# Probabilistic Joint Recovery Method for CO2 Plume Monitoring

arXiv: https://arxiv.org/abs/2501.18761 · HTML: https://mlsanity.com/paper/2501.18761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks](https://mlsanity.com/paper/1810.03982/) (2018)

## Built on by (most cited first, 1 of 1)

- [SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior](https://mlsanity.com/paper/2604.00307/) (2026)

## Abstract (arXiv)

Reducing CO2 emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO2 emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO2 plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.