306 results about "Distributed acoustic sensing" patented technology

The invention discloses a planning method and system of a power distribution communication access network, particularly relates to the technical field of power distribution communication network dynamic topology management, and is used for solving the problems of fault misjudgment and inaccurate positioning caused by topology update delay in the prior art. A dynamic topological data set is generated by dynamically monitoring the operation state of each optical fiber node and a link transmission signal, a real-time topological view with a dynamic version identifier is generated based on a preset topological planning strategy, and a network change area is quickly locked by combining a topological difference identification technology of an alarm moment and a historical view. Candidate fault links are screened through the conflict relation between link switching frequency and service priority, and the vibration characteristics of the construction machinery are further analyzed by using distributed acoustic sensing data and external force damage interference is eliminated; and finally, optical cable breakage faults are confirmed through optical power interruption verification, heartbeat signal loss detection and redundant optical fiber phase mirror symmetry verification. And multi-dimensional accurate positioning of the topological data fault root cause is realized.

In some embodiments, a system for producing fluid from a well can include an electrical submersible pump (ESP) disposed in a wellbore of the well and configured to pump the fluid. The system may further include a distributed acoustic sensing (DAS) system, for example having an interrogator unit and a fiber optic cable extending downhole in the wellbore. An end of the fiber optic cable can be disposed downhole relative to the ESP. In embodiments, the system may further include a controller configured to receive data from the DAS system, process the data to detect a slug, determine a parameter of the detected slug, and alter operation of the ESP in response to determining that the parameter exceeds a threshold.

The invention discloses a signal-to-noise ratio improvement method based on a multi-core optical fiber dual-channel distributed acoustic sensing system, and the method comprises the steps: carrying out the light splitting of light emitted by a narrow linewidth laser through a first coupler C1, enabling one path of light to generate a pulse signal through an acoustic optical modulator (AOM), enabling the other path of light to pass through a second coupler C2, and enabling the other path of light to pass through a second coupler C1; the pulse light is divided into two beams of transmission light, the two beams of transmission light are injected into a connected sensing optical fiber through two groups of erbium-doped optical fiber amplifiers (EDFA), two groups of filters, two groups of circulators and a fan-in coupler Fan-in Coupler, and the pulse light is subjected to backward Rayleigh scattering in the sensing optical fiber; based on a dual-channel DAS system, a multi-core optical fiber is used as a sensing optical fiber, three cores and four cores of the multi-core optical fiber are connected in series to form two sensing channels, seven-core space division signals are multiplexed, the equivalent self-noise pressure suppression effect of the seven-core space division signals is analyzed, after noise correlation is removed through a time domain dislocation-frequency domain phase shift method, the signal-to-noise ratio gain can be increased to 8.65 dB, and the signal-to-noise ratio gain can be increased to 8.65 dB. And the detection capability of the system on low-frequency weak signals can be improved.

A method for monitoring pipeline integrity is provided. The method includes obtaining, using at least one hardware processor, acoustic signal captured by at least one optical fiber arranged along a pipeline. The method includes inputting, using the at least one hardware processor, the acoustic signal to a machine learning model. The machine learning model is trained to execute a first analysis on a first representation of the acoustic signal and a second analysis on a second representation of the acoustic signal, the first analysis being independent to the second analysis. An output of the trained machine learning model is at least one of a first result based on the first analysis or a second result based on the second analysis. The method includes determining, using the at least one hardware processor, an indication of pipeline integrity based on the at least one of the first result or the second result.

Disclosed are a device and method for detecting and identifying shallow-stratum foreign objects based on distributed acoustic sensing. The device includes a vibrating sensitivity-enhanced fiber optic cable, a distributed acoustic sensing demodulator, a vibrator system, a vibration data processing unit, a velocity structure inversion unit and an AI-aided locating and identification unit. The distributed acoustic sensing demodulator transmits vibration signals to the vibration data processing unit for preprocessing to obtain waveform signals; the velocity structure inversion unit performs inversion to obtain the shallow-stratum underground velocity structure; positions of shallow-stratum foreign objects are identified through the underground velocity structure and abnormal waveforms; and the types of shallow-stratum foreign objects are identified. A distributed acoustic sensing method for fiber optic cables is used to obtain vibration signals, velocity imaging is performed on a shallow-stratum velocity structure, and AI is utilized to achieve locating and type identification of shallow-stratum underground foreign objects.

