Recent studies have shown that learning theories have been very successful in
hydrocarbon exploration. Inversion of seismic into various attributes through
the relationship of 1D well-logs and 3D seismic is an essential step in
reservoir description, among which, acoustic impedance is one of the most
critical attributes, and although current deep learningbased impedance
inversion obtains promising results, it relies on a large number of logs (1D
labels, typically more than 30 well-logs are required per inversion), which is
unacceptable in many practical explorations. In this work, we define acoustic
impedance inversion as a regression task for learning sparse 1D labels from 3D
volume data and propose a voxel-wise semisupervised contrastive learning
framework, ContrasInver, for regression tasks under sparse labels. ConstraInver
consists of several key components, including a novel pre-training method for
3D seismic data inversion, a contrastive semi-supervised strategy for diffusing
well-log information to the global, and a continuous-value vectorized
characterization method for a contrastive learning-based regression task, and
also designed the distance TopK sampling method for improving the training
efficiency. We performed a complete ablation study on SEAM Phase I synthetic
data to verify the effectiveness of each component and compared our approach
with the current mainstream methods on this data, and our approach demonstrated
very significant advantages. In this data we achieved an SSIM of 0.92 and an
MSE of 0.079 with only four well-logs. ConstraInver is the first purely
data-driven approach to invert two classic field data, F3 Netherlands (only
four well-logs) and Delft (only three well-logs) and achieves very reasonable
and reliable results.Comment: This work has been submitted to journal for possible publication.
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