This invention relates to the field of geological monitoring technology, specifically to an intelligent
analysis method and
system for geological
monitoring data. The method first deploys multiple types of monitoring terminals, including those for seismic
waves, displacement, seepage, and stress, in the exploration area. Then, it performs multi-scale
decomposition on the time-
series data, matching the geological body evolution process feature
library to divide the evolution stages and generate feature threshold intervals. Anomaly
feature extraction and
verification are completed through a dual-
branch cross-validation architecture, obtaining anomaly
feature matching degrees and anomaly point identification sequences. A neural network with geomechanical constraints is constructed to fit the evolution trend and predict
instability time intervals. Finally, through multi-dimensional index weighted fusion, a quantitative value of geological activity risk and a
risk level identifier are output. This invention can achieve spatiotemporal benchmark unification of data collected by multiple types of monitoring terminals and basic
geological exploration data, complete the normalization and integration of multi-
source data, quantify the reliability of
measurement point data, and improve the integrity and reliability of
monitoring data.