The automated interpretation and inversion of seismic data have advanced significantly with the development of Deep Learning (DL) methods. However, these methods often require numerous costly well logs, limiting their application only to mature or synthetic data. This paper presents ContrasInver, a method that achieves seismic inversion using as few as two or three well logs, significantly reducing current requirements. In ContrasInver, we propose three key innovations to address the challenges of applying semi-supervised learning to regression tasks with ultra-sparse labels. The Multi-dimensional Sample Generation (MSG) technique pioneers a paradigm for sample generation in multi-dimensional inversion. It produces a large number of diverse samples from a single well, while establishing lateral continuity in seismic data. MSG yields substantial improvements over current techniques, even without the use of semi-supervised learning. 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. The Impedance Vectorization Projection (IVP) vectorizes impedance values and performs semi-supervised learning in a compressed space. We demonstrated that 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 in the synthetic data SEAM I. In the field data with two or three well logs, only the methods based on the components proposed in this paper were able to achieve reasonable results. It's the first data-driven approach yielding reliable results on the Netherlands F3 and Delft, using only three and two well logs respectively.

翻译：摘要：随着深度学习（DL）方法的发展，地震数据的自动解释与反演技术取得了显著进展。然而，这些方法通常依赖大量昂贵的测井数据，限制了其仅适用于成熟或合成数据应用。本文提出ContrasInver方法，该方法仅需使用两到三个测井数据即可实现地震反演，大幅降低了现有需求。在ContrasInver中，我们提出了三项关键创新以应对将半监督学习应用于超稀疏标签回归任务的挑战。多维样本生成（MSG）技术开创了多维反演中样本生成的范式，可从单口井生成大量多样化样本，同时建立地震数据的横向连续性。即便不采用半监督学习，MSG相比现有技术仍展现出显著优势。区域生长训练（RGT）策略利用地震数据固有的连续性，基于测井数据邻近性有效将精度从近区传递至远区。阻抗向量化投影（IVP）技术将阻抗值向量化，并在压缩空间中进行半监督学习。我们证明，该空间导出的雅可比矩阵可滤除伪标签向量中的离群分量，从而解决半监督回归学习中的数值混淆问题。实验表明，ContrasInver在合成数据SEAM I上达到了最先进性能。在仅含两三个测井数据的实际数据中，仅基于本文提出组件的模型能够获得合理结果。这是首个仅分别利用荷兰F3与Delft数据中三口和两口测井数据即产出可靠结果的数据驱动方法。