DiGiT: A Diffusion-based Modular Geophysical Toolkit for On-device Multi-modal Data Generation

Full-wave inversion (FWI), as a fundamental scientific approach to deducing unknown or unobservable subsurface properties, holds significant value in geophysics applications. Traditional FWI methods rely on physics-driven approaches that demand substantial computational resources. Recently, with the breakthroughs in machine learning (ML) and the prevalence of AI for science, data-driven approaches have been applied to FWI, showing promising results. However, as these applications often necessitate deployment in diverse regions with remote and extreme environments, localization of ML models on edge devices becomes imperative. A promising approach involves leveraging Generative AI models and governing wave equations to generate paired training data, including geophysical measurements (i.e., seismic waveform) as data and corresponding velocity maps as labels for model fine-tuning. However, the limited resources on edge devices pose significant challenges to achieving high software efficiency and low latency. In this paper, we present a toolkit, namely DiGiT, a di ffusion-based modular g eophys i cal t oolkit platform. One key component is a library of decomposed modules from the widely used geophysical designs. Benefiting from the flexibility of combining modules, we composite a toolkit for the generation of on-device diffusion-based paired geophysical training data. The toolkit includes a 1-in-2-out network structure and diffusion model distillation, both of which can significantly reduce the computational time. Experiments on the OpenFWI dataset show that the DiGiT toolkit can generate paired seismic waveform and velocity map in seconds, which is over 100 × speedup compared with the sequential execution of the diffusion model and the wave equation-based forward modeling.