Fiber sensing using supervisory path of submarine cables
Systems, and methods for automatically identifying an underground optical fiber cable length from DFOS systems in real time and pair it with GPS coordinates that advantageously eliminate the need for in-field inspection/work by service personnel to make such real-time distance/location determinations. As such, inefficient, error-prone and labor-intensive prior art methods are rendered obsolete. Operationally, our method disclosure involves driving vehicles including GPS to generate traffic patterns and automatically mapping traffic trajectory signals from a deployed buried fiber optic cable to locate geographic location(s) of the buried fiber optic cable. Traffic patterns are automatically recognized; slack in the fiber optic cable is accounted for; location of traffic lights and other traffic control devices/structures may be determined; and turns in the fiber optic cable may likewise be determined.
1. A vehicle-assisted, buried cable localization method using distributed fiber optic sensing (DFOS), the method comprising:
providing a length of optical fiber having a portion thereof buried underground;
providing a DFOS system, said system including
a DFOS interrogator and analyzer in optical communication with the length of optical fiber, said DFOS interrogator configured to generate optical pulses from laser light, introduce the pulses into the length of optical fiber and detect/receive Rayleigh reflected signals from the optical fiber, said analyzer configured to analyze the Rayleigh reflected signals and generate location/time waterfall plots from the analyzed Rayleigh reflected signals;
operating a set of at least three vehicles in coordination with one another, said vehicles being operated sufficiently proximate to at least a portion of the length of optical fiber to provide mechanical excitations to the portion of the length of optical fiber, wherein each vehicle of the set of at least three vehicles includes a global positioning system (GPS) that generates GPS coordinates for the vehicle in which it is included;
operating the DFOS system while the set of at least three vehicles are being operated, thereby providing mechanical excitations to the portion of the optical fiber thereby producing vibration events in the optical fiber and recording DFOS signals received during DFOS operation;
continuously operating the DFOS system and the vehicles and determining time/location of vibration events from waterfall plots a associating the vibration events to a GPS location of the buried portion of the optical fiber.
2. The method of claim 1 wherein the time/location of vibration events from waterfall plots are determined without human intervention.
3. The method of claim 2 wherein the set of at least three vehicles are operated in substantially a straight line, one after another.
4. The method of claim 3 wherein the set of at least three vehicles are operated along a route having a manhole or other utility hole interposed between at least two of the vehicles.
5. The method of claim 4 wherein an amount of slack optical fiber located in the manhole or other utility hole is determined.
6. The method of claim 3 wherein traffic control device locations are determined from the waterfall data.
7. The method of claim 6 wherein the traffic control device is a traffic light and at least two of the set of at least three vehicles are stopped during operation in response to the traffic light while at least one of the set of at least three vehicles is/are not stopped during operation in response to the traffic light.
8. The method of claim 3 wherein the vehicles are operated upon a roadway that is located proximate to the buried optical cable, and a path of the buried optical cable diverges from the roadway and such divergence is determined.
9. The method of claim 8 further comprising generating a map showing the optical fiber locations by both GPS coordinates and length from the interrogator.
10. The method of claim 9 wherein other portions of the optical fiber that are not sufficiently proximate to the operation of the set of at least three vehicles are mapped to time/locations by an artificial intelligence analyzer using known time/locations.