Forward modeling method for wavefield response characteristics of distributed acoustic fiber optic sensing

Abstract

The present invention discloses a forward modeling method for distributed acoustic fiber optic sensing wavefield response characteristics. The forward modeling method includes: establishing a particle velocity response formula, establishing a fiber optic sensing wavefield response formula based on the particle velocity response formula, obtaining a seismic wavefield response value, calculating the azimuth angle in the particle velocity response formula based on the seismic wavefield response value, substituting the azimuth angle into the fiber optic sensing wavefield response formula, and obtaining a fiber optic sensing wavefield response value. The present invention, starting from the perspective of numerical simulation research on the optical fiber's own receiving elastic wave characteristic response, can well fit laboratory data and provide technical guidance for subsequent observation system design and data processing. Through in-depth research on the analysis of the distributed fiber optic acoustic sensor's receiving elastic wave field response characteristics and applicable observation deployment methods, a certain reference basis is provided for active source underground space detection.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical exploration seismic data processing, and more specifically, relates to a forward simulation method for distributed acoustic fiber optic sensing wave field response characteristics. Background Art

[0002] Distributed acoustic fiber sensing (DAS) systems are a popular new type of sensing device that can receive and record elastic wave signals reflecting underground strata. They offer advantages such as wide bandwidth, high sensitivity, resistance to high temperatures and pressures, immunity to electromagnetic interference, and a wide dynamic range. Therefore, fiber optic sensors can overcome the challenges faced by traditional seismic detectors. Fiber optic sensors are stable even under varying geological conditions; signals received at the same location are highly repeatable; and short optical pulses enable high-spatial-resolution monitoring. A single receiver can quickly acquire large amounts of data, providing rich information. Simple stacking and processing can provide more information about the current underground geology, enabling real-time, dynamic monitoring and positioning. Distributed fiber optic acoustic sensing, currently used to sense ground motion, has been applied to geophysical research.

[0003] While the received data from distributed optical fibers is relatively consistent with those from seismic geophones, some complex responses are also present, such as sensitivity only to axial strain. The complexity of these responses is not only related to the demodulation mode of the optical fiber interrogator and the type of optical fiber, but is also primarily due to the unique characteristics of the optical fiber's elastic wavefield response. Existing research rarely involves forward modeling experiments on optical fibers. Most studies have instead focused on actual fiber testing to investigate response parameters, which are significantly affected by the instrument itself.

[0004] Therefore, we hope to invent a forward simulation method for the wave field response characteristics of distributed acoustic fiber optic sensing to provide technical support for the study of the characteristics of received elastic wave data. Summary of the Invention

[0005] The purpose of the present invention is to provide a forward simulation method for the wave field response characteristics of distributed acoustic fiber sensing, which provides technical support for the research on the characteristics of received elastic wave data.

[0006] To achieve the above objectives, the present invention provides a forward modeling method for distributed acoustic fiber sensing wavefield response characteristics, comprising:

[0007] Establish a particle velocity response model;

[0008] Based on the particle velocity response model, an initial optical fiber sensing wave field response model is established;

[0009] Acquiring a seismic wavefield response value, and calculating an azimuth in the particle velocity response model based on the seismic wavefield response value;

[0010] The azimuth angle is substituted into the initial optical fiber sensing wavefield response model to obtain a final optical fiber sensing wavefield response model.

[0011] Optionally, the particle velocity response model includes a surface wave particle velocity response model and a body wave particle velocity response model;

[0012] The body wave particle velocity response model includes a P wave particle velocity response model, an SV wave particle velocity response model and an SH wave particle velocity response model.

