A DAS data adaptive pitch reconstruction and multi-resolution imaging method

Abstract

The application provides a DAS data adaptive gauge length reconstruction and multi-resolution imaging method, and belongs to the technical field of optical fiber sensing and signal processing. The method comprises the following steps: step S1: a seismic source vehicle is used to generate seismic waves in an active source mode; step S2: a DAS system is used to complete VSP data acquisition at a specified gauge length, and original strain rate data with a high spatial sampling rate are obtained; step S3: the original strain rate data are preprocessed, and preprocessed strain rate data are obtained; the preprocessing comprises normalization and denoising; step S4: the preprocessed strain rate data are subjected to continuous wave field reconstruction, and a continuous wave field function is obtained; and step S5: a gauge length is adaptively generated, multi-resolution data fusion and imaging are performed. Only data collected at a minimum gauge length is relied on, and optical fibers or demodulation hardware do not need to be changed, so that the adaptability of an existing DAS-VSP system can be directly improved, and the method can be applied to other distributed optical fiber sensing scenes, and higher observation quality and better engineering usability are achieved.

Description

Technical Field

[0001] This invention belongs to the field of fiber optic sensing and signal processing technology, specifically to distributed fiber acoustic sensing (DAS) and its data processing and reconstruction methods in vertical seismic profile (VSP) observation, and particularly relates to a method for adaptive gauge length reconstruction and multi-resolution imaging of DAS data. Background Technology

[0002] Distributed fiber optic acoustic sensing (DAS) technology is widely used in vertical seismic profile (VSP) observation, oil and gas wellbore imaging, underground structure monitoring, and other geophysical exploration missions. A DAS system obtains the axial strain along the fiber direction by demodulating the phase difference between adjacent scattering segments on the fiber; therefore, the gauge length becomes a crucial parameter for spatial sampling characteristics and waveform response. The gauge length determines the system's integral length of the strain along the fiber, directly impacting spatial resolution, signal-to-noise ratio (SNR), and sensitivity to seismic waves of different wavelengths.

[0003] Existing research and practical engineering studies show that when the DAS gauge length is too large, strain along the gauge length is averaged, potentially causing waveform flattening, attenuation of reflected energy, and loss of high-frequency components, especially near the wellhead or in areas with rapid formation changes, leading to reduced spatial resolution. Conversely, when the gauge length is too small, the number of scattering points decreases, resulting in relatively increased noise and a lower SNR, making it difficult to reliably identify weak signals. Furthermore, comparisons with traditional seismic detector records and cross-product cross-correlation analysis show that the optimal gauge length is affected by factors such as formation velocity, wavelength, source type, and well location. Simultaneously, the DAS tap length should be close to one-third of the dominant seismic wavelength to achieve a higher SNR; however, if the tap length exceeds a significant proportion of the wavelength, the waveform will be severely smoothed. Existing methods cannot continuously adjust the gauge length based on well location changes within the same downhole record, nor can they flexibly reconstruct the gauge length during data processing. For VSP observations with significant formation velocity variations, complex well structures, or the need to simultaneously identify high-frequency and low-frequency events, a fixed gauge length often fails to balance spatial resolution and signal-to-noise ratio.

[0004] Current DAS-VSP technology generally uses a fixed gauge length for data acquisition. However, the optimal gauge length depends on formation wave velocity, dominant frequency, wellbore location, and noise conditions. Different well sections often require different gauge lengths to obtain the best reflection imaging quality and waveform reliability. Therefore, how to overcome the limitations of fixed gauge length acquisition, achieve flexible gauge length adjustment, customize its variation along the wellbore location, and obtain multi-resolution DAS data corresponding to different gauge lengths during data processing has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a method for adaptive gauge length reconstruction and multi-resolution imaging of DAS data. This method can generate multi-resolution DAS data reconstruction at any location with arbitrary gauge length, dynamically providing the optimal resolution, enhancing the engineering adaptability and observation efficiency of the DAS system, and solving the technical problem in the prior art where the fixed gauge length in DAS-VSP results in the inability to simultaneously achieve spatial resolution and signal-to-noise ratio.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0007] A method for adaptive gauge length reconstruction and multi-resolution imaging of DAS data, the method comprising the following steps:

