Underground cable location using high-speed time series template matching

Abstract

A method for underground cable location using fast time series template matching and distributed fiber optic sensing (DFOS) includes providing a DFOS system including an optical sensor fiber, a DFOS interrogator configured in optical communication with the optical sensor fiber to generate optical pulses, couple the generated pulses into the optical sensor fiber, and receive backscattered signals from the optical sensor fiber, and an intelligent analyzer configured to analyze the DFOS data received by the DFOS interrogator and determine vibration activity occurring at a location along the optical sensor fiber from the backscattered signals, disposing a programmable vibration generator at a field location proximate to the optical sensor fiber, transmitting a unique vibration pattern generated by the vibration generator to the programmable vibration generator, activating the programmable vibration generator to generate the transmitted unique vibration pattern, activating the DFOS system, and collecting / analyzing the determined vibration activity to further determine vibration activity indicative of the unique vibration pattern generated by the vibration generator.

Description

[Technical Field]

[0001] The present disclosure relates generally to distributed fiber optic sensing (DFOS) systems, methods, and structures. More specifically, the present disclosure relates to underground cable localization using high-speed time series template matching combined with DFOS. [Background technology]

[0002] A significant challenge facing communication service providers worldwide is efficiently managing the millions of miles of buried and suspended optical fiber that comprise their network infrastructure. Accordingly, systems, methods, and structures that facilitate the location of buried and suspended optical fiber would be a welcome addition to the art. Summary of the Invention

[0003] An advancement in the art is made by aspects of the present disclosure relating to distributed fiber optic sensing underground cable localization using high speed time series data template matching.

[0004] In contrast to conventional techniques, systems and methods according to embodiments of the present invention provide cable location based on time-series similarity search. The inventive technique can be used to rapidly search either specially designed vibration patterns created with on / off patterns distinguishable from background noise, or actual field vibration signals collected from secondary point sensors (e.g., cell phones, tablets, accelerometers, etc.). Furthermore, the inventive system and method works with a variety of signal patterns, of varying durations, generated by different vibrators, and in different locations (e.g., buried or aerial sections of the cable), using resource-limited platforms (e.g., laptops or edge devices) so that field technicians do not have to wait more than a few seconds to receive results.

[0005] A method for underground cable location using high-speed time series template matching and distributed fiber optic sensing (DFOS) includes providing a DFOS system including an optical sensor fiber; a DFOS interrogator in optical communication with the optical sensor fiber, the DFOS interrogator configured to generate optical pulses, couple the generated pulses into the optical sensor fiber, and receive backscattered signals from the optical sensor fiber; and an intelligent analyzer configured to analyze DFOS data received by the DFOS interrogator and determine vibration activity occurring at a location along the optical sensor fiber from the backscattered signals; disposing a programmable vibration generator at a field location proximate to the optical sensor fiber; transmitting a unique vibration pattern to the programmable vibration generator to be generated by the vibration generator; activating the programmable vibration generator to generate the transmitted unique vibration pattern; and operating the DFOS system and collecting / analyzing the determined vibration activity to further determine vibration activity indicative of the unique vibration pattern generated by the vibration generator. [Brief explanation of the drawings]

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

[0007] [Figure 1(A)] FIG. 1 is a schematic diagram illustrating a DFOS system according to an embodiment of the present disclosure.

[0008] [Figure 1(B)] FIG. 1 is a schematic diagram illustrating a coded constant amplitude DFOS system with out-of-band signal generation, according to an aspect of the present disclosure.

[0009] [Figure 2] FIG. 1 is a schematic diagram outlining a method according to an aspect of the present disclosure.

[0010] [Figure 3]1 is a graph of an exemplary periodic square wave signal with a 4 second period and on / off cycles of equal length, with a sampling rate of 100 Hz, in accordance with an embodiment of the present disclosure.

[0011] [Figure 4] 1 is a graph of an exemplary periodic square wave signal with a 4 second period and on / off cycles of equal length, with a sampling rate of 100 Hz, in accordance with an embodiment of the present disclosure.

