基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量...基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量结果质量较低,对嵌入算法要求更高.针对这一问题,本文提出一种更适用于MEMS强震仪的改进长短时窗均值比(Short Term Average/Long Term Average,STA/LTA)算法.首先,通过构建抗干扰(Anti-interference,AR)特征函数抑制基线漂移和低频噪声的干扰,提高STA/LTA算法拾取地震事件的抗干扰能力;其次,提出采用“延时长窗”的方式,提高STA/LTA算法的计算效率和拾取精度,减少STA/LTA算法对MEMS集成资源的占用;最后,结合时窗位置进一步探究不同时窗大小对STA/LTA算法拾取效率的影响.实际地震资料处理结果表明,本文提出的改进STA/LTA算法计算效率更高,实时性和抗干扰能力更强,更适用于集成资源有限的MEMS强震仪.展开更多
With the recent development of digital Micro Electro Mechanical System （MEMS） sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record...With the recent development of digital Micro Electro Mechanical System （MEMS） sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record a seismic event depends upon the efficiency of triggering algorithm, apart from the sensor＇s sensitivity. There are several classic triggering algorithms developed to detect seismic events, ranging from basic amplitude threshold to more sophisticated pattern recognition. Algorithms based on STA/LTA are reported to be computationally efficient for real time monitoring. In this paper, we analyzed several STA/LTA algorithms to check their efficiency and suitability using data obtained from the Quake Catcher Network （network of MEMS accelerometer stations）. We found that most of the STA/LTA algorithms are suitable for use with MEMS accelerometer data to accurately detect seismic events. However, the efficiency of any particular algorithm is found to be dependent on the parameter set used （i.e., window width of STA, LTA and threshold level）.展开更多
文摘基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量结果质量较低,对嵌入算法要求更高.针对这一问题,本文提出一种更适用于MEMS强震仪的改进长短时窗均值比(Short Term Average/Long Term Average,STA/LTA)算法.首先,通过构建抗干扰(Anti-interference,AR)特征函数抑制基线漂移和低频噪声的干扰,提高STA/LTA算法拾取地震事件的抗干扰能力;其次,提出采用“延时长窗”的方式,提高STA/LTA算法的计算效率和拾取精度,减少STA/LTA算法对MEMS集成资源的占用;最后,结合时窗位置进一步探究不同时窗大小对STA/LTA算法拾取效率的影响.实际地震资料处理结果表明,本文提出的改进STA/LTA算法计算效率更高,实时性和抗干扰能力更强,更适用于集成资源有限的MEMS强震仪.
基金IIT Roorkee under the Faculty Initiation Grant No.100556
文摘With the recent development of digital Micro Electro Mechanical System （MEMS） sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record a seismic event depends upon the efficiency of triggering algorithm, apart from the sensor＇s sensitivity. There are several classic triggering algorithms developed to detect seismic events, ranging from basic amplitude threshold to more sophisticated pattern recognition. Algorithms based on STA/LTA are reported to be computationally efficient for real time monitoring. In this paper, we analyzed several STA/LTA algorithms to check their efficiency and suitability using data obtained from the Quake Catcher Network （network of MEMS accelerometer stations）. We found that most of the STA/LTA algorithms are suitable for use with MEMS accelerometer data to accurately detect seismic events. However, the efficiency of any particular algorithm is found to be dependent on the parameter set used （i.e., window width of STA, LTA and threshold level）.