[0008] Step S1: The seismic source vehicle generates seismic waves using an active source method;

[0009] Step S2: The DAS system uses the specified gauge length Complete VSP data acquisition to obtain raw strain rate data with high spatial sampling rate;

[0010] Step S3: Preprocess the raw strain rate data to obtain preprocessed strain rate data; preprocessing includes normalization and noise reduction;

[0011] Step S4: Reconstruct the continuous wave field from the preprocessed strain rate data to obtain the continuous wave field function;

[0012] Step S5: Adaptively generate gauge length and perform multi-resolution data fusion and imaging.

[0013] Furthermore, the original strain rate data in step S2 is expressed as follows: ,in, Indicates the location of the sensor channel. It is a time series; the first The raw strain rate data for each sensing channel are , representing the Location of each sensor channel The original strain rate data at the location; among which, Indicates the first The location of each sensor channel.

[0014] Further, step S3 includes the following steps:

[0015] Step S31: Normalize the raw strain rate data to compress it into a range. The normalized strain rate data were obtained.

[0016] No. Location of each sensor channel The formula for normalizing the original strain rate data at the location is as follows:

[0017]

[0018] in, The normalized strain rate data represents the first strain rate. Location of each sensor channel Normalized strain rate data; This indicates taking the minimum value. This indicates taking the maximum value;

[0019] Step S32: Perform frequency domain bandpass filtering on the normalized strain rate data to suppress low-frequency and high-frequency noise that will be severely amplified during the integration process, and obtain the preprocessed strain rate data.

[0020] No. Location of each sensor channel Normalized strain rate data The formula for performing frequency domain bandpass filtering is as follows:

[0021]

[0022] in, The frequency spectrum of the strain rate data represents the first... Location of each sensor channel Frequency spectrum of strain rate data at the location; Represents the Fourier transform function; Indicates frequency; Represents the imaginary unit; The data after bandpass filtering in the frequency domain represents the first... Location of each sensor channel Data after bandpass filtering in the frequency domain; This represents the response function of a bandpass filter; This represents the filtered data, i.e., the preprocessed strain rate data, representing the... Location of each sensor channel Preprocessed strain rate data at the location;

[0023] The raw strain rate data from all sensing channels are preprocessed to obtain preprocessed strain rate data. .

[0024] Further, step S4 includes the following steps:

[0025] Step S41: Perform an integral transform on the preprocessed strain rate data to obtain the particle wave function:

[0026]

[0027] in, Let f be the wave function of a particle, representing the wave function of the first particle. Location of each sensor channel The wave function of a particle at a given location; This represents a proportionality coefficient related to formation velocity. The initial depth of the integration;

[0028] Step S42: Using a spatial interpolation algorithm, perform continuous wave field recovery on the particle wave function to obtain the continuous wave field function:

[0029]

[0030] in, Represents the continuous wave field function; This represents the spatial interpolation operator.

[0031] Further, step S5 includes the following steps:

[0032] Step S51: For deep regions, use high signal-to-noise ratio data with large gauge length to generate virtual trace data, form a stratigraphic structure image, and achieve high signal-to-noise ratio imaging;

[0033] Step S52: For shallow areas, high-resolution data with small gauge length is used to generate high-resolution virtual trace data, forming a stratigraphic structure image to achieve high-resolution imaging;

[0034] Step S53: In the transition region, imaging is performed using data of different gauge length resolutions to generate virtual track data of different gauge length resolutions, thereby achieving multi-resolution imaging and forming the final adaptive fusion data.

