Method, device, equipment, storage medium and computer program for correcting frequency response of das data

Abstract

This disclosure relates to a method and apparatus for correcting the frequency response of distributed optical fiber sensing data, as well as a computer device, a computer-readable storage medium, and a computer program product. The correction method includes: acquiring background noise and effective signal of distributed optical fiber sensing data; extracting a spectrum correction function from the background noise; performing spectrum correction on the effective signal using the spectrum correction function to obtain a spectrum-corrected effective signal; and outputting the spectrum-corrected effective signal. This disclosure achieves effective recovery of the frequency response of DAS data through the spectrum correction function. Furthermore, the spectrum-corrected effective signal closely matches the spectrum of the detector data.

Description

Technical Field

[0001] This disclosure pertains to the application field of fiber optic sensing technology, and specifically relates to a method, device, storage medium, and computer program for correcting the frequency response of distributed fiber optic sensing (DAS) data. Background Technology

[0002] Distributed fiber optic sensing (DAS) technology, as an emerging and transformative technology, has developed rapidly in fields such as oil and gas exploration and development and engineering monitoring, but there are still some problems that need to be solved.

[0003] Due to the fiber demodulation mechanism and the influence of the internal and external environment, the response of DAS to vibration is affected not only by the vibration itself, but also by factors such as temperature, strain, and demodulation algorithm. This response causes significant distortion in the DAS spectrum. Compared with the response of conventional electronic detectors, if the DAS data is strain rate data, the amplitude increases with the frequency; if it is strain data, the amplitude decreases with the frequency.

[0004] Because of the numerous influencing factors, in practical applications, the method of increasing the excitation source energy is generally adopted to make the amplitude of the effective signal much greater than the amplitude change caused by the frequency, thereby reducing the impact on the effective signal. However, in reality, the excitation energy of the source cannot be increased indefinitely. In addition, even if the excitation energy of the source is increased, in many cases, the impact of spectral distortion problem still cannot be ignored. Summary of the Invention

[0005] Based on the above, in order to solve the problem that the frequency response spectrum of fiber optic sensing data to vibration signals differs from the actual vibration response spectrum due to the fiber demodulation mechanism and the influence of the internal and external environment, resulting in frequency response distortion of DAS data in applications, the purpose of this disclosure is to propose a frequency response correction method and device for distributed fiber optic sensing data. This frequency response correction method achieves true recovery of the frequency response of DAS data.

[0006] In a first aspect, this disclosure provides a method for correcting the frequency response of distributed optical fiber sensing data, comprising:

[0007] S1. Acquire background noise and effective signal of distributed optical fiber sensing data;

[0008] S2. Extract the spectral correction function from the background noise;

[0009] S3. Perform spectrum correction on the effective signal using the spectrum correction function to obtain the spectrum-corrected effective signal;

[0010] S4. Output the effective signal after spectrum correction.

[0011] In this disclosure, the background noise is a segment of distributed optical fiber sensing data signal collected under non-production conditions without any external human interference.

[0012] In some embodiments, acquiring the background noise and effective signal of the distributed optical fiber sensing data includes:

[0013] S11. Using the same acquisition parameters, the background noise and effective signal of the distributed optical fiber sensing data are acquired under non-production conditions and production conditions respectively. The acquisition parameters include time sampling interval and / or spatial sampling interval.

[0014] In some embodiments, the effective signal and the background noise have the same demodulation parameters, the demodulation parameters including the demodulation gauge.

[0015] In some embodiments, step S2, extracting the spectral correction function from the background noise, includes:

[0016] S21. Superimpose the sample points in all sampling channels of the background noise to obtain a superimposed channel;

[0017] S22. Perform a positive Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In;

[0018] S23. Based on the real part vector Rn and the imaginary part vector In, calculate the corresponding magnitude vector An and the optional phase angle vector Pn using the following formula:

[0019] Pn i =atan(In i / Rn i (II)

[0020] In equations (I) and (II), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i An is the element at position i in the imaginary vector In; i Pn is the element at position i in the magnitude vector An; i The element at position i in the phase angle vector Pn;

[0021] S24. Fit the amplitude vector An to obtain the fitting function f. i , where i is the vector element number, i = 1, ..., N, and N is the number of vector elements.

[0022] In some embodiments, in step S24, the fitting function f is obtained by fitting the amplitude vector An. i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements, including: fitting the amplitude vector An using a nonlinear curve fitting algorithm to obtain the fitting function f. i .

[0023] In some embodiments, the curve nonlinear fitting algorithm includes one of polynomial fitting and least squares fitting.

[0024] In some embodiments, step S2, extracting the spectral correction function from the background noise, further includes: S25, adjusting the fitted function f i Verification is required.

