Iterative Gaussian mixture model and multi‐channel attributes for arrival picking in extremely noisy environments

Abstract

ABSTRACTArrival picking is a traditional and important topic in the field of seismic exploration. Depending on the quality of seismic data, the picking results are prone to large errors. As commonly used arrival‐picking strategies, conventional clustering methods, such as K‐means, suffer from insufficient flexibility, for example the clustering boundaries are fixed given a set of attribute vectors. In order to overcome this drawback, we propose a new strategy which can provide flexible clustering results (i.e. the variable clustering boundaries) based on an iterative Gaussian mixture model and utilize the local correlation among adjacent traces to enhance the anti‐noise ability. First, we use local principal component analysis to roughly distinguish the areas of signal waveforms. Then, two multi‐channel attributes are calculated and input to the iterative Gaussian mixture model. These two attributes can correctly identify the arrivals and avoid redundant computation caused by the high‐dimensional attribute vectors. The final step is to establish an optimized Gaussian mixture model and to iteratively select the proper boundaries for each trace. Although the iterative Gaussian mixture model takes a longer calculation time, the synthetic and real data tests have shown superior results over conventional methods even in situations of extremely low signal‐to‐noise ratios.