<p>Efficient arrival picking and precise source imaging are crucial components of seismic data processing in both active and passive seismology. In this study, we employ the unsupervised Fuzzy C-Means clustering approach to improve the picking of the arrivals from small magnitude earthquake events. The waveform-based source image is then computed using the grouped time-reversal approach, which first splits the receivers into groups and then backward propagates the wavefield. Once the arrivals are autopicked, P-wave signals are identified and extracted from the entire waveforms. These extracted segments are then used to construct a high-resolution source image through multi-dimensional cross-correlation of the wavefields, from which the event location is determined at the point of maximum coherence. The performance of the integrated approach is first tested on a suite of synthetic data and then on field datasets obtained from the North-West Himalayan region of Jammu and Kashmir. The estimated uncertainties reveal improvements in event locations compared to the conventional travel-time based inversion method. We demonstrate that the integrated approach can be effectively used to analyze seismological datasets in complex media, and can even work with sparse seismic networks for locating the small-magnitude earthquakes. Moreover, the present approach enables the location of seismic sources utilizing the single-component waveform data.</p>