A near-surface modeling method and system based on microseismic spectral ratio interface imaging constraints

Abstract

The present application belongs to the technical field of oil seismic exploration, and discloses a near-surface modeling method and system based on microseismic spectral ratio interface imaging constraint, wherein the method comprises the following steps: collecting continuous microseismic data of a set time length in the whole work area and performing pretreatment; performing spectral ratio calculation on the pretreated microseismic data, smoothing the spectral ratio, and obtaining peak frequency from the smoothed spectral ratio; obtaining the shear wave velocity structure from the microseismic signal inversion, converting the peak frequency under each station into interface depth according to the shear wave velocity structure, and obtaining the interface depth information of the whole work area; taking the interface depth information as a constraint condition, adding the first arrival wave travel time tomography inversion, and completing the near-surface velocity modeling. The present application provides fine lithology interface constraint, makes up for the defect of insufficient micro-logging spatial sampling, improves the vertical resolution and lateral continuity of the near-surface velocity model, and solves the problem of insufficient accuracy of the near-surface model caused by lack of constraint in complex areas.

Description

Technical Field

[0001] This invention belongs to the field of petroleum seismic exploration technology, and specifically relates to a near-surface modeling method and system based on micro-motion spectral ratio interface imaging constraints. Background Technology

[0002] Confirming traps is crucial for ensuring continued breakthroughs in exploration in Area A. In recent years, 20 out of 28 failed exploration wells in the region failed to confirm traps, accounting for a staggering 71.4%. This data clearly demonstrates that trap confirmation has become a core bottleneck restricting the success rate of oil and gas exploration in this area. Therefore, although the target traps are in deep formations, the problems first appear in the shallow layers. The accuracy of the shallow velocity model directly determines the reliability of deep structural imaging. The core of solving the problem lies in velocity modeling technology, and improving the accuracy of shallow velocity is of paramount importance. This has become the primary task for current exploration efforts in Area A.

[0003] Currently, the mainstream near-surface modeling technology adopts the first-arrival travel-time tomography inversion method based on microlog constraints. For example, patent application CN117310806A discloses a near-surface pseudo-3D velocity modeling method and system based on microlog constraints. By picking up the first arrival time of seismic waves and combining it with the prior velocity information provided by micrologs, a near-surface velocity model is established, which has good applicability under conventional surface conditions. However, while a single first-arrival tomography inversion method can usually reflect the lateral trend of velocity, in complex abrupt change zones without microlog constraints, the vertical accuracy of the inverted shallow surface velocity is often insufficient, making it difficult to accurately depict the spatial distribution characteristics of near-surface lithological interfaces, resulting in systematic biases in deep imaging.

[0004] The central section of Area A is a typical complex area with both surface and subsurface features, presenting numerous technical challenges for near-surface modeling. In some complex surface areas, such as alluvial channels covered with gravel and very steep hillsides, the terrain is highly undulating, and surface lithology changes rapidly, making micrologging extremely difficult. Often, it is impossible to deploy the logging points according to the design locations, resulting in insufficient constraint density for subsequent first-arrival tomography inversion. This further leads to inaccurate near-surface velocity models, distortion of deep structural morphology, and affects subsequent imaging processing and geological interpretation, ultimately causing drilling failures.

[0005] In summary, existing near-surface modeling techniques suffer from insufficient constraints in complex areas, resulting in inadequate modeling accuracy, distortion in deep structural imaging, and difficulties in identifying traps, which severely restricts the success rate of oil and gas exploration. Summary of the Invention

[0006] To address the above problems, this invention provides a near-surface modeling method based on micro-motion spectral ratio interface imaging constraints, comprising the following steps: Collect continuous micro-motion data for a set duration across the entire work area; Preprocess the continuous micro-motion data; The spectral ratio of the preprocessed micro-motion data is calculated, the spectral ratio is smoothed, and the peak frequency is obtained from the smoothed spectral ratio. The shear wave velocity structure is obtained by inversion from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area. By using interface depth information as a constraint and incorporating first-arrival wave travel-time tomography inversion, near-surface velocity modeling is completed.

[0007] Furthermore, continuous micro-motion data for a set duration is collected across the entire work area, including the following steps: Micro-motion data collection was conducted throughout the entire work area. In addition to the regular blasting data collection, continuous micro-motion data collection was carried out for a period of more than 10 hours.

[0008] Furthermore, the continuous micro-motion data is preprocessed, including the following steps: After removing the mean, trend, and spikes from the continuous micro-motion data, the STA / LTA algorithm is applied to remove seismic signals, resulting in preprocessed micro-motion data.

[0009] Furthermore, the spectral ratio of the preprocessed micro-motion data is calculated, including the following steps: The continuous micromotion records are divided into segments of fixed duration. For each segment of micromotion data, the micromotion seismic data is transformed from the time domain to the frequency domain using Fourier transform. Then, the micromotion spectral ratio is calculated based on the square root average algorithm.

