185 results about "Distributed acoustic sensing" patented technology

Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) to monitor and provide real-time estimation of ice thickness on fiber optic communications facilities, and which integrate DSA data with a hybrid processing technique that combines frequency domain decomposition (FDD) and stochastic subspace identification (SSI). Aspects of our innovative systems and methods include: i) Hybrid Signal Processing Techniques; ii) Real-time, Continuous Ice Monitoring; iii) Enhanced Noise Robustness and Non-linear Dynamics Handling; and iv) Adaptability and Scalability.

Disclosed are systems and methods employing distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) that continuously monitor the buried / exposed status of undersea power cables for offshore wind farms. Our DAS systems and methods continuously monitor dynamic conditions characterizing undersea power cables and advantageously detect dynamic strain of an optical fiber that may be part of a composite cable with the power cable. By measuring and monitoring these dynamic conditions, a change in status from buried to exposed is determined and service personnel deployed before damage occurs to the power cable.

The invention relates to the technical field of industrial safety monitoring, in particular to a fallen object accurate identification FOD calculation method based on a fixed tower and application of the fallen object accurate identification FOD calculation method. Comprising the following steps: synchronously acquiring multi-source heterogeneous sensing data; carrying out multi-modal data preprocessing and feature extraction; performing cross-modal feature fusion and representation construction; modeling and reasoning a time-space diagram neural network; and performing multi-dimensional threat quantification and decision output. According to the design of the invention, multi-modal sensor groups are deployed at the tower bottom, the tower middle and the tower top of a fixed tower in a layered manner, and a high-definition multispectral camera, a high-density laser radar and a distributed acoustic sensor are integrated, so that 7 * 24-hour full-automatic data acquisition is realized; according to the design, the time and environment limitation of manual inspection is eliminated, and the complex weather detection capability is improved by 80%; meanwhile, the sensor synchronously triggers the mechanism and the grading detection process, the single-frame data processing delay is less than or equal to 50ms, and the real-time monitoring requirement of an aircraft take-off and landing high-frequency scene is met.

Distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) that illustrate inventive techniques—applicable to all embedded systems—that deliver high-bandwidth traffic from a DFOS system to a cloud or other processing resources in real-time, employ firmware that operates at a register-transfer level and writes data repeatedly into the embedded host memory directly—without software intervention after initial configuration. This technique advantageously enables the use of the relatively large host memory to compensate for any software jitter. Preferably configured, the firmware employs only a small buffer to compensate for a transaction and host-memory arbitration latency. A second dedicated thread sends out data from the user space buffer to a cloud, or a connected server. Remote procedures that are executed on a remote system (a computer, network of computers, or cloud), receive data transmitted from the embedded system, and perform further processing as necessary.

The invention discloses a city toughness real-time determination method and system based on distributed sound wave sensing, and the method comprises the steps: carrying out the optical cable distribution of a target region, carrying out the connection with a data channel, and carrying out the channel number mapping; acquiring a strain rate signal of each channel of the optical cable; caching the acquired DAS data; detecting, screening and judging the signal quality of each channel; if the SNR value and the DR value of one channel are lower than 80% of the whole channel, the coupling is abnormal; performing multi-domain feature extraction on the data; rendering the evolution trend of the activity intensity, the disturbance duration and the regional synchronization index along with time and space; constructing a multi-index fusion matrix; different indexes are mapped to color depth and saturation, an abnormal region track is identified, and high-intensity cooperative event prompting and recording are triggered; and obtaining a regional risk evolution situation. The system comprises a sensing optical fiber unit, a DAS demodulation unit, a data storage unit, a data processing and analysis unit, a toughness collaborative analysis unit and a toughness evolution visualization unit. The method is low in cost, wide in space coverage, high in timeliness and friendly in privacy.

