基于改进混沌果蝇优化小波阈值法地震信号随机噪声压制

由于野外采集地震资料往往带有较多的随机噪声,给资料解释造成困难。针对小波阈值去噪的阈值选取通常需要对信号进行先验估计,带有较强猜测性,阈值选取难以获得最优结果。本文提出基于改进混沌果蝇优化的小波阈值法,将基于广义交叉验证(GCV)函数设定为阈值选取目标函数,在混沌果蝇优化算法中引入调节系数实现对该目标函数的迭代寻优,在无先验信息前提下,获取最优小波阈值。通过将本文算法用于合成地震记录和实际地震记录进行去噪处理,并对比常用小波阈值去噪算法,证明了本文算法的有效性。

Suppression of seismic random noise based on the improved wavelet threshold method using chaotic fruit fly optimization

It is usually difficult to interpret the seismic data collected in the field bearing a large amount of random noise. Although the wavelet threshold method can be used to remove such noise, it requires prior estimation of the signal, which is largely conjecture, hard to obtain optimal results. In this article, we propose a wavelet threshold denoising method based on the improved chaotic fruit fly optimization. This method selects the objective function based on the generalized cross validation (GCV) as the threshold selection function. In order to optimize this objective function, adjustment coefficient is introduced into the optimization algorithm of the chaotic fruit fly. Then the optimal wavelet threshold can be obtained without any prior information. By denoising tests of synthetic seismic records and measured seismic data and comparing with the commonly-used wavelet threshold denoising method, the effectiveness of this new method is proved .