[0013] Optionally, the surface wave particle velocity response model is:

[0014]

[0015] The P-wave particle velocity response model is:

[0016]

[0017] The SV wave particle velocity response model is:

[0018]

[0019] The SH wave particle velocity response model is:

[0020]

[0021] in, is the particle velocity, is the angle between the wavefront of the surface wave and the horizontal direction, θ is the angle between the receiving line and the horizontal direction, A and B are constants, A / B represents the ellipticity of the surface wave, O RL is the surface wave oscillation coefficient, O PS is the body wave oscillation coefficient, c is the phase velocity, k is the wave number, g is the measurement length, α and β are the irrotational field and rotatory field of the elastic wave equation respectively, β1 is the group velocity, x, y and z are the horizontal center coordinates of the sensor, A1 is the amplitude parameter, is the angle between the wavefront of the body wave and the horizontal direction, is the first azimuth angle of the body wavefront relative to the vertical direction.

[0022] Optionally, establishing an initial optical fiber sensing wavefield response model based on the particle velocity response model includes:

[0023] Based on the surface wave particle velocity response model, an initial optical fiber sensing wave field response model of the surface wave is established;

[0024] Based on the P-wave particle velocity response model, an initial optical fiber sensing wave field response model of the P-wave is established;

[0025] Based on the SV wave particle velocity response model, an initial optical fiber sensing wave field response model of the SV wave is established;

[0026] Based on the SH wave particle velocity response model, an initial fiber optic sensing wave field response model of SH is established.

[0027] Optionally, the initial optical fiber sensing wave field response model of the surface wave is:

[0028]

[0029] The initial optical fiber sensing wave field response model of the P wave is:

[0030]

[0031] The initial fiber optic sensing wave field response model of the SV wave is:

[0032]

[0033] The initial optical fiber sensing wave field response model of the SH wave is:

[0034]

[0035] in, Represents the wavefield response.

[0036] Optionally, obtaining a seismic wavefield response value includes:

[0037] Establishing an observing system;

[0038] Determine the source wavelet function;

[0039] Based on the observation system and the source wavelet function, the elastic wave equation is used to simulate and synthesize the seismic wave field response value.

[0040] Optionally, the source wavelet function is a Ricker wavelet function, and the expression of the Ricker wavelet function is: Among them, f m is the dominant frequency of the Ricker wavelet, and t represents time.

[0041] Optionally, the azimuth angle includes a second azimuth angle of the surface wave propagating relative to the survey line. The third azimuth of the body wave propagating relative to the survey line and a first azimuth angle of the wavefront of the body wave relative to the vertical direction

[0042] Calculating the azimuth angle in the particle velocity response model based on the seismic wavefield response value includes:

[0043] Based on the seismic wave field response value, the A / B value is obtained according to the H / V spectrum ratio method;

[0044] The second azimuth is obtained based on the surface wave particle velocity response model, the seismic wave field response value and the value of A / B.

[0045] Based on the seismic wave field response value, the third azimuth angle is determined according to the ray path.

[0046] Based on the body wave particle velocity response model, the seismic wave field response value and the third azimuth angle The value of the first azimuth is obtained

[0047] An electronic device, comprising:

[0048] a memory storing executable instructions;

[0049] A processor runs the executable instructions in the memory to implement the forward modeling method for distributed acoustic fiber sensing wavefield response characteristics.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the forward modeling method for distributed acoustic fiber optic sensing wave field response characteristics.

[0051] The beneficial effects of the present invention are:

[0052] The forward modeling method for distributed acoustic fiber optic sensing wavefield response characteristics of the present invention includes: establishing a particle velocity response model, establishing an initial fiber optic sensing wavefield response model based on the particle velocity response model, obtaining a seismic wavefield response value, calculating the azimuth angle in the particle velocity response model based on the seismic wavefield response value, substituting the azimuth angle into the initial fiber optic sensing wavefield response model, and obtaining a final fiber optic sensing wavefield response model. The present invention, starting from the perspective of numerical simulation of the optical fiber's own receiving elastic wave characteristic response, can well fit laboratory data and provide technical guidance for subsequent observation system design and data processing. Through in-depth research on the analysis of the distributed fiber optic acoustic sensor's receiving elastic wave field response characteristics and applicable observation deployment methods, a certain reference basis is provided for active source underground space detection.

