Method and apparatus for high definition-distributed acoustic sensing (HD-das)

Abstract

An optical interrogation system for high definition-distributed acoustic sensing (HDDAS) of a vibration that changes a length of an optical fiber sensor in contact with a material or structure includes an optical interferometer, optical detection circuitry, coupled to the optical interferometer, for detecting optical interferometric measurement signals for a length of the optical fiber sensor, and data processing circuitry. The data processing circuitry receives interferometric measurement signals from the optical detection circuitry and generate, based on the interferometric measurement signals, interferometric measurement data for a length of the optical fiber sensor. It processes the interferometric measurement data to determine a strain state of the optical fiber sensor at one or more locations along the optical fiber sensor and determines a vibration component of the strain state of the optical fiber sensor at the one or more locations along the optical fiber sensor.

Description

METHOD AND APPARATUS FOR HIGH DEFINITION-DISTRIBUTED ACOUSTIC SENSING (HD-DAS)

[0001] This application claims priority from U.S. provisional patent application serial number 63 / 719,163, filed November 12, 2024, the contents of which are incorporated herein by reference.

[0002] Multi-Channel Analysis of Surface Waves (MASW) uses a collection of discrete geophones to measure vibrations in the ground or other material that vibrations are propagating through and then processes them to determine characteristics of the material (strength, modulus, etc.). A geophone is a discrete device that converts ground motion / velocity into data. Using discrete devices means a limited number of points of measurement, uncertainty on the relative position of the points of measurement, and minimum separation of the points of measurement, which leads to noise and / or systematic error in the results.

[0003] A better approach is optical fiber-based distributed acoustic sensing (DAS) which uses information that comes from changes in the fiber from propagating vibrations to replace the discrete geophones in MASW. Compared to MASW, DAS many more “points” of measurement with better spatial resolution. The term “gauge” may be used to define a point or location of measurement in an optical fiber. Because the fiber is physically contiguous, the spatial separation between points of measurement is always known. Thus, an optical fiber functions as a distributed sensor which is more cost effective and efficient than many point sensors, especially for monitoring vibration over longer distances and in structures like pipelines, wells, railways, roads, conveyors, bridges, tunnels, buildings, and fences.

[0004] The technology in this application performs distributed acoustic sensing of optical path length changes along an optical fiber sensor caused by, for example, acoustic, elastic, seismic, vibrational, thermal or kinetic energy. For ease of reference, such energy and similar other types of energy that can cause optical path length changes along an optical fiber sensor are collectively referred to in this application as vibration. Distributed acoustic sensing (DAS) uses Rayleigh backscattering that occurs due to small variations in the refractive index of the glass fib er that are intentionally or unintentionally introduced during the fiber manufacturing process to measure changes in physical properties of the fiber along the length of the fiber in a distributedmanner. For example, a vibration may stretch or contract the optical fiber which causes a slight change in the flight time of reflected light in the fiber which can be measured by an optical interrogator.

[0005] Some example embodiments include an optical interrogation system for high definition-distributed acoustic sensing (HD-DAS) of a vibration that changes a length of an optical fiber sensor in contact with a material or structure. The optical interrogation system also includes an optical interferometer. The system also includes optical detection circuitry, coupled to the optical interferometer, for detecting optical interferometric measurement signals for a length of the optical fiber sensor. The system also includes data processing circuitry configured to perform the following operations: (a) receive interferometric measurement signals from the optical detection circuitry; (b) generate, based on the interferometric measurement signals, interferometric measurement data for a length of the optical fiber sensor; (c) process the interferometric measurement data to determine a strain state of the optical fiber sensor at one or more locations along the optical fiber sensor; (d) determine a vibration component of the strain state of the optical fiber sensor at the one or more locations along the optical fiber sensor; (e) identify one or more portions of the vibration component that is changing as a function of time; (f) determine whether each portion of the changing vibration component satisfies at least one predetermined criterion; and (g) determine information about the material or the structure based on one or more portions of the changing vibration component that satisfies the at least one predetermined criterion.

[0006] Example implementations may include the data processing circuitry being configured to generate a multi-dimensional representation in space and frequency for each vibration component and each portion of the changing vibration component, where the received interferometric measurement signals are associated with one or more sources of acoustic energy that includes one or both of transient acoustic energy and continuous acoustic energy.

[0007] In an example implementation, the optical interferometer is an optical frequency domain reflectometry (OFDR) interrogator configured to receive different wavelengths of light from the tunable laser over the range of wavelengths, and where the optical interferometric measurement signals indicate back scatter amplitude as a function of time along the optical fiber sensor, where data acquisition of the interferometric measurement signals includes one or more sweeps of the wavelength laser which are either separated or overlapping in time. The dataprocessing circuitry is configured to perform one or more of the following: (a) determine one or more propagation characteristics of the vibration component detected along the length of the optical fiber sensor; (b) based on the one or more propagation characteristics, determine a propagation velocity of the vibration component in the material or the structure; and (c) based on the propagation velocity of the vibration, determine a frequency composition of each vibration component detected at each location along the fiber.

[0008] In example implementations, the at least one predetermined criterion includes one or more of: (a) an amplitude of each portion of the changing vibration component; (b) a spectral content of each portion of the changing vibration component; (c) a rate of change of amplitude of the vibration component of the strain state of the optical fiber sensor at one or more locations; (d) a velocity with which the vibration component of the strain state of the optical fiber sensor at one ore more locations propagates along the fiber; (e) a difference between velocities of different frequencies of the vibration component of strain of the optical fiber sensor at one or more locations; (f) a time at which one or more frequencies of the vibration component of the strain state of the optical fiber sensor is observed at one or more locations; and (g) a derivative of any of (a)-(f) with respect to time or location.

[0009] In example implementations, the information about the material or the structure includes one or more of: a spatial and / or temporal map of a 3-dimensional seismic model of the material or the structure; a density, rigidity, or strength of the material or structure; an integrity of material or structure; a change in the properties of the material or structure over time; one or more of the time or location of the source of the vibration component of strain in the optical fiber sensor; a differentiating characteristic of the source of the vibration component of the strain in the optical fiber sensor; resonant modes of the structure or material; and characteristics of the resonant modes of the structure or material.