[0025] In some embodiments, in step S25, the fitting function f i Verification includes:

[0026] S251. Based on the amplitude vector An, the corrected amplitude vector An' is obtained by correcting it using the following formula:

[0027] An i '=An i —f i (VIII)

[0028] In equation (VIII), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; An i 'The element at position i in the corrected amplitude vector An'; An i The element at position i in the magnitude vector An;

[0029] S252. Based on the corrected amplitude vector An', the corrected real part vector Rn' and the corrected imaginary part vector In' are calculated using the following formula:

[0030] Rn i '=An i 'sin(Pn i (IX), In i '=An i 'cos(Pn i (X)

[0031] In equations (IX) and (X), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i The element at position i in the vector element Rn after correction; In i'The element at position i in the corrected imaginary part vector In';

[0032] S253. Based on the corrected real part vector Rn' and the corrected imaginary part vector In', perform an inverse Fourier transform to obtain the real part vector Rn'", which is the noise signal on the sampling channel after spectral correction. If the trend of the Rn' curve is flattened, it indicates that the calculated correction function f i It is effective.

[0033] In some embodiments, step S3, which involves performing spectral correction on the effective signal using the spectral correction function to obtain the spectral-corrected effective signal, includes:

[0034] S31. Perform a positive Fourier transform on the sampling points of the sampling channel of the effective signal to obtain the real part vector Rs and the imaginary part vector Is.

[0035] S32. Based on the real part vector Rs and the imaginary part vector Is, calculate the corresponding magnitude vector As and phase vector Ps using the following formula:

[0036] Ps i =atan(Is i / Rs i (IV)

[0037] In equations (III) and (IV), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps;

[0038] S33. Based on the amplitude vector As, the corrected amplitude vector As' is obtained by using the following formula:

[0039] As i '=As i -f i (V)

[0040] In equation (V), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As' i The element at position i in the corrected amplitude vector As';

[0041] S34. Based on the corrected amplitude vector As', calculate the corrected real part vector Rs' and the corrected imaginary part vector Is' using the following formula:

[0042] Rs i '=As i 'sin(Ps i (VI), Is i '=As i 'cos(Ps i (VII)

[0043] In equations (VI) and (VII), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs' i Rs' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction.

[0044] S35. Perform an inverse Fourier transform based on the corrected real part vector Rs' and the corrected imaginary part vector Is' to obtain the real part vector Rs", which is the effective signal on the sampling channel after spectral correction.

[0045] Secondly, this disclosure provides a frequency response correction device for distributed optical fiber sensing data, used to implement the aforementioned frequency response correction method for distributed optical fiber sensing data, comprising:

[0046] The acquisition module is used to acquire the background noise and effective signal of distributed optical fiber sensing data.

[0047] A spectrum correction function extraction module is used to extract the spectrum correction function from the background noise;

[0048] A spectrum correction module is used to perform spectrum correction on the effective signal using the spectrum correction function to obtain a spectrum-corrected effective signal.

[0049] The output module is used to output the effective signal after spectrum correction.

[0050] In some embodiments, the acquisition module performs the acquisition of background noise and valid signals of distributed optical fiber sensing data, including: acquiring background noise and valid signals of distributed optical fiber sensing data under non-production conditions and production conditions using the same acquisition parameters. Preferably, the acquisition parameters include time sampling interval and / or spatial sampling interval.

[0051] In some embodiments, the effective signal and the background noise have the same demodulation parameters, preferably, the demodulation parameters include the demodulation gauge.

[0052] In some embodiments, the extracted spectrum correction function module includes:

[0053] The superposition unit is used to superimpose the sample points in all sampling channels of the background noise to obtain a superposition channel;

[0054] The first positive transform unit is used to perform a positive Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In;

[0055] The first calculation unit is used to calculate the corresponding magnitude vector An and the optional phase angle vector Pn based on the real part vector Rn and the imaginary part vector In using the following formula:

[0056] Pn i =atan(In i / Rn i (II)

[0057] In equations (I) and (II), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i An is the element at position i in the imaginary vector In; i Pn is the element at position i in the magnitude vector An; i The element at position i in the phase angle vector Pn;

[0058] A fitting unit is used to fit the magnitude vector An to obtain a fitting function f. i , where i is the vector element number, i = 1, ..., N, and N is the number of vector elements.

[0059] In some embodiments, the fitting unit is used to fit the magnitude vector An to obtain a fitting function f. i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements, including: fitting the amplitude vector An using a nonlinear curve fitting algorithm to obtain the fitting function f. i .

[0060] In some embodiments, the curve nonlinear fitting algorithm includes one of polynomial fitting and least squares fitting.