[0053] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0055] Figure 1 A flow chart of a forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to an embodiment of the present invention is shown.

[0056] Figure 2 A three-dimensional stereogram of a three-dimensional viscoelastic medium model in the prior art is shown.

[0057] Figure 3 A top view of the layout of a fiber optic observation system in the prior art is shown.

[0058] Figure 4 The spatial distribution of geophone and fiber receiving locations is shown.

[0059] Figure 5a A diagram showing the relationship between the gathers of the original records of the geophone and time in a forward simulation method of the wavefield response characteristics of distributed acoustic fiber sensing according to an embodiment of the present invention is shown.

[0060] Figure 5b A diagram showing the relationship between the gathers of the original records of the geophone and time in a forward simulation method of the wavefield response characteristics of distributed acoustic fiber sensing according to an embodiment of the present invention is shown.

[0061] Figure 6a FIG2 shows the fk spectrum recorded by the geophone in a forward modeling method of wavefield response characteristics of distributed acoustic fiber sensing according to an embodiment of the present invention.

[0062] Figure 6b The figure shows the fk spectrum recorded by the DAS of a forward modeling method for wavefield response characteristics of distributed acoustic fiber sensing according to an embodiment of the present invention.

[0063] Figure 7 A distribution diagram of the signal-to-noise ratio of a geophone and a DAS receiving signal in a forward simulation method of a distributed acoustic fiber sensing wavefield response characteristic according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Instead, these embodiments are provided to make the present invention more thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0065] A forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to the present invention includes:

[0066] Establish a particle velocity response model;

[0067] Based on the particle velocity response model, the initial fiber optic sensing wave field response model is established;

[0068] Obtaining a seismic wavefield response value, and calculating an azimuth angle in a particle velocity response model based on the seismic wavefield response value;

[0069] The azimuth angle is substituted into the initial fiber optic sensing wavefield response model to obtain the final fiber optic sensing wavefield response model.

[0070] Specifically, the forward modeling method for distributed acoustic fiber optic sensing wavefield response characteristics of the present invention includes: establishing a particle velocity response model, establishing an initial fiber optic sensing wavefield response model based on the particle velocity response model, obtaining a seismic wavefield response value, calculating the azimuth angle in the particle velocity response model based on the seismic wavefield response value, substituting the azimuth angle into the initial fiber optic sensing wavefield response model, and obtaining a final fiber optic sensing wavefield response model; the present invention, starting from the perspective of numerical simulation research on the characteristic response of receiving elastic waves of the optical fiber itself, can well fit laboratory data and provide technical guidance for the design of subsequent observation systems and data processing. Through in-depth research on the analysis of the wavefield response characteristics of distributed fiber optic acoustic sensors receiving elastic waves and applicable observation deployment methods, a certain reference basis is provided for active source underground space detection.

[0071] Furthermore, a particle velocity response model is established based on the optical fiber response characteristics. First, a three-layer uniform three-dimensional viscoelastic medium model including free surface conditions is defined. The three-dimensional viscoelastic medium model is as follows: Figure 2 As shown in Figure 1, the 3D viscoelastic medium model has a size of 300m*50m*100m. The spatial grid size is 1m, the source frequency is 50Hz, and the time step is 0.1ms. The trace spacing is 1m, with a total of 300 traces. Specific parameters of the particle velocity response model are shown in Table 1.

[0072] Table 1

[0073]

[0074] In one example, the particle velocity response model includes a surface wave particle velocity response model and a body wave particle velocity response model;

[0075] The body wave particle velocity response model includes the P-wave particle velocity response model, the SV-wave particle velocity response model and the SH-wave particle velocity response model.

