The quality of the wavelet significantly affects the accuracy of full-waveform inversion (FWI). In practice, seismic data are often contaminated by non-ideal wavelets with complex side lobes and ambiguous dominant frequencies. Directly employing such data in conventional FWI workflows, which typically assume an ideal Ricker wavelet, can induce severe cycle-skipping problems and lead to inversion failure. Existing remedies, such as sophisticated source wavelet estimation or the design of wavelet-independent objective functions, often suffer from instability in estimation or the loss of waveform details. To address this issue at its root from a data-driven perspective, this study proposes a novel deep learning-based approach. We introduce a multi-scale Swin Transformer transposed U-Net (MSTU) network to intelligently transform seismic records containing unknown, non-ideal wavelets into universal records composed solely of ideal Ricker wavelets. Through end-to-end learning, our method establishes a direct nonlinear mapping from the complex wavelet domain to the universal wavelet domain, effectively stripping the inherent wavelet ambiguity from the raw data. Numerical examples demonstrate that data universal by the MSTU network can be directly fed into standard FWI routines, yielding superior inversion results. For data originally generated with Klauder or Ormsby wavelets, the inversion results obtained after our standardization process significantly outperform those from direct inversion using the incorrect wavelet or even using the original non-ideal wavelet itself. Moreover, the accuracy approaches the theoretical upper limit achievable by inversion with the ideal Ricker wavelet as the ground truth. Requiring no prior wavelet estimation or modification to the core inversion algorithm, our method serves as an efficient, robust, and “plug-and-play” data preprocessing tool, offering a highly practical solution to enhance the reliability of FWI for field seismic data.