Location/detection method: In this study, we revisit the continuous recordings that are acquired by 69 three-component nodes at a Hydraulic Fracturing (HF) site in Alberta, Canada, taking advantage of a machine learning-based seismic detection and location workflow (LOC-FLOW). This workflow integrates four key steps to generate earthquake catalogs, including phase picking using neural network (PhaseNet), rapid association of seismic phases (REAL), seismic location (e.g., VELEST) as well as relocation (e.g., hypoDD). The catalog contains 21,619 earthquakes with magnitudes down to -2 and achieves a high location accuracy at meter scale. The improved earthquake locations allow us to better delineate the fractures and faults activated during the HF. Our results further reveal the complexity of the triggering mechanism of HF-induced earthquakes according to the statistical analysis of the frequency-magnitude distribution of earthquakes. Our study suggests that multiple factors, including pore fluid pressure due to fluid injection and perturbation in the stress field caused by earthquakes, can jointly control the spatiotemporal variations of regional seismicity during the multi-stage HF operation.