[0076] In one example, the surface wave particle velocity response model is:

[0077]

[0078] The P-wave particle velocity response model is:

[0079]

[0080] The SV wave particle velocity response model is:

[0081]

[0082] The SH wave particle velocity response model is:

[0083]

[0084] in, is the particle velocity, is the angle between the wavefront of the surface wave and the horizontal direction, θ is the angle between the receiving line and the horizontal direction, A and B are constants, A / B represents the ellipticity of the surface wave, O RL is the surface wave oscillation coefficient, O PS is the body wave oscillation coefficient, c is the phase velocity, k is the wave number, g is the measurement length, α and β are the irrotational field and rotatory field of the elastic wave equation respectively, β1 is the group velocity, x, y and z are the horizontal center coordinates of the sensor, A1 is the amplitude parameter, is the angle between the wavefront of the body wave and the horizontal direction, is the first azimuth angle of the body wavefront relative to the vertical direction.

[0085] Specifically, (1) a surface wave particle velocity response model is established. The specific method is as follows: Assume that the direction of surface wave propagation is The sensor is horizontally arranged with the center coordinates (x, y, z), and the direction of the received signal is (cosθ, sinθ, 0). is the angle between the surface wave front and the horizontal direction, and θ is the angle between the receiving line and the horizontal direction. According to the theory of Eileen R. (2018):

[0086] The propagation direction of Rayleigh surface waves is The displacement of the particle is

[0087]

[0088] From this, the vibration speed of the point particle is deduced to be

[0089]

[0090] Then the particle velocity response of the geophone in the receiving direction of (cosθ, sinθ, 0) is:

[0091]

[0092] (2) Establish a P-wave particle velocity response model. The specific method is as follows: Assume that the direction of wave propagation The DAS is arranged horizontally (x, y, z), and the direction of the received signal is (cosθ, sinθ, 0). (1) The propagation direction of the P wave is The displacement of the particle is

[0093]

[0094] From this, the vibration speed of the point particle is deduced to be

[0095]

[0096] Then the particle velocity response of the geophone in the receiving direction of (cosθ, sinθ, 0) is:

[0097]

[0098] (3) Establish the SV wave particle velocity response model. The specific method is as follows: (2) The propagation direction of the SV wave is Then the position of the particle is

[0099]

[0100] From this, the vibration speed of the point particle is deduced to be

[0101]

[0102] Then the particle velocity response of the geophone in the receiving direction of (cosθ, sinθ, 0) is:

[0103]

[0104] (4) Establish the SH wave particle velocity response model. The specific method is as follows: (3) The propagation direction of the SH wave is Then the position of the particle is

[0105]

[0106] From this, we can infer that the vibration speed of the point particle is

[0107]

[0108] Then the particle velocity response of the geophone in the receiving direction of (cosθ, sinθ, 0) is:

[0109]

[0110] In one example, establishing an initial fiber optic sensing wave field response model based on a particle velocity response model includes:

[0111] Based on the surface wave particle velocity response model, the initial fiber optic sensing wave field response model of the surface wave is established;

[0112] Based on the P-wave particle velocity response model, the initial fiber optic sensing wave field response model of P-wave is established;

[0113] Based on the SV wave particle velocity response model, the initial fiber optic sensing wave field response model of SV waves is established;

[0114] Based on the SH wave particle velocity response model, the initial fiber optic sensing wave field response model of SH is established.

[0115] In one example, the initial fiber-optic sensing wavefield response model for surface waves is:

[0116]

[0117] The initial fiber optic sensing wave field response model of P wave is:

[0118]

[0119] The initial fiber optic sensing wavefield response model of SV waves is:

[0120]

[0121] The initial fiber optic sensing wave field response model of SH wave is:

[0122]

[0123] in, Represents the wavefield response.

