首页|基于Zynq的微地震数据采集优化技术研究

基于Zynq的微地震数据采集优化技术研究

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为解决微地震监测系统长时间连续采集导致的数据存储压力大的问题,设计了一种以Zynq为核心的微地震数据采集优化系统.在Zynq XC7010 芯片的PL部分完成 4 路ADC并行采样控制,在PS部分内嵌修正能量比(MER)算法实现对连续采集的震动信号进行自动识别,并增加动态阈值方法来降低震动信号识别的漏判率,进一步提高该算法识别精度,从而较大程度减少无效噪声数据的存储.实验结果表明:该系统对震动信号响应灵敏,即使是低信噪比的震动信号也能准确识别;同时与IMS微震监测系统相比,经过16 h的连续采集,该系统的数据存储量仅为IMS系统的31%,有效地降低了连续采集过程中的数据存储压力.
Research on Optimization Techniques for Microseismic Data Acquisition Based on Zynq
To address the issue of significant data storage pressure caused by long-term continuous collection in microseismic monitoring systems,a microseismic data acquisition optimization system based on Zynq was designed.This system achieves four-channel ADC parallel sampling control in the PL part of the Zynq XC7010 chip and implements automatic identification of contin-uous seismic signals using the Modified Energy Ratio(MER)algorithm in the PS part.Furthermore,the algorithm incorporates a dynamic threshold method to reduce the misjudgment rate in seismic signal identification,further improve the recognition accuracy of this algorithm,thereby significantly reducing the storage of ineffective noise data.Experimental results demonstrate that this sys-tem exhibits a sensitive response to seismic signals,accurately identifying even low signal-to-noise ratio seismic signals.Moreover,compared to the IMS microseismic monitoring system,this system's data storage volume is only 31%of the IMS system after 16 hours of continuous collection,effectively alleviating data storage pressure during continuous acquisition.

microseismic monitoring systemdata storage pressureZynq XC7010MER algorithmautomatic identificationdy-namic threshold method

阮波、沈统、徐垒、杨兰、阳刚

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西南科技大学信息工程学院

成都理工大学核技术与自动化工程学院

微地震监测系统 数据存储压力 Zynq XC7010 MER算法 自动识别 动态阈值方法

四川省自然科学基金青年基金西南科技大学博士基金黑龙江省重点研发计划项目四川省科技计划项目

2022NSFSC111019zx71592022ZX01A162022YFG0148

2024

仪表技术与传感器
沈阳仪表科学研究院

仪表技术与传感器

CSTPCD北大核心
影响因子:0.585
ISSN:1002-1841
年,卷(期):2024.(1)
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