| 本文已被：浏览 262次 下载 299次 |
码上扫一扫！ |
|
|
| 海底地震仪自噪声分析 |
|
贾正大1,2, 李军3,4, 金星1,4, 林彬华1,4
|
|
1.中国地震局工程力学研究所, 哈尔滨 150080;2.中国地震局地震工程与工程振动重点实验室, 哈尔滨 150080;3.福建省地震局, 福州 350003;4.中国地震局厦门海洋地震研究所, 福建厦门 361000
|
|
| 摘要: |
| 海底地震仪是海上观测天然地震的主要手段,仪器所记录到的噪声分为环境噪声和仪器噪声。本文利用2018年9月3日—2019年7月1日期间浯屿岛海底地震观测台所采集到的数据,对海洋环境下的强震型海底地震采集站和宽频带海底地震采集站进行仪器自噪声分析,利用两道互相关模型,采用加窗平均周期图法计算功率谱,通过1/3倍频程积分作平滑处理,得到仪器自噪声功率谱密度图。对比分析相同型号仪器的自噪声功率谱,发现相同仪器的自噪声变化趋势一致,但自噪声大小有差异,且这种差异对于不同仪器、相同仪器的不同分量也存在不同。 |
| 关键词: 海底地震仪 两道互相关模型 自噪声 功率谱 |
| DOI： |
| 分类号:P315 |
| 基金项目:地震科技星火计划项目(XH21015)、国家自然科学基金重大项目(41790461)、国家自然科学基金面上项目(41774068)共同资助 |
|
| Self-noise Analysis of Seismometer Ocean Bottom |
|
Jia Zhengda1,2, Li Jun3,4, Jin Xing1,4, Lin Binhua1,4
|
|
1.Institute of Engineering Mechanics, CEA, Harbin 150080, China;2.Key Laboratory of Earthquake Engineering and Engineering Vibration, CEA, Harbin 150080, China;3.Fujian Earthquake Agency, Fuzhou 350003, China;4.Xiamen Institute of Marine Seismology, CEA, Xiamen 361000, Fujian, China
|
| Abstract: |
| Ocean bottom seismometer (OBS) is a major means of natural earthquake observation in marine. The noise recorded by the instrument is divided into environmental noise and instrument noise. Using the data collected by the Wuyu Island Submarine Seismic Observatory from September 3,2018 to July 1,2019,we analyzed the instrument self-noise of the strong seismic OBS and the broadband OBS in the sea environment. Two channel cross-correlation model is used to calculate the power spectrum by windowed average periodogram method,and then 1/3 octave integral is used as smoothing processing. Finally,the self noise power spectrum density of the instrument is obtained. By comparing and analyzing the power spectrum of self-noise of the same type of instrument,we found that the variation trend of self-noise of the same instrument is the same,but the size of self-noise is different,and the difference also exists for different instruments and different components of the same instrument. |
| Key words: Ocean bottom seismometer Two cross correlation model Self-noise Power spectrum |
