器学习方法预测地震。该文基于AETA数据的临震模型预报,针对多分量地震监测预测系统(Acoustic and Electromagnetic Testing All in one system,AETA)在川滇地区记录的电磁和地声数据,提取时域和频域特征,采用基于随机森林算法、轻量级梯度提升决策树和极度随机树的集成学习方法共同预测该区域的发震情况,选取发震概率最大的子区域中心位置作为震中预测结果,进一步训练LightGBM回归模型以预测此子区域的震级,按周对地震三要素进行预测。实验结果表明,该方法在川滇地区地震风险预测上,准确率可达0.64,震级预测的平均误差为0.38,最小误差为0.00,具有良好的预测效果。展开更多
Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite method...Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.展开更多
文摘地震是极具破坏性与不确定性的自然现象,在人们毫无察觉的情况下地震发生在人口稠密区时,将严重危害人们生命财产安全。人们不断努力了解地震的物理特征和物理危害与环境之间的相互作用,以便在地震发生前发出适当的警报。可靠的地震预测应包含对地震信号的分析,但是这些信号在地震发生前不明显;因此使用数据驱动机器学习的方法来分析这些信号与地震的联系并预测地震。通过建立观测台网连续监测与地震发生相关的各种物理量或化学量,据此获取的地震前兆信息是地震预测的研究基础。地震发生前,地球物理场发生显著变化,伴随电磁和地声等多种前兆信号,其中电磁和地声信号具有临震特性,是开展地震临震观测预测研究的重要数据来源;因此对地下的电磁扰动和地声信号进行实时监测,获取长期观测数据用于数据驱动机器学习方法预测地震。该文基于AETA数据的临震模型预报,针对多分量地震监测预测系统(Acoustic and Electromagnetic Testing All in one system,AETA)在川滇地区记录的电磁和地声数据,提取时域和频域特征,采用基于随机森林算法、轻量级梯度提升决策树和极度随机树的集成学习方法共同预测该区域的发震情况,选取发震概率最大的子区域中心位置作为震中预测结果,进一步训练LightGBM回归模型以预测此子区域的震级,按周对地震三要素进行预测。实验结果表明,该方法在川滇地区地震风险预测上,准确率可达0.64,震级预测的平均误差为0.38,最小误差为0.00,具有良好的预测效果。
基金Supported by the National Natural Science Foundation of China(Nos.61301240,61271406)
文摘Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.