[0124] Specifically, (1) based on the surface wave particle velocity response model, the initial fiber optic sensing wave field response model of the surface wave is established. The surface wave particle velocity response model is rotated through the Cartesian coordinate system to convert the surface wave particle velocity into the spatial strain rate. The initial fiber optic sensing wave field response model of the surface wave can be obtained as follows:

[0125]

[0126] (2) Based on the P-wave particle velocity response model, the initial fiber optic sensing wave field response model of the P-wave is established. The P-wave particle velocity response model is rotated through the Cartesian coordinate system to convert the P-wave particle velocity into the spatial strain rate. The initial fiber optic sensing wave field response model of the P-wave can be obtained as shown below:

[0127]

[0128] (3) Based on the SV wave particle velocity response model, the initial fiber optic sensing wave field response model of the SV wave is established. The SV wave particle velocity response model is rotated through the Cartesian coordinate system to convert the SV wave particle velocity into the spatial strain rate. The initial fiber optic sensing wave field response model of the SV wave can be obtained as shown below:

[0129]

[0130] (4) Based on the SH wave particle velocity response model, the initial fiber optic sensing wave field response model of the SH wave is established. The SH wave particle velocity response model is rotated through the Cartesian coordinate system to convert the SH wave particle velocity into the spatial strain rate. The initial fiber optic sensing wave field response model of the SH wave can be obtained as shown below:

[0131]

[0132] In one example, obtaining a seismic wavefield response value includes:

[0133] Establishing an observing system;

[0134] Determine the source wavelet function;

[0135] Based on the observation system and source wavelet function, the elastic wave equation is used to simulate the synthetic seismic wavefield response numerically.

[0136] Specifically, obtaining the seismic wavefield response value includes establishing an observation system, Figure 3 A top view of the fiber optic observation system layout. Figure 4 is the spatial distribution diagram of the seismic detector and optical fiber receiving position, such as Figure 3 As shown in Figure 2, five optical fibers are laid out in a serpentine pattern at equal intervals (10 m) on the surface (on the XOY plane), and the seismic line and fiber 3 are placed at the same position, as shown in Figure 2. Figure 4 As shown, the distance between the geophone and the optical fiber receiving channel is 1 meter. The entire observation system parameters are set to meet the stability conditions for elastic wave numerical simulation. Finite-difference PML absorbing boundary conditions are used for grid data processing. Numerical simulations are performed using a staggered grid high-order finite difference method with second-order time difference accuracy and twelfth-order spatial difference accuracy.

[0137] Furthermore, based on previous data collection and analysis experiments, the wavelet dominant frequency was set to 50 Hz. In the numerical simulations presented here, a P-wave source was primarily used. This is achieved by applying a source to the model's normal stress component. For example, explosive source excitation can be considered a P-wave source. This simulates the radial force generated at each point within the grid as a source, with the force directed from that point toward the surrounding underground medium. Consequently, only P-wave components are generated.

[0138] In one example, the source wavelet function is the Ricker wavelet function, and the expression of the Ricker wavelet function is Among them, f m is the dominant frequency of the Ricker wavelet, and t represents time.

[0139] In one example, the azimuth angle includes a second azimuth angle of the surface wave propagating relative to the survey line. The third azimuth of the body wave propagating relative to the survey line The first azimuth angle of the body wave front relative to the vertical direction

[0140] The azimuth angles in the particle velocity response model based on the numerical calculation of seismic wave field response include:

[0141] Based on the seismic wavefield response values, the A / B value is obtained according to the H / V spectrum ratio method;

[0142] Based on the surface wave particle velocity response model, the seismic wave field response value and the value of A / B, the second azimuth is obtained

[0143] Based on the seismic wavefield response value, the third position angle is determined according to the ray path

[0144] Based on the body wave particle velocity response model, seismic wave field response value and third-party azimuth The value of the first azimuth is obtained

[0145] An electronic device, comprising:

[0146] a memory storing executable instructions;

[0147] The processor runs the executable instructions in the memory to implement the forward simulation method of the distributed acoustic fiber optic sensing wave field response characteristics.

[0148] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the forward modeling method for distributed acoustic fiber optic sensing wave field response characteristics.

[0149] Example 1

[0150] Figure 1 A flow chart of a forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to an embodiment of the present invention is shown.