[0061] In some embodiments, the extraction spectrum correction function module further includes a verification unit for verifying the fitting function f. i Verification is required.

[0062] In some embodiments, the verification unit includes:

[0063] The correction component is used to correct the amplitude vector An based on the amplitude vector An using the following formula to obtain the corrected amplitude vector An':

[0064] An i '=An i —f i (VIII)

[0065] In equation (VIII), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; An i 'The element at position i in the corrected amplitude vector An'; An i The element at position i in the magnitude vector An;

[0066] The first calculation unit is used to calculate the corrected real part vector Rn' and the corrected imaginary part vector In' based on the corrected amplitude vector An' using the following formula:

[0067] Rn i '=An i 'sin(Pn i (IX), In i '=An i 'cos(Pn i (X)

[0068] In equations (IX) and (X), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i The element at position i in the vector element Rn after correction; In i 'The element at position i in the corrected imaginary part vector In';

[0069] The inverse transform unit performs an inverse Fourier transform based on the corrected real part vector Rn' and the corrected imaginary part vector In' to obtain the real part vector Rn'", which is the noise signal on the sampling channel after spectral correction. If the trend of the Rn' curve is flattened, it indicates that the calculated correction function f i It is effective.

[0070] In some embodiments, the spectrum correction module includes:

[0071] The second forward transform unit is used to perform a forward Fourier transform on the sample points of the sampling channel of the effective signal to obtain the real part vector Rs and the imaginary part vector Is.

[0072] The second calculation unit is used to calculate the corresponding magnitude vector As and phase vector Ps based on the real part vector Rs and the imaginary part vector Is using the following formula:

[0073] Ps i =atan(Is i / Rs i (IV)

[0074] In equations (III) and (IV), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps;

[0075] The correction unit is used to correct the amplitude vector As according to the amplitude vector As using the following formula to obtain the corrected amplitude vector As':

[0076] As i '=As i -f i (V)

[0077] In equation (V), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As i The element at position i in the vector element number of the corrected amplitude vector As;

[0078] The third calculation unit is used to calculate the corrected real part vector Rs' and the corrected imaginary part vector Is' based on the amplitude vector As' using the following formula:

[0079] Rs i '=As i 'sin(Ps i (VI), Is i '=As i 'cos(Ps i (VII)

[0080] In equations (VI) and (VII), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i ' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction;

[0081] The inverse transform unit is used to perform an inverse Fourier transform based on the corrected real part vector Rs' and the corrected imaginary part vector Is' to obtain the real part vector Rs", which is the effective signal on the sampling channel after spectral correction.

[0082] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described correction method.

[0083] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described correction method.

[0084] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described correction method.

[0085] The beneficial effects of this disclosure are as follows:

[0086] This disclosure proposes a method for correcting the frequency response of distributed optical fiber sensing data, which achieves effective recovery of the frequency response of DAS data, and the effective signal after spectrum correction is highly consistent with the spectrum of detector data. Attached Figure Description

[0087] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0088] Figure 1 This is a schematic diagram of an application scenario for a method for correcting the frequency response of distributed optical fiber sensing data according to an embodiment of this disclosure.

[0089] Figure 2 This is a flowchart of a second embodiment of the method for correcting the frequency response of distributed optical fiber sensing data according to the present disclosure.

[0090] Figure 3 This is a cross-sectional view of the background noise data of DAS data.

[0091] Figure 4 for Figure 3 A comparison chart of the original spectrum of the background noise data, the extracted correction function, and the spectrum corrected by the correction function.

[0092] Figure 5a This is the valid signal data before spectrum correction.

[0093] Figure 5b This is the valid signal data before spectrum correction.

[0094] Figure 6This is a comparison chart of the effective signal data spectrum, the corrected spectrum after the correction function, and the detector data spectrum in the DAS data.

[0095] Figure 7 This is a simplified structural diagram of a frequency response recovery device for distributed optical fiber sensing data provided according to an embodiment of the present disclosure.

[0096] Figure 8 This is a schematic diagram of an electronic device provided according to an embodiment of the present disclosure.

[0097] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0098] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0099] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0100] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0101] Example 1

[0102] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include server 4, network 5, and terminal devices.

[0103] The terminal device can be either hardware or software. When the terminal device is hardware, it can be various electronic devices with an LED display screen that support communication with the server 4, including but not limited to smartphones 1, tablets 3, laptops 2, and desktop computers; when the terminal device is software, it can be installed in the aforementioned electronic devices. The terminal device can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not limit this. Furthermore, various applications can be installed on the terminal device, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0104] Server 4 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 4 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This disclosure embodiment does not limit this.

