Vehicle-assisted buried cable location using distributed optical fiber sensing

Abstract

A system and method for automatically identifying underground fiber optic cable lengths in real time from a DFOS system and combining same with GPS coordinates, thereby advantageously eliminating the need for on-site inspection / operation by service personnel to make such real-time distance / location determinations. Thus, the inefficient, error prone, and laborious prior art methods are no longer used. In operation, the disclosed method includes driving a vehicle including a GPS to generate traffic patterns and automatically mapping traffic trajectory signals from deployed buried fiber optic cables to identify the geographic location of the buried fiber optic cables. Traffic patterns are automatically recognized, fiber optic cable slack is taken into account, and the locations of traffic lights and other traffic control devices / structures can be determined, as well as fiber optic cable turns.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE This disclosure relates generally to optical fiber communication equipment. More specifically, this disclosure describes a system and method for vehicle-assisted buried cable location using distributed optical fiber sensing (DFOS). [Background technology]

[0002] As one skilled in the art can readily appreciate, optical time domain reflectometry (OTDR) is a useful technique for determining the length and loss of modern optical fiber communication equipment. For cable location determination, the OTDR, coupled with the technician's experience, provided an educated guess at the location of the optical fiber equipment corresponding to the cable length. Given the proliferation of optical fiber communication equipment and their enablement of modern communication services, systems and methods that provide easier and more accurate fiber equipment location information would be a welcome addition to the art. Summary of the Invention

[0003] An advancement in the art is provided in accordance with aspects of the present disclosure directed to a system and method for automatically identifying underground fiber optic cable lengths in real time from a DFOS system and combining same with GPS coordinates.

[0004] In stark contrast to the prior art, systems and methods according to aspects of the present disclosure eliminate the need for on-site inspection / operation by service personnel to perform such real-time distance / location determinations, and thus the inefficient, error-prone, and labor-intensive methods of the prior art are no longer used.

[0005] In operation, the present disclosure involves driving a vehicle including a GPS to generate traffic patterns and automatically mapping traffic trajectory signals from a deployed buried fiber optic cable to identify the geographic location of the buried fiber optic cable. As further disclosed, several advantageous aspects of the present system and method become apparent: traffic patterns are automatically recognized, fiber optic cable slack is taken into account, the locations of traffic lights and other traffic control devices / structures can be determined, and fiber optic cable turns can be determined as well. [Brief description of the drawings]

[0006] A more complete understanding of the present disclosure may be realized by reference to the accompanying drawings.

[0007] [Figure 1] FIG. 1 is a schematic diagram of an exemplary distributed optical fiber sensing system according to aspects of the present disclosure.

[0008] [Figure 2(A)] FIG. 1 is a schematic diagram showing a system layout of a sensor layer overlaid on an existing deployed optical fiber. [Figure 2(B)] 1 shows an illustration of a mechanism according to an aspect of the present disclosure. [Figure 2(C)] 1 illustrates an example waterfall trace according to an aspect of the present disclosure.

[0009] [Diagram 3] FIG. 1 is a schematic diagram illustrating the cable section under investigation for Case-1 (straight line) according to an embodiment of the present disclosure.

[0010] [Figure 4(A)] FIG. 13 is a schematic diagram illustrating a cable section under investigation for Case-2 (straight line, manhole in between) according to an embodiment of the present disclosure. [Figure 4(B)] FIG. 1 illustrates waterfall data collected by an exemplary DFOS operation according to an aspect of the present disclosure.

[0011] [Diagram 5] FIG. 13 is a schematic diagram showing the cable section under investigation for Case-3 (traffic light (all vehicles stopped due to signal on the right)) according to an embodiment of the present disclosure.

[0012] [Figure 6] FIG. 13 is a schematic diagram illustrating a cable section under investigation for Case-4 (traffic lights (one car passing, two cars stopping due to right-hand traffic light)) according to an embodiment of the present disclosure.

[0013] [Figure 7] FIG. 13 is a schematic block diagram illustrating a cable section under investigation for Case-4 (cable direction change) according to an embodiment of the present disclosure.

[0014] [Figure 8] FIG. 2 is a schematic flow diagram of an exemplary overall process according to an aspect of the present disclosure.

[0015] [Figure 9] FIG. 1 is a schematic diagram illustrating an architecture for automatically mapping the geographic locations of fiber cables, according to an aspect of the present disclosure.