[0035] Further, step S51 includes the following steps:

[0036] Step S511: Based on the imaging target requirements, determine the target position of the virtual track for the deep region, and generate a gauge length array using a large gauge length;

[0037] Step S512: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths in the gauge length array;

[0038] The virtual channel data is calculated as follows:

[0039]

[0040] in, Represents virtual channel data; For the first The position of each sensor channel indicates the target position of the virtual channel; This is the proportionality coefficient; Represents the first element in the gauge array. One virtual gauge length; Indicates moving to the right Continuous wave field function; Indicates moving to the left Continuous wave field function;

[0041] Step S513: Calculate the signal-to-noise ratio of all virtual trace data, adaptively select the optimal gauge length based on the signal-to-noise ratio, take the virtual gauge length corresponding to the maximum signal-to-noise ratio as the optimal gauge length, and the virtual trace data corresponding to the maximum signal-to-noise ratio is the stratigraphic structure image;

[0042] The formula for calculating the signal-to-noise ratio of virtual channel data is:

[0043]

[0044] in, Indicates the noise window. Indicates the signal window; Represents virtual channel data Noise segment in the middle; Represents virtual channel data The signal segment in; This is the window length.

[0045] Further, step S52 includes the following steps:

[0046] Step S521: Based on the imaging target requirements, determine the target position of the virtual track in the shallow region, and generate a gauge array using a small gauge length;

[0047] Step S522: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths in the gauge length array;

[0048] Step S523: Calculate the signal-to-noise ratio (SNR) of all virtual trace data, adaptively select the optimal gauge length based on the SNR, and take the virtual gauge length corresponding to the maximum SNR as the optimal gauge length. The virtual trace data corresponding to the maximum SNR is the stratigraphic structure image.

[0049] Further, step S53 includes the following steps:

[0050] Step S531: Based on the imaging target requirements, determine the target position of the virtual track for the transition area, divide the strata of the transition area into 4 layers on average, and generate a gauge array for each layer using the virtual gauge length between the large and small gauge lengths.

[0051] Step S532: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths within each gauge length array;

[0052] Step S533: Calculate the signal-to-noise ratio of all virtual trace data, adaptively select the optimal gauge length based on the signal-to-noise ratio, take the virtual gauge length corresponding to the maximum signal-to-noise ratio of each layer as the optimal gauge length of that layer, and the virtual trace data corresponding to the maximum signal-to-noise ratio of each layer is the stratigraphic structure image of that layer.

[0053] Compared with the prior art, the present invention has the following beneficial technical effects:

[0054] 1) This invention requires only a single high-density data acquisition to generate data with an infinite number of gauge length configurations through reconstruction, greatly improving field operation efficiency and equipment adaptability. It ensures that the generated virtual data strictly conforms to the physical principles of DAS sensing, avoiding signal distortion caused by simple mathematical averaging. The generated fused data volume combines the advantages of strong deep signals and rich shallow details, resulting in simultaneous improvement in both longitudinal resolution and lateral continuity of the final imaging profile, particularly beneficial for identifying complex structures and thin reservoirs. Gauge length selection can be dynamically adjusted based on the characteristics of the data itself or geological targets, achieving adaptability to geological problems.

[0055] 2) This invention can significantly reduce the equivalent gauge length in high-resolution areas such as the wellhead, achieving spatial resolution several times greater than traditional systems. At deeper wells, an even higher signal-to-noise ratio can be obtained by increasing the gauge length. Through continuous adjustment of the gauge length, this invention suppresses waveform flattening caused by a fixed gauge length while preserving high-frequency details, making reflection recognition and wavefield acquisition more stable and reliable. Since the algorithm only relies on data acquired at the minimum gauge length, it does not require changes to the optical fiber or demodulation hardware. Therefore, it can directly improve the adaptability of existing DAS-VSP systems and can be migrated to other distributed fiber optic sensing scenarios, achieving higher observation quality and better engineering usability. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the DAS data acquisition structure of an exemplary embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of the steps of the DAS data adaptive gauge length reconstruction and multi-resolution imaging method of the present invention.

[0059] Figure 3 This is a schematic diagram of the original gauge length of an exemplary embodiment of the present invention.