[0012] [Figure 5(A)] 10 is a pair of graphs of amplitude versus time of an actual vibration signal with a specified pattern overlaid after Z-normalization processing, according to an embodiment of the present disclosure. [Figure 5(B)] 10 is a pair of graphs of amplitude versus time of an actual vibration signal with a specified pattern overlaid after Z-normalization processing, according to an embodiment of the present disclosure.

[0013] [Figure 6] FIG. 10 illustrates an example sliding window that iterates through all n-m+1 sliding windows in ts to find the sliding window position that provides the maximum correlation to the query, according to aspects of the present disclosure.

[0014] [Figure 7(A)] 1 is a pair of graphs of amplitude versus time of actual vibration signals from a waterfall image, where the vertical lines are the start and end positions of a sliding window in which the subsequence is most similar to the query, according to aspects of the present disclosure. [Figure 7(B)] 10A-10C are a pair of graphs of amplitude versus time of actual vibration signals from a waterfall image showing an overlay of both a query and a subsequence, in accordance with aspects of the present disclosure.

[0015] [Figure 8] FIG. 10 is an exemplary flow diagram illustrating tuple iteration according to an aspect of the present invention.

[0016] [Figure 9] FIG. 1 is an exemplary flow diagram illustrating the overall operation of systems and methods according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following is merely illustrative of 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.

[0018] Furthermore, all examples and conditional language set forth 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.

[0019] 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.

[0020] 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.

[0021] Unless otherwise specified herein, the figures comprising the drawings are not drawn to scale.

[0022] As additional background, we begin by noting that distributed fiber optic sensing (DFOS) is an important and widely used technology for detecting environmental conditions (e.g., temperature, vibration, acoustic excitation, and strain levels) anywhere along a fiber optic cable that is in turn connected to an interrogator. As known, a modern interrogator is a system that generates an input signal into the fiber, detects and analyzes the reflected / scattered, and then received signal. The signal is analyzed, and an output is generated that indicates the environmental conditions encountered along the fiber. Such received signals can result from reflections within the fiber, such as Raman backscattering, Rayleigh backscattering, and Brillion backscattering. DFOS can also use forward signals that exploit the velocity differences of multiple modes. Without loss of generality, the following discussion assumes reflected signals, but the same approach is equally applicable to forward signals.

[0023] Figure 1(A) is a schematic diagram of a generalized prior art DFOS system. 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.

[0024] At locations along the fiber, a small portion of the signal is reflected back to the interrogator. The reflected signal carries information that the interrogator uses to detect, for example, changes in power level indicative of mechanical vibrations. Although not shown in detail, the interrogator can include a coded DFOS system that can employ a coherent receiver configuration known in the art, such as that shown in Figure 1(B).

[0025] The reflected signal is converted to the electrical domain and processed within the interrogator. Based on the time of pulse injection and the time the signal is detected, the interrogator can determine which location on the fiber the signal is coming from and sense the activity at each location on the fiber.

[0026] Those skilled in the art will understand and appreciate that by implementing signal coding on the interrogation signal, more optical power can be transmitted into the fiber, thereby advantageously improving the signal-to-noise ratio (SNR) of Rayleigh scattering-based systems (e.g., distributed acoustic sensing, or DAS) and Brillouin scattering-based systems (e.g., Brillouin optical time-domain reflectometry, or BOTDR).

[0027] As currently practiced in many modern implementations, a dedicated fiber is allocated to the DFOS system in a fiber optic cable, physically separated from existing optical communications signals carried on different fibers. However, given the exponential growth in bandwidth demand, it is becoming increasingly difficult to economically operate and maintain optical fiber solely for DFOS operations. As a result, there is growing interest in integrating communications and sensing systems on a common fiber that is part of a larger multi-fiber cable, or that simultaneously carries live communications traffic in addition to DFOS data.

