Automatic stack velocity picking using a semi-supervised ensemble learning method

Abstract

Picking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi-data-driven methods has the potential to efficiently solve this problem. Thus, we propose a semi-supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi-supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few-shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi-supervised ensemble learning achieves more reliable and precise picking than traditional clustering-based techniques and the currently popular convolutional neural network method.