[0016] [Figure 10] 1 illustrates an example waterfall trace having a vehicle trajectory, according to an aspect of the present disclosure.

[0017] [Figure 11] 1 is a schematic plot illustrating a vehicle trajectory from a waterfall trace in accordance with an aspect of the present disclosure.

[0018] [Figure 12] 1 is a schematic plot illustrating constructing a geographic trajectory on a waterfall trace, according to an aspect of the present disclosure.

[0019] [Figure 13] 1 is a schematic plot illustrating the movement of a geographic trajectory on a waterfall trace, according to an aspect of the present disclosure.

[0020] [Figure 14] 1 is a schematic plot illustrating finding a plateau on a waterfall trace, according to an aspect of the present disclosure.

[0021] [Figure 15] 1 is a schematic plot illustrating a fitting path according to an aspect of the present disclosure.

[0022] [Figure 16] 1 is a schematic plot illustrating an optimal path on a waterfall trace according to an aspect of the present disclosure.

[0023] [Figure 17] 11 is a plot illustrating the dependence of integrated waterfall strength on horizontal offset, according to an aspect of the present disclosure.

[0024] [Figure 18] FIG. 1 is a schematic diagram illustrating automatic cable mapping by DFOS (DAS / DVS) according to an aspect of the present disclosure.

[0025] [Figure 19] FIG. 1 is a flow diagram illustrating the overall process of automatic cable mapping by DFOS, according to an aspect of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Exemplary embodiments are more fully described in the drawings and detailed description, however, embodiments in accordance with the present disclosure may be embodied in various forms and are not limited to the specific or exemplary embodiments set forth in the drawings and detailed description.

[0027] The following merely illustrates the principles of the present disclosure, and it will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the present disclosure and are included within its spirit and scope.

[0028] Furthermore, all examples and conditional terms described herein are intended to be for educational purposes only to aid the reader in understanding the concepts contributed by the inventors to further the principles and techniques of the present disclosure, and should not be construed as being limited to such specifically recited examples and conditions.

[0029] Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents as well as equivalents developed in the future, i.e., elements developed that perform the same function, regardless of structure.

[0030] Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure.

[0031] Unless otherwise specified herein, the figures making up the drawings are not drawn to scale.

[0032] As some additional background, note that a distributed fiber optic sensing system interconnects an optoelectronic integrator to an optical fiber (or cable), transforming the fiber into an array of sensors distributed along the length of the fiber. In effect, the fiber becomes the sensor, and the interrogator generates / injects laser light energy into the fiber to sense / detect events along the fiber length.

[0033] As those skilled in the art will understand and appreciate, DFOS technology can be deployed to continuously monitor vehicle movement, human traffic, drilling activity, seismic activity, temperature, structural integrity, liquid and gas leaks, and many other conditions and activities. It is used worldwide to monitor power plants, communication networks, railroads, roads, bridges, borders, critical infrastructure, onshore and undersea power and pipelines, and downhole applications in oil, gas, and enhanced geothermal power generation. Advantageously, distributed fiber optic sensing is not constrained by line of sight or remote power access, and depending on the system configuration, can be deployed at continuous lengths of over 30 miles, with sensing / detection possible at all points along that length. Thus, the cost per sensing point over long distances is typically incomparable to competing technologies.

[0034] Fiber optic sensing measures changes in the "backscatter" of light that occurs in an optical sensing fiber when the sensing fiber encounters a vibration, strain, or temperature change event. As mentioned above, the sensing fiber acts as a sensor along its entire length, providing real-time information about the physical / environmental surroundings, and the integrity / security of the fiber. Additionally, distributed fiber optic sensing data pinpoints the exact location of events and conditions occurring at or near the sensing fiber.

[0035] In Figure 1, a schematic diagram showing a generalized configuration and operation of a distributed fiber optic sensing system including artificial intelligence analysis and cloud storage / services is shown. With reference to Figure 1, one can observe an optical sensing fiber connected to an interrogator. As is known, a modern interrogator is a system that generates an input signal to the fiber and detects / analyzes the received signal after being reflected / scattered. The signal is analyzed and an output is generated that is indicative of the environmental conditions encountered along the fiber. The signal thus received may result from reflections within the fiber such as Raman backscattering, Rayleigh backscattering, and Brillion backscattering. It may also be a forward signal that utilizes the velocity difference of multiple modes. Without loss of generality, the following description assumes a reflected signal, but the same approach can be applied to a forward signal as well.