[0105] It should be noted that server 4 can be either hardware or software. When server 4 is hardware, it can be any electronic device that provides various services to the terminal device. When server 4 is software, it can implement multiple software programs or software modules that provide various services to the terminal device, or it can implement a single software program or software module that provides various services to the terminal device. This disclosure does not impose any limitations on this aspect.

[0106] Network 5 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This disclosure does not limit the scope of the embodiments.

[0107] Users can establish a communication connection with server 4 via network 5 through terminal devices to receive or send information, etc.

[0108] Specifically, firstly, server 4 can acquire the raw data from the distributed fiber optic sensing. Secondly, server 4 can perform a first transformation process, converting the raw data from a phase response into a strain response to obtain first data. Thirdly, server 4 can perform a second transformation process, converting the first data from a strain response into a displacement response to obtain second data. Fourthly, server 4 can perform a third transformation process, converting the second data from a displacement response into a velocity response to obtain third data. Finally, server 4 can output the third data.

[0109] It should be noted that the specific types, quantities, and combinations of server 4, network 5, and terminal devices can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0110] Example 2

[0111] Continue to refer to Figure 2 , Figure 2 A flowchart illustrating a method for correcting the frequency response of distributed optical fiber sensing data according to an embodiment of this disclosure is shown. This method can be... Figure 1 It is executed by electronic devices within the system. For example... Figure 2 As shown, the method for correcting the frequency response of distributed fiber optic sensing data includes:

[0112] S1. Acquire background noise and effective signal of distributed optical fiber sensing data;

[0113] S2. Extract the spectral correction function from the background noise;

[0114] S3. Perform spectrum correction on the effective signal using the spectrum correction function to obtain the spectrum-corrected effective signal;

[0115] S4. Output the effective signal after spectrum correction.

[0116] In some embodiments, the entity performing the correction method (e.g. Figure 1 The electronic device shown can connect to the target device via a wired or wireless connection, and then acquire the raw data from the distributed fiber optic sensing. The raw data from the distributed fiber optic sensing can refer to the unprocessed data collected by the distributed fiber optic sensing, such as… Figure 3 and Figure 5a The data in the middle.

[0117] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0118] In this embodiment, the background noise is a segment of distributed fiber optic sensing data signal collected under non-production conditions without any external human interference.

[0119] In some embodiments, acquiring the background noise and valid signal of distributed optical fiber sensing data includes: S11, acquiring the background noise and valid signal of distributed optical fiber sensing data under non-production conditions and production conditions using the same acquisition parameters, wherein the acquisition parameters include time sampling interval and / or spatial sampling interval.

[0120] In some embodiments, the effective signal and the background noise have the same demodulation parameters.

[0121] In some embodiments, the demodulation parameters include the demodulation caliper.

[0122] In some embodiments, step S2, extracting the spectral correction function from the background noise, includes:

[0123] S21. Superimpose the sample points in all sampling channels of the background noise to obtain a superimposed channel;

[0124] S22. Perform a positive Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In;

[0125] S23. Based on the real part vector Rn and the imaginary part vector In, the corresponding magnitude vector An is calculated using the following formula:

[0126]

[0127] In equation (I), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i An is the element at position i in the imaginary vector In; i The element at position i in the magnitude vector An;

[0128] S24. Fit the amplitude vector An to obtain the fitting function f. i , where i is the vector element number, i = 1, ..., N, and N is the number of vector elements.

[0129] In some embodiments, fitting the magnitude vector An to obtain the fitting function f i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements, including: fitting the amplitude vector An using a nonlinear curve fitting algorithm to obtain the fitting function f. i .

[0130] In some embodiments, the curve nonlinear fitting algorithm includes one of polynomial fitting and least squares fitting.

[0131] In some embodiments, step S2, extracting the spectral correction function from the background noise, further includes: S25, adjusting the fitted function f i Verification.

[0132] In some embodiments, in step S25, the fitting function f i Verification includes:

[0133] S250. Based on the real part vector Rn and the imaginary part vector In, the corresponding phase angle vector Pn is calculated using the following formula:

[0134] Pn i =atan(In i / Rn i (II)

[0135] In equation (II), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i Pn is the element at position i in the imaginary vector In; i The element at position i in the phase angle vector Pn;

[0136] S251. Based on the amplitude vector An, the corrected amplitude vector An' is obtained by correcting it using the following formula:

[0137] An i '=An i —f i (VIII)

[0138] In equation (VIII), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; An i 'The element at position i in the corrected amplitude vector An'; An i The element at position i in the magnitude vector An;

[0139] S252. Based on the corrected amplitude vector An', the corrected real part vector Rn' and the corrected imaginary part vector In' are calculated using the following formula:

[0140] Rn i '=An i 'sin(Pn i (IX), In i '=Ani 'cos(Pn i (X)

[0141] In equations (IX) and (X), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i The element at position i in the vector element Rn after correction; In i 'The element at position i in the corrected imaginary part vector In';

[0142] S253. Based on the corrected real part vector Rn' and the corrected imaginary part vector In', perform an inverse Fourier transform to obtain the real part vector Rn'", which is the noise signal on the sampling channel after spectral correction. If the trend of the Rn' curve is flattened, it indicates that the calculated correction function f i It is effective.

