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1.
地震P波、S波到时是精确分析地震水平位置、深度与速度结构等的重要参数,如何准确拾取P波和S波到时是地震学的一项重要的基础工作.大数据量与强噪声环境给地震到时的自动拾取带来了很大挑战.在频率域中可将信号与噪声分离,但会造成震相的偏移.针对上述问题,本文在STA/LTA、AIC方法的基础上,引入了标准时频变换(Normal Time-Frequency Transform,NTFT),结合信号时间域与频率域特征,提出了基于NTFT的STA/LTA方法,以及基于NTFT的AIC方法来拾取P波和S波的到时.基于NTFT的STA/LTA方法通过构建即时频率约束的特征函数,以增强地震信号振幅响应的变化特征.基于NTFT的AIC方法则根据NTFT的变换系数定位即时频率-时间基准点,通过滑动窗口直接对标准时频谱进行AIC处理拾取最佳到时.本文采用了不同强度噪声的60组合成数据和105组实测地震数据对方法的可靠性进行检验.以人工拾取到时为参考,实测数据中NTFT-STA/LTA方法拾取P波、S波到时的均方根误差分别为0.36 s和0.56 s;NTFT-AIC方法拾取P波、S波到时的均方根误差分别为0.25 s和0.35 s.相比于STA/LTA、AIC方法,NTFT改进后的方法提高了P波和S波到时的拾取准确率,为强噪声环境下的地震波形到时拾取提供了新思路.  相似文献   
2.
基于1951—2018年衢州市椪柑采摘期降水量、雨日、日照时数、相对湿度等逐日气象资料,应用统计分析和小波分析方法,分析椪柑采摘期连阴雨天气变化特征及其大气环流背景。结果表明:1951—2018年衢州椪柑采摘期连阴雨日数、次数和强度呈略微增加趋势、滑动3 d无雨次数呈减少趋势;滑动3 d无雨次数存在明显的5 a、7 a和15 a左右的年际和年代际周期变化规律,并且均呈现缩短趋势;椪柑采摘期连阴雨较强年亚欧地区呈两高一低的径向型环流,强冷空气南下活动较频繁、东移缓慢,偏南暖湿气流活跃,致使冷暖空气在中国长江中下游地区长时间交汇,导致连阴雨天气。  相似文献   
3.
SWAN2.0系统的设计与实现   总被引:2,自引:0,他引:2  
强对流天气短时临近预报系统(Severe Weather Automatic Nowcasting,SWAN)是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0基于MICAPS4(Meteorological Information Comprehensive Analysis and Processing System Version 4.0,人机交互气象信息处理和天气预报制作系统)二次开发框架,采用C/S架构,服务器部署在省级,负责收集数据,运算SWAN产品;客户端部署在气象台站,实现具体的预报业务,并形成算法二次开发接口。SWAN2.0新增了三维变分风场反演、基于分雨团技术的雷达降水估测、冰雹识别等方法,实现了算法管理、产品生成、分析处理、资料检索显示、实时监控报警、预警产品制作等功能。SWAN2.0业务系统已在全国试用,在强对流天气监测、分析和短时临近预报预警中发挥了重要作用。  相似文献   
4.
As the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-to-noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation.  相似文献   
5.
The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.  相似文献   
6.
在油气田开发过程中,微震监测是获得水力压裂引起裂缝分布的一种较为有效的方法。微震的定位成像与裂缝解释需要利用有效微震信号位置,而微震信号具有低信噪比的特点,传统信号拾取方法无法有效实现较低信噪比条件下初至时刻的准确拾取。本文提出一种基于时频谱熵的初至拾取新方法,该方法首先通过S变换获取含噪信号的时频谱;然后对谱内各个采样点沿频率方向进行分帧操作,并计算每帧频段内的近似负熵值,以最小近似负熵值作为该谱点的负熵值;最后沿时间方向比较各谱点的负熵值,最小值对应的时刻即为初至时刻。本文利用不同信噪比的合成地震数据对该方法进行效果验证,并与长短时窗能量比(STA/LTA)法进行拾取结果对比,结果表明:信噪比在-5 dB时,两种方法拾取效果都很好;信噪比在-10 dB时,时频谱熵法拾取效果更好。时频谱熵法更适合低信噪比情况下的信号初至拾取。  相似文献   
7.
海岸带是受人类活动和全球海平面上升影响的敏感地带,海岸线的提取和监测是海岸带生态系统研究和社会管理的重要内容。本文在遥感和地理信息系统的支持下,以修正的归一化水体指数(Modified Normalized Difference Water Index,MNDWI)为基础,结合遥感影像处理和直方图均衡化等技术,实现了大连市獐子岛1985—2016年海岸线的自动化提取。结果表明:(1)通过与三位专家目视解译的成果比对,本文提取海岸线的精度能满足后续研究的要求(相对误差分别为0.045%,0.032%和0.023%);(2)近30年来,獐子岛海岸线总体呈现蚀退趋势,岸线长度与岛屿面积分别呈现变短和变小的趋势,獐子岛(主岛)和大耗岛的岸线蚀退速率最大,褡裢岛次之,小耗岛最小;在人类活动较为密集的区域,海岸线呈现出较为强烈的增长趋势,海水养殖和圈海建坝是岸线增长的主要驱动力;(3)獐子岛海岸线具有显著的分形性质,分形维数随时间呈现增大的趋势,獐子岛(主岛)的分形维数最大,褡裢岛的分形维数最小。  相似文献   
8.
Despite the recent development in radiometric dating of numerous zircons by LA-ICPMS, mineral separation still remains a major obstacle, particularly in the search for the oldest material on Earth. To improve the efficiency in zircon separation by an order of magnitude, we have designed/developed a new machine-an automatic zircon separator(AZS). This is designed particularly for automatic pick-up of100 μm-sized zircon grains out of a heavy mineral fraction after conventional separation procedures. The AZS operates in three modes:(1) image processing to choose targeted individual zircon grains out of all heavy minerals spread on a tray,(2) automatic capturing of the individual zircon grains with microtweezers, and(3) placing them one-by-one in a coordinated alignment on a receiving tray. The automatic capturing was designed/created for continuous mineral selecting without human presence for many hours. This software also enables the registration of each separated zircon grain for dating, by recording digital photo-image, optical(color) indices, and coordinates on a receiving tray. We developed two new approaches for the dating; i.e.(1) direct dating of zircons selected by LA-ICPMS without conventional resin-mounting/polishing,(2) high speed U-Pb dating, combined with conventional sample preparation procedures using the new equipment with multiple-ion counting detectors(LA-MIC-ICPMS).With the first approach, Pb-Pb ages obtained from the surface of a mineral were crosschecked with the interior of the same grain after resin-mounting/polishing. With the second approach, the amount of time required for dating one zircon grain is ca. 20 s, and a sample throughput of 150 grains per hour can be achieved with sufficient precision(ca. 0.5%).We tested the practical efficiency of the AZS, by analyzing an Archean Jack Hills conglomerate in Western Australia with the known oldest(4.3 Ga) zircon on Earth. Preliminary results are positive; we were able to obtain more than 194 zircons that are over 4.0 Ga out of ca. 3800 checked grains, and 9 grains were over 4300 Ma with the oldest at 4371 ± 7 Ma. This separation system by AZS, combined with the new approaches, guarantees much higher yield in the hunt for old zircons.  相似文献   
9.
基于岩石图像深度学习的岩性自动识别与分类方法   总被引:8,自引:3,他引:5  
张野  李明超  韩帅 《岩石学报》2018,34(2):333-342
岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。  相似文献   
10.
The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.  相似文献   
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