[0036] As will be appreciated, modern DFOS systems include an interrogator that periodically generates optical pulses (or any coded signal) and injects them into an optical fiber. The injected optical pulse signal is transmitted along the optical fiber.

[0037] At each location along the fiber, a small portion of the signal is scattered / reflected back to the interrogator. The scattered / reflected signal carries information that the interrogator uses to detect, e.g., changes in power level indicative of mechanical vibrations.

[0038] The reflected signal is converted to the electrical domain and processed inside the interrogator. Based on the time of pulse injection and the time the signal is detected, the interrogator can determine from which location along the fiber the signal came, and thus sense activity at each location along the fiber.

[0039] Distributed Acoustic Sensing (DAS) / Distributed Vibration Sensing (DVS) systems detect vibrations and capture acoustic energy along optical sensing fibers. Advantageously, existing, traffic-carrying optical fiber networks can be utilized to convert into distributed acoustic sensors and capture real-time data. Additionally, classification algorithms can be used to detect and locate events such as leaks, cable faults, intrusive activity, or other anomalous events involving both acoustic and / or vibration.

[0040] Currently, various DAS / DVS techniques are used, the most common being based on coherent optical time domain reflectometry (C-OTDR). C-OTDR utilizes Rayleigh backscattering and can detect acoustic frequency signals over long distances. The interrogator transmits coherent laser pulses along an optical sensor fiber (cable). Due to scattering sites in the fiber, the fiber acts as a dispersive interferometer with a gauge length equal to the pulse length (e.g. 10 meters). Acoustic / mechanical disturbances acting on the sensor fiber cause microscopic stretching or compression (microstrain) of the fiber, resulting in changes in the phase relationship and / or amplitude of the optical pulses traveling in the fiber.

[0041] Before the next laser pulse is sent, the previous pulse must have time to travel the entire length of the sensing fiber and its scattering / reflection back. Thus, the maximum pulse rate is determined by the length of the fiber. It is therefore possible to measure acoustic signals that vary in frequency up to the Nyquist frequency, which is usually half the pulse rate. Higher frequencies decay very quickly, so most of the frequencies relevant for event detection and classification are in the low 2kHz range.

[0042] As will now be shown and described, and as previously mentioned, the systems and methods of the present invention automatically detect / interpret vibration signals resulting from DFOS operations using a deployed fiber optic sensor cable to detect / locate vibrations in the cable caused, for example, by a vehicle traveling in sufficient proximity to the deployed fiber optic sensor cable.

[0043] As is known, telecommunications carriers and network providers own and operate millions of miles of deployed optical fiber. Those skilled in the art will understand and appreciate that it is important for telecommunications carriers to geographically locate fiber cables in the event of a fiber cable failure (e.g., fiber cut). In many cases, maintenance operations rely on prior information and knowledge of the location and orientation of the cable, which may be obtained from construction maps of the deployment site or from notes and photographs made / recorded during the construction / deployment of the fiber cable. In the absence or unavailability of such prior knowledge, it is difficult for telecommunications carriers and their workers to identify and pinpoint the exact segment or location of the fiber cable that needs to be inspected or repaired. Thus, locating such portions of optical cable is crucial for telecommunications carriers to efficiently maintain their facilities.

[0044] Currently, the most widely used cable location methods generally use optical time domain reflectometry (OTDR) technology to measure fiber length and loss. OTDR, coupled with the technician's experience, is used to infer / determine the geographic location of an object by mapping it to fiber length. Unfortunately, modern OTDR measurement systems and methods do not provide the location of an object on a geographical map, and are unable to pinpoint the location of an object precisely, in part due to the presence of slack cable along a particular cable route.

[0045] Already, we have described mechanisms that use Distributed Fiber Optic Sensing (DFOS) technology and AI algorithms to recognize cable location by externally exciting signals using mechanical vibrators. However, such methods still rely on humans / technicians manually operating mechanical vibrators in the field. These procedures remain inefficient, error-prone, and laborious. For large-scale field deployments, a self-determining method is needed to detect target vibration signals from the entire fiber cable path and differentiate it in different scenarios. Thus, as mentioned above, one aspect of the present disclosure is to describe an inventive system and method that automatically identifies underground cable length in real time from waterfall data by a DFOS system and combines it with GPS coordinates associated with a field vehicle. Advantageously, the system and method according to the present disclosure can eliminate field deployment to determine fiber cable location as in the case of the prior art / current technology.