[0143] In some embodiments, step S3, which involves performing spectral correction on the effective signal using the spectral correction function to obtain the spectral-corrected effective signal, includes:

[0144] S31. Select a sampling channel of the effective signal, and perform a forward Fourier transform on the sample points of the sampling channel to obtain the real part vector Rs and the imaginary part vector Is.

[0145] S32. Based on the real part vector Rs and the imaginary part vector Is, calculate the corresponding magnitude vector As and phase vector Ps using the following formula:

[0146] Ps i =atan(Is i / Rs i (IV)

[0147] In equations (III) and (IV), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps;

[0148] S33. Based on the amplitude vector As, the corrected amplitude vector As' is obtained by using the following formula:

[0149] As i '=As i -fi (V)

[0150] In equation (V), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As' i The element at position i in the corrected amplitude vector As';

[0151] S34. Using the corrected amplitude vector As', calculate the corrected real part vector Rs' and the corrected imaginary part vector Is' using the following formula:

[0152] Rs i '=As i 'sin(Ps i (VI), Is i '=As i 'cos(Ps i (VII)

[0153] In equations (VI) and (VII), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i ' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction;

[0154] S35. Perform an inverse Fourier transform using the corrected real part vector Rs' and the corrected imaginary part vector Is' to obtain the real part vector Rs", which is the effective signal on the sampling channel after spectral correction;

[0155] S36. Following steps S31 to S35 above, traverse all sampling channels of all valid signals in the sampling channel direction to obtain the valid signals of all sampling channels after spectral correction.

[0156] The beneficial effects of one of the above embodiments of this disclosure include at least the following: effective recovery of the DAS data frequency response is achieved through the spectrum correction function. Furthermore, the spectrum-corrected effective signal closely matches the detector data spectrum.

[0157] The present disclosure is illustrated below through a specific embodiment:

[0158] Figure 3 This is a profile of the background noise data from actual DAS data collected in a certain work area. Figure 4 yes Figure 3 A comparison chart of the original spectrum of the background noise data, the extracted correction function, and the spectrum corrected using the correction function. Based on... Figure 3 The data is processed by executing steps S21 to S24 in step S2 of the above embodiment to obtain... Figure 4The extracted correction function is then used to continue executing step S25 in step S2 of the above embodiment to obtain... Figure 4 The spectrum corrected using the correction function. From Figure 4 As can be seen above, the amplitude energy of the original spectrum increases nonlinearly with the increase of frequency. After correction by the extracted correction function, the energy of each frequency band of the background noise is basically at the same level, which is consistent with the characteristics of the noise spectrum. This shows that the obtained correction function can effectively correct the background noise.

[0159] Figure 5a Is with Figure 3 The data uses the original data of valid signals acquired with the same acquisition parameters. Figure 5b yes Figure 5a The original data of the effective signal in the DAS data is frequency corrected after steps S3 and S4. As can be seen from the figure, the effective signal energy of the corrected DAS data is improved and the signal-to-noise ratio is enhanced.

[0160] Figure 6 yes Figure 5a The original data spectrum of the effective signal, Figure 5b The comparison chart of the spectrum of DAS data after spectrum correction and the spectrum of electronic detector data acquired simultaneously shows that the spectrum of DAS data after spectrum correction and the spectrum of electronic detector data are highly consistent. In addition, the signal-to-noise ratio of the effective signal of DAS data has been greatly improved after spectrum correction.

[0161] Example 3

[0162] This embodiment describes some further implementation steps of the distributed fiber optic sensing data frequency response correction method according to this disclosure. The distributed fiber optic sensing data frequency response correction method includes:

[0163] S100, Acquire background noise and effective signals from distributed fiber optic sensing data, including:

[0164] S110. Background noise and effective signal of distributed optical fiber sensing data are collected under non-production conditions and production conditions using the same acquisition parameters. The acquisition parameters include time sampling interval and spatial sampling interval. The effective signal and the background noise have the same demodulation parameters, including demodulation gauge length.

