Abstract: Seismic source locations can characterize the spatial and temporal distributions of seismic sources, and can provide important basic data for earthquake disaster monitoring, fault activity characterization, and fracture growth interpretation. Waveform stacking-based location methods invert the source locations by focusing the source energy with multichannel waveforms, and these methods exhibit a high level of automation and noise-resistance. Taking the cross-correlation stacking (CCS) method as an example, this work attempts to study the influential factors of waveform stacking-based methods, and introduces a comprehensive performance evaluation scheme based on multiple parameters and indicators. The waveform data are from field monitoring of induced microseismicity in the Changning region (southern Sichuan Basin of China). Synthetic and field data tests reveal the impacts of three categories of factors on waveform stacking-based location: velocity model, monitoring array, and waveform complexity. The location performance is evaluated and further improved in terms of the source imaging resolution and location error. Denser array monitoring contributes to better constraining source depth and location reliability, but the combined impact of multiple factors, such as velocity model uncertainty and multiple seismic phases, increases the complexity of locating field microseismic events. Finally, the aspects of location uncertainty, phase detection, and artificial intelligence-based location are discussed.