MACHINE LEARNING DAS MICROSEISMIC PROCESSING

Systems and methods for operating a distributed acoustic sensing (DAS) system are disclosed that process the DAS signal by downsampling the received signal and stacking the channels, generating a plurality of sliding windows of the processed signal, analyzing the windows to either identify a microseismic event or determine that the window contains only noise, then discarding all noise windows. A convolutional neural network is used to determine an onset time and a peak channel of each microseismic event and to reduce the dimensionality of the data in time and space around the onset time. A convolutional neural network is used to identify a first arrival pick within the truncated window of all recorded phases, which are then used to determine the physical location of the source fracture.