[0010] Further, the spectral ratio is smoothed, and the peak frequency is obtained from the smoothed spectral ratio, including the following steps: An adaptive logarithmic frequency smoothing algorithm is used to smooth the calculated spectral ratio values; The frequency value corresponding to the largest amplitude value is obtained from the smoothed spectral ratio and used as the peak frequency.

[0011] Furthermore, the shear wave velocity structure is obtained by inversion from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into interface depth to obtain the interface depth information of the entire work area, including the following steps: The transverse wave velocity structure was obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm. After obtaining the shear wave velocity structure below each station, the peak frequency below each station is combined and converted into interface depth using a frequency-depth conversion algorithm.

[0012] Furthermore, the micro-motion spectrum ratio is calculated based on the square root averaging algorithm, including the following steps: The square root of the power spectral density of the east-west and north-south component micro-motion signals is taken after arithmetic averaging, to obtain the isotropic horizontal Fourier spectrum value. The horizontal Fourier spectrum value is then compared with the spectral density of the vertical component micro-motion signal to obtain the micro-motion spectrum ratio.

[0013] Furthermore, the transverse wave velocity structure is obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm, including the following steps: The micro-motion signal is restored to a passive source surface wave signal; An initial shear wave velocity model is constructed based on a passive source surface wave signal; An approximate inverse matrix of the Hessian matrix is ​​constructed using the BFGS quasi-Newton method, and the shear wave velocity model is updated through an iterative formula. During the iteration process, the BFGS correction rule is used to update the approximate inverse matrix to ensure that the matrix is ​​positive definite and maintains a stable descent direction. When the objective function converges to a preset threshold, the iteration terminates and the optimal shear wave velocity structure is output.

[0014] Furthermore, the peak frequency is converted into interface depth using a frequency-depth conversion algorithm, including the following steps: Based on the shear wave velocity structure beneath each station, the average shear wave velocity of the entire work area is determined. The ratio of the average shear wave velocity to the product of the empirical constant and the peak frequency is then used to determine the interface depth.

[0015] This invention also provides a near-surface modeling system based on micro-motion spectral ratio interface imaging constraints, comprising: The data acquisition module is used to collect continuous micro-motion data for a set duration across the entire work area; The data preprocessing module is used to preprocess continuous micro-motion data; The calculation module is used to calculate the spectral ratio of the preprocessed micro-motion data, smooth the spectral ratio, and obtain the peak frequency from the smoothed spectral ratio. The frequency-depth conversion and interface imaging module is used to invert and obtain the shear wave velocity structure from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area. The tomographic inversion modeling module is used to incorporate interface depth information as a constraint, add first-arrival wave travel time tomographic inversion, and complete near-surface velocity modeling.

[0016] Furthermore, the computing module is specifically used for: The continuous micromotion records are divided into segments of fixed duration. For each segment of micromotion data, the micromotion seismic data is transformed from the time domain to the frequency domain using Fourier transform. Then, the micromotion spectral ratio is calculated based on the square root average algorithm.

[0017] Furthermore, the frequency-depth conversion and interface imaging module is specifically used for: The transverse wave velocity structure was obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm. After obtaining the shear wave velocity structure below each station, the peak frequency below each station is combined and converted into interface depth using a frequency-depth conversion algorithm.

[0018] The beneficial effects of this invention are: This invention effectively improves the strong dependence of existing near-surface modeling techniques on micrologging data by integrating the micro-motion spectral ratio method into the near-surface modeling process. By acquiring continuous micro-motion data, the peak frequency of the subsurface lithological interface is extracted from the micro-motion signal using the micro-motion spectral ratio method. Combined with the frequency-depth conversion relationship, interface depth information is obtained. This interface depth information provides fine-grained lithological interface constraints across the entire work area, effectively compensating for the insufficient spatial sampling of micrologging, significantly improving the vertical resolution and lateral continuity of the near-surface velocity model, and effectively solving the problem of insufficient near-surface model accuracy due to lack of constraints in complex areas.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0020] 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.

[0021] Figure 1 A flowchart illustrating a near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to an embodiment of the present invention is shown. Figure 2 The diagram shows a schematic before and after smoothing the spectral ratio value using an adaptive logarithmic frequency smoothing algorithm according to an embodiment of the present invention; Figure 3 A schematic diagram of the shear wave velocity structure inversion process based on the BFGS quasi-Newton gradient method according to an embodiment of the present invention is shown. Figure 4 A schematic diagram illustrating the acquisition of work area interface depth based on frequency-depth conversion according to an embodiment of the present invention is shown; Figure 5A schematic diagram of a near-surface modeling system based on micro-motion spectral ratio interface imaging constraints according to an embodiment of the present invention is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0023] It should be noted that the terms "first," "second," etc., used in this application 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 for the embodiments of this application described herein.