[0165] S200, Extracting the spectral correction function from the background noise, including:

[0166] S210. Superimpose the sample points in all sampling channels of the background noise to obtain a superimposed channel;

[0167] S220. Perform a forward Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In;

[0168] S230. Based on the real part vector Rn and the imaginary part vector In, the corresponding magnitude vector An and phase vector Pn are calculated using the following formula:

[0169] Pn i =atan(In i / Rn i (II)

[0170] In equations (I) and (II), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i An is the element at position i in the imaginary vector In; i Pn is the element at position i in the magnitude vector An; i The element at position i in the phase angle vector Pn;

[0171] S240. Fit the amplitude vector An to obtain the fitting function f. i , where i is the vector element number, i = 1, ... N, and N is the number of vector elements;

[0172] S250, the fitted function f i Verification includes:

[0173] S251. Based on the amplitude vector An, the corrected amplitude vector An' is obtained by correcting it using the following formula:

[0174] An i '=An i —f i (VIII)

[0175] In equation (VIII), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; An i 'The element at position i in the corrected amplitude vector An'; An i The element at position i in the magnitude vector An;

[0176] S252. Based on the corrected amplitude vector An', the corrected real part vector Rn' and the corrected imaginary part vector In' are calculated using the following formula:

[0177] Rn i '=An i 'sin(Pn i (IX), In i '=Ani 'cos(Pn i (X)

[0178] In equations (IX) and (X), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i The element at position i in the vector element Rn after correction; In i 'The element at position i in the corrected imaginary part vector In';

[0179] S253. Perform an inverse Fourier transform based on the corrected real part vector Rn' and the corrected imaginary part vector In' to obtain the real part vector Rn'", which is the noise signal on the sampling channel after spectral correction. If the trend of the Rn' curve is flattened, it indicates that the calculated correction function f i It is effective.

[0180] S300. Perform spectrum correction on the effective signal using the spectrum correction function to obtain a spectrum-corrected effective signal, including:

[0181] S310. Select a sampling channel of the effective signal, and perform a forward Fourier transform on the sample points of the sampling channel to obtain the real part vector Rs and the imaginary part vector Is.

[0182] S320. Based on the real part vector Rs and the imaginary part vector Is, the corresponding magnitude vector As and phase vector Ps are calculated using the following formula:

[0183] Ps i =atan(Is i / Rs i (IV)

[0184] In equations (III) and (IV), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps;

[0185] S330. Based on the amplitude vector As, the amplitude vector As' is obtained by correction using the following formula:

[0186] As i '=As i -f i(V)

[0187] In equation (V), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As i The element at position i in the vector element number of the corrected amplitude vector As;

[0188] S340. Based on the amplitude vector As', calculate the corrected real part vector Rs' and the corrected imaginary part vector Is' using the following formula:

[0189] Rs i '=As i 'sin(Ps i (VI), Is i '=As i 'cos(Ps i (VII)

[0190] In equations (VI) and (VII), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i ' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction;

[0191] S350. Perform an inverse Fourier transform using the corrected real part vector Rs' and the corrected imaginary part vector Is' to obtain the real part vector Rs", which is the effective signal on the sampling channel after spectral correction;

[0192] S360. Following steps S31 to S35 above, traverse all sampling channels of all valid signals in the sampling channel direction to obtain the valid signals of all sampling channels after spectrum correction.

[0193] S400, Output the effective signal after spectrum correction.

[0194] In some embodiments, the specific implementation of steps S100 to S400 and the resulting technical effects can be referred to... Figure 2 The steps in the corresponding embodiments will not be repeated here.

[0195] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0196] Example 4

[0197] This embodiment describes some structural embodiments of the frequency response correction device for fractional fiber optic sensing data according to this disclosure. It can be used to perform the method embodiments of this disclosure. For details not disclosed in the device embodiments of this disclosure, please refer to the method embodiments of this disclosure.

[0198] refer to Figure 7 The distributed optical fiber sensing data frequency response correction device 6 provided in this disclosure includes:

[0199] Acquisition module 7 is used to acquire background noise and effective signal of distributed optical fiber sensing data;

[0200] Module 8 for extracting spectrum correction functions is used to extract spectrum correction functions from background noise.

[0201] The spectrum correction module 9 is used to perform spectrum correction on the effective signal through the spectrum correction function to obtain the spectrum-corrected effective signal.

[0202] Output module 10 is used to output the effective signal after spectrum correction.

[0203] In some embodiments, the acquisition module 7 is specifically used to acquire background noise and effective signals of distributed optical fiber sensing data under non-production conditions and production conditions using the same acquisition parameters, wherein the acquisition parameters include time sampling interval and / or spatial sampling interval.

[0204] In some embodiments, the effective signal and the background noise have the same demodulation parameters, the demodulation parameters including the demodulation gauge.