[0010] In example implementations, information includes one or more material properties of the material or the structure such as a spatial and / or temporal map of a 3-dimensional seismic model of the material or the structure. The one or more material properties is determined based on differences in propagation of the vibration component of the strain state of the optical fiber sensor at different time varying frequencies to characterize the material properties of the material or the structure at varying depths within the material or the structure. The one or more material properties may be an integrity of the material or the structure based onwhether the vibration component of the strain state of the optical fiber sensor propagates along the material or the structure.

[0011] Example implementations may include one or more of the following features where the data processing circuitry is configured to: (a) identify one or both of a source time and a source location of an excitation for the vibration component of the strain state of the optical fiber sensor based on or from the portions of the changing vibration component that satisfies the at least one predetermined criterion, (b) determine a cause of the excitation based on or from patterns in the portions of the changing vibration component that satisfies the at least one predetermined criterion, (c) characterize a source of the excitation based on or from patterns in the portions of the changing vibration component that satisfies the at least one predetermined criterion, (d) perform vibratory modal analysis of the material or the structure based on resonant responses contained in the interferometric measurement signals, and / or (e) use a relative response of interferometric measurement signals generated from multiple stimuli applied to the material or structure to generate a multi-dimensional map of physical characteristics of the material or structure.

[0012] In example implementations, optical detection circuitry is coupled to the multiple optical interferometers and is configured to detect optical interferometric measurement signals for a length of each optical fiber sensor; where the data processing circuitry is configured to receive interferometric measurement signals from the optical detection circuitry associated with each optical fiber sensor and combine responses from each optical fiber sensor.

[0013] In other example embodiments, another type of optical interrogator for distributed acoustic sensing may use coherent Optical Time Domain Reflectometry (c-OTDR). Although C-OTDR can operate over long distances, e.g., many km of fiber, it has coarser spatial resolution than OFDR, e.g., gauges (points of measurements) are 10’ s to 100’s of cm apart. The OTDR or C-OTDR receive pulses of light from the light source, where the optical interferometric measurement signals indicate back scatter amplitude as a function of time along the optical fiber sensor. The data processing circuitry is configured to: transform the interferometric measurement data set into a spectral domain; extract a phase response corresponding to the interferometric measurement data set into the spectral domain; and determine the vibration component from the phase response.

[0014] In example implementations, distributed vibration signals are used to determine the time, location, and magnitude of impacts, vibration sources, and acoustic emissions of a structure, such as an inflatable space habitat, to monitor structural health.

[0015] In example implementations, the data processing circuitry is configured to perform machine learning to perform one or more of operations (c)-(g).

[0016] In example implementations, the data processing circuitry is configured to process the interferometric measurement data to determine a strain state of the optical fiber sensor in distributed locations along the optical fiber sensor, where the distributed locations may be spaced apart by a distance ranging from 10 microns to tens of meters.

[0017] This Summary is provided to introduce a selection of concepts that are further described below in the Detailed Description. This Summary is intended neither to identify key features or essential features of the claimed subject matter, nor to be used to limit the scope of the claimed subject matter; rather, this Summary is intended to provide an overview of the subject matter described in this document. Accordingly, it will be appreciated that the abovedescribed features are merely examples, and that other features, aspects, and advantages of the subject matter described herein will become apparent from the following Detailed Description, Figures, and Claims.BRIEF DESCRIPTION OF THE FIGURES

[0018] These and other features and advantages will be better and more completely understood by referring to the following detailed description of example non-limiting illustrative embodiments in conjunction with the drawings. Unless specific dimensions are given in a figure, the figures are not necessarily to scale and are schematic representations.

[0019] Figure 1 shows an example seismic environment in which HD-DAS may be used to detect high and low frequency acoustic energy propagating through a material or structure as a result of acoustic energy impacting the material or structure.

[0020] Figures 2A-2C show examples of different types of acoustic waveforms that may propagate through a material or structure as a result of acoustic energy impacting the material or structure.

[0021] Figure 3 shows an example seismic environment in which HD-DAS may be used to detect different acoustic waves propagating through different layers of a material or structure as a result of acoustic energy impacting the material or structure.

[0022] Figure 4 shows example seismic environment in which HD-DAS may be used to detect different acoustic waves propagating through different layers of different earth materials as a result of acoustic energy impacting the material or structure.

[0023] Figures 5A-5D show example graphs (e.g., vibragrams) plotting time (vertical axis) against fiber length (horizontal axis) of acoustic waveforms propagating through material or structure depending on different properties of the material or structure.

[0024] Figures 6A and 6B show different cross-sectional views of an HD-DAS example where a sensing fiber is embedded in a material or structure.

[0025] Figure 7 is a flowchart diagram showing HD-DAS example procedures.

[0026] Figure 8 is an example OFDR optical interrogation system for HD-DAS.

[0027] Figure 9 is a flowchart diagram showing example OFDR procedures for the OFDR optical interrogation system shown in Figure 8.

[0028] Figure 10 is a flowchart diagram showing example HD-DAS procedures for the OFDR optical interrogation system shown in Figure 8 in accordance with an example embodiment.

[0029] Figure 11 is a flowchart diagram showing further example HD-DAS procedures related to an example vibration function described in Figure 10.

[0030] Figure 12 is a diagram showing various examples of information that may be determined about the material or the structure based on one or more portions of the changing vibration component satisfying one or more predetermined criteria.

[0031] Figures 13A-13D show example graphs illustrating four different laser scans of the OFDR system in Figure 8 and associated detected vibration amplitude and acoustic wavefront propagation.

[0032] Figure 14A is a flowchart showing example procedures analyzing a single scan vibragram shown in Figure 14B for fast moving traveling waves and a graph of a vibration shock front in Figure 1 C.