The invention discloses a method and system for monitoring the abnormal state of a belt conveyor based on deep learning, and the method comprises the steps: laying sensing optical fibers along the line of a rack of the belt conveyor, and collecting the operation sound signals of a plurality of spatial point locations along the line through the sensing optical fibers by a distributed acoustic sensing DAS device; the method comprises the following steps: in a normal operation state of a belt conveyor, collecting normal sound signals of each point location, preprocessing the signals, and constructing a normal sample data set; for a normal sample of each spatial point location, training a corresponding unsupervised learning model; in real-time monitoring, sound signals collected at each point location are input into a corresponding unsupervised learning model, a reconstruction error is calculated, and the reconstruction error is compared with a dynamic threshold value based on normal sample statistical characteristics to obtain an abnormal result; and performing comprehensive judgment on an abnormal result based on statistical logic of the time window, and triggering an alarm if an abnormal proportion in the window exceeds a set value. High-accuracy abnormity early warning is realized, and system deployment is convenient.

The invention relates to the technical field of building construction, and discloses a BIM (Building Information Modeling) deepening-based steel reinforced concrete composite column ultra-large-span corridor construction method, which comprises the following steps of: constructing a multi-modal sensing building information model, and integrating a fiber bragg grating and a distributed acoustic sensor on a steel member to form an intelligent member; establishing a data link between the intelligent component and the sensing BIM center and calibrating the data link; dynamic optimization control based on strain feedback is carried out on collaborative hoisting, and internal quality monitoring based on multi-modal sensing fusion is carried out on concrete pouring; after construction is completed, digital twin baseline calibration is carried out, and a data packet containing a structure initial state vector is generated. According to the method, the multi-modal sensing system and the BIM are deeply fused, and the whole construction process is penetrated, so that real-time, closed-loop and quantitative control of key procedures is realized, the safety, accuracy and quality of super-large complex structure construction are remarkably improved, and a high-fidelity digital twinning baseline is established for structural health monitoring of the whole life cycle of a building.

This application relates to a Rayleigh scattering noise suppression method and system for a distributed acoustic wave detection system. The method includes: performing optical phase sinusoidal modulation on a high-coherence continuous laser output from a narrow-linewidth laser in an FBG-based distributed acoustic detection system according to a set modulation scheme using an optical phase modulator; the set modulation scheme is Csin(2πf m t), C is the phase modulation amplitude, f m The phase modulation frequency and satisfying f m τ = k, where τ is the time difference between two adjacent reflected light pulses being detected, t is time, and k is an integer. A highly coherent continuous laser beam with sinusoidal phase modulation is passed through an unbalanced interferometer to generate dual light pulses. These dual light pulses are then injected into an FBG array for distributed acoustic wave detection. This achieves low-noise distributed acoustic wave detection and effectively improves the detection performance of FBG-based distributed acoustic wave detection.

The application relates to an environment noise monitoring and event identification method, system and medium of a distributed acoustic sensing system, which comprises the following steps: obtaining real-time environment sound signals f(t) at different distances from the distributed acoustic sensing system; obtaining a frequency domain signal S in the sound signal through Fourier transform; extracting characteristic values corresponding to each event based on the frequency domain signal by adopting a dual-domain adaptive signal peak extraction method based on dynamic morphological coupling; classifying the sound signal characteristics through a convolutional neural network machine learning and determining the corresponding event; and reporting the noise determination result. The application can effectively process diversified noise types and realize real-time monitoring of key infrastructures.

Methods and apparatus for estimating a position of a boundary of a queue of traffic are disclosed, the queue extending along a roadway. Distributed acoustic sensing is used to generate, as a function of time and of position along the roadway, a signal representing acoustic vibration at a sensing optical fibre that extends along the roadway. A queue signature is detected in the signal of either the slowing down of a plurality of vehicles as they approach the boundary of the queue, or speeding up of a plurality of vehicles as they advance from the departed boundary of the queue. A position of the boundary of the queue is then estimated from the detected queue signature. Methods and apparatus are also disclosed for controlling one or more road traffic signals by using distributed acoustic sensing to detect acoustic vibration at one or more sensing optical fibers disposed along a roadway, as a function of time and of position along a roadway, and controlling the one or more road traffic signals responsive to the detected acoustic vibration.