[0028] Operationally, DFOS systems are assumed to be Rayleigh scattering-based systems (e.g., distributed acoustic sensing, or DAS) and Brillouin scattering-based systems (e.g., Brillouin optical time-domain reflectometry, or BOTDR) that may include coding implementations. With such coding designs, such systems are more likely to be integrated with fiber communication systems due to their low-power operation and greater sensitivity to the response time of optical amplifiers.

[0029] The exemplary arrangement shown in the block diagram assumes that the coded interrogation sequence is generated digitally and modulated onto the sensing laser via a digital-to-analog converter (DAC) and an optical modulator. The modulated interrogation sequence may be amplified to an optimal operating power before being sent down the fiber for interrogation.

[0030] Advantageously, DFOS operations can also be integrated with communication channels via WDM in the same fiber. Within the sensing fiber, the interrogation sequence and the returned sensing signal can be optically amplified using either discrete (EDFA / SOA) or dispersive (Raman) methods. The returned sensing signal undergoes amplification and optical bandpass filtering before being sent to a coherent receiver. The coherent receiver detects the optical fields of both polarizations of the signal and downconverts them to four baseband lanes for analog-to-digital conversion (ADC) sampling and digital signal processor (DSP) processing. As those skilled in the art will readily understand and appreciate, a decoding operation is performed in the DSP to generate the interrogated Rayleigh or Brillouin response of the fiber, and any changes in that response are then identified and interpreted as sensor readings.

[0031] Continuing with the diagram, because the coded interrogation sequence is generated digitally, the out-of-band signal is also generated digitally and then combined with the code sequence before the waveform is created by the DAC. When generated together digitally, the out-of-band signal is generated only outside of the code sequence, so when added together, the combined waveform has a constant amplitude.

[0032] As will be understood and appreciated by those skilled in the art, DFOS / DAS systems have been shown to detect, record, and listen for acoustic vibrations in the audible frequency range, although one of the limiting factors in sensitivity is the physical layout of the fiber optic cables used as sensors.

[0033] For outdoor applications, thick telecommunications-grade fiber optic cables are physically insensitive to low-amplitude vibrations in the audible range, so the quality of the acoustic signal depends heavily on the fiber type, layout, and how the acoustic pressure waves are coupled into the fiber cable.

[0034] As can be readily appreciated, fiber optic cables are widely deployed in both urban and rural areas, advantageously offering greater bandwidth, transmission distances, and reliability.

[0035] To support such deployments, we disclose a novel underground cable location method based on time-series similarity search. As will be described, our method rapidly searches either specially designed vibration patterns (created with on / off patterns distinguishable from background noise) or actual field vibration signals collected from secondary point sensors (e.g., cell phones, tablets, accelerometers, etc.). Advantageously, it works with different signal patterns generated by different vibrators, representing different vibration durations, occurring at different locations along the fiber optic cable (e.g., buried or overhead sections of the cable). By providing instant feedback on resource-limited platforms (e.g., laptops or edge devices), our method enables field technicians to locate underground fiber cables more quickly and easily than conventional techniques, eliminating the need for field technicians to wait more than a few seconds to receive results.

[0036] Limited differences between the present disclosure and the prior art can be highlighted.

[0037] Problems / differences with existing approaches and prior art

[0038] Manual Localization and Matching

[0039] A manual search process can be performed by a human through visual inspection, but such an operation requires extra effort, is time consuming, and is prone to errors.

[0040] Pattern recognition using image processing

[0041] Several image pattern detection methods can be employed to automate this process. However, traditional edge detection methods only work for patterns with simple shapes and structures, such as vertical bars of a specific width and length on a waterfall image. This pattern can be generated by using a vibrator to continuously vibrate near the fiber for a certain period of time. Depending on the test location, the waterfall image may be very noisy due to continuous traffic signals or other vibration signals from the surroundings. To distinguish a simple shape pattern from other sources, it must be long and wide enough, which increases the requirements for test equipment and test time. Traditional image pattern detectors struggle to design special structure patterns, cannot obtain consistent results under various conditions, and their complexity negatively impacts computational performance.

