Since quantitative correlation association between in-panel faults and high-stress areas have not been well understood, we propose a workflow to quantitatively estimate this spatial association through Monte Carlo simulations and point process statistics using measured fault traces and tomographic seismic velocities as inputs. According to different distribution scenarios of fault traces and high-stress areas based on in situ characteristics, we build three different spatial statistical models: a no spatial correlation model, an anti-correlation model and a correlation model to analyze and compare with the observed data. By estimating and cross plotting RHA (Ratio of High-stress Areas over total area) and RFL (Ratio of included Fault-trace Length over total fault-trace length) pairs for Monte Carlo realizations of those models, we generate a template to estimate the correlation association between in-panel faults and high-stress areas for the study panel. After comparing the observed cross plots of RHA vs. RFL pairs with the template, we find that the in-panel faults and high-stress areas have positive correlation association and yield an estimate of correlation radius for the study panel. This result is in accordance with previous geological analysis. However, the estimated correlation radius can be affected by velocity artifacts and inaccurate interpreted faults. Considering the influence of velocity artifacts, we achieve a calibrated template to better estimate the correlation radius between in-panel faults and high-stress areas. This estimate could be a practical parameter to optimize mining methods and to minimize stress related rock failures.