The invention discloses a distributed acoustic sensing earthquake monitoring data stream processing method and system, and belongs to the technical field of earthquake monitoring and data processing. The method comprises the following steps: after preprocessing original seismic monitoring data, slicing the original seismic monitoring data by adopting a sliding processing time window to obtain data fragments suitable for real-time detection; designing a self-adaptive short-time average / long-time average detection algorithm, detecting data segments, and dynamically adjusting self-adaptive parameters in the algorithm according to data characteristics; and finally, multi-channel fusion detection is performed on the initial candidate list of the seismic events, so that a final detection list of the seismic events is generated. The method is based on a distributed streaming data processing architecture, combines the innovation advantages of a self-adaptive earthquake detection algorithm, can improve the earthquake detection precision, guarantees the real-time performance of a detection result, and can provide efficient and reliable technical support for a large-scale earthquake monitoring task.

A computer-implemented method is disclosed for monitoring traffic using a distributed acoustic sensing (DAS) model trained with video input. The method includes receiving DAS training data representing strain or strain rate along a fiber optic cable and video training data captured from a segment of road covered by the same cable. A machine learning-based object detection model (e.g., YOLOv11) is applied to the video data to generate vehicle attribute data labels, which are aligned with the DAS training data. A neural network model (e.g., UNet) is trained using the aligned data to associate DAS signal patterns with vehicle classification outcomes. The trained DAS model is stored in memory and subsequently applied to operational DAS data to detect and classify vehicles in real time, producing model output such as synthetic strain maps or detection maps. Once trained, the model functions without requiring operational video data.

The application discloses a kind of rock mechanics test in the collection device of acoustic emission signal, including computer, measurement and control and signal processing equipment, distributed acoustic wave sensing equipment and collection probe: the collection probe is composed of single-mode optical fiber and probe skeleton, single-mode optical fiber is fixed around probe skeleton, when testing, collection probe is fixed on the surface of rock sample for testing;The measurement and control and signal processing equipment include: radio frequency amplifier, digital-to-analog conversion module, analog-to-digital conversion module, filter amplifier, field programmable logic gate array and CPU-GPU data processing module;The distributed acoustic sensing equipment includes narrow linewidth laser, fiber coupler, acoustooptic modulator, erbium-doped fiber amplifier, circulator, polarization diversity receiver, first balanced photodetector and second balanced photodetector;Collection signal band is wide, can be conveniently installed on any rock sample.Collecting main frequency is lower, collection probe is connected closely with rock sample, coupling is good, without additional smearing coupling agent.

This invention belongs to the field of road traffic safety monitoring technology, and provides a road surface black ice detection and early warning system and method based on distributed acoustic sensing. It includes multiple sets of sensing optical fibers buried under the road surface and multiple sets of sensing optical fibers installed on the roadside guardrail. The system preprocesses the detection signals acquired by the distributed acoustic sensing subsystem, decomposes the signals, removes interfering intrinsic mode components, and processes the remaining components using an adaptive noise cancellation algorithm to obtain a clean signal containing road surface condition characteristics. Time-domain, frequency-domain, and time-frequency-domain features are extracted from the clean signal. A pre-trained recognition model is used to process the extracted features to identify the road surface condition, and an early warning is issued based on the recognition results. This invention can achieve real-time detection, accurate differentiation, location, and graded early warning of road surface black ice and black snow, ensuring road traffic safety.

Disclosed are systems and methods for optimizing natural resource production. The system includes a data input system configured to receive input data from one or more channels of a data transmission cable. The wellbore data includes one or more noise patterns corresponding to one or more perforations in a well casing of a wellbore. The system further includes an analysis system configured to determine one or more cluster amplitudes corresponding to each of the one or more noise patterns and generate, using the one or more cluster amplitudes, a flow distribution model. The system also includes an output generation system configured to output the flow distribution model corresponding to the one or more perforation clusters.

A distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) system and method employing a fiber optic sensor cable that collects vibrational data of individual utility poles suspending the fiber optic sensor cable and stores the vibrational data in a central office (CO). Machine learning (ML) models are developed, trained, and utilized to analyze vibrational features of the utility poles and determine their integrity. Additionally, DFOS / DAS systems and methods according to the present disclosure determine the location(s) of fiber coils that exist along a length of a fiber optic sensor cable.

The invention relates to a vehicle-type-based traffic flow monitoring DAS system and a signal processing method, and belongs to the technical field of intelligent traffic monitoring. A DAS system for monitoring traffic flow based on different vehicle types is characterized by comprising a sensing module based on a phase-sensitive optical time domain reflection technology and a self-adaptive signal processing module, according to the system, firstly, road vibration data are collected by using an optical fiber distributed acoustic sensing technology, vehicle vibration characteristics are selectively enhanced and background interference is suppressed in a key frequency band of 10-80 Hz through DAS-PCSE based on enhanced ultra-wavelet transform, and high-stability input characterization is generated; then, the preprocessed data are input into a lightweight DAS Trajectory Net, the network takes an encoder-decoder as a skeleton and introduces an adaptive feature enhancement module, and accurate recognition of a vehicle track is achieved; and finally, through a DAS Veple Classifier Net, a double-current signal processing module and lightweight convolution are utilized to efficiently fuse spatio-temporal information, and vehicle type discrimination is completed. According to the invention, the influence of low signal-to-noise ratio and complex background disturbance can be effectively overcome, and wide-area and all-weather vehicle-type-based traffic flow and vehicle speed monitoring can be realized with low calculation cost.

The present application relates to a kind of well down distributed fiber (Distributed Acoustic Sensing, DAS) anisotropic shale reservoir guided wave dispersion curve inversion method and medium, comprising: obtaining the guided wave seismic data of shale reservoir, and according to the guided wave seismic data obtained, the guided wave dispersion energy diagram is calculated, and the observed guided wave dispersion curve is extracted from it;Establish the initial anisotropic elastic parameter model of the shale reservoir;According to the initial or updated anisotropic elastic parameter model of the shale reservoir, theoretical guided wave dispersion curve is obtained by forward;Construct objective function, according to the residual between theoretical guided wave dispersion curve and observed guided wave dispersion curve, calculate and judge whether objective function converges;If objective function converges, then output the anisotropic elastic parameter model of current shale reservoir;If objective function does not converge, then anisotropic elastic parameter model is updated using gradient algorithm until objective function converges;Output the optimized shale reservoir anisotropic elastic parameter model.

A method for mapping acoustic characteristics of an underwater sediment includes penetrating a pile into the underwater sediment at a first location of a plurality of locations. A first distributed acoustic sensor is inserted near the first post. At the second location, the second pile is driven into the deposit. When the second pile is penetrated, an acoustic signal is detected at the first acoustic sensor. While driving a second pile, a depth indexed to the second pile is detected. A second acoustic sensor is inserted near the second post. Penetrating the pile, detecting the acoustic signal at each acoustic sensor, and inserting the sensors are repeated until the pile is driven at all locations. The signals are used to generate a map of characteristics of the deposit defined by a plurality of locations. Characteristics include attenuation that excludes the effects of diffractors in deposits.

