标题： 一种用于不平衡训练数据集的成对地理科学数据生成的新扩散模型 显示英文标题

标题： A Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset

摘要： 最近，生成式AI技术的出现对我们的日常生活产生了变革性的影响，但其在科学应用中的应用仍处于早期阶段。数据稀缺是数据驱动科学计算中的一个主要且众所周知的障碍，因此物理引导的生成式AI具有重要的前景。在科学计算中，大多数任务研究多种数据模态的转换以描述物理现象，例如地震成像中的空间和波形，信号处理中的时间和频率，以及气候建模中的时间和光谱；因此，需要高度的多模态成对数据生成，而不是通常用于自然图像（例如人脸、风景）的单模态数据生成。此外，在实际应用中，数据在模态方面的不平衡普遍存在；例如，地震成像中的空间数据（即速度图）可以轻松模拟，但真实世界的地震波形却严重缺乏。尽管最近的努力使强大的扩散模型能够生成多模态数据，但如何利用不平衡的可用数据仍然不清楚。在本工作中，我们以地下地球物理学中的地震成像为载体，提出“UB-Diff”，一种用于多模态成对科学数据生成的新扩散模型。一个主要创新是一个一进二出的编码器-解码器网络结构，该结构可以确保从共同潜在表示中获得成对数据。然后，共同潜在表示将被扩散过程用于成对数据生成。在OpenFWI数据集上的实验结果表明，UB-Diff在Fréchet Inception距离（FID）分数和成对评估方面显著优于现有技术，表明生成了可靠且有用得多模态成对数据。 显示英文摘要

摘要： Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages. Data scarcity is a major, well-known barrier in data-driven scientific computing, so physics-guided generative AI holds significant promise. In scientific computing, most tasks study the conversion of multiple data modalities to describe physical phenomena, for example, spatial and waveform in seismic imaging, time and frequency in signal processing, and temporal and spectral in climate modeling; as such, multi-modal pairwise data generation is highly required instead of single-modal data generation, which is usually used in natural images (e.g., faces, scenery). Moreover, in real-world applications, the unbalance of available data in terms of modalities commonly exists; for example, the spatial data (i.e., velocity maps) in seismic imaging can be easily simulated, but real-world seismic waveform is largely lacking. While the most recent efforts enable the powerful diffusion model to generate multi-modal data, how to leverage the unbalanced available data is still unclear. In this work, we use seismic imaging in subsurface geophysics as a vehicle to present ``UB-Diff'', a novel diffusion model for multi-modal paired scientific data generation. One major innovation is a one-in-two-out encoder-decoder network structure, which can ensure pairwise data is obtained from a co-latent representation. Then, the co-latent representation will be used by the diffusion process for pairwise data generation. Experimental results on the OpenFWI dataset show that UB-Diff significantly outperforms existing techniques in terms of Fr\'{e}chet Inception Distance (FID) score and pairwise evaluation, indicating the generation of reliable and useful multi-modal pairwise data.