[0042] Supervised learning approaches such as neural networks

[0043] Neural network-based image pattern detectors can perform well if the model is pre-trained using a sufficient number of good training examples. However, such training requires both time and effort to collect a sufficiently large dataset in various locations and conditions, as such signals do not naturally exist in waterfall images.

[0044] In contrast to these approaches, our AI module for fast time series template matching automatically detects signal patterns within large-scale waterfall images of entire routes (tens of kilometers). As will be appreciated by those skilled in the art, such operation has the following advantages: it does not require annotated training data, it operates on customized signal patterns, it is robust to variations caused by sensing distance, environmental factors, and the strength of vibration sources, and it provides immediate / real-time results while requiring only limited computing resources.

[0045] As those skilled in the art will understand and appreciate, these features provide many practical advantages.

[0046] Low false alarm rate

[0047] Because our technique does not require training data and can work with any pattern, a set of synthetic signals can be designed in advance and validated with historical daily waterfall data to select several candidate patterns that yield the lowest false positive rate.

[0048] Customizable patterns

[0049] If the signal later changes, our technique transitions seamlessly without retraining the model.

[0050] Reduced operating time and equipment weight

[0051] The designed signal pattern is more easily detected, even for shorter, weaker patterns or patterns captured from noisy environments. As a result, the overall task can be completed more easily by using a small, portable vibration source as the signal source. Field workers appreciate a lighter, smaller, and more compact vibration source device that is easier to carry than current alternatives. Unfortunately, such vibrators are usually inconsistent and easily affected by other sources, resulting in broken patterns appearing in the waterfall image.

[0052] Real-time feedback The technology and system of the present invention is lightweight, requiring only a mid-range laptop processor. As a result, the necessary calculations are performed locally (no need to send data to a cloud computing system), making real-time results available in the field, a key benefit for the user. As a result, the user can instantly know whether the target cable is underground or not.

[0053] 2 is a schematic diagram outlining a method according to an embodiment of the present disclosure. Referring to this diagram, it can be seen that the hardware elements include:

[0054] Fiber optics, which can use existing installed telecommunications fiber cables or newly installed dedicated fiber.

[0055] Distributed Fiber Optic Sensing System (DFOS), which can be a DAS or DVS, capable of detecting vibration signals along an optical fiber and generating waterfall signal images continuously in real time.

[0056] A handheld vibrating device such as a hammer drill or vibrator acts as the signal source.

[0057] A programmable controller that can operate the vibrator in predefined modes or as an audio recorder to collect signals of interest in the field.

[0058] The AI ​​analysis steps include:

[0059] A time series template matching procedure searches for signal patterns of interest on raw waterfall data. The time series template matching procedure outputs N regions on the waterfall image, one of which contains a signal corresponding to the vibration of the test vibrator, or none of which contains a signal corresponding to the vibration of the test vibrator. This is a key element of the present invention: the module can operate on any signal pattern without pre-training, and the computation is efficient enough to provide results in real time.

[0060] A region selection procedure processes the N regions from the previous module. This module selects the regions that contain signals modified by the probe vibrator. If the probe vibrator does not modify the deformation of the monitoring fiber, no region is selected.

[0061] The operation procedure proceeds as follows:

[0062] Step 1: Use the DFOS system to monitor vibrations around the fiber cable.

[0063] For real-time monitoring, the field fiber is connected to the DFOS system at the remote terminal.

[0064] Step 2: Pattern design and signal control

[0065] To shorten the vibrator's operating time without compromising the visibility of the waterfall image, a special pattern can be used. The main idea is to program a metronome that can control the vibration rhythm of the vibrator device, which can generate a unique signal pattern on the waterfall image.

[0066] Design a pattern pool to maximize detection rates.

[0067] The goal of this step is to generate a special pattern with maximum uniqueness at minimum length. When designing the signal, the characteristics of the vibrator (such as inertia) and the characteristics of the DFOS must be taken into account. For example, generating a 10 Hz square wave is meaningless because it is impossible to control the vibrator to operate at this speed, and if the DFOS pulse rate is low, such a pattern may not be preserved in the waterfall image.