A method for eliminating phase drift in distributed acoustic sensing, comprising: acquiring each signal component in a distributed acoustic sensing system and performing phase demodulation to obtain the phase value of each signal component at the current moment (S1); initializing a Kalman filter and, for each signal component, by means of a Kalman filter time update equation, updating the priori estimate covariance at the current moment on the basis of the posteriori estimate covariance at the previous moment (S2); for each signal component, substituting the priori estimate covariance at the current moment into a Kalman gain equation to calculate a Kalman gain coefficient (S3); on the basis of the phase value and the Kalman gain coefficient of each signal component at the current moment, calculating a phase drift estimated value of each signal component at the current moment and updating the posteriori estimate covariance of each signal component at the current moment (S4); and using the phase value and the phase drift estimated value of each signal component at the current moment to calculate drift-free phase values at the current moment and outputting same, thus eliminating the phase drift of each signal component at the current moment (S5). The method is applicable to various distributed acoustic sensing systems, and can eliminate the phase drift of each signal component to keep the phase consistency of each signal component in real time.

A photonic-integrated distributed acoustic sensing system (DAS) with long-distance and wide-frequency response includes: a low-voltage DC electric drive module, an optical transmitter chip, an integrated optical amplification module, an optical receiver chip, a signal conditioning and acquisition chip, and a digital signal processing chip that are sequentially connected. The photonic-integrated DAS system has the features of extended monitoring distance, broad frequency response bandwidth, high signal fidelity, ultra-sensitive detection capabilities, low power consumption, compact form factor, and robust environmental adaptability.

The invention discloses a sampling frequency offset estimation method and system based on multichannel node information, and belongs to the technical field of voice signal processing. The method comprises the following steps: acquiring a microphone array signal received by each node; resampling a microphone array signal received by the node to be detected by taking the sampling frequency deviation candidate value as a reference; and constructing a multi-channel cross-correlation function containing the combination of all microphones of the reference node and the to-be-tested nodes in pairs, and estimating the sampling frequency deviation value of each to-be-tested node relative to the reference node through correlation maximization. According to the invention, beam forming preprocessing can be carried out on node signals so as to suppress noise interference and enhance signal quality; based on the filtered signal, a cross-correlation function between the resampled signal and the reference signal is calculated, and the sampling frequency offset is estimated by correlation maximization. According to the method, the accuracy and robustness of sampling frequency offset estimation are improved by utilizing multi-channel information, and the method is suitable for node synchronization in a distributed acoustic sensor network.

There is provided a method and system of frequency dithered distributed acoustic sensing (FD-DAS) using group data signal processing, where the continuous wave (CW) coherent laser's optical frequency or optical phase for the FD-DAS interrogator may be set in evenly or randomly distributed manner for different data groups but they are kept as constant or with negligibly variation within each data group. Such FD-DAS may be used to improve acoustic or vibration signal measurement reliability, to allow to use a low-cost single-frequency laser such as a DFB laser, and / or to suppress polarization fading effect.

An object is to monitor a plurality of optical fibers in a distributed acoustic sensing system with a simple system configuration. A plurality of optical fibers for distributed acoustic sensing are disposed in areas where a target is detected. A measurement unit outputs probe light and receives backscattered light to sensing for each optical fiber. A light distribution unit switches an optical path between the measurement unit and the plurality of the optical fibers in such a manner that the probe light is input to one optical fiber in the plurality of the optical fibers and the measurement unit receives the backscattered light from the one optical fiber. A control unit controls the light distribution unit to connect each optical fiber to the measurement unit in a sequential manner.

Disclosed are signal processing methods for an optical detection system, and corresponding systems. An example is a signal processing method for a distributed acoustic sensing system which utilizes spread spectrum pulses transmitted along an optical path, where a scattered signal that was scattered at a scattering location along an optical path is received and interfered with a local oscillator signal to generate a first carrier signal that is modulated by a phase difference between the local oscillator and scattered signals. The first carrier signal is then processed to generate a second carrier signal that is modulated by a spatial differential of the phase difference. Pulse compression is then performed on the second carrier signal. The spatial differential of the phase difference is directly related to the strain (or acoustic environment) of the optical path at the scattering location, and so enables the strain at the scattering location to be estimated.

