In many cities in the world, underground pipe network always suffers from serious external damage. The detection method of excavation devices based on acoustic signal has been extensively studied in the past research. It is important for preventing the destruction of urban underground pipeline network during disorderly underground excavation. However, the existing excavation devices detection methods have little attention to distance estimation, but only focus on the recognition results or direction-of-arrival (DOA) estimation. In the actual monitoring system, there may be frequent misinformation alarm, because of the lack of convincing distance-of-arrival (DisOA) estimation to achieve accurate source localization. In this paper, a new intelligent distance estimation algorithm, based on acoustic attenuation property for excavation devices localization, is brought forward. Specially, the extreme learning machine-based auto-encoder is used to obtain more robust feature representation from the acoustic signal frequency domain amplitude spectrum, and the regularized extreme learning machine (RELM) is utilized to train the regression model. Experimental results show that the proposed algorithm is effective.