Automatic Stack Velocity Picking Using an Unsupervised Ensemble Learning Method

TL;DR

This paper introduces an unsupervised ensemble learning method for automatic stack velocity picking in seismic data, achieving higher accuracy and efficiency without extensive manual labeling.

Contribution

The novel Unsupervised Ensemble Learning approach balances label reliance and accuracy, utilizing physical knowledge and clustering for improved velocity picking.

Findings

Outperforms traditional clustering methods in accuracy.

More reliable and precise than CNN-based methods.

Effective on both synthetic and real data sets.

Abstract

Seismic velocity picking algorithms that are both accurate and efficient can greatly speed up seismic data processing, with the primary approach being the use of velocity spectra. Despite the development of some supervised deep learning-based approaches to automatically pick the velocity, they often come with costly manual labeling expenses or lack interpretability. In comparison, using physical knowledge to drive unsupervised learning techniques has the potential to solve this problem in an efficient manner. We suggest an Unsupervised Ensemble Learning (UEL) approach to achieving a balance between reliance on labeled data and picking accuracy, with the aim of determining the stack velocity. UEL makes use of the data from nearby velocity spectra and other known sources to help pick efficient and reasonable velocity points, which are acquired through a clustering technique. Testing on both the synthetic and field data sets shows that UEL is more reliable and precise in auto-picking than traditional clustering-based techniques and the widely used Convolutional Neural Network (CNN) method.