[0033] Figure 15A is a flowchart showing example procedures for detecting density discontinuities based on a single scan vibragrams shown in Figures 15B and 15D with graphs of vibration shock fronts shown in Figures 15C and 15E at respective time steps i and i+1.

[0034] Figure 16A is a flowchart showing example procedures for identifying an acoustic excitation and Figure 16B shows examples of different excitation sources.

[0035] Figure 17A is a flowchart showing example procedures for modal analysis and Figures 17B-17D provide example illustrations for several of the steps in Figure 17A.

[0036] Figure 18A is a flowchart showing example procedures for structural health monitoring and Figures 18B-18D provide example illustrations for several of the steps in Figure 18A.

[0037] Figure 19 shows an example embodiment with the data processor 14 using machine learning.DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0038] Specific embodiments are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description are not intended to limit the claims to the particular embodiments disclosed, even where only a single embodiment is described with respect to a particular feature. On the contrary, the intention is to cover all modifications, equivalents and alternatives that would be apparent to a person skilled in the art having the benefit of this disclosure. Examples of features provided in the disclosure are intended to be illustrative rather than restrictive unless stated otherwise.

[0039] Terms, such as first, second, and the like, may be used herein to describe various components. Each of these terminologies is not used to define an essence, order or sequence of a corresponding component but used merely to distinguish the corresponding component from other component(s). For example, a "first" component may be referred to as a "second" component, and similarly, the "second" component may be referred to as the "first" component. “Based on” as used herein covers based at least on. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises / comprising" and / or "includes / including" when used herein, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0040] The same name may be used to describe an element included in the embodiments described above and an element having a common function. Once a component or function isdescribed for one embodiment, that description is not repeated for other embodiments where that component or function operates or performs similarly. Unless disclosed to the contrary, a configuration of components disclosed in any embodiment may be applied to other embodiments, and the specific description of the repeated configuration will be omitted.

[0041] Unless otherwise defined, all terms used herein including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which an example belongs. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0042] Example embodiments include a high definition distributed acoustic sensing (HD-DAS) system that analyzes and interprets spatial and frequency components of optical interrogator vibration measurements from an optical fiber sensor in contact with a material or structure to determine information about the material or the structure based on one or more portions of changing vibration components that satisfies one or more predetermined criterion. The high definition distributed acoustic sensing system can generate a 2D and / or a 3D model of the material or structure experiencing vibration. Such a 2D and / or a 3D model is referred to as a “vibragram” and includes a partitioning of the time varying strain (vibration) in the material or structure which is separated by both location (position along the fiber) and time (instant when the vibration is being characterized). A vibragram is used to measure the propagation of a vibration along a material or structure.

[0043] Figure 1 shows an example seismic environment in which HD-DAS may be used to detect high and low frequency acoustic energy propagating through a material or structure, in this case the ground, as a result of acoustic energy impacting the ground. An optical fiber is laid on the surface of the ground. A hammer striking the ground causes acoustic impulse that spreads as it propagates laterally through the ground. The small counterclockwise circles represent high frequency acoustic waves associated with the impulse, and the larger counterclockwise circles represent low frequency acoustic waves associated with the impulse. The high and low frequency acoustic waves propagate through the ground from left to right and are detected using the optical fiber sensor and the HD-DAS detection and processing system which is described in detail below.

[0044] Figures 2A-2C show examples of different types of acoustic waveforms that may propagate through a material or structure as a result of acoustic energy impacting the material or structure. Figure 2A shows a broadband wave packet that includes Rayleigh waves which are a type of surface acoustic wave that travel along the surface of material or structure. A wave packet is a short duration impulse of vibratory energy that travels through a material, i.e., a travelling ripple, and includes a broadband collection of individual frequencies of vibration. Rayleigh waves are a type of mechanical vibration that takes place in a material or structure and are a combination of compression and shear waves. The different diameter circles represent local vibrations that change with time and / or location as the wave packet propagates from left to right. Figure 2B shows a broadband wave packet that includes a Lamb wave which is an elastic wave whose particle motion lies in the plane that contains the direction of wave propagation and the direction perpendicular to the material or structure. Lamb waves are relatively complex and have a symmetric wave mode and an antisymmetric wave mode. Figure 2C shows a broadband wave packet that includes a Love wave. Love waves are surface seismic waves that travel faster than Rayleigh waves and are observed when there is a low velocity layer overlying a high velocity layer / sublayers in the material or structure. An optical sensor in contact with a material or structure may be used to detect these and other types of acoustic waveforms propagating through the material or structure.

[0045] Figure 3 shows an example seismic environment in which HD-DAS may be used to detect different acoustic waves propagating through different layers of a material or structure as a result of acoustic energy impacting the material or structure. Three distinct layers in the ground are shown with an HD-DAS fiber optic sensor in contact with the ground. A stimulus, e.g., an impact, to the ground results in a head wave propagating in Layer 1 reflecting at the Boundary 1 with Layer 2 (R2) and also reflecting at the ground surface. The head wave also refracts into Layer 2 at Boundary 1 and from Layer 2 into Layer 3 at Boundary 3. The head wave in each layer reflects as shown in the examples for Layer 2 and Layer 3. Each head wave in each layer may have a different wave velocity that depends on the material(s) of that layer.

[0046] Figure 4 shows an example seismic environment in which HD-DAS may be used to detect different acoustic waves propagating through different layers of different earth materials as a result of acoustic energy impacting the material or structure. In this example, aseismic HD-DAS system is used to detect a water reservoir amongst loam, limestone, air, salt water, and shale layers.

