马海涛,
李月,
吉林大学信息工程系, 长春 130012
基金项目: 国家高技术研究发展"863"计划重大项目"深部矿产资源探测技术"第05号课题（2014AA06A605）"和国家自然科学基金项目（41574096）联合资助
详细信息
作者简介: 郑升, 男, 1992年生, 硕士研究生, 从事信号处理研究.E-mail:18844189152@163.com
通讯作者: 李月, 女, 博士生导师, 教授, 从事信号处理和地震勘探数字处理研究.E-mail:liyue@jlu.edu.cn
中图分类号: P631收稿日期:2018-07-05
修回日期:2019-08-29
上线日期:2019-10-05
Reduction of seismic random noise in mountainous metallic mines based on adaptive threshold RCSST
ZHENG Sheng,MA HaiTao,
LI Yue,
Department of Information and Engineering, Jilin University, Changchun 130012, China
More Information
Corresponding author: LI Yue,E-mail:liyue@jlu.edu.cn
MSC: P631--> Received Date: 05 July 2018
Revised Date: 29 August 2019
Available Online: 05 October 2019
摘要
摘要:随着陆地地震勘探工作的加深，勘探环境变得越来越复杂，获得的地震信号信噪比越来越低，这给地震成像和数据解释带来了巨大的困难.为了解决这一技术难题，本文针对云南山地金属矿区的勘探环境提出了一种基于自适应阈值递归循环平移的Shearlet变换去噪算法（Recursive Cycle Spinning Shearlet Transform，RCSST）.首次将递归循环平移与Shearlet变换相结合，利用Shearlet变换的多尺度多方向特性对平移后的地震资料进行分解变换，之后，我们又提出了一种全新的自适应阈值，避免了信号系数被过度扼杀，同时也保护了有效信号.实验表明基于自适应阈值的RCSST算法克服了传统Shearlet变换去噪算法在低信噪比下易出现假轴的弊端并且能够有效地保护信号的幅度.在处理较低信噪比的模拟和实际云南山地地区地震资料的过程中，本文方法能够较好的压制随机噪声和保护有效信号.
关键词: 低信噪比地震信号/
随机噪声压制/
Shearlet变换/
递归循环平移/
自适应阈值
Abstract:The suppression of random noise in seismic data is an essential step in processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals gets weaker and the Signal to Noise Ratio(SNR)of seismic data is much lower which brings great difficulty to seismic data processing and interpretation. In order to solve this problem, a Shearlet transform denoising algorithm based on adaptive threshold recursive cycle spinning is proposed in view of the exploration environment of metal mines in Yunnan mountainous regions. In this algorithm, the Shearlet transform is combined with recursive cycle spinning. By virtue of multiscale and multidirection features of Shearlet transform, the seismic signals are transformed into different scales and directions. Then, we propose a new adaptive threshold to prevent the coefficients being killed excessively and protect the amplitude of the effective signals. Experiments show that this adaptive threshold RCSST algorithm can overcome the disadvantages of conventional Shearlet transform denoising algorithm and protect the amplitude of signals effectively. Application to of simulative and real seismic data in Yunnan mountainous regions with low SNR demonstrates that this algorithm can suppress the random noises effectively and protect the amplitude of valid signals.
Key words:Low SNR seismic signals/
Random noise suppression/
Shearlet transform/
Recursive cycle spinning/
Adaptive threshold
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