基于超虚干涉法约束的模糊C均值聚类地震初至自动提取

在静校正和层析成像等地震数据处理中，准确并快速地拾取初至是随后速度结构成像和地震资料综合解释的前提。手动拾取方法难以适用于海量地震数据处理，且存在人为误差。对于低信噪比地震数据，相关法、能量比值法（STA/LTA）、分形维法等常规自动拾取方法需不断调整参数以达到设定拾取精度，导致稳定性变差。为此，提出一种基于超虚干涉（SVI）约束的模糊C均值（FCM）聚类地震初至自动拾取方法。FCM聚类分析是一种非监督的机器学习方法，仅依赖数据本身进行分类，可更灵活、方便地应用于实际地震初至拾取；对于低信噪比数据，须预先利用SVI法加强远炮检距等弱初至信号的能量，提高地震数据的信噪比，以实现地震初至的准确、稳定拾取。理论模型数据和实际地震资料测试结果进一步表明了该方法的稳定性和高效性。

Automatic pick-up of seismic P-wave first arrivals via fuzzy C-means method constrained by super-virtual interferometry

In seismic data processing，such as static correction and seismic tomography，accurate and quick pick-up of first arrivals is the basic premise of velocity structure imaging and comprehensive interpretation of seismic data.It is difficult to finish big data through manual picking,and manual method may cause man-made errors.For seismic data with low signal-to-noise ratio (SNR),conventional automatic picking methods,such as correlation method，energy ratio method (STA/LTA)，and fractal method,should keep adjusting parameters to achieve desired accuracy,therefore resulting in poor stability.This paper proposes a method to automatically pick up first arrivals based on fuzzy C-means and super-virtual interferometry.Fuzzy C-means (FCM) clustering analysis is an unsupervised machine learning method.It only depends on data themselves for classification,so it can be more flexibly and conveniently applied for picking actual seismic first arrivals.For data with low SNR,to get accurate and stable first arrivals,first super-virtual interferometry (SVI) is used to enhance the energy of weak first arrival signals,such as far offsets,and improve the SNR of the seismic data.Tests on theoretical model data and actual land seismic data show that the method provides a stable and efficient technical means for automatically picking up first arrivals from big seismic data.