[0068] Note that the amplitude of the patterns is arbitrary and has no effect on the search results, as we advantageously use Pearson's correlation coefficient as a metric with a built-in normalization step.

[0069] FIG. 3 is a graph of an exemplary periodic square wave signal with a 4 second period and on / off cycles of equal length, having a sampling rate of 100 Hz, in accordance with an embodiment of the present invention.

[0070] FIG. 4 is a graph of an exemplary periodic square wave signal with a sampling rate of 100 Hz and a 4 second period with on / off cycles of equal length, according to an embodiment of the present disclosure.

[0071] Ambient data is used to select patterns to minimize false alarm rates.

[0072] Historical real-world data is used as test data to evaluate the uniqueness of each design pattern. Uniqueness is defined as the number of highly correlated subsequences with the pattern. The test data should be as complete as possible to cover a variety of ambient signals from various types of background sources.

[0073] Step 3: Use the vibrator as a signal source to generate the designed vibration pattern.

[0074] In this step, the vibrator device is programmed based on the designed pattern so that the signal captured by the DFOS when the vibrator is operating is highly correlated with the designed pattern. This can be done by using another piece of hardware, such as a metronome, to control the duration of the device's on / off cycles.

[0075] 5(A) and 5(B) are a pair of graphs of amplitude versus time of an actual vibration signal with a specified pattern overlaid after processing with Z-normalization according to an embodiment of the present disclosure. Fig. 5(A) is an ideal case where the vibration signal and the pattern are 94% correlated. Fig. 5(B) is an average case where traffic noise has corrupted the signal and the correlation with the pattern is only 41%.

[0076] Step 4: Template Matching

[0077] Figures 5(A) and 5(B) provide a high-level overview of each component. As shown in these figures, the template matching module is a key component of the software of the present invention. It processes the waterfall image, a 2D matrix with the designed pattern, and finds all signals that correlate with the pattern template.

[0078] Next, we will explain in detail how to use template matching with distributed optical fiber sensing data.

[0079] Waterfall Data Description

[0080] As one skilled in the art will appreciate, in an exemplary waterfall image generated from an operational DFOS system, the horizontal axis represents different sensing locations (collected sensing points) on the fiber, the vertical axis represents different timestamps, and the origin is the most recent instant the image was generated. Each pixel (x,y) on the waterfall image is the amplitude of the signal at sensing point x at time y. Each column of pixels can also be treated as a sample of a time series sequence. If there are a total of N columns in the waterfall image, there will be N time series, and template matching is applied to these N time series.

[0081] Time series similarity search

[0082] In this task, we use a similarity search algorithm based on Pearson correlation for template matching. Using Pearson correlation coefficient as a metric has several advantages:

[0083] First, it is based on lockstep search rather than elastic search like DTW, which is better suited to this task, which requires one-to-one coordination, as shown in Figure 4(A) and Figure 4(B).

[0084] Second, unlike Euclidean distance, which takes absolute values, correlation only considers the relative trends between two series, which is very important because the amplitude of signals generated by the same vibrator will be different in different geological environments underground.

[0085] Finally, from a computational point of view, the algorithm is highly efficient and capable of real-time processing.

[0086] Search processing

[0087] Next, we will explain how the search process is performed.

[0088] Given a query to search, denoted as "q" and assumed to have length m, a longer time search "ts" of length n is required as the search space in which the algorithm will search the query. The search process is performed using a sliding window held in ts, and since Pearson's correlation is used as the metric, the length of this sliding window must be the same as q. This sliding window starts at index 0 and covers elements of ts from index 0 to index m-1. At each step i, it excludes the first element of the previous window and covers the element at index m-1+i. Figure 6 shows how the sliding window moves.

[0089] FIG. 6 illustrates an example sliding window that iterates through all n-m+1 sliding windows in ts to find the sliding window position that provides the maximum correlation to the query, according to an aspect of the present disclosure.