[0205] In some embodiments, the extracted spectrum correction function module includes:

[0206] The superposition unit is used to superimpose the sample points in all sampling channels of the background noise to obtain a superposition channel;

[0207] The first positive transform unit is used to perform a positive Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In;

[0208] The first calculation unit is used to calculate the corresponding magnitude vector An and phase vector Pn based on the real part vector Rn and the imaginary part vector In using the following formula:

[0209] Pn i =atan(In i / Rn i (II)

[0210] In equations (I) and (II), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i Let i be the element at position i in the real part vector Rn; In i An is the element at position i in the imaginary vector In; iPn is the element at position i in the magnitude vector An; i The element at position i in the phase angle vector Pn;

[0211] A fitting unit is used to fit the magnitude vector An to obtain a fitting function f. i , where i is the vector element number, i = 1, ..., N, and N is the number of vector elements.

[0212] In some embodiments, the fitting unit is used to fit the magnitude vector An to obtain a fitting function f. i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements, including: fitting the amplitude vector An using a nonlinear curve fitting algorithm to obtain the fitting function f. i .

[0213] In some embodiments, the curve nonlinear fitting algorithm includes one of polynomial fitting and least squares fitting.

[0214] In some embodiments, the extraction spectrum correction function module further includes a verification unit for verifying the fitting function f. i Verification is required.

[0215] In some embodiments, the verification unit includes:

[0216] The correction component is used to correct the amplitude vector An based on the amplitude vector An using the following formula to obtain the corrected amplitude vector An':

[0217] An i '=An i —f i (VIII)

[0218] In equation (VIII), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; An i 'The element at position i in the corrected amplitude vector An'; An i The element at position i in the magnitude vector An;

[0219] The first calculation unit is used to calculate the corrected real part vector Rn' and the corrected imaginary part vector In' based on the corrected amplitude vector An' using the following formula:

[0220] Rn i '=An i 'sin(Pn i (IX), In i '=An i 'cos(Pn i(X)

[0221] In equations (IX) and (X), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rn i The element at position i in the vector element Rn after correction; In i 'The element at position i in the corrected imaginary part vector In';

[0222] The inverse transform unit performs an inverse Fourier transform based on the corrected real part vector Rn' and the corrected imaginary part vector In' to obtain the real part vector Rn'", which is the noise signal on the sampling channel after spectral correction. If the trend of the Rn' curve is flattened, it indicates that the calculated correction function f i It is effective.

[0223] In some embodiments, the spectrum correction module includes:

[0224] The second forward transform unit is used to perform a forward Fourier transform on the sample points of the sampling channel of the effective signal to obtain the real part vector Rs and the imaginary part vector Is.

[0225] The second calculation unit is used to calculate the corresponding magnitude vector As and phase vector Ps based on the real part vector Rs and the imaginary part vector Is using the following formula:

[0226] Ps i =atan(Is i / Rs i (IV)

[0227] In equations (III) and (IV), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps;

[0228] The correction unit is used to correct the amplitude vector As according to the amplitude vector As using the following formula to obtain the corrected amplitude vector As':

[0229] As i '=As i -f i (V)

[0230] In equation (V), i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As i The element at position i in the vector element number of the corrected amplitude vector As;

[0231] The third calculation unit is used to calculate the corrected real part vector Rs' and the corrected imaginary part vector Is' based on the corrected magnitude vector As' using the following formula:

[0232] Rs i '=As i 'sin(Ps i (VI), Is i '=As i 'cos(Ps i (VII)

[0233] In equations (VI) and (VII), i is an independent vector element number, i = 1, ..., N, where N is the number of vector elements; Rs i ' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction;

[0234] The inverse transform unit is used to perform an inverse Fourier transform based on the corrected real part vector Rs' and the corrected imaginary part vector Is' to obtain the real part vector Rs", which is the effective signal on the sampling channel after spectral correction.

[0235] It is understandable that the modules described in the distributed fiber optic sensor data conversion device 6 are similar to those in the reference... Figure 2 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the conversion device 6 and the modules contained therein, and will not be repeated here.

[0236] Example 5

[0237] like Figure 8 As shown, the electronic device 11 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage device 1108 into a random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for the operation of the electronic device 11. The processing unit 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0238] Typically, the following devices can be connected to I / O interface 1105: input devices 1106 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1109. Communication device 1109 allows electronic device 11 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 11 with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead. Figure 7 Each box shown can represent a device or multiple devices as needed.

[0239] Specifically, according to this embodiment, the process described in the above-mentioned flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program or instructions carried on a computer-readable medium, the computer program or instructions containing program code for performing the methods shown in the flowchart. In this embodiment, the computer program can be downloaded and installed from a network via communication device 1109, or installed from storage device 1108, or installed from ROM 1102. When the computer program is executed by processing device 1101, the steps of the above-described correction method can be performed.

