稳健M估计用于瞬变电磁数据抽道叠加与噪声压制

虽然国内外大部分瞬变电磁仪器都针对原始数据采取了一系列的数据处理方法来压制噪声、提高信噪比.但是当天电干扰、人文干扰严重时,噪声仍有可能残留到周期叠加后的数据中. 时间窗口抽道叠加处理是周期叠加后瞬变电磁数据处理中的一项重要内容. 本文提出将稳健M估计算法用于时间窗口内采样数据的抽道叠加处理. 通过对其在模拟数据与实测瞬变电磁数据中的应用效果进行对比分析,发现其相比于传统的算术平均算法、几何平均算法等,可进一步压制类高斯随机噪声与尖峰脉冲噪声,提高数据质量.

Robust M estimates for transient electromagnetic samples stacking and denosing in time window

Although most of transient electromagnetic ( TEM) apparatuses in China and abroad have taken various methods to suppress noise and improve SNR, noise may still remain in TEM data after stacking of all the periods. Stacking samples in time windows is an important technique for TEM data after stacking of all the periods. This paper proposes that Robust M Estimates methods can be applied to stacking samples in time windows. M Estimates are respectively adopted to process TEM data, and perform simulation and measure-ment in the field. The results and errors are investigated and compared.The experimental results show that M Estimates could effectively remove the Gaussian random noise and impulse noise, the M Estimates method is superior to the Arithmetic mean and geometric mean stack, and thus can improve the quality of data preprocessing.