The invention provides a broadband response distributed sound wave sensing method and device, and relates to the technical field of optical fiber sensing. Single-frequency continuous light beams are split, double-sideband frequency sweep modulation is performed on a first path to generate double-sideband frequency sweep light, short-pulse-width chirped pulse light and long-pulse-width chirped pulse light are obtained through frequency band selection and pulse interception, and the short-pulse-width chirped pulse light and the long-pulse-width chirped pulse light are synthesized and injected into a sensing optical fiber; backward Rayleigh scattering light signals are collected, long-pulse-width and short-pulse-width Rayleigh scattering light signals are separated through optical filtering, and spontaneous emission noise is suppressed and amplified; coherent beat frequency detection and phase demodulation are carried out on the long-pulse-width Rayleigh scattering light signal, spectrum correlation processing is carried out on the short-pulse-width Rayleigh scattering light signal to obtain time offset, disturbance information recovery is achieved, and a sound wave measurement result distributed along the sensing optical fiber is output.

A system for autonomously deploying geophysical cables in sand dunes includes a surface station positioned at the sand dunes, one or more subsurface burrowing robots including distributed acoustic sensing geophysical cables laying robot, and a geo-locating radio system (GLRS). The one or more subsurface burrowing robots digs in the sand dunes to a target depth located between a base of the sand dunes and a base of loose sand, autonomously moves under a surface of the loose sand along a predefined path, exchanges data with the surface station via an unspooling cable, and receives commands from the surface station via the unspooling cable. The GLRS transmits telemetry data from the one or more subsurface burrowing robots to the surface station. The GLRS comprises a first GLRS positioned on the base of the sand dunes and a second GLRS disposed on the one or more subsurface burrowing robots.

The application discloses a distributed acoustic sensing data processing method, device and computer program product, wherein the method comprises: performing time-frequency transformation on Rayleigh backscattering original time domain signals collected by a distributed acoustic sensing system to obtain a frequency spectrum; extracting a main lobe part with concentrated energy from the frequency spectrum to obtain a main lobe frequency spectrum; quantizing conjugate representation components of each frequency spectrum coefficient in the main lobe frequency spectrum to form a binary symbol sequence; performing entropy coding on the binary symbol sequence to generate a compressed code stream; in response to a reconstruction requirement, performing entropy decoding on the compressed code stream to recover the binary symbol sequence and performing inverse quantization to reconstruct a main lobe frequency spectrum estimation; and performing inverse time-frequency transformation on the main lobe frequency spectrum estimation to reconstruct Rayleigh backscattering time domain signals. Through the synergistic effect of main lobe extraction, extreme quantization and entropy coding, the application realizes efficient compression and high-fidelity reconstruction of massive DAS data under the premise of retaining key phase information, significantly reduces storage and transmission overheads, and is suitable for hardware deployment.

The application discloses a wireless node cooperative communication low-power control method based on distributed optical fiber sensing and belongs to the technical field of distributed optical fiber sensing and Internet of Things communication fusion. The method relies on a heterogeneous cooperative system composed of a distributed acoustic sensing (DAS) host, a center server and a wireless acoustic wave acquisition terminal, and realizes cooperative control through wireless terminal time synchronization, hardware silent cycle recording, adaptive wake-up instruction generation, historical data cross-border backtracking, narrowband feature extraction and heterogeneous data fusion decision. The application solves the problems of high energy consumption of wireless nodes, missing event starting data and bandwidth waste in the prior art, realizes low-power long-term deployment, complete event capture and high-reliability identification through on-demand wake-up and data processing guided by DAS, and is suitable for long-distance pipeline, power line and other safety monitoring scenes with strict requirements for real-time response and low power consumption.

A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (ϕ-OTDR) coupled to the optical fiber, and a processor cooperating with the ϕ-OTDR. The processor may be configured to train a plurality of machine learning networks with DAS data from the ϕ-OTDR based upon different respective optimizers, select a trained machine learning network from among the plurality thereof based upon a game theoretic model, and generate an acoustic event report from the DAS data using the selected trained machine learning network.