[0240] It should be noted that the computer-readable medium described above in this embodiment can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this embodiment, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0241] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0242] The aforementioned computer-readable medium may be included in the aforementioned device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the steps of the aforementioned correction method.

[0243] Computer program code for performing the operations of this embodiment can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0244] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0245] The modules described in this embodiment can be implemented in software or hardware. The described modules can also be located in a processor; for example, they can be described as:

[0246] The module comprises an acquisition module, a generation module, and a calculation module. For example, the acquisition module can also be described as "a module for acquiring seismic wave data collected by distributed optical fibers for a target area."

[0247] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0248] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for correcting the frequency response of distributed optical fiber sensing data, characterized in that, include: Acquire background noise and effective signal from distributed fiber optic sensing data; Extract the spectral correction function from the background noise; The effective signal is spectrally corrected using the spectral correction function to obtain the spectrally corrected effective signal. Output the effective signal after spectrum correction.

2. The correction method according to claim 1, characterized in that, The acquisition of background noise and effective signals from distributed fiber optic sensing data includes: The background noise and effective signal of distributed optical fiber sensing data are collected under non-production and production conditions using the same acquisition parameters, which include time sampling interval and / or spatial sampling interval.

3. The correction method according to claim 1, characterized in that, The effective signal and the background noise have the same demodulation parameters, including the demodulation gauge.

4. The correction method according to claim 1, characterized in that, Extracting the spectral correction function from the background noise includes: The sample points from all sampling channels of the background noise are superimposed to obtain a superimposed channel; Perform a positive Fourier transform on the superimposed channel to obtain the real part vector Rn and the imaginary part vector In; Based on the real part vector Rn and the imaginary part vector In, the corresponding magnitude vector An is calculated using the following formula: where i is the vector element number, i = 1,...N, N is the number of vector elements; Rn i is the element at the corresponding position of the vector element number i in the real part vector Rn; In i is the element at the corresponding position of the vector element number i in the imaginary part vector In; An i is the element at the corresponding position of the vector element number i in the amplitude vector An; fitting the amplitude vectors Anresults in a fit function f i where i is the vector element number, i = 1,... N, N is the number of vector elements.

5. The correction method according to claim 4, characterized in that, The fitting function f is obtained by fitting the amplitude vector An. i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements, including: fitting the amplitude vector An using a nonlinear curve fitting algorithm to obtain the fitting function f. i The nonlinear curve fitting algorithm includes one of polynomial fitting and least squares fitting.

6. The correction method according to claim 4 or 5, characterized in that, The step of performing spectral correction on the effective signal using the spectral correction function to obtain the spectral-corrected effective signal includes: Perform a forward Fourier transform on the sample points of the sampling channel of the effective signal to obtain the real part vector Rs and the imaginary part vector Is; Based on the real part vector Rs and the imaginary part vector Is, the corresponding magnitude vector As and phase vector Ps are calculated using the following formula: Ps i =atan(Is i / Rs i ) Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements; Rs i Is is the element at position i in the real part vector Rs; i As is the element at position i in the imaginary vector Is; i Ps is the element at position i in the magnitude vector As; i The element at position i in the phase angle vector Ps; The corrected amplitude vector As' is obtained by correcting the amplitude vector As using the following formula: As i '=As i -f i Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements; As' i The element at position i in the corrected amplitude vector As'; Based on the corrected magnitude vector As', the corrected real part vector Rs' and the corrected imaginary part vector Is' are calculated using the following formula: Rs i '=As i 'sin(Ps i ),Is i '=As i 'cos(Ps i ) Where i is the vector element index, i = 1, ..., N, and N is the number of vector elements; Rs' i Rs' is the element at position i in the real part vector Rs' after correction; Is' is the element at position i in the imaginary part vector Is' after correction. Based on the corrected real part vector Rs' and the corrected imaginary part vector Is', an inverse Fourier transform is performed to obtain the real part vector Rs” as the effective signal after spectral correction on the sampling channel.

7. A correction device for the frequency response of distributed optical fiber sensing data, characterized in that, include: The acquisition module is used to acquire the background noise and effective signal of distributed optical fiber sensing data. A spectrum correction function extraction module is used to extract the spectrum correction function from the background noise; A spectrum correction module is used to perform spectrum correction on the effective signal using the spectrum correction function to obtain a spectrum-corrected effective signal. The output module is used to output the effective signal after spectrum correction.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the correction method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the correction method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the correction method according to any one of claims 1 to 6.

Images