[0090] For each sliding window, we calculate the correlation between the elements in the sliding window and the query. To find the sliding window position with the maximum correlation with the query, we iterate through all n-m+1 sliding windows in ts and consider the subsequence of this sliding window to be the time series most similar to the query in ts.

[0091] 7(A) and 7(B) are a pair of graphs of amplitude versus time of an actual vibration signal from a waterfall image according to an embodiment of the present disclosure, where the vertical lines in FIG. 7(A) are the start and end positions of the sliding window in which the subsequence is most similar to the query, and FIG. 7(B) shows an overlay of both the query and the subsequence.

[0092] The same operation is then performed for each column of the waterfall image. The position and Pearson correlation value of the most similar subsequence for each column are saved for further processing in the following steps.

[0093] Performance optimization

[0094] A waterfall image of a 40-second time frame of a 100 km optical fiber cable contains 25 million pixels, so time efficiency must be optimized to achieve real-time processing. The state-of-the-art algorithm for general time series similarity search is MASS. MASS is

number

number

[0095] Structurally, our procedure incorporates an automated procedure that estimates the optimal batch size based on the input data size and hardware platform. Our optimized implementation for underground fiber optic localization applications is significantly faster, allowing real-time processing locally on a regular laptop. The performance of our customized implementation compares favorably with the open-source implementation of MASS.

[0096] In summary, the procedure employed in our AI model finds the subsequence that is most similar to a template for each column of a waterfall image. If the waterfall image has N columns, N tuples are generated. Each tuple contains the following information: the column index, the start and end indexes of the subsequence that is most correlated with the pattern, and the correlation value between said subsequence and the pattern.

[0097] Step 4: Region Estimation

[0098] Based on the results of the previous step, we want to find clusters of sequences that are spatially contiguous and within the same time window, where all sequences in the cluster are highly correlated. For example, such clusters can be represented as regions surrounded by white lines on a waterfall image. When estimating the vibration signal of interest in the next step, regions are of interest rather than individual subsequences because they contain more information than individual sequences. Such regions exist because disturbances affect the extent of a fiber cable containing multiple sensing points.

[0099] To estimate these regions, we use the results of the previous step. We iterate through the tuples in descending order based on their correlation values, stopping when we reach the required number of regions. Each tuple records the column index, row index, and Pearson correlation value of the subsequence. For each tuple, we follow the flow diagram in Figure 8.

[0100] FIG. 8 illustrates an exemplary flow diagram illustrating tuple iteration according to an aspect of the present disclosure.

[0101] To make the localized region more complete, we use the actual sequence as a template instead of the synthetic design pattern to search its neighboring space. Although the synthetic design pattern works well in ideal situations (high SNR and low noise), in most cases the signal is distorted by other background noises such as traffic.

[0102] Step 5: Area selection

[0103] In this final step, a region corresponding to the vibration signal of interest is selected, or none is selected if none of the regions from the previous steps correspond to a vibration signal.

[0104] We describe two approaches for this step: one based on threshold and the other by region overlap.

[0105] Threshold-based approach

[0106] In the threshold-based method, the following values ​​are first calculated for each region and then thresholded: SNR, regional Pearson correlation, and autocorrelation.

[0107] For SNR, it is the signal-to-noise ratio of this region to the surrounding region. The next value calculated is the similarity of the region to the designed pattern, which is calculated as the Pearson correlation value of the spatially and temporally smoothed subsequence of this region to the designed pattern. The spatially smoothed sequence is generated by adding (as a vector) the entire time series from each column in the region and dividing by the number of columns. The spatially smoothed sequence is then temporally smoothed using a sliding averaging window.

[0108] Similar to the calculation above, we calculate both the top-correlated sequence and a spatiotemporally smoothed version. Here, the full time series is used instead of a subsequence. This allows us to consider a wider time range, since signals are typically generated by stationary vibration sources from the surrounding area and typically persist for a long time. The results are compared with the autocorrelation of the zero-filled design pattern, and similarity is still measured by Pearson's correlation.