A fiber optic distributed sensing system and method. The fiber optic distributed sensing system includes: an optical fiber extending through an underwater gas field; an interrogator connected to the optical fiber so that light transmitted through the optical fiber is capable of being sensed by the interrogator; and a computer system configured to obtain sensor data captured by the interrogator and determine an amount or a characteristic of gas within the underwater gas field based on the sensor data. The method includes: capturing sensor data using a fiber optic distributed sensor extending through an acoustic field; performing a wavelet transform on the sensor data in order to obtain spatiotemporal data; and matching the spatiotemporal data to an event template of a predetermined set of event templates in order to identify a spatiotemporal event.

A system may include a first DAS station, comprising a first plurality of interrogator units to launch a first plurality of DAS signals in a first direction, wherein a first interrogator unit is configured to launch a first DAS signal at a first interrogation rate and first wavelength, wherein a second interrogator unit is configured to launch a second DAS signal at a second wavelength and second interrogation rate, less than the first interrogation rate. The system may include a loopback array, comprising a plurality of loopbacks, arranged over a plurality of spans, wherein a first set of proximate loopbacks nearest to the first DAS station are configured to route back the first DAS signal to the first DAS station, and wherein a first set of intermediate loopbacks, located further from the first DAS station, are configured to route back the second DAS signal to the first DAS station.

A distributed acoustic system (DAS) that may include two or more lasers that each transmit a continuous wave (CW) light, a pulser disposed after the two the two or more lasers and optically connected to each of the two or more lasers to receive the CW light from each of the two or more lasers and form a light pulse, and an optical amplifier, wherein the pulser is optically connected to the optical amplifier to receive the light pulse from the pulser as an input. The DAS may further include a splitter optically connected to the optical amplifier to optically split the light pulse into two or more light pulses for each output of the splitter a circulator optically connected to each output of the splitter, and a sensor fiber attached to each of the circulators as an output for each circulator.

The disclosed method includes: determining a subsurface space of interest; arranging a source sensor array relative to a receiver sensor array of a Surface Distributed Acoustic Sensing (S-DAS); transmitting, from the source sensor array, a first set of seismic signals indicating baseline measurements; receiving, using the receiver sensor array, the baseline measurements and thereby generate baseline data; injecting fluid into the subsurface space of interest; transmitting, using the source sensor array, a second set of seismic signals indicating fluid plume measurements; receiving, using the receiver sensor array, the fluid plume measurements and thereby generate fluid plume data; determining, based on the baseline data and the fluid plume data, time-shift data and thereby generate fluid plume extent data; formatting the fluid plume extent data into data values that characterize an extent or rate of spread of the fluid plume; and visualizing the extent or rate of spread of the fluid plume.

The invention discloses an equivalent self-noise pressure suppression method based on a dual-channel distributed acoustic sensing system, and the method comprises the steps: carrying out the light splitting of light emitted by a narrow-linewidth laser through a first coupler C1, enabling one path of light to generate a pulse signal through an acoustic optical modulator (AOM), enabling the other path of light to pass through a second coupler C2, and enabling the other path of light to pass through a second coupler C1; the pulse light is divided into two beams of transmission light, the two beams of transmission light are injected into a connected sensing optical fiber through two groups of erbium-doped optical fiber amplifiers (EDFA), two groups of filters, two groups of circulators and a fan-in coupler Fan-in Coupler, and the pulse light is subjected to backward Rayleigh scattering in the sensing optical fiber; according to the invention, based on a dual-channel DAS system, two fiber cores of the multi-core optical fiber are used as sensing optical fibers, and the space division signals of the two cores are multiplexed, so that the performance that the equivalent self-noise pressure drop is 2.54 dB in the frequency band of 100-1000Hz is realized under the condition that the noise correlation coefficient of the dual-core signals is 0.11, and the detection capability of the system on low-frequency weak signals can be improved.

