Time-window-based filtering method for near-surface detection of leakage from geologic carbon sequestration sites

We use process-based modeling techniques to characterize the temporal features of natural biologically controlled surface CO 2 fluxes and the relationships between the assimilation and respiration fluxes. Based on these analyses, we develop a signal-enhancing technique that combines a novel time-window splitting scheme, a simple median filtering, and an appropriate scaling method to detect potential signals of leakage of CO 2 from geologic carbon sequestration sites from within datasets of net near-surface CO 2 flux measurements. The technique can be directly applied to measured data and does not require subjective gap filling or data-smoothing preprocessing. Preliminary application of the new method to flux measurements from a CO 2 shallow-release experiment appears promising for detecting a leakage signal relative to background variability. The leakage index of ±2 was found to span the range of biological variability for various ecosystems as determined by observing CO 2 flux data at various control sites for a number of years.