[0060] Figure 4This is a schematic diagram of adaptive gauge reconstruction and multi-resolution imaging, an exemplary embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] This invention proposes an adaptive gauge length reconstruction and multi-resolution imaging method for DAS data. The original DAS data with a fixed gauge length is regarded as a discrete sample of a continuous wave field. By establishing a physical conversion model between DAS strain rate and particle vibration velocity, and using adaptive gauge length reconstruction technology, DAS data with any desired gauge length can be virtually synthesized at any spatial location. This allows for dynamic adjustment of the DAS trace spacing for different geological conditions, thereby achieving multi-resolution imaging.

[0063] A schematic diagram of the DAS data acquisition system is shown below. Figure 1 As shown, the DAS data acquisition system includes a DAS (Distributed Acoustic Sensing) system, a seismic source vehicle, and optical fibers laid along the cylinder wall. The seismic source vehicle generates seismic waves using an active source method. The DAS system is connected to the optical fiber, and the seismic waves cause strain in the optical fiber, resulting in corresponding changes in the optical signal. The DAS system detects the seismic wave signal by demodulating the changes in the optical signal.

[0064] The DAS data adaptive gauge reconstruction and multi-resolution imaging method proposed in this invention, such as Figure 2 As shown, the method includes the following steps:

[0065] Step S1: The seismic source vehicle generates seismic waves by using an active source method.

[0066] Step S2: The DAS system uses the specified gauge length Complete VSP (Vertical Seismic Profile) data acquisition to obtain raw strain rate data with a high spatial sampling rate, such as... Figure 3 As shown.

[0067] Furthermore, the original strain rate data is DAS-VSP data, also known as DAS data, specifying the gauge length. The gauge length ranges from 0.5m to 1m.

[0068] The original strain rate data are expressed as ,in, Indicates the location of the sensor channel. This is a time series. (The...) The raw strain rate data for each sensing channel are , representing the Location of each sensor channel The original strain rate data at the location; among which, Indicates the first The location of each sensor channel.

[0069] Step S3: Preprocess the raw strain rate data to obtain preprocessed strain rate data. Preprocessing includes normalization and noise reduction.

[0070] Step S31: Normalize the raw strain rate data to compress it into a range. The normalized strain rate data were obtained.

[0071] No. Location of each sensor channel The formula for normalizing the original strain rate data at the location is as follows:

[0072]

[0073] in, The normalized strain rate data represents the first strain rate. Location of each sensor channel Normalized strain rate data; This indicates taking the minimum value. This indicates taking the maximum value.

[0074] Step S32: Perform frequency domain bandpass filtering on the normalized strain rate data to suppress low-frequency and high-frequency noise that will be severely amplified during the integration process, and obtain the preprocessed strain rate data.

[0075] No. Location of each sensor channel Normalized strain rate data The formula for performing frequency domain bandpass filtering is as follows:

[0076]

[0077] in, The frequency spectrum of the strain rate data represents the first... Location of each sensor channel Frequency spectrum of strain rate data at the location; Represents the Fourier transform function; Indicates frequency; Represents the imaginary unit; The data after bandpass filtering in the frequency domain represents the first... Location of each sensor channel Data after bandpass filtering in the frequency domain; This represents the response function of a bandpass filter; This represents the filtered data, i.e., the preprocessed strain rate data, representing the... Location of each sensor channel The strain rate data after preprocessing.

[0078] The raw strain rate data from all sensing channels are preprocessed to obtain preprocessed strain rate data. .

[0079] Step S4: Reconstruct the continuous wave field from the preprocessed strain rate data to obtain the continuous wave field function.

[0080] Continuous wave field reconstruction includes integral transform and continuous wave field recovery.

[0081] Step S41: Perform an integral transform on the preprocessed strain rate data to obtain the particle wave function.

[0082] For a wave propagating along the fiber optic axis, the original strain rate data is approximately proportional to the spatial derivative of the particle vibration velocity. By numerically integrating the preprocessed strain rate data along the spatial direction, it is converted into a quantity related to the particle velocity, yielding the particle wave function, as follows:

[0083]

[0084] in, Let f be the wave function of a particle, representing the wave function of the first particle. Location of each sensor channel The wave function of a particle at a given location; This represents a proportionality coefficient related to formation velocity. The initial depth of the integration; This represents the preprocessed strain rate data, which uses a cumulative effect to transform noisy derivative data into more stable velocity data.

