The invention discloses a CO2
storage area intelligent monitoring method based on multi-
source data combination, and the method comprises the steps: obtaining a hyperspectral image of a CO2
storage area through the macroscopic monitoring of the hyperspectral image, and extracting a
spectral index which is high in CO2 sensitivity and correlation; the method comprises the following steps: inverting underground
resistivity distribution of a CO2 sequestration area through a high-density electrical method, then measuring
carbonate contents in soil at different positions, respectively determining a relationship between the
carbonate contents and spectral indexes to establish a spectram-CO2 sequestration correlation model, determining a relationship between the
carbonate contents and resistivity, and establishing a resistivity-CO2 sequestration correlation model; and finally, fusing various parameter data to construct a
random forest joint monitoring model, and realizing CO2
storage area leakage prediction. According to the method, single
data dimension limitation is broken through through spectrum-electrical characteristic
coupling, monitoring of the full-area storage state of the CO2 storage area from the
earth surface to the deep part is achieved, long-term stability of the CO2 storage area is guaranteed, and
technical support is provided for safe implementation of
coal mine carbon waste storage
engineering.