The invention relates to the technical field of audio processing, and discloses an intelligent control system and method for a wireless microphone, and the method comprises the steps: collecting multi-point acoustic data through a distributed microphone network, and constructing a spatial acoustic topology model; based on a topological analysis result, constructing an adaptive notch filter bank to realize howling suppression; environment changes are monitored in real time through a distributed acoustic sensor network, and suppression parameters are dynamically optimized; integrating a lightweight convolutional neural network on microphone equipment to identify acoustic events; low-delay acoustic event processing and howling prevention are realized by adopting a layered edge computing architecture; according to the invention, the technical problems of unstable howling suppression, weak environmental adaptability and lack of spatial perception in a complex environment are effectively solved.

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.

The embodiment of the invention provides a fault positioning method and device for a communication optical cable, equipment and a medium, and the method comprises the steps: carrying out the preprocessing of distributed acoustic sensing data, and obtaining the processed distributed acoustic sensing data; detecting abnormal data in the processed distributed acoustic sensing data by using a dual-threshold threshold algorithm; performing analysis of different domains on the abnormal data to obtain multi-dimensional features; and identifying the multi-dimensional features by using a trained support vector machine model, and determining the fault position of the distributed acoustic sensing data, so as to solve the problem that the real-time dangerous case positioning of the communication optical cable line cannot be realized at present.

A method for performing one or more capture utilization and sequestration (CCUS) operations. The method includes obtaining, from a vibration sensor, ambient noise data associated with a subsurface of a resource site. The method also includes generating, from the ambient noise data, passive virtual shot data indicating temporal properties or interactions present in the ambient noise data. The method also includes determining, using the passive virtual shot data, shot analysis data. The method also includes generating, using the shot analysis data, a model representing changes in past CCUS operations for the resource site. The method also includes initiating, based on the model, one or more additional CCUS operations for the resource site.

Earthquake detection via fiber sensing is provided using using a supervisory path of submarine cables wherein the supervisory system / path of a submarine optical cable conveys portion(s) of an optical signal back to an origin location periodically—i.e., at every repeater location. Advantageously, since it is known where a returning signal is coming from, a resolution equivalent to an undersea span length may be determined—which is sufficient for wide area disturbances such as earthquakes. The returned signal is sufficiently strong such that the signal-to-noise ratio of a returned / received signal is not limited by the ASE noise of the amplifiers. The returned signal is much larger as compared to a normal distributed acoustic sensing (DAS) return signal since the return signal according to aspects of the present disclosure is directed backward via an optical coupler / reflector / circulator having a much larger coupling ratio as compared to normal Rayleigh back scattering utilized in DAS.

Methods and systems for managing operation of a well are disclosed. The method may include obtaining distributed acoustic sensing (DAS) data based on a measurement made using a DAS sensing element positioned in the well. A qualification process for the DAS data may be performed to identify whether any portion of the DAS sensing element was acoustically coupled to a structure of the well during the measurement in a manner that introduced at least one artifact to the DAS data. If at least one portion of the DAS sensing element was coupled in such a manner, a remediation process may be performed to manage impacts of the at least one artifact on downstream use of the DAS data.

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.

A system and method for using a Distributed Acoustic Sensor (DAS) system to receive signals transmitted from remote autonomous sensors and to locate the autonomous sensors are disclosed. The method includes installing a DAS system in a borehole consisting of at least one fiber-optic cable connected to at least one corresponding interrogator, deploying at least one autonomous sensor and conducting at least one measurement. The methods also include encoding the at least one measurement in at least one encoded acoustic signal, transmitting the at least one encoded acoustic signal to the at least one fiber-optic cable, and detecting the at least one encoded acoustic signal with the DAS system. Furthermore, the methods include recording the at least one encoded acoustic signal received by the DAS system at a surface location and processing the at least one encoded acoustic signal with a processing unit to decode and obtain the at least one measurement.

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.

A method for monitoring pipeline integrity is provided. The method includes obtaining, using at least one hardware processor, acoustic signal captured by at least one optical fiber arranged along a pipeline. The method includes inputting, using the at least one hardware processor, the acoustic signal to a machine learning model. The machine learning model is trained to execute a first analysis on a first representation of the acoustic signal and a second analysis on a second representation of the acoustic signal, the first analysis being independent to the second analysis. An output of the trained machine learning model is at least one of a first result based on the first analysis or a second result based on the second analysis. The method includes determining, using the at least one hardware processor, an indication of pipeline integrity based on the at least one of the first result or the second result.

