Abstract: Disclosed herein are wireless networks of gunshot detector arrays providing the firing location and trajectory of a gunshot, while being tolerant of clock drift among the network. Individual gunshot detectors may be battery-powered employing one or more microphones. Detectors may time-synchronize infrequently to conserve power. Detectors may communicate wirelessly to a central device that evaluates gunshot candidates, associates those candidates between detectors to individual gunshot events, and performs calculations to arrive at a solution for a gunshot direction and/or origin. Combinations of contemporaneous gunshot candidates may be reviewed for a best positional solution. The location of gunshot events may be displayed on an overhead image or map, to persons providing remedial actions, optionally with trajectories and potential zones of impact and damaged assets.

Abstract: Disclosed herein are wireless networks of gunshot detector arrays providing the firing location and trajectory of a gunshot, while being tolerant of clock drift among the network. Individual gunshot detectors may be battery-powered employing one or more microphones. Detectors may time-synchronize infrequently to conserve power. Detectors may communicate wirelessly to a central device that evaluates gunshot candidates, associates those candidates between detectors to individual gunshot events, and performs calculations to arrive at a solution for a gunshot direction and/or origin. Combinations of contemporaneous gunshot candidates may be reviewed for a best positional solution. The location of gunshot events may be displayed on an overhead image or map, to persons providing remedial actions, optionally with trajectories and potential zones of impact and damaged assets.

Abstract: Various aspects described herein relate to a system that utilized deep learning and neural networks to estimate/predict an amount of natural resource production in a well given a set of parameters indicative of physical changes to the well. In one aspect, a virtual flow meter includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to receive one or more input parameters indicative of physical changes to at least one well; apply the one or more input parameters to a trained neural network architecture; and determine one or more outputs of the trained neural network architecture, the one or more outputs corresponding to predicted fluid output of the at least one well.

Abstract: Various aspects described herein relate to a system that utilized deep learning and neural networks to estimate/predict an amount of natural resource production in a well given a set of parameters indicative of physical changes to the well. In one aspect, a virtual flow meter includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to receive one or more input parameters indicative of physical changes to at least one well; apply the one or more input parameters to a trained neural network architecture; and determine one or more outputs of the trained neural network architecture, the one or more outputs corresponding to predicted fluid output of the at least one well.