The ample size of time-lapse data often requires significant event detection and source location efforts, especially in areas such as shale gas exploration regions where a large number of microseismic events are often recorded. In many cases, real-time monitoring and location of these events are essential to production decisions. Conventional methods face considerable drawbacks. For example, traveltime-based methods require traveltime picking of often noisy data, whereas migration and waveform inversion methods require expensive wavefield solutions and event detection. Both tasks require some human intervention, and this becomes a big problem when too many sources need to be located, which is common in microseismic monitoring. Machine learning has recently been used to identify microseismic events or locate their sources once they are identified and picked. We have used a novel artificial neural network framework to directly map seismic data, without any event picking or detection, to their potential source locations. We train two convolutional neural networks (CNNs) on labeled synthetic acoustic data containing simulated microseismic events to fulfill such requirements. One CNN, which has a global average pooling layer to reduce the computational cost while maintaining high-performance levels, aims to classify the number of events in the data. The other network predicts the source locations and other source features such as the source peak frequencies and amplitudes. To reduce the size of the input data to the network, we correlate the recorded traces with a central reference trace to allow the network to focus on the curvature of the input data near the zero-lag region. We train the networks to handle single-, multi-, and no-event segments extracted from the data. Tests on a simple vertical varying model and a more realistic Otway field model demonstrate the approach’s versatility and potential.