ContrasInver: Ultra-Sparse Label Semi-Supervised Regression for Multidimensional Seismic Inversion

Data-driven seismic inversion has achieved certain advancements. However, these methods often require a large number of expensive well logs, limiting their application only to mature or synthetic data. This article presents ContrasInver, a method that achieves seismic inversion using as few as two or three well logs, significantly reducing the current requirements. In ContrasInver, two key innovations are proposed to address the challenges of applying semi-supervised learning to regression tasks with ultra-sparse labels: 1) the region-growing training (RGT) strategy leverages the inherent continuity of seismic data, effectively propagating accuracy from closer to more distant regions based on the proximity of well logs. To realize this concept, a multidimensional sample generation (MSG) method is also proposed that produces a large number of diverse samples from a single well, while establishing lateral continuity within the seismic data; 2) the impedance vectorization projection (IVP) vectorizes impedance values and performs semi-supervised learning in a compressed space. The Jacobian matrix derived from this space can filter out some outlier components in pseudo-label vectors, thereby solving the value confusion issue in semi-supervised regression learning. In the experiments, ContrasInver achieved state-of-the-art performance on the synthetic SEAM I data. In the field data with two or three well logs, only the methods based on the components proposed in this article were able to achieve reasonable results. It is the first data-driven approach yielding reliable results on the Netherlands F3 and Delft, using only three and two well logs, respectively.