[0046] Figure 2(A) is a schematic diagram showing a system layout of a sensor layer overlaid on an existing deployed optical fiber. Figure 2(B) shows an illustration of a mechanism according to an embodiment of the present disclosure. Figure 2(C) shows an example waterfall trace according to an embodiment of the present disclosure.

[0047] Referring to FIG. 2(A), a configuration is shown that includes a sensing layer overlaid on an existing deployed fiber network. A distributed fiber optic sensing system (DFOS) (101), which may use distributed acoustic sensing (DAS) and / or distributed vibration sensing (DVS) technologies, is shown installed at a control station (CO) / central office (100) for remote monitoring of the entire cable route. As can be seen, the DFOS system is connected to field optical fibers to provide sensing capabilities. Advantageously, the fiber may be dark fiber or operational fiber carrying live traffic from one or more service providers.

[0048] To create a recognizable traffic pattern, three trucks (201) are shown participating in the survey and traveling together along the cable route. As shown in Figure 2(B), the traffic pattern of the following three trucks is different from normal road traffic, which can be identified by using artificial intelligence (AI) systems, methods, and algorithms. GPS devices (202) are installed in all trucks (201) and transmit the GPS coordinates and timestamps of each truck to a pairing system (102) located at the central office (CO). By matching the timestamps of the GPS devices and the DFOS system, the geographic location of the target location can be combined with the fiber distance from the waterfall data and the GPS coordinates of the AI ​​platform (103).

[0049] Figure 2(C) shows an example of waterfall data collected by an exemplary DFOS system with one tracked vehicle trajectory. For one tracked vehicle, the traffic pattern is distinguishable from other road traffic. For three trucks traveling together, the differentiation becomes even more pronounced.

[0050] Those skilled in the art will appreciate that the use of three vehicles provides many advantages, including: First, it is easier to distinguish the target traffic trajectory from the waterfall trace than from a normal road traffic signal. Second, the location of the buried cable can be found by driving the truck. Even if the fiber cable is several meters away from the road, the vibrations generated by the heavy truck can be detected by the DFOS system. Third, if some locations are inaccessible, the AI ​​analysis platform has an adaptation algorithm based on two points, front and back. Fourth, the direction of travel of the vehicle can be determined by two GPS positions. Finally, certain facilities can be detected, such as the location of manholes, the amount of slack in the fiber, traffic lights (when the survey truck stops at a red light), and the location of cable turns. It should be noted that this list of advantages above is abbreviated, and further benefits and advantages of the method, system, and technique of the present invention will become more apparent in the following description.

[0051] FIG. 3 is a schematic diagram showing the cable section under survey for Case-1 (straight line) according to an embodiment of the present disclosure. With reference to this figure, it can be seen that the survey truck passes through two locations (L1, L1') at different timestamps (T1, T2). At T1, the pairing system can map GPS1 coordinates and cable length L1 onto the GIS system, while GPS1' coordinates are paired with L1' at the timestamp of T2. Since it is a straight cable, the distance from GPS (GPS1'~GPS1) will be the same as the cable length (L1'~L1) from DFOS. Also, the second and third trucks will not only generate traffic trajectories, but also perform system validation, such as distances GPS2'-GPS2=L2'-L2 and GPS3'-GPS3=L3'-L3.

[0052] FIG. 4(A) is a schematic diagram showing the cable section under investigation for Case-2 (straight line, manhole in between) according to an embodiment of the present disclosure. FIG. 4(B) is a diagram showing waterfall data collected by an exemplary DFOS operation according to an embodiment of the present disclosure. As can be seen, the diagram shows a manhole along the optical fiber cable path. There is a slope in the traffic trajectory as the vehicle travels along the road. However, the slope becomes flat when passing through the manhole with the slack fiber inside. It can be seen from FIG. 1 (including the field data collected from DFOS). In this example, the cable length (L1'-L1) is longer than the GPS distance (GPS1'-GPS1). The difference is due to the slack fiber (x) in the manhole (x=(L1'-L1)-(GPS1'-GPS1)). And the information (coordinates and cable length) of the manhole can be determined at T1 (start point of flat slope) with the corresponding GPS1 and L1.

