Neural-network-based mapping of potential leakage pathways of subsurface carbon dioxide storage

An apparatus, comprising:

• a device including at least one memory having processor-executable code stored therein, and at least one processor that is adapted to execute the processor-executable code, wherein the processor-executable code includes processor-executable instructions that, in response to execution, enable the device to perform actions, including:
• training a first neural network with synthetic data that is associated with seismic images of synthetic simulated subsurfaces, wherein the first neural network extracts features from multiple resolutions of the seismic images of the synthetic simulated subsurfaces, with ground truth including synthetic labels that indicate probabilities of potential carbon dioxide leakage pathways of the synthetic simulated subsurfaces, and wherein the synthetic data includes simulated elastic deformations of the potential carbon dioxide leakage pathways of the synthetic simulated subsurfaces;
• receiving a seismic image of a first subsurface;
• and
• using at least the first neural network to generate output labels that indicate probabilities of potential leakage pathways of carbon dioxide storage of the first subsurface.