[0047] Figures 5A-5D show example graphs (e.g., 2D vibragrams) plotting time (vertical axis) against fiber length (horizontal axis) of acoustic waveforms propagating through material or structure depending on different properties of the material or structure for impacts occurring at four different locations. For impacts occurring perpendicular to the fiber, the HD-DAS system can determine the location of the impact event along the fiber's length and the distance to the impact event with assumptions of the surface wave velocity. With multiple impacts at varying locations, the HD-DAS system can determine a subsurface surface wave velocity model for the region providing information on subsurface density and voids. Here the impact happened off to the side of the fiber, and the time at which the vibration arrives at different parts of the fiber is detected to determine where and when the impact occurred. Figure 5A shows an impact 1 and uniform wave propagation to the fiber because the material is uniform, and a lower density region does not affect the acoustic wave propagation. Subsequent impacts 2-4 are shown in gray to the right of impact 1. Figures 5B and 5C show subsequent impacts 2 and 3 with a nonuniform wave propagation to the fiber because the low-density region is now affecting the acoustic wave propagation. Figure 5D shows a further subsequent impact 4 and uniform wave propagation to the fiber because the material is again uniform, and a lower density region no longer affects the acoustic wave propagation. Thus, discontinuities in a composite material or structure from voids, uncured resin, cracks, or other features can be detected and identified.

[0048] Figures 6A and 6B show different cross-sectional views of an HD-DAS example where a sensing fiber is embedded in a material or structure. In one instance this structure is a composite bond line bonding a ‘T’ joint, seen end on, as in Fig. 6A or between two plates, seen from the side, as in Fig. 6B. A vibration source shown as a vibration generator imparts vibration to the material or structure. The embedded fiber optic sensor may be interrogated using an HD-DAS technique described in detail below to identify discontinuities in acoustic wave propagation indicating defects in the material or structure such as delamination, incomplete cure, high porosity, etc.

[0049] Figure 7 is a flowchart diagram of example high level HD-DAS procedures. In step SOI, optical data is detected for a single scan of an optical fiber using an interferometer, e.g., OTDR, or multiple scans, e.g., OFDR. In step S02, each scan is processed for acoustic wavedetection and propagation. In step S03, the acoustic response is interpreted, e.g., a detected speed of an acoustic wave may be used to determine a strength parameter of a material of structure. Another example is a detected discontinuity used to determine cracks, breaks, or low / high density region in a material or structure. Further example applications are described below. Step 04 outputs, and if desired, records / stores that processed data and / or the interpreted acoustic response.

[0050] Figure 8 is an example OFDR optical interrogation system 10 for HD-DAS. OFDR based DAS system has much higher spatial resolution than other types of acoustic sensing systems like MASW and c-OTDR DAS, providing for example mm size gauges (points of measurement). An OFDR based DAS system offers better velocity measurement accuracy because of the size and uniformity of the gauge positions and measurement of a higher maximum frequency which results in better characterization of the material or structural layer near the surface. An OFDR based DAS system can handle a broader range of input strains than other types of acoustic sensing systems.

[0051] A time varying disturbance occurs near a particular location of one or more fiber optic sensors 12- 12c (the term fiber is used for convenience, but the technology applies to any suitable waveguide). A tunable light source 16 is swept through a range of optical frequencies. This light routed to one or more interferometric interrogators 18 each connected to a corresponding fiber optic sensor 12. Here a single interferometric interrogator 18 is connected to fiber optic sensor 12a. Light enters fiber optic sensor 12a through a measurement arm of the interferometric interrogator 18. Scattered light along the length of the fiber optic sensor 12a is then interfered with light that has traveled along the reference arm of the interferometric interrogator 18. A second interferometer within a laser monitor network 22 measures fluctuations in the tuning rate as the light source 16 scans through a frequency range. The laser monitor network 22 provides an absolute wavelength reference throughout the measurement scan. Each of a plurality of optical detectors (e.g., diodes) converts detected light signals from the laser monitor network 22 and the interference pattern from the fiber optic sensor 12a into electrical signals for a data acquisition unit 20. A system controller data processor 14 uses the acquired electrical signals from the data acquisition unit 20 to extract a scattering profile along the length of the fiber optic sensor 12a as is explained in more detail in conjunction with Figures 9 and 10 below. The system controller data processor 14 may include one or more computersthat can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that, in operation, causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by the system controller data processor 14, cause the system controller data processor 14 to perform the actions.

[0052] Figure 9 is a flowchart diagram showing example OFDR procedures for the OFDR optical interrogation system shown in Figure 8. These operations are described in USP 9841269 which is incorporated herein by reference.

[0053] In step SI, the system controller data processor 14 causes the tunable light source to be swept through a range of optical frequencies and directed into the fiber optic sensor 12 via the measurement arm of the interferometric interrogator (step S2). Scattered light along the length of the fiber optic sensor 12 interferes with light that has traveled through the reference path of the interferometric interrogator. An absolute wavelength reference is provided for the measurement scan (step S3), and tuning rate variations are measured (step S4). Optical detectors convert detected optical signals into electrical signals (step S5) for processing by the system controller data processor 14. The interference pattern of the sensing fiber is preferably resampled using the laser monitor signal to ensure the detected signals are sampled with a constant increment of optical frequency (step S6). Once resampled, a Fourier transform is performed to produce a fiber optic sensor 12 scatter signal in the temporal domain. In the temporal domain, the scatter signal depicts the amplitude of the scattering events as a function of delay along the length of the fiber optic sensor 12 (step S7). Using the distance light travels in a given increment of time, this delay is converted to a signal measure of length along the fiber optic sensor 12. In other words, this signal depicts each scattering event as a function of distance along the fiber optic sensor 12. The sampling period is referred to as the spatial resolution and is inversely proportional to the frequency range that the tunable light source 16 was swept through during the measurement.

[0054] As the fiber optic sensor 12 is strained, local scatters shift as the fiber changes in physical length. It can be shown that these distortions are highly repeatable. Hence, an OFDR measurement can be retained in memory that serves as a reference pattern of the fiber in an unstrained state. A subsequent measurement can be compared to this reference pattern to gain ameasure of shift in delay of the local scatters along the length of the sensing fiber (step S8). This shift in delay manifests as a continuous, slowly varying optical phase signal when compared against the reference scatter pattern. The derivative of this optical phase signal is directly proportional to a change in physical length of the sensing core. Change in physical length can be scaled to strain producing a continuous measurement of strain along the fiber optic sensor 12.