[0151] like Figure 1 As shown, the forward modeling method of the distributed acoustic fiber optic sensing wave field response characteristics includes:

[0152] Step 1: Establish a particle velocity response model;

[0153] Among them, the particle velocity response model includes the surface wave particle velocity response model and the body wave particle velocity response model;

[0154] The body wave particle velocity response model includes the P-wave particle velocity response model, the SV-wave particle velocity response model and the SH-wave particle velocity response model.

[0155] Step 2: Based on the particle velocity response model, establish the initial fiber optic sensing wave field response model;

[0156] Among them, based on the particle velocity response model, the initial fiber optic sensing wave field response model is established, including:

[0157] Based on the surface wave particle velocity response model, the initial fiber optic sensing wave field response model of the surface wave is established;

[0158] Based on the P-wave particle velocity response model, the initial fiber optic sensing wave field response model of P-wave is established;

[0159] Based on the SV wave particle velocity response model, the initial fiber optic sensing wave field response model of SV waves is established;

[0160] Based on the SH wave particle velocity response model, the initial fiber optic sensing wave field response model of SH is established.

[0161] Step 3: Obtain the seismic wave field response value, and calculate the azimuth angle in the particle velocity response model based on the seismic wave field response value;

[0162] Among them, obtaining the seismic wave field response value includes:

[0163] Establishing an observing system;

[0164] Determine the source wavelet function;

[0165] Based on the observation system and source wavelet function, the elastic wave equation is used to simulate the synthetic seismic wavefield response numerically.

[0166] The azimuth angle includes the second azimuth angle of the surface wave propagating relative to the survey line The third azimuth of the body wave propagating relative to the survey line The first azimuth angle of the body wave front relative to the vertical direction

[0167] The azimuth angles in the particle velocity response model based on the numerical calculation of seismic wave field response include:

[0168] Based on the seismic wavefield response values, the A / B value is obtained according to the H / V spectrum ratio method;

[0169] Based on the surface wave particle velocity response model, the seismic wave field response value and the value of A / B, the second azimuth is obtained

[0170] Based on the seismic wavefield response value, the third position angle is determined according to the ray path

[0171] Based on the body wave particle velocity response model, seismic wave field response value and third-party azimuth The value of the first azimuth is obtained

[0172] Specifically, after obtaining the seismic wave field response value, the method further includes:

[0173] Based on the seismic wave field response value, the apparent velocity V1 is obtained using the shallow velocity minimum principle;

[0174] Based on the apparent velocity V1, using the formula The travel time of the wave is obtained, where S is the shortest path of the observation system.

[0175] Step 4: Substitute the azimuth angle into the initial fiber optic sensing wavefield response model to obtain the final fiber optic sensing wavefield response model.

[0176] Specifically, Figure 5(a) is the original record of the geophone, Figure 5(b) is the original single-shot record of the DAS, Figure 6(a) is the fk spectrum recorded by the geophone, and Figure 6(b) is the fk spectrum recorded by the DAS. Figure 7is the signal-to-noise ratio of the received signals of the geophone and DAS. As shown in Figure 5(a) and Figure 5(b), the three lines received by source2-fiber1, 3 and 5 survey lines (sorted from left to right) can be used to obtain different offset distances. The amplitude strength of the fiber received signal at different azimuths is quite different. As the offset distance increases, the direct wave received by the geophone only has a delay phenomenon, while the direct wave received by the fiber, in addition to the delay phenomenon, has weak energy at the closest offset distance (black frame), that is, the fiber response gradually weakens, and it can be found that near the sampling point closest to the offset distance on the survey line, whether it is the direct wave or the lower interface reflection wave, the energy of the P wave and the S wave weakens with the increase of the offset distance. As shown in Figure 6(a) and Figure 6(b), the DAS obtained by forward simulation has the same response bandwidth as the geophone, but the overall amplitude is weak, and the fk spectrum energy needs to be improved. Figure 7 As shown, the signal-to-noise ratio of the DAS response is approximately 0.85 of that of the geophone. Therefore, the results of the present invention's method are lower than the signal-to-noise ratio of elastic wave reception by optical fibers, as previously reported in actual production testing experiments using distributed acoustic fiber sensors (DASs). This is consistent with the response characteristics of the received signal, such as its unidirectional nature and strong dependence on spatial reception angles.

