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Based on an analysis of the correlativity between Qinghai-Tibet Plateau thermal infrared remote sensing data (QPTIRSD) and underground temperature field distribution, the main factors which obviously influence underground-layer temperatures were derived. Using neural network technology, a model was built to compute underground temperatures via parameters out of the inversion of thermal infrared remote sensing (TIRS) and then analyze the correlativity between above-ground parameters and underground temperatures. This method offers a new way to apply TIRS in monitoring the suture zone of a large-area massif as well as to research structural thermal anomalies.