[0053] FIG. 5 is a schematic diagram illustrating a cable section under investigation for Case-3 (traffic light (all vehicles stopped due to right-side signal)) according to an embodiment of the present disclosure. As can be seen, this figure shows an example where the investigation truck is stopped at a traffic light. From the waterfall data, the traffic trajectory is broken. At T1, when the investigation truck is stopped at a red light, the traffic pattern is also deleted. The pattern continues after green light and T2. Thus, the traffic light information can be detected at T1 with the corresponding GPS1 and L1. The red light period can be known as T2~T1. It should be noted that the traffic light can be detected only when the investigation traffic is stopped by a red light or other similar traffic control device.

[0054] FIG. 6 is a schematic diagram illustrating a cable section under investigation for Case-4 (traffic lights (one through, two stopped due to right-hand light)) according to an embodiment of the present disclosure. As can be seen, the diagram illustrates an example of surveyed trucks stopped at a traffic light, but not all trucks are stopped. As illustrated in the example, vehicle 1 (V1) passes the traffic light, but V2 and V3 are stopped. This scenario can be detected when the pairing data does not match, e.g., GPS1'-GPS1>GP2'-GPS2 in two time windows, GPS2'-GPS2=0. From the waterfall trace, one traffic trajectory continues, but two traffic trajectories are interrupted. At Tl, two surveyed trucks are stopped at a red light, and the traffic pattern is stopped. The pattern continues after the green light and T2. Thus, the traffic light information can be detected at Tl with the corresponding GPS2 and L2. The duration of the red light can be known as T2-T1. Also, the vehicle distance (D) can be determined by GPS1'-GPS2', and even if there are some vehicles between V1 and V2, their trajectories can be identified and the new traffic pattern can be used for investigation. In this case, the AI ​​algorithm changes the target pattern from Pattern A (three tracks with equal spacing) to Pattern B (one with large spacing and two with equal spacing).

[0055] FIG. 7 is a schematic block diagram illustrating the cable section under investigation for Case-4 (cable turnaround) according to an embodiment of the present disclosure. As can be seen, this diagram illustrates an example scenario when the fiber optic cable under investigation is bent. This is a common case in urban areas and can be recognized when T2 cannot pair with GPS1' (no L1'). In this case, the AI ​​platform can send a message to the truck driver, informing him that he is on a wrong route. The point of the cable turnaround can be known at T1 using the corresponding GPS1 and L1. Thus, the investigation truck can turn back to the cable turnaround location and follow the cable under investigation.

[0056] FIG. 8 is a schematic flow diagram of an exemplary overall optical fiber survey process according to an embodiment of the present disclosure. With reference to this figure, it can be seen that the system and method of the present invention includes connecting a DFOS system located at a management (central) station to the field fiber used in the survey. Three trucks (vehicles), each equipped with a global positioning device (GPS), are operated to generate traffic patterns and thereby perform the field survey. It is noted that in a preferred operation, the three trucks are operated together, typically following each other in single file as much as possible. As these vehicles are operated, the vibration signals collected / analyzed by the DFOS system include ambient noise, normal road traffic, road construction, and other traffic patterns along the fiber optic cable route.

[0057] During and after signal collection / analysis, unique traffic patterns are recognized / determined by artificial intelligence systems and algorithms to, among other things, identify the cable distances of vibrations generated by the operation of survey vehicles and associated with the known GPS coordinates of those vehicles. Once such vibration / GPS locations are determined, a survey map can be generated that includes such information along with other map-related data.

[0058] As one skilled in the art can readily appreciate, the systems, methods, and structures according to aspects of the present disclosure can be further refined to provide even more information.

[0059] As previously mentioned, one aspect of the present disclosure is directed to a system and method that uses a GPS equipped vehicle as a signal source, where the GPS instantly determines / records the geographic location and the DFOS system records the waterfall trace of the vehicle while the vehicle is driven in proximity to and along the length of the fiber optic cable under investigation.