[0085] Step S42: Using a spatial interpolation algorithm, the continuous wave field is recovered from the particle wave function to obtain the continuous wave field function.

[0086] Using spatial interpolation algorithms, the particle wave function at discrete points is... Reconstruct a spatially continuous wave field function As the best estimate of the velocity wave field of a real continuous particle, it is expressed as follows:

[0087]

[0088] in, Represents the continuous wave field function; This represents the spatial interpolation operator.

[0089] For each fixed time slice Using Kirchhoff integral interpolation based on the wave equation, the discrete form of the continuous wave field function is obtained as follows:

[0090]

[0091] in, express The continuous wave field function at time t; The known number of elements involved in the interpolation; It is a time shift quantity used to correct the position of the wave. propagate to target location Time difference; These are the weighting coefficients; Indicates the location Time shift The subsequent continuous wave field function.

[0092] Step S5: Adaptively generate gauge length and perform multi-resolution data fusion and imaging.

[0093] Because DAS-VSP data with a larger gauge length provides clearer details and a higher signal-to-noise ratio, while DAS-VSP data with a smaller gauge length offers higher spatial resolution but a lower signal-to-noise ratio, the imaging process uses a smaller gauge length in shallow regions (to improve spatial resolution), a larger gauge length in deep regions (to improve signal-to-noise ratio), and a medium gauge length or multi-gauge fusion in transitional regions to achieve adaptive selection of spatial resolution along the wellbore.

[0094] Step S51: For deep regions, high signal-to-noise ratio (SNR) data with large gauge lengths are primarily used to generate virtual trace data, forming a stratigraphic structure image and achieving high SNR imaging, such as... Figure 4 As shown in A in the diagram.

[0095] Step S511: Based on the imaging target requirements, determine the target position of the virtual track for the deep region, and generate a gauge array using a large gauge length.

[0096] Specifically, the deep region has a depth range greater than 600m, and the virtual gauge length range for large gauge lengths is 15m~25m. The gauge length array is represented as follows: ; Indicates the first A virtual gauge length, Indicates the number of virtual gauge lengths.

[0097] Step S512: By simulating the DAS physical measurement process, virtual track data is generated for all virtual gauge lengths in the gauge length array.

[0098] The virtual channel data is calculated as follows:

[0099]

[0100] in, This represents virtual trace data, which is gauge length data generated based on different locations within the wellbore. For the first The position of each sensor channel indicates the target position of the virtual channel; This is a scaling factor that controls the amplitude of the virtual track data; Indicates moving to the right Continuous wave field function; Indicates moving to the left The continuous wave field function.

[0101] Step S513: Calculate the signal-to-noise ratio (SNR) of all virtual trace data, adaptively select the optimal gauge length based on the SNR, and take the virtual gauge length corresponding to the maximum SNR as the optimal gauge length. The virtual trace data corresponding to the maximum SNR is the stratigraphic structure image.

[0102] The formula for calculating the signal-to-noise ratio of virtual channel data is:

[0103]

[0104] in, Indicates the noise window. Indicates the signal window; Represents virtual channel data Noise segment in the middle; Represents virtual channel data The signal segment in; This is the window length.

[0105] Step S52: For shallow areas, high-resolution data with small gauge lengths are mainly used to generate high-resolution virtual trace data, forming a stratigraphic structure image and achieving high-resolution imaging, such as... Figure 4 As shown in B in the diagram.

[0106] Step S521: Based on the imaging target requirements, determine the target position of the virtual track in the shallow region, and generate a gauge array using a small gauge length.

[0107] Specifically, the shallow region has a depth range of 1m to 200m, and the virtual gauge length for small gauge lengths ranges from 0.5m to 6m.

[0108] Step S522: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths in the gauge length array.