[0055] As detailed above, during an OFDR measurement the optical interference between a reference path and a measurement path is recorded as a laser is swept through a range of optical frequencies. The resulting interference pattern at the optical detectors of the system contains information about both the amplitude and the phase of the light reflected from the sensing fiber. The phase and amplitude of the light is recorded as a function of time through the laser sweep. A basic assumption of the system is that the interferometer system under interrogation, which includes the fiber optic sensor 12, does not change during the sweep, and that the phase and amplitude response of the system as a function of optical frequency is encoded in time as the laser is swept. Therefore, if the laser frequency is known as function of time, the phase and amplitude response of the system are known as a function of laser wavelength.

[0056] Example steps to identify a time varying signal as a result of motion arc now described. The term vibration is used to encompass all kinds of time varying signal components. Starting at step S, a windowing operation is performed on a complex valued OFDR data set in the time delay domain about a reflective event, e.g., at the beginning of the data set. An inverse Fourier transform is applied to transform the data set into the frequency domain (step S10). The phase response in the spectral domain is extracted by unwrapping the phase signal (step SI 1). The total accumulated phase is proportional to the location of the reflective event in the delay domain. Each index of delay in the delay domain accumulates a 2n phase change in the spectral domain. Subtracting a linear fit from this accumulated phase response removes the phase associated with the location of the reflective event in the delay domain providing a measure of the vibration (e.g., a non-linear time varying signal that describes the motion of the sensor) (step S12). The vibration is isolated from the phase of the original measurement OFDR data set in the spectral domain (step SI 3). The isolated spectral response of the vibration is then transformed into the time delay domain using a Fourier transform operation (step SI 4).

[0057] Figure 10 is a flowchart diagram showing example HD-DAS procedures for the OFDR optical interrogation system shown in Figure 8 in accordance with an exampleembodiment. The process in Figure 10 uses the procedures outlined in Figure 9 to identify the vibration components from the interferometric measurement data. In step S21 , an OFDR measurement is performed including generating a swept laser signal, sending it down an optical fiber sensor, with its reflections interfering with reference reflections. The interference response from the optical interferometer is detected by optical detection circuitry (step S22), which generates optical interferometric measurement signals in which data from locations along the length of the optical fiber sensor are identified and segregated (step S23). The optical interferometric measurement signals are processed to determine a strain state of the optical fiber sensor (or another parameter which correlates with strain, such as spectral shift) at one or more locations along the optical fiber sensor (step 24). One or more vibration components of the strain state of the optical fiber sensor are determined at the one or more locations along the optical fiber sensor in step S25 (see Figure 9). One or more portions of the vibration component that is changing as a function of time is then identified (step S26), and for each portion of the changing vibration component, the data processor 14 determines whether that portion of changing vibration satisfies at least one predetermined criterion (step S27). The data processor 14 then determines information about the material or the structure based on one or more portions of the changing vibration component that satisfies the at least one predetermined criterion, e.g., interpret the spatial and temporal distribution of satisfied criterion(a) to infer the information (step S28).

[0058] Figure 11 is a flowchart diagram showing further example HD-DAS procedures related to an example vibration function described in Figure 10 and performed by the data processor 14. For each gauge spectral shift data (each fiber location designated as a point of measurement), remove the background (static) signal leaving the dynamic time varying signal corresponding to the vibration component of that gauge (step S31). For each gauge, transform the data back into the optical spectral (true time) domain (step S32). The data for that gauge is partitioned into multiple slices of time (corresponding to portions of the laser wavelength sweep) and generate a representative strength of the vibration component signal for that portion of time for that specific gauge (step S33). In step S34, the data processor 14 may combine the data from multiple sweeps of the data acquisition to synthesize a longer total data set. The data processor 14 collects the data from all the gauges of the sensor for all the time segments for those gauges to create a vibragram such as a 2-dimensional (position in the fiber versus time of measurement)representation of the time varying spectral shift corresponding to vibrations (step S35). A 3-dimcnsional model may also be created. The data processor 14 detects features in the vibragram that are indicative of the vibration meeting at least one criterion (step S36). The data processor 14 identifies one or more patterns in detected features that correspond to propagation of the vibration (step S37). The data processor 14 then may interpret the identified patterns to extract information about the mechanical characteristics of the material or structure under test (step S38).

[0059] Figure 12 is a diagram showing various examples of information corresponding to example applications A1-A7 of the HD-DAS system that may be determined about the material or the structure (referred to below as substance under test (SUT)) based on one or more portions of the changing vibration component satisfying one or more predetermined criteria. The processing input for the data processor 14 shown on the left includes times and locations within the optical fiber sensor in which the acoustic response analyzed by the data processor 14 satisfies one or more of the predetermined criteria. One example is determining whether the speed at which the detected wavefront propagates along the fiber (Fig. 13-A-D) exceeds a specific value, which would indicate a material strength which is sufficient for that application. Another example would be determining if there is an abrupt change in vibrational strength at a location in space, (Fig 15B-E), which indicates a discontinuity in the structure being tested. From this input, many applications are possible for interpretation of the information.

[0060] One example application is shown in Al in which the data processor 14 determines a group velocity at some or all frequencies of the acoustic response input to determine the modulus of the SUT or the variation of the modulus of the SUT as a function of depth in the SUT. Group velocity is the speed with which an acoustic wave packet propagates through the SUT and is directly related to the Young’s modulus of the SUT, i.e., the strength of the material, that the wave packet is traveling through. Another example application A2 includes the data processor 14 identifying from the acoustic response input, spatial variations of the strength or integrity of the SUT based on the propagation of vibrations past a given location along the optical fiber sensor. For example application A3, the data processor 14 compares current measurements of the acoustic response of a SUT against historical measurements for the SUT to perform structural health monitoring of the SUT. For example application A4, the data processor 14 may process the input to identify the source of the vibration in position and / or time,and in a further example application A5, to characterize or identify the source of the vibration such as a person a light vehicle, heavy vehicle, etc. In a further example application A6, the data processor 14 processes the input acoustic response data from multiple vibrational sources to produce a multidimensional map, e.g., 3-dimensional, of the SUT. In another example application A7, the data processor 14 processes the input acoustic response data to perform modal analysis of the SUT. Many other applications exist and are possible.