[0060] As previously mentioned, to accurately identify the vehicle's trajectory from other traffic or events that generate vibration signals that excite the fiber optic cable under investigation, the mapping system requires at least one reference point with GPS coordinates and the linear distance of the cable from that point to the central station mapped in other ways. As previously mentioned, a communications technician can use a mobile power tool such as a jackhammer to generate a recognizable signal, and the fiber sensing system can use machine learning based algorithms to identify the linear distance of the vibration source while simultaneously mapping the GPS coordinates of the technician. It is noted that there are many problems associated with developing an automated system such as that disclosed in accordance with the present disclosure. Specifically, the following are included:

[0061] Traffic Pattern

[0062] Slow moving trucks are recommended for on-site investigations in order to distinguish the trajectory of the survey vehicle from other traffic. Larger survey trucks can generate a stronger signal for DFOS and their slightly slower speed compared to normal traffic makes their trajectory easier to distinguish.

[0063] Reference point

[0064] The GPS and fiber length of at least one reference point on the straight cable section needs to be determined. The reference point is usually picked as the end point of the straight section of cable.

[0065] mapping

[0066] Map GPS and DFOS timestamps to extract a virtual trajectory that matches the actual waterfall trace.

[0067] FIG. 9 is a schematic diagram illustrating an architecture for automatically mapping the geographic location of a fiber cable, according to an embodiment of the present disclosure. As can be seen in this figure, a distributed fiber optic sensing system (DFOS) (101), which can be distributed acoustic sensing (DAS) and / or distributed vibration sensing (DVS), is located in a control station (CO) / central station (100) for remotely monitoring the entire cable route. The DFOS system is connected to the field optical fiber and provides sensing capabilities. The fiber can be dark fiber or a service provider's operational fiber. To create a recognizable traffic pattern, a truck (201) is involved in the survey, driving parallel to and close to the cable. A GPS unit (202) is carried by the truck (201) and transmits GPS coordinates and timestamps to the CO's pairing system (102). By matching the timestamps of the GPS unit and the DFOS system, the geographic location of the target location can be combined with the fiber distance and GPS coordinates from the waterfall data at the AI ​​platform (103). In order to determine the trajectory of a vehicle in use from other traffic, one or more reference points (302) on the route are needed. The reference points are usually picked as the end points of a straight section of a cable.

[0068] FIG. 10 illustrates an exemplary waterfall trace with vehicle trajectory according to an embodiment of the present disclosure. As shown in the figure, the trace is collected in a 3-minute window from the DFOS system. Vehicle trajectory can be extracted from buried cables as well as suspended aerial cables. Therefore, this exemplary architecture can be used for the entire cable path mapping except for cable sections that are inaccessible to vehicles and / or buried underwater. The path match algorithm is generally described in the drawings / figures as follows:

[0069] 11 is a schematic plot showing a vehicle trajectory from a waterfall trace according to an embodiment of the present disclosure. The figure shows a schematic graph of a vehicle trajectory from a waterfall trace including a target vehicle, other road traffic, and two reference points. The two reference points are selected at the start and end of a straight cable section. More reference points may be required if the cable bends or crosses the road to another side.

[0070] FIG. 12 is a schematic plot showing constructing a geographical trajectory on a waterfall trace according to an embodiment of the present disclosure. In this scenario, a vehicle moves from a first reference point to a second reference point, and the GPS at that time is recorded. A virtual trajectory of the vehicle is created by an instant GPS list. By matching the timestamps from the DFOS and GPS device with the positions of the reference points, a geographical trajectory can be constructed on the waterfall trace and the corresponding vehicle trajectory of the target vehicle can be found.

[0071] Please note that there are sections where the geographic trajectory and the target vehicle trajectory do not match. Section A represents the difference from the geographic location and cable length. Typically, the cable length is 20% longer than the geographic location, which is due to the slack fiber (fiber coil) in the field. Furthermore, the fiber coil in the manhole can be seen from section B.

[0072] FIG. 13 is a schematic plot showing the movement of a geographic trajectory on a waterfall trace according to an embodiment of the present disclosure. By searching for a horizontal offset, the geographic path can be matched to the waterfall trajectory. Furthermore, the signal strength along the virtual trajectory is integrated and shifted along the driving direction due to the offset. A plot of integrated strength versus offset is created. The peak locations correspond to the overlapping portion of the virtual trajectory with the actual waterfall trajectory. The distance between the two peak locations indicates a slack fiber coil.

