四川盐源盆地短周期密集台阵背景噪声分布特征分析   总被引：2，自引：0，他引：2       下载免费PDF全文

田原  瞿辰  王伟涛  于常青  李丽 《地球物理学报》2020,63(6):2248-2261

短周期密集台阵的高频背景噪声互相关函数(NCF)是探查地球浅层精细结构的重要数据.然而高频背景噪声成分复杂且容易分布不均,分析其对NCF信号提取的影响,有助于获取可靠成像结果.本文基于布设于川滇地区盐源盆地的209个短周期台站组成的盐源台阵,利用密集台阵的噪声水平评估以及基于NCF的相干噪声分析两种方法,分析了其记录到的噪声波场特征及其对NCF的影响.结果表明,盐源台阵的整体噪声水平呈现北低南高的不均匀分布,高频噪声水平的强弱受控于当地的人类活动,亦受到浅部松散沉积层的影响.台阵垂直分量NCF中主要信号为基阶Rayleigh波,且产生该信号的相干噪声源的优势方位在不同频带具有较大区别:0.3～0.5Hz的噪声源强度较强且随时间变化较为稳定,主要能量来自台阵的南侧;0.5～1Hz的相干噪声源强度较低,有两个优势方向,其中较强的一个来自于台阵南侧,可能与0.3～0.5 Hz的噪声同源,较弱的一个来自于台阵北偏东方向;1～1.5Hz的背景噪声有四个较弱的优势方向,在台阵的不同区域有不同的优势方向,可能受到不同的局部噪声源的控制.垂向NCF中Rayleigh波的信噪比主要受控于波场的复杂程度,台阵南部受人文活动及沉积层影响,噪声水平较高,且由于盆山边缘复杂的反射、散射作用,其NCF波形复杂,信噪比偏低.受高频噪声源分布不均与及复杂地质结构的共同影响,盐源台阵的高频NCF中的信号复杂,后续对面波频散特征的提取应充分考虑噪声源对NCF的影响以获取可靠结果.  相似文献   

新疆和田台阵PSD与PDF分析   总被引：2，自引：0，他引：2       下载免费PDF全文

杨千里  郝春月  田鑫 《地球物理学报》2019,62(7):2591-2606

和田台阵是我国第一个自主建成并运行至今的小孔径台阵,承担着监测印巴地区核试验以及我国西部地震活动的重要使命.台阵波形数据中充斥着背景噪声,直接影响着数据质量.为了评估台阵噪声水平,本文利用Welch平均周期法对9个子台记录的数十万条噪声样本进行功率谱估计,对出现的谱异常进行了总结归纳,通过绘制概率密度函数图以及单频曲线来研究背景噪声变化范围和规律,最后针对台阵降噪提出了建设性意见.研究结果表明,中心台长周期噪声功率谱密度随季节变化显著,具有周期性;受温度和气压影响,水平分量长周期噪声变化幅度较大,局部频段超出新高噪声模型,建议改善仪器安装条件,或者利用数学方法进行校正.所有子台短周期噪声变化规律与长周期相反,受到采石场影响,谱密度曲线在4~8 Hz之间出现形态规则的高频尖刺,A1、B3、B4子台最为明显,可以通过窄带滤波或者聚束予以压制.本文取得的研究成果为台阵运维提供重要依据,除此之外,总结出的不同地震频谱特征也为地震解释工作提供重要参考.  相似文献   

基于小波分析的河北省及邻区流动重力场时空动态变化分析     

侯晓真  祝意青  刘芳  马栋  屈曼  陈建国  刘洪良  张展伟  高晨 《中国地震》2019,35(3):455-464

搜集河北省及其邻近地区2010～2014年8期流动重力观测数据和周围绝对重力观测数据,利用小波多尺度分解方法获取河北及邻近区域(110°～120°E,36°～42°N)范围内不同尺度、不同场源深度下的分解结果,并结合该时间段内震例,通过对多期实验结果进行分析,对研究区内的重力变化情况及异常特征有更为清晰的认识,其结论可为河北省及邻近地区后续震情分析、研判提供一定参考。  相似文献   

基于PPSD方法的广东阳江小孔径井下型地震监测台阵环境地噪声计算与分析     

陈建涛  谢剑波  吕仲杭  廖一帆 《华北地震科学》2019,37(2):21-29

选取2014—2018年广东阳江小孔径井下型地震监测台阵记录的连续波形三分量seed观测数据,使用概率功率谱密度(Probabilistic Power Spectral Densities,以下简称PPSD)方法,对该地震监测台阵所属10个子台进行环境地噪声计算,统计数据月连续率和年连续率;通过PPSD计算获得每小时、每天、每月、每年、5年的环境地噪声结果;汇总结果并分析环境地噪声稳定性和异常性。结果显示:①各子台这5年的环境地噪声水平稳定,说明其井下台基变化和仪器运行稳定,井下观测手段能够避免地面生产生活等人为干扰,解决了因城镇长期建设发展破坏地震监测环境的难题;②存在靠近海边的子台易受台风严重干扰、无线网桥通信方式的子台数据连续率较差、仪器垂直分量相对容易出现运行异常、井下设备故障维修周期较长等问题。  相似文献   

2016年12月25日智利M7.6地震球型自由振荡检测     

曹欢  万永革 《华南地震》2019,39(1):50-58

智利M7.6的地震激发了地球自由振荡,利用中国数字台网(CDSN)的10个台站记录到7 d的VHZ波形数据,采用功率谱密度估计方法,在没有滤波的情况下,提取了2016年12月25日智利地震激发的地球球型自由振荡基频振型_0S_4～_0S₅₃和一系列的谐频振型,并与PREM模型的理论自由振荡频率峰值进行对比,发现2.5～4.5×10¹³Hz的频率值与PREM模型预测的球型自由振荡周期符合得很好,说明本次地震确实激发了全球范围内的自由振荡,而4.5～6×10¹³ Hz的频率值稍微有偏离,这是由于地壳的横向不均匀所致,可以用来研究地球内部的构造。  相似文献   

Spatial rules that generate urban patterns: Emergence of the power law in the distribution of axial line length     

Hani Rezayan  Mahmoud Reza Delavar  Andrew Ulrich Frank  A. Mansouri 《International Journal of Applied Earth Observation and Geoinformation》2010

This paper studies emergence/generation of power law in rank-order distribution of axial line length, which is a global pattern observed in real cities, due to interaction of a set of seven simple spatial rules at a local scale. These rules and their interactions form a model expected to simulate the morphological structure of free spaces in unplanned organic pedestrian small cities. Effects of each of the seven rules are discussed through repeated simulations of eight possible combinations of the rules, using a bottom-up process. The results show that the rules generate environments with statistically stable rank-order distribution of axial line length that follows the power law. It means that the axial maps of the simulated environments have a scale-free hierarchical structure such that their distributions lean toward short axial lines. It also represents dominance of local spatial structure, as the model renders a faster rate of growth at a local scale while allowing a steady growth at a global scale.  相似文献   

Multi-hazard susceptibility mapping based on Convolutional Neural Networks     

《地学前缘(英文版)》2022,13(5):101425

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.  相似文献