[0109] Step S523: Calculate the signal-to-noise ratio (SNR) of all virtual trace data, adaptively select the optimal gauge length based on the SNR, and take the virtual gauge length corresponding to the maximum SNR as the optimal gauge length. The virtual trace data corresponding to the maximum SNR is the stratigraphic structure image.

[0110] Step S53: In the transition region, imaging is performed using data of different gauge length resolutions to generate virtual track data of different gauge length resolutions, thereby achieving multi-resolution imaging, such as... Figure 4 As shown in C, the final adaptive fused data volume is formed.

[0111] Step S531: Based on the imaging target requirements, determine the target position of the virtual track for the transition area, divide the strata of the transition area into 4 layers on average, and generate a gauge array for each layer using the virtual gauge length between the large and small gauge lengths.

[0112] Specifically, the transition zone has a depth range of 200m to 600m. The strata in the transition zone are divided into four layers on average. When the depth range is 200m to 300m, the virtual gauge length between the large and small gauge lengths is 6m to 8m; when the depth range is 300m to 400m, the virtual gauge length between the large and small gauge lengths is 8m to 10m; when the depth range is 400m to 500m, the virtual gauge length between the large and small gauge lengths is 10m to 12m; and when the depth range is 500m to 600m, the virtual gauge length between the large and small gauge lengths is 12m to 15m.

[0113] Step S532: By simulating the DAS physical measurement process, virtual track data is generated for all virtual gauge lengths in each gauge length array.

[0114] Step S533: Calculate the signal-to-noise ratio of all virtual trace data, adaptively select the optimal gauge length based on the signal-to-noise ratio, take the virtual gauge length corresponding to the maximum signal-to-noise ratio of each layer as the optimal gauge length of that layer, and the virtual trace data corresponding to the maximum signal-to-noise ratio of each layer is the stratigraphic structure image of that layer.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive gauge length reconstruction and multi-resolution imaging of DAS data, characterized in that, The method includes the following steps: Step S1: The seismic source vehicle generates seismic waves using an active source method; Step S2: The DAS system uses the specified gauge length Complete VSP data acquisition to obtain raw strain rate data with high spatial sampling rate; DAS refers to Distributed Fiber Acoustic Sensing, and VSP refers to Vertical Seismic Profile. Step S3: Preprocess the raw strain rate data to obtain preprocessed strain rate data; preprocessing includes normalization and noise reduction; Step S4: Reconstruct the continuous wave field from the preprocessed strain rate data to obtain the continuous wave field function; Step S5: Adaptively generate gauge length, perform multi-resolution data fusion and imaging; Step S5 includes the following steps: Step S51: For deep regions, use high signal-to-noise ratio data with large gauge length to generate virtual trace data, form a stratigraphic structure image, and achieve high signal-to-noise ratio imaging; Step S52: For shallow areas, high-resolution data with small gauge length is used to generate high-resolution virtual trace data, forming a stratigraphic structure image to achieve high-resolution imaging; Step S53: In the transition region, imaging is performed using data of different gauge length resolutions to generate virtual track data of different gauge length resolutions, thereby achieving multi-resolution imaging and forming the final adaptive fusion data. Step S51 includes the following steps: Step S511: Based on the imaging target requirements, determine the target position of the virtual track for the deep region, and generate a gauge length array using a large gauge length; Step S512: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths in the gauge length array; The virtual channel data is calculated as follows: in, Represents virtual channel data; For the first The position of each sensor channel indicates the target position of the virtual channel; This is the proportionality coefficient; Represents the first element in the gauge array. One virtual gauge length; Indicates moving to the right Continuous wave field function; Indicates moving to the left Continuous wave field function; Step S513: Calculate the signal-to-noise ratio of all virtual trace data, adaptively select the optimal gauge length based on the signal-to-noise ratio, take the virtual gauge length corresponding to the maximum signal-to-noise ratio as the optimal gauge length, and the virtual trace data corresponding to the maximum signal-to-noise ratio is the stratigraphic structure image; The formula for calculating the signal-to-noise ratio of virtual channel data is: in, Indicates the noise window. Indicates the signal window; Represents virtual channel data Noise segment in the middle; Represents virtual channel data The signal segment in; This is the window length.