[0177] Example 2

[0178] The present disclosure provides an electronic device including: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned linear noise attenuation method.

[0179] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0180] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0181] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory.

[0182] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0183] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0184] Example 3

[0185] The present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for attenuating linear noise is implemented.

[0186] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the embodiments of the present disclosure.

[0187] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0188] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A forward modeling method for wavefield response characteristics of distributed acoustic fiber sensing, characterized in that: include: Establish the particle velocity response formula; Based on the particle velocity response formula, a fiber optic sensing wave field response formula is established; Acquiring a seismic wavefield response value, and calculating the azimuth angle in the particle velocity response formula based on the seismic wavefield response value; Substituting the azimuth angle into the optical fiber sensing wave field response formula to obtain an optical fiber sensing wave field response value; Wherein, the particle velocity response formula includes a surface wave particle velocity response formula and a body wave particle velocity response formula; The body wave particle velocity response formula includes a P wave particle velocity response formula, an SV wave particle velocity response formula and an SH wave particle velocity response formula; The surface wave particle velocity response formula is: The P-wave particle velocity response formula is: The SV wave particle velocity response formula is: The SH wave particle velocity response formula is: in, represents the seismic wavefield response, is the angle between the wavefront of the surface wave and the horizontal direction, θ is the angle between the receiving line and the horizontal direction, A and B are constants, A / B represents the ellipticity of the surface wave, O RL is the surface wave oscillation coefficient, O PS is the body wave oscillation coefficient, c is the phase velocity, k is the wave number, g is the measurement length, α and β are the irrotational field and rotatory field of the elastic wave equation respectively, β1 is the group velocity, x, y and z are the horizontal center coordinates of the sensor, A1 is the amplitude parameter, is the angle between the wavefront of the body wave and the horizontal direction, is the first azimuth angle of the body wave front relative to the vertical direction; The optical fiber sensing wave field response formula of the surface wave is: The optical fiber sensing wave field response formula of the P wave is: The fiber optic sensing wave field response formula of the SV wave is: The optical fiber sensing wave field response formula of the SH wave is: in, represents the wavefield response; The azimuth angle includes the second azimuth angle of the surface wave propagating relative to the survey line. The third azimuth of the body wave propagating relative to the survey line and a first azimuth angle of the wavefront of the body wave relative to the vertical direction The azimuth angle in the particle velocity response formula calculated based on the seismic wave field response value includes: Based on the seismic wave field response value, the A / B value is obtained according to the H / V spectrum ratio method; Based on the surface wave particle velocity response formula, the seismic wave field response value and the value of A / B, the second azimuth angle is obtained. Based on the seismic wave field response value, the third azimuth angle is determined according to the ray path. Based on the body wave particle velocity response formula, the seismic wave field response value and the third azimuth angle The value of the first azimuth is obtained 2. The forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to claim 1 is characterized in that: The obtaining of seismic wave field response values ​​includes: Establishing an observing system; Determine the source wavelet function; Based on the observation system and the source wavelet function, the elastic wave equation is used to simulate and synthesize the seismic wave field response value.

3. The forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to claim 2, characterized in that: The source wavelet function is the Ricker wavelet function, and the expression of the Ricker wavelet function is: Among them, f m is the dominant frequency of the Ricker wavelet, and t represents time.

4. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor is configured to execute the executable instructions in the memory to implement the forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the forward modeling method for distributed acoustic fiber sensing wavefield response characteristics according to any one of claims 1 to 3.

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