[0073] FIG. 14 is a schematic plot illustrating finding a horizontal section on a waterfall trace according to an embodiment of the present disclosure. To match the geographic location and cable length, the slack fiber length needs to be found from the waterfall trace. The fiber coil can be detected based on the horizontal (flat, slope=0) section. Therefore, a vertical moving window can be used to find the fiber coil and integrate the waterfall intensity along the trajectory. The total intensity can be shown as a peak value at the location that matches the DFOS waterfall trace and geographic trajectory. This can be implemented as follows: create a list of virtual trajectories corresponding to the offset at the peak of integrated intensity, and determine the horizontal path section by searching for the maximum integrated intensity on two consecutive virtual trajectories.

[0074] FIG. 15 is a schematic plot showing adapted pathways according to an embodiment of the present disclosure.

[0075] 16 is a schematic plot showing an optimal path on a waterfall trace according to an embodiment of the present disclosure. An example of a light path from GPS and a vehicle trajectory detected from DFOS is shown, and the method of the present invention can be very well adapted to the vehicle trajectory in the field.

[0076] 17 is a plot showing the horizontal dependence of integrated waterfall intensity. The peaks indicate the coincidence of the geographic locus with the waterfall trajectory. The distance between the two peaks is the length and offset of the fiber coil that may be on the aerial cable and / or in the manhole according to an embodiment of the present disclosure.

[0077] Note that at this point a plot may be generated showing the results of searching for the optimal location of the horizon based on integrated signal strength, with the peak indicating the targeted location.

[0078] FIG. 18 is a schematic diagram illustrating automatic cable mapping by DFOS (DAS / DVS) according to an embodiment of the present disclosure.

[0079] FIG. 19 is a flow diagram illustrating the overall process of automatic cable mapping by DFOS, according to an embodiment of the present disclosure.

[0080] At this point, the present disclosure has been presented using some specific examples, but those skilled in the art will recognize that the present teachings are not so limited. Accordingly, the present disclosure should be limited only by the scope of the appended claims.

Claims

1. 1. A vehicle-assisted buried cable location method using distributed fiber optic sensing (DFOS), comprising: Providing an optical fiber partially buried underground; providing a DFOS system, the DFOS system including a DFOS interrogator and analyzer in optical communication with the optical fiber, the DFOS interrogator configured to generate optical pulses from a laser light, couple the pulses into the optical fiber, and detect / receive a Rayleigh return signal from the optical fiber, the analyzer configured to analyze the Rayleigh return signal, and generate a position / time waterfall plot from the analyzed Rayleigh return signal; operating a set of at least three vehicles in coordination with one another, the vehicles operating in sufficient proximity to at least a portion of the optical fiber to provide mechanical excitation to the portion of the optical fiber, each vehicle of the set of at least three vehicles including a Global Positioning System (GPS), the GPS generating GPS coordinates of the vehicle in which the GPS is included; operating the DFOS system while the set of at least three vehicles is being driven, thereby providing a mechanical excitation to a portion of the optical fiber, thereby generating a vibration event in the optical fiber, and recording DFOS signals received during DFOS operation; and continuously operating the DFOS system and the vehicle, identifying time / location of vibration events from a waterfall plot, and correlating the vibration events with a GPS location of the buried portion of the optical fiber.

2. The method of claim 1 , wherein the time / location of a vibration event from a waterfall plot is determined without human intervention.

3. The method of claim 2 , wherein the set of at least three vehicles are driven sequentially in a substantially straight line.

4. The method of claim 3 , wherein the set of at least three vehicles is driven along a route that includes a manhole or other utility hole between at least two of the vehicles.

5. The method of claim 4 , wherein an amount of slack optical fiber located within the manhole or other utility hole is determined.

6. The method of claim 3 , wherein the location of a traffic control device is determined from the waterfall data.

7. 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 stop while driving in response to the traffic light, and at least one of the set of at least three vehicles does not stop while driving in response to the traffic light.

8. 4. The method of claim 3, wherein the vehicle is driven on a road located proximate to the buried portion of the optical fiber, a route of the buried portion of the optical fiber diverges from the road, and the divergence is determined.

9. 9. The method of claim 8, further comprising generating a map showing the location of said optical fiber by both GPS coordinates and length from said DFOS interrogator.

10. The method of claim 9, wherein other portions of the optical fiber that are not sufficiently close to the operation of the set of at least three vehicles are mapped to time / location by an artificial intelligence analyzer using the known time / location.

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