2. The DAS data adaptive gauge reconstruction and multi-resolution imaging method according to claim 1, characterized in that, The original strain rate data in step S2 is expressed as follows: ,in, Indicates the location of the sensor channel. It is a time series; the first The raw strain rate data for each sensing channel are , representing the Location of each sensor channel The original strain rate data at the location; among which, Indicates the first The location of each sensor channel.

3. The DAS data adaptive gauge reconstruction and multi-resolution imaging method according to claim 2, characterized in that, Step S3 includes the following steps: Step S31: Normalize the raw strain rate data to compress it into a range. The normalized strain rate data were obtained. No. Location of each sensor channel The formula for normalizing the original strain rate data at the location is as follows: in, The normalized strain rate data represents the first strain rate. Location of each sensor channel Normalized strain rate data; This indicates taking the minimum value. This indicates taking the maximum value; Step S32: Perform frequency domain bandpass filtering on the normalized strain rate data to suppress low-frequency and high-frequency noise that will be severely amplified during the integration process, and obtain the preprocessed strain rate data. No. Location of each sensor channel Normalized strain rate data The formula for performing frequency domain bandpass filtering is as follows: in, The frequency spectrum of the strain rate data represents the first... Location of each sensor channel Frequency spectrum of strain rate data at the location; Represents the Fourier transform function; Indicates frequency; Represents the imaginary unit; The data after bandpass filtering in the frequency domain represents the first... Location of each sensor channel Data after bandpass filtering in the frequency domain; This represents the response function of a bandpass filter; This represents the filtered data, i.e., the preprocessed strain rate data, representing the... Location of each sensor channel Preprocessed strain rate data at the location; The raw strain rate data from all sensing channels are preprocessed to obtain preprocessed strain rate data. .

4. The DAS data adaptive gauge reconstruction and multi-resolution imaging method according to claim 3, characterized in that, Step S4 includes the following steps: Step S41: Perform an integral transform on the preprocessed strain rate data to obtain the particle wave function: in, Let f be the wave function of a particle, representing the wave function of the first particle. Location of each sensor channel The wave function of a particle at a given location; This represents a proportionality coefficient related to formation velocity. The initial depth of the integration; Step S42: Using a spatial interpolation algorithm, perform continuous wave field recovery on the particle wave function to obtain the continuous wave field function: in, Represents the continuous wave field function; This represents the spatial interpolation operator.

5. The DAS data adaptive gauge reconstruction and multi-resolution imaging method according to claim 4, characterized in that, Step S52 includes the following steps: Step S521: Based on the imaging target requirements, determine the target position of the virtual track in the shallow region, and generate a gauge array using a small gauge length; Step S522: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths in the gauge length array; Step S523: Calculate the signal-to-noise ratio (SNR) of all virtual trace data, adaptively select the optimal gauge length based on the SNR, and take the virtual gauge length corresponding to the maximum SNR as the optimal gauge length. The virtual trace data corresponding to the maximum SNR is the stratigraphic structure image.

6. The DAS data adaptive gauge reconstruction and multi-resolution imaging method according to claim 5, characterized in that, Step S53 includes the following steps: Step S531: Based on the imaging target requirements, determine the target position of the virtual track for the transition area, divide the strata of the transition area into 4 layers on average, and generate a gauge array for each layer using the virtual gauge length between the large and small gauge lengths. Step S532: By simulating the DAS physical measurement process, generate virtual track data for all virtual gauge lengths within each gauge length array; Step S533: Calculate the signal-to-noise ratio of all virtual trace data, adaptively select the optimal gauge length based on the signal-to-noise ratio, take the virtual gauge length corresponding to the maximum signal-to-noise ratio of each layer as the optimal gauge length of that layer, and the virtual trace data corresponding to the maximum signal-to-noise ratio of each layer is the stratigraphic structure image of that layer.

Images