Abstract: Passive microseismic monitoring (PMM) serves as a fundamental technology for assessing hydraulic fracturing (HF) effectiveness, with a key focus on accurate and efficient phase detection/arrival picking and source location. In PMM data processing, the data-driven paradigm (deep learning based) outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification. Monte Carlo Dropout, a Bayesian uncertainty quantification technique, performs stochastic neuron deactivation through multiple forward propagation samplings. Therefore, this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration, establishing an optimized workflow spanning automated phase detection to robust source location. The methodology implementation comprises two principal components: (1) The MD-Net employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and uncertainty estimation; (2) an integrated hybrid-driven workflow with a traveltime-based inversion method for source location. Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions, maintaining detection accuracy exceeding 99% for both P- and S-waves. The phase arrival picking precision shows significant improvement, with a 40% reduction in standard deviation compared to the baseline model (P-S time difference decreasing from 12.0 ms to 7.1 ms), while providing quantifiable uncertainty metrics for manual calibration. Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions, with 100% of microseismic events clustering along the primary fracture expanding direction. This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven methods in spatial rationality. This study establishes an interpretable, high-precision automated framework for HF-PMM applications, demonstrating potential for extension to diverse geological settings and monitoring configurations.