Automatic Stack Velocity Picking Using an Unsupervised Ensemble Learning Method
arxiv(2022)
摘要
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.
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