A hydraulic fracturing system and method uses a model trained with external pressure gauge data and LFDAS sensor data to identify pressure communication events based on LFDAS sensor data. One or more monitoring wells are established in proximity to a well undergoing well stimulation, each monitoring well including one or more LFDAS sensors. LFDAS sensor data is received from the LFDAS sensors of the one or more monitoring wells, the received LFDAS sensor data including data received after start of a well stimulation operation. Occurrences of pressure communication events at each monitoring well may then be identified based on the model and on the received LFDAS sensor data.

A distributed acoustic system (DAS) that may include two or more lasers that each transmit a continuous wave (CW) light, a pulser disposed after the two the two or more lasers and optically connected to each of the two or more lasers to receive the CW light from each of the two or more lasers and form a light pulse, and an optical amplifier, wherein the pulser is optically connected to the optical amplifier to receive the light pulse from the pulser as an input. The DAS may further include a splitter optically connected to the optical amplifier to optically split the light pulse into two or more light pulses for each output of the splitter a circulator optically connected to each output of the splitter, and a sensor fiber attached to each of the circulators as an output for each circulator.

The invention provides a down-sampling method and device for distributed acoustic sensing monitoring data, and the corresponding method comprises the steps: carrying out the spectrum analysis of the pre-received distributed acoustic sensing monitoring data of a target work area, so as to determine the frequency band range; performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result; and down-sampling the filtering result according to a proportional relation between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data. By using the distributed acoustic sensing monitoring data downsampling method provided by the invention, the signal after the distributed acoustic sensing monitoring data is downsampled can be better close to the expected signal of the time domain, meanwhile, the frequency component loss of the signal is small, the calculation process is simple and efficient, and the method is suitable for popularization and application. And thus, the cost of storage and processing of distributed optical fiber acquisition data is reduced.

The present invention relates to the field of audio processing technology, and discloses a wireless microphone intelligent control system and method, wherein a wireless microphone intelligent control method includes: collecting multi-point acoustic data through a distributed microphone network to construct a spatial acoustic topology model; constructing an adaptive notch filter group to achieve howling suppression based on the topology analysis results; monitoring environmental changes in real time through a distributed acoustic sensor network to dynamically optimize suppression parameters; integrating a lightweight convolutional neural network on the microphone device to identify acoustic events; and adopting a layered edge computing architecture to achieve low-latency acoustic event processing and howling prevention. The present invention effectively solves the technical problems of unstable howling suppression, weak environmental adaptability and lack of spatial perception in complex environments.

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 present invention relates to systems and methods for monitoring microseismic activity in a geothermal well system using fiber optic sensing technology. The system includes fiber optic cables installed in multiple wells of the geothermal well system. The fiber optic cables enable simultaneous collection of distributed acoustic sensing (DAS) data, strain measurements, and temperature data. By detecting and localizing microseismic events during stimulation and operation of the geothermal well system, seismic activity is maintained within safety thresholds, providing for safe and effective extraction from the geothermal well system.

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.

The invention provides a method and system for detecting a signal response in distributed optical fibre sensing data. The method comprises receiving, from a distributed optical fibre sensing system, distributed optical fibre sensing data, where the distributed optical fibre sensing system comprises a plurality of spatial channels, each spatial channel associated with a respective scattering location along an optical fibre of the distributed optical fibre sensing system. The distributed optical fibre sensing data comprises a plurality of measurement signals as a function of time, each of the plurality of measurement signals corresponding to a respective one of the spatial channels. The method further comprises providing the distributed optical fibre sensing data to a machine learning model, where the machine learning model is configured to detect in the distributed optical fibre sensing data a signal response corresponding to an event of a predetermined type. The machine learning model was trained using training data comprising simulated signal responses for events of the predetermined type. If the machine learning model detects a signal response corresponding to an event of the predetermined type, the method comprises outputting, by the machine learning model, information indicative of the detected signal response. The invention further provides a method and system for training the machine learning model using simulated distributed acoustic sensing data.