[0061] Figures 13A-13D show example graphs illustrating four different laser scans of the OFDR system in Figure 8 and associated detected vibration amplitude and acoustic wavefront propagation. The four consecutive laser scans N+0 to N+4 show example vibrational amplitude for a single frequency (solid gray lines in left side plots) crossing threshold (dotted black line). Using multiple laser scans detects variations that travel slower than the duration of a single scan of the laser. Note that the dotted threshold line curves because the signal weakens the further it gets from the original source beyond the end of the fiber. The shaded regions in the right side of fiber correspond to times and locations within the fiber where the vibration amplitude exceeds the specified threshold. The slope of that boundary is the speed of sound of the vibrations (at the frequency in question) though the material.

[0062] Figure 14A is a flowchart showing example procedures analyzing a single scan vibragram shown in Figure 14B for fast moving traveling waves and a graph of a vibration shock front in Figure 14C. Surface waves in materials with high Young’s modulus may result in noisy frequency content as well as surface waves that are not measured over multiple frames due to the propagation of the wave over the measured length of fiber occurring in less time than the tuning period of the swept laser that may be used for HD-DAS measurements. The vibragram in Figure 14B, the velocity of the fastest surface wave can be determined by measuring the slope of the transition from a vibrationally quiet region to highly perturbed region of the vibragram as shown in Figure 14C. In this example, the impact occurred in line with and beyond the left end of the fiber at about -1 meters as can be discerned from Figure 14 B, and the wavefront propagates along the fiber from left to right. Returning to Figure 14A, starting with a single scan vibragram, the data processor 14 identifies regions of the vibragram having an amplitude that is greater than a threshold amplitude (All). The data processor 14 identifies the shock front corresponding to the boundary between the portion of the vibragram with vibrations and that without vibrations (A 12) and fits a line to earlier portions of the shock front (A 13) as shown in the example ofFigure 14B. It determines the slope of the line which indicates speed at which traveling wave propagates through the SUT (A14), where a high propagation speed may be interpreted as indicating a high modulus or denser material (A15).

[0063] Figure 15 A is a flowchart showing example procedures for detecting density discontinuities based on a single scan vibragrams shown in Figures 15B and 15D with graphs of vibration shock fronts shown in Figures 15C and 15E at respective time steps i and i+1. When waves are generated and traveling through a media, they will reflect off of larger density discontinuities causing a significant decrease in vibrational amplitude from one side of the discontinuity to the next. Analyzing the vibragrams in Figures 15B and 15D, the data processor 14 can identify the locations of these discontinuities by detecting a consistent drop of amplitude at a single location that is consistent across multiple laser sweeps. Returning to Figure 15 A, starting with a single scan vibragram (A21), the data processor 14 identifies regions of the vibragram having an amplitude that is greater than a threshold amplitude (A22). The data processor 14 identifies the shock front corresponding to the boundary between the portion of the vibragram with vibrations and that without vibrations (A23) and fits a line to earlier portions of the shock front (A24) as shown in the example of Figure 15B. It determines if any of the data which includes the shock front has regions in which the shock front line is discontinuous, which can be detected for example where the shock front stops or changes slope abruptly (A25).Locations where the shock front stops are discontinuities in the SUT, and locations where there is an abrupt change in slope of the shock front line indicates a change in material density or rigidity (A26).

[0064] Figure 16A is a flowchart showing example procedures for identifying an acoustic excitation, and Figure 16B shows examples of different excitation sources. In Figure 16A, starting with a single scan or multiple scan vibragram (A30), the data processor 14 identifies regions of the vibragram having local vibrations which do not propagate along fiber at high speed, i.e., they are not part of a traveling wave (A31). Pulsed vibrations packets that occur at intervals may be detected and interpreted by the data processor 14 as footsteps, e.g., with heel / toe pattern (A32). Smoothly continuous traveling vibrations may be detected and interpreted by the data processor 14 as wheeled vehicles, e.g., a bicycle (A33). Parallel smooth continuous traveling vibrations may be detected and interpreted by the data processor 14 asmulti-axle wheeled vehicles, e.g., an automobile (A34). A slope of vibration propagation may be detected and interpreted by the data processor 14 as a velocity of detected object (A35).

[0065] Figure 17A is a flowchart showing example procedures for modal analysis and Figures 17B-17D provide example illustrations for several of the steps in Figure 17A. Figure 17D shows an example of a fiber instrumenting a suspension bridge which is vibrating according to some mode pattern. Figure 17C shows how the modes of the structure will result in average vibrations patterns which are localized (the vertical stripes) with the darkness of the stripe corresponding to the strength of the motion. For motion detected at any location, the frequency content (obtained by performing a Fourier transform, for example, an FFT) is characteristic of the structural characteristics of the bridge.

[0066] Figure 17A illustrates example procedures for the data processor 14 to perform the modal analysis. Starting with a single scan vibragram (A40), the data processor 14 identifies regions of the vibragram having an amplitude that is greater than a threshold amplitude (A41) and locations in the fiber in which the vibration remains in the same location over time (although amplitude could vary) (A42). When a structure is vibrating in a resonant mode, the locations which do not move are called “nodes,” and the locations of maximum motion are called “antinodes.” The data processor 14 identifies regions of no vibration and interprets them as the nodes of the structure’s modes (A43) and regions of strong vibration as the anti-nodes of the structure (A44). Taking an FFT of the strong vibrational regions provides the frequency content of the structure at that location, as shown in the graphs of Figures 17B and 17C. Because the vibration of the bridge structure at modal resonance is oscillating, the energy of the vibration at a given location along the bridge can be obtained by calculating the root mean squared vibrations (as opposed to a simple average in which negative signals would cancel out the positive ones). The root mean squared (RMS) vibrations shown in the time v. length graph of Figure 17C and the amplitude v. frequency graph of Figure 17B are related by the Fourier transform. In this case, the strong vibrations seen, for example at location 9m in Fig. 17C, are made up of three distinct frequencies of vibration shown in Fig. 17B. Although the intensity of the RMS vibration in Fig.17C is shown as a constant over time for any given location, in actuality the vertical stripes would have time varying content which was suppressed by the RMS. Performing the FFT on that time varying content for that location allows the separation of the motion into the underlying frequencies. The data processor 14 may analyze the frequency content of the vibrations at theanti-nodes to determine the local stiffness of an oscillating object such as the bridge shown in Figure 17D (resonant frequency) (A45).

