AI 中文总结
针对少样本声阻抗成像难题，提出RD - SCL框架，通过正则化反卷积与半监督交叉学习相结合，利用潜变量介导，在SEAM和Marmousi 2基准测试中优于现有方法，提供了实用高效的解决方案。
AI 中文摘要
声阻抗成像在地下分析中是一个基本但严重不适定的问题，地震子波未知、观测有带宽限制且标记的测井样本极少。现有半监督深度学习方法存在不足。本文提出RD - SCL框架，将正则化反卷积与半监督交叉学习相结合。其核心是可微的闭式一阶蒂霍诺夫反卷积算子，在训练时动态估计频域中的潜子波。在此基础上设计对称交叉学习，利用大量未标记数据。实验表明RD - SCL优于现有方法，以较低计算成本取得显著增益，为声阻抗成像提供实用、物理一致且高效的解决方案。
英文摘要
Acoustic impedance imaging is a fundamental yet severely ill-posed problem in subsurface analysis: the seismic wavelet is unknown, observations are band-limited, and labeled well-log samples are extremely scarce (typically <1% of all traces). Existing semi-supervised deep learning methods mitigate few-shot problem by incorporating forward modeling, yet they either rely on inaccurate prior wavelet assumptions or introduce auxiliary networks, leading to unstable optimization and degraded performance. We propose RD-SCL, a novel framework that integrates regularized deconvolution with semi-supervised cross-learning. At its core lies a differentiable, closed-form first-order Tikhonov deconvolution operator that dynamically estimates the latent wavelet in the frequency domain during training, providing stable physics-guided feedback without explicit auxiliary networks and fixed wavelet priors. Building on this operator, we design a symmetric cross-learning that enforces consistency between predictions on labeled and unlabeled data, thereby effectively exploiting abundant unlabeled traces. Extensive experiments on the SEAM and Marmousi 2 benchmarks demonstrate that RD-SCL consistently outperforms state-of-the-art supervised and semi-supervised methods, achieving substantial gains with lower computational cost. With only 56.5k learnable parameters and competitive runtime, RD-SCL offers a practical, physically consistent, and efficient solution for acoustic impedance imaging.
CommentsThe manuscript is currently under review