[0109] Finally, a threshold is set for each measurement to assess whether the region is a region of interest, in other words whether the signal in this region is caused by the probe vibrator.

[0110] Area overlap

[0111] Another approach is region overlap, which is done by searching for two or more designed patterns separately in the same location. The idea behind this is that the designed patterns should appear in the same location on the waterfall image. Also, it is highly unlikely that one background noise region will be correlated with all the designed patterns, meaning that the top few estimated regions of different design patterns should be separated from the target region.

[0112] FIG. 9 is an exemplary flow diagram illustrating the overall operation of systems and methods according to aspects of the present disclosure.

[0113] With reference to this figure, note that the procedure of the present invention begins by connecting the DFOS system from the control (central) station to the field test fiber cable.

[0114] Next, a technician will carry out an on-site survey with a vibration generator.

[0115] Such a survey involves a technician activating a vibration generator near the test fiber cable route, for example, in a manhole where the field test fiber is deployed, and involves generating / transmitting a named sequence of unique on / off (vibration) patterns / signatures (e.g., 5 seconds on, 5 seconds off, etc.) from a central office via Wifi / LTE / 5G, etc., to the field vibration generator.

[0116] The vibration patterns, along with ambient noise such as road traffic and other vibration sources occurring along the test fiber in the field, result in characteristic scattering that is detected during DFOS operation.

[0117] The operation of the DFOS and associated analysis system is provided by a central station and involves recognizing the unique vibration patterns mechanically affected by vibration sources in the field and detected by the test fiber in the field. AI algorithms in machine learning procedures can identify the distance associated with the vibrations generated in the field.

[0118] These distances are correlated with the GPS coordinates of the cable distance data to generate, among other items, a graphical location display / data.

[0119] The graphical locations so determined may then be displayed on a graphical user interface to direct subsequent technicians to such locations.

[0120] While the present disclosure has been presented using some specific examples, 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. A method for underground cable location by fast time series template matching and distributed fiber optic sensing (DFOS), comprising: an optical sensor fiber; a DFOS interrogator in optical communication with the optical sensor fiber, the DFOS interrogator configured to generate optical pulses, couple the generated pulses into the optical sensor fiber, and receive backscattered signals from the optical sensor fiber; an intelligent analyzer configured to analyze the backscattered signals received by the DFOS interrogator and determine from the backscattered signals vibrational activity occurring at positions along the optical sensor fiber; providing a DFOS system including: disposing a programmable vibration generator at a field location proximate to said optical sensor fiber; transmitting to the programmable vibration generator a unique designed vibration pattern to be generated by the vibration generator; periodically operating the programmable vibration generator to generate the transmitted unique vibration pattern, the period of operation of the vibration generator being determined by a metronome instrument; operating the DFOS system to collect and analyze the vibration activity determined from the backscattered signals to further determine vibration activity indicative of the unique vibration pattern produced by the vibration generator; analyzing the oscillatory activity includes generating a waterfall image from the backscattered signals and applying the fast time series template matching to pattern match the waterfall image; The pattern matching includes correlating a 2D matrix having a designed pattern with a pattern template; the waterfall image includes N columns of N time series, and the pattern matching is applied to all of the N time series; The method, wherein the pattern matching includes determining a similar subsequence of the pattern template for each of the N columns of the waterfall image.

2. The method of claim 1 , further comprising associating the vibration activity indicative of the unique vibration pattern generated by the vibration generator with a physical location along the optical sensor fiber.

3. The method of claim 2 , wherein the physical location is correlated with a GPS coordinate.

4. 4. The method of claim 3, further comprising repositioning the vibration generator to a different position along the optical sensor fiber and transmitting a different vibration pattern to the vibration generator to generate, and activating the vibration generator and a DFOS system to determine the new position of the vibration generator.

5. The method of claim 3 , further comprising generating the unique vibration pattern that is transmitted to the vibration generator.

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