  Geological carbon storage represents one of the few truly scalable
technologies capable of reducing the CO2 concentration in the atmosphere. While
this technology has the potential to scale, its success hinges on our ability
to mitigate its risks. An important aspect of risk mitigation concerns
assurances that the injected CO2 remains within the storage complex. Amongst
the different monitoring modalities, seismic imaging stands out with its
ability to attain high resolution and high fidelity images. However, these
superior features come, unfortunately, at prohibitive costs and time-intensive
efforts potentially rendering extensive seismic monitoring undesirable. To
overcome this shortcoming, we present a methodology where time-lapse images are
created by inverting non-replicated time-lapse monitoring data jointly. By no
longer insisting on replication of the surveys to obtain high fidelity
time-lapse images and differences, extreme costs and time-consuming labor are
averted. To demonstrate our approach, hundreds of noisy time-lapse seismic
datasets are simulated that contain imprints of regular CO2 plumes and
irregular plumes that leak. These time-lapse datasets are subsequently inverted
to produce time-lapse difference images used to train a deep neural classifier.
The testing results show that the classifier is capable of detecting CO2
leakage automatically on unseen data and with a reasonable accuracy.
