<p>The limited resolution of surface seismic data remains a significant bottleneck in delineating fine-scale geological features, such as thin interlayers and small-scale faults. While DAS-VSP provides high-resolution imaging, its limited spatial coverage restricts its utility in large-scale exploration. Conversely, surface seismic data offer broader illumination but suffer from substantial resolution loss. Furthermore, existing deep learning approaches often fail to effectively bridge these modalities, as they rely heavily on fully paired training data or synthetic priors, leading to poor generalization and “model mismatch” under real-world conditions. To address these challenges, we propose a novel semi-supervised resolution enhancement framework that integrates joint borehole and surface exploration via a domain adaptation paradigm. A key component is the unsupervised Degradation Transformation Module (DTM), which models the resolution gap by mapping real-world data from an unknown degradation domain to a known domain defined by simulated priors. Unlike conventional methods, this transformation is learned using a Generative Adversarial Network (GAN) on unpaired data, allowing the network to capture complex degradation patterns, such as frequency attenuation and distortion, without requiring strict sample-level alignment. Once transformed into the known domain, a Transformer-CNN hybrid U-shaped Degradation Inverting Model (UDIM) performs the inversion to reconstruct high-resolution seismic profiles. By uniquely decoupling degradation modeling from the reconstruction process, the framework avoids overfitting to synthetic priors and ensures robustness in scenarios of data scarcity. Experiments on two field datasets validate the superior performance in seismic events restoration, fault continuity, and fidelity compared to state-of-the-art baselines. This study demonstrates the potential of borehole-guided domain transformation as a scalable, high-fidelity strategy for seismic imaging and resolution enhancement.</p>