[0067] Figure 18A is a flowchart showing example procedures for structural health monitoring, e.g., of a bridge, and Figures 18B-18D provide example illustrations for several of the steps in Figure 18A. The data processor 14 collects measurement data for a baseline scan to detect the impulse affecting the bridge shown in Figure 18B when the structure is in known good condition with an impulse traveling along the structure (A50) to generate the example vibragram shown in Figure 18C. The data processor 14 collects measurement data for new scans with the same excitation source (a same impulse is applied to the bridge) (A51) to generate the example vibragram shown in Figure 18D. It then identifies regions in the new vibragram which do not match the original response within some predetermined tolerance (A52). Regions of the vibragram shown in Figure 18D where the propagation of the shock front is slower than baseline may be detected and interpreted by the data processor 14 as indicating a weakened structure (A53). New discontinuities in the vibragram (see the new response in Figure 18D) may be detected and interpreted by the data processor 14 as indicating a crack formation.

[0068] Figure 19 shows an example embodiment with the data processor 14 using machine learning. In this example, an input vibragram is processed through multiple stages to determine the likelihood that the data contains evidence of any of a variety of damage signatures. The input vibragram is input into two layers (two layers is just an example) of convolutional neural networks (CNNs), which detect features in the vibragram such as shockwaves, edge detections, discontinuities, etc. The outputs of the CNNs are a set of identified features which are then passed to a deep neural network (shown as a two-layer network in this example), which predicts whether the original input data contains markers of damage events (e.g., hoop breaks).

[0069] EXAMPLE ADVANTAGES

[0070] Example advantages of the technology described above include: considerably higher spatial resolution than can be achieved with discrete sensors which can result in improved 2D and / or 3D mapping of the ground, structures, and other materials; sensitivity to very small scale acoustic signals; inexpensive sensing elements for permanent installations resulting in reduced errors due to lack of repeatable positioning on sensing elements; the ability to use disposable sensing fiber; sensing fiber is insensitive to electrical interference and weather; the small size and weight of sensing fiber allows for easier transport to difficult to access locations;the impact of monitoring instrumentation of an application does not affect the normal use and / or operation of that location; reduced cost associated with only instrumenting a site once; sensing fiber can be built into composite structures or woven into straps without weakening them; and others.

[0071] Although various embodiments have been shown and described in detail, the claims are not limited to any particular embodiment or example. None of the above description should be read as implying that any particular element, step, range, or function is essential. All structural and functional equivalents to the elements of the above-described embodiments that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed. Features of the embodiments described above may be combined unless clearly technically impossible. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present invention, for it to be encompassed by the invention. No embodiment, feature, element, component, or step in this document is intended to be dedicated to the public.

[0072] All methods described herein can be performed in any suitable order unless otherwise indicated herein. The use of any and all examples, or example language (e.g., “such as”) provided herein, is intended merely to better illuminate the example embodiments and does not pose a limitation on the scope of the claims appended hereto unless otherwise claimed.

[0073] The term “about” or “approximately” means an acceptable error for a particular recited value, which depends in part on how the value is measured or determined. In certain embodiments, “about” can mean one or more standard deviations. When the antecedent term "about" is applied to a recited range or value it denotes an approximation within the deviation in the range or value known or expected in the art from the measurement’s method. For removal of doubt, it is understood that any range stated herein that does not specifically recite the term “about” before the range or before any value within the stated range inherently includes such term to encompass the approximation within the deviation noted above.

Claims

What is claimed is:

1. An optical interrogation system for high definition-distributed acoustic sensing (HD-DAS) of a vibration that changes a length of an optical fiber sensor in contact with a material or structure, comprising:an optical interferometer;optical detection circuitry, coupled to the optical interferometer, for detecting optical interferometric measurement signals for a length of the optical fiber sensor; anddata processing circuitry configured to perform the following operations:(a) receive interferometric measurement signals from the optical detection circuitry;(b) generate, based on the interferometric measurement signals, interferometric measurement data for a length of the optical fiber sensor;(c) process the interferometric measurement data to determine a strain state of the optical fiber sensor at one or more locations along the optical fiber sensor;(d) determine a vibration component of the strain state of the optical fiber sensor at the one or more locations along the optical fiber sensor;(e) identify one or more portions of the vibration component that is changing as a function of time;(f) determine whether each portion of the changing vibration component satisfies at least one predetermined criterion; and(g) determine information about the material or the structure based on one or more portions of the changing vibration component that satisfies the at least one predetermined criterion.

2. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to generate a multi-dimensional representation in space and frequency for each vibration component and each portion of the changing vibration component.

3. The optical interrogation system of claim 1 , wherein the received interferometric measurement signals arc associated with one or more sources of acoustic energy that includes one or both of transient acoustic energy and continuous acoustic energy.

4. The optical interrogation system of claim 1, further comprising:a tunable laser configured to scan through a range of wavelengths,wherein the optical interferometer is an optical frequency domain reflectometry (OFDR) interrogator configured to receive different wavelengths of light from the tunable laser over the range of wavelengths, and wherein the optical interferometric measurement signals indicate back scatter amplitude as a function of time along the optical fiber sensor,wherein data acquisition of the interferometric measurement signals includes one or more sweeps of the wavelength laser which are either separated or overlapping in time.

