Probabilistic inversion with Flow Matching

Probabilistic inversion represents a qualitative leap in the interpretation of geophysical data, especially in problems such as full waveform inversion (FWI). Traditionally, deterministic methods assume a single optimal solution, but the reality of the subsurface is full of uncertainties. This is where advanced generative artificial intelligence techniques, such as Flow Matching, offer a novel approach: modeling complete probability distributions of model parameters, rather than a single value. This method, originally developed to generate images and audio, adapts elegantly to the geophysical context, allowing multiple solutions to be sampled and uncertainty to be quantified in a computationally efficient manner. In practice, a Flow Matching model learns to transform a simple distribution (noise) into the posterior distribution of subsurface parameters, conditioned on observed seismic data. This opens the door to more robust risk analyses in hydrocarbon exploration, CO2 storage, or geothermal energy. For companies looking to implement this type of solution, having AI for businesses like those we develop at Q2BSTUDIO is key, since we integrate AI agents and generative models into custom software platforms, connected with AWS and Azure cloud services to scale massive data processing. Furthermore, the correct interpretation of results requires business intelligence services such as Power BI, which allow visualizing probability distributions and supporting decision-making. Cybersecurity is also a fundamental pillar when handling sensitive geophysical data. At Q2BSTUDIO, we offer custom applications that combine artificial intelligence, automation, and advanced analytics, facilitating the adoption of methodologies such as Flow Matching in real business environments.