Abstract: In order to comprehensively evaluate the seismic resilience of the urban gas network, this paper proposes a quantitative assessment framework in three dimensions: technical, organizational and social dimensions, which accounts for the uncertainties in ground motion input and includes network connectivity performance assessment and repair process. The connectivity of the gas network is calculated based on Monte Carlo simulation which uses the ground motion prediction equation (GMPE) as input. Then the real-time repair process of the gas network is obtained by randomly allocating the repair resource under each simulated damage condition. The performance recovery curve in three dimensions is given, and the corresponding indices such as post-earthquake performance, repair rate and resilience are calculated. Repeating the above steps N times, the expectation of the above indicators and the probability distribution of the recovery time for different performance levels are obtained. Taking a northern city of China as an example, the whole procedure is applied to assess the seismic resilience of a gas network system. The results show that the post-earthquake performance of the gas network in three dimensions roughly follows the normal distribution, and the performance recovery time for 75%, 90%, and 100% original performance levels in organizational and social dimensions roughly follows log-normal distribution. Neglecting the connectivity of the pipe network, the resilience results in technical dimension underestimate the actual damage level, and the recovery curve is close to linear-type. On the other hand, the results in organizational and social dimensions are close to the reality, and are greatly affected by repair order and resource allocation. The proposed seismic resilience quantification procedure comprehensively considers the uncertainty of various aspects after the earthquake, which can provide a reference framework for probabilistic resilience assessment of gas networks, and could be expanded to other lifeline network systems.