5. The optical interrogation system of claim 4, wherein the data processing circuitry is configured to perform one or more of the following:(a) determine one or more propagation characteristics of the vibration component detected along the length of the optical fiber sensor;(b) based on the one or more propagation characteristics, determine a propagation velocity of the vibration component in the material or the structure; and(c) based on the propagation velocity of the vibration, determine a frequency composition of each vibration component detected at each location along the fiber.

6. The optical interrogation system of claim 1, wherein the at least one predetermined criterion includes one or more of:(a) an amplitude of each portion of the changing vibration component;(b) a spectral content of each portion of the changing vibration component;(c) a rate of change of amplitude of the vibration component of the strain state of the optical fiber sensor at one or more locations;(d) a velocity with which the vibration component of the strain state of the optical fiber sensor at one ore more locations propagates along the fiber;(e) a difference between velocities of different frequencies of the vibration component of strain of the optical fiber sensor at one or more locations;(f) a time at which one or more frequencies of the vibration component of the strain state of the optical fiber sensor is observed at one or more locations; and(g) a derivative of any of (a)-(f) with respect to time or location.

7. The optical interrogation system of claim 1, wherein the information about the material or the structure includes one or more of:a. a spatial and / or temporal map of a 2-dimensional or 3-dimensional seismic model of the material or the structure;b. a density, rigidity, or strength of the material or structure;c. an integrity of material or structure;d. a change in the properties of the material or structure over time;e. one or more of the time or location of the source of the vibration component of strain in the optical fiber sensor;f. a differentiating characteristic of the source of the vibration component of the strain in the optical fiber sensor;g. resonant modes of the structure or material; andh. characteristics of the resonant modes of the structure or material.

8. The optical interrogation system of claim 1, wherein the information includes one or more material properties of the material or the structure.

9. The optical interrogation system of claim 8, wherein the one or more material properties includes a spatial and / or temporal map of a 2-dimensional or 3-dimensional seismic model of the material or the structure.

10. The optical interrogation system of claim 8, wherein the one or more material properties is determined based on differences in propagation of the vibration component of the strain state of the optical fiber sensor at different time varying frequencies to characterize the material properties of the material or the structure at varying depths within the material or the structure.

11. The optical interrogation system of claim 8, wherein the one or more material properties is an integrity of the material or the structure based on whether the vibration component of the strain state of the optical fiber sensor propagates along the material or the structure.

12. The optical interrogation system of claim 8, wherein the data processing circuitry is configured to compare a current vibration component of the strain state of the optical fiber sensor to previous measurements of the vibration component of the strain state of the optical fiber sensor to determine changes in material properties of the material or the structure over time.

13. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to identify one or both of a source time and a source location of an excitation for the vibration component of the strain state of the optical fiber sensor based on or from the portions of the changing vibration component that satisfies the at least one predetermined criterion.

14. The optical interrogation system of claim 13, wherein the data processing circuitry is configured to determine a cause of the excitation based on or from patterns in the portions of the changing vibration component that satisfies the at least one predetermined criterion.

15. The optical interrogation system of claim 13, wherein the data processing circuitry is configured to characterize a source of the excitation based on or from patterns in the portions of the changing vibration component that satisfies the at least one predetermined criterion.

16. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to perform vibratory modal analysis of the material or the structure based on resonant responses contained in the interferometric measurement signals.

17. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to use a relative response of interferometric measurement signals generated from multiple stimuli applied to the material or structure to generate a multi-dimensional map of physical characteristics of the material or structure.

18. The optical interrogation system of claim 1, further comprising:multiple optical interferometers each associated with a corresponding optical fiber sensor; wherein the optical detection circuitry is coupled to the multiple optical interferometers and is configured to detect optical interferometric measurement signals for a length of each optical fiber sensor;wherein the data processing circuitry is configured to receive interferometric measurement signals from the optical detection circuitry associated with each optical fiber sensor and combine responses from each optical fiber sensor.

19. The optical interrogation system in claim 1, further comprising:a light source,wherein the optical interferometer includes an optical time domain reflectometer (OTDR) or a coherent OTDR configured to receive pulses of light from the light source, and wherein the optical interferometric measurement signals indicate back scatter amplitude as a function of time along the optical fiber sensor.

20. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to:transform the interferometric measurement data set into a spectral domain;extract a phase response corresponding to the interferometric measurement data set into the spectral domain; anddetermine the vibration component from the phase response.

21. The optical interrogation system of claim 1, wherein distributed vibration signals are used to determine the time, location, and magnitude of impacts, vibration sources, and acoustic emissions of a structure, such as an inflatable space habitat, to monitor structural health.

22. The optical interrogation system of claim 1, wherein the data processing circuitry is configured to perform machine learning to perform one or more of operations (c)-(g).

23. The optical interrogation system of claim 1 , wherein the data processing circuitry is configured to process the interferometric measurement data to determine a strain state of the optical fiber sensor in distributed locations along the optical fiber sensor.

24. The optical interrogation system of claim 23, wherein the distributed locations are spaced apart by distances ranging from 10 microns to tens of meters.

25. A method for high definition-distributed acoustic sensing (HD-DAS) of a vibration that changes a length of an optical fiber sensor in contact with a material or structure, comprising: detecting from an optical interferometer optical interferometric measurement signals for a length of the optical fiber sensor;receiving interferometric measurement signals from the optical detection circuitry; generating, based on the interferometric measurement signals, interferometric measurement data for a length of the optical fiber sensor;processing the interferometric measurement data to determine a strain state of the optical fiber sensor at one or more locations along the optical fiber sensor;determining a vibration component of the strain state of the optical fiber sensor at the one or more locations along the optical fiber sensor;identifying one or more portions of the vibration component that is changing as a function of time;determining whether each portion of the changing vibration component satisfies at least one predetermined criterion; anddetermining information about the material or the structure based on one or more portions of the changing vibration component that satisfies the at least one predetermined criterion.

Images