[0024] This invention provides a near-surface modeling method and system based on micro-motion spectral ratio interface imaging constraints. Utilizing the ease of acquiring micro-motion data in complex dual-region areas, the method extracts the resonant frequency information of subsurface lithological interfaces from micro-motion signals using the micro-motion spectral ratio method. Combined with frequency-depth conversion relationships, it provides refined lithological interface constraints across the entire work area. This invention effectively compensates for the insufficient spatial sampling in micro-logging, significantly improves the vertical resolution and lateral continuity of near-surface velocity models, and effectively solves the problem of insufficient modeling accuracy caused by inadequate constraints in existing near-surface modeling techniques for complex areas (complex areas with abrupt topographic changes).

[0025] Explanation of relevant terms: Micro-vibration signal: a weak ground vibration signal excited by natural sources (wind, waves, human activities, etc.), with significant advantages such as no need for artificial seismic source, strong anti-interference ability, and adaptability to complex terrain.

[0026] Micro-logging: This method involves sequentially drilling at different depths within a well to determine the near-surface velocity and thickness. It is currently the most direct and accurate method for surface surveying. The near-surface generally has a multi-layered structure, which can be broadly categorized into low-velocity layers, decreasing-velocity layers, and high-velocity layers.

[0027] Micromotion Spectral Ratio Interface Imaging: The micromotion spectral ratio method (hereinafter referred to as the spectral ratio method) is a key technique for analyzing site response and subsurface interfaces. Its formal name is the Horizontal-Vertical Spectral Ratio (HVSR). Numerous observational data confirm that when bedrock is overlain by sedimentary layers, the resonance frequency of the sedimentary layers coincides with the peak frequency of the spectral ratio. Therefore, the interface depth of subsurface structures can be inferred from the peak frequency of the spectral ratio. The calculation formula is as follows:

[0028] in, The horizontal Fourier spectrum value, The vertical Fourier spectrum values ​​are... This represents the micro-motion spectrum ratio.

[0029] like Figure 1 As shown, a near-surface modeling method based on micro-motion spectral ratio interface imaging constraints includes the following steps: S1. Collect continuous three-component micro-motion data for a set duration across the entire work area for subsequent processing.

[0030] In this step, nodal instruments deployed during seismic exploration are typically used to collect micromotion data throughout the entire work area. Additional continuous data collection is conducted after conventional blasting data collection, with the collection time usually lasting 10 hours or more, to ensure the stability and reliability of subsequent micromotion spectrum ratio calculations.

[0031] S2. Preprocessing the continuous micro-motion data: By preprocessing the acquired continuous micro-motion data, interference signals are removed, and only background noise is retained to obtain a high-quality spectral ratio. This includes the following steps: First, conventional processing such as mean removal, trend removal, and glitch removal is performed on the continuous micro-motion data to remove low-frequency trends and some outliers. Then, the STA / LTA algorithm is applied to remove seismic signals to obtain preprocessed micro-motion data.

[0032] The STA / LTA algorithm compares the energy ratio of a short time window (sensitive to signal variations) and a long time window (reflecting the level of fretting noise). R This is used to detect whether the signal has changed from micro-motion noise to a seismic event, thereby achieving the purpose of eliminating seismic signals and retaining only noise signals. The principle is as follows:

[0033] in, s(t) Indicates a short time window. l(t) Indicates a long window. This represents a given characteristic function, typically taken as the mean amplitude of the micro-motion record. If the ratio... If the threshold is exceeded, the record segment is excluded. The calculation typically involves dividing the entire record into several short time periods, calculating the spectral ratio for each period, and then summing the results. Each time period should be at least 10 data sampling cycles long.

[0034] S3. Calculate the spectral ratio of the preprocessed micro-motion data, smooth the spectral ratio, and obtain the peak frequency from the smoothed spectral ratio, including the following steps: S31. The continuous micromotion records are divided into segments of fixed duration. For the micromotion data of each segment, the micromotion seismic data is transformed from the time domain to the frequency domain by Fourier transform. Then, the micromotion spectrum ratio is calculated based on the square root average algorithm.

[0035] The square root averaging algorithm takes the square root of the arithmetic mean of the power spectral densities of the east-west and north-south component micro-motion signals to obtain the isotropic horizontal Fourier spectrum value. This horizontal Fourier spectrum value is then compared with the spectral density of the vertical component micro-motion signal to obtain the micro-motion spectrum ratio. The calculation formula is as follows:

[0036] in, and These are the power spectral densities of the east-west and north-south component micro-motion signals, respectively. The value represents the horizontal Fourier spectrum.

[0037] The advantages of calculating the micro-motion spectrum ratio by the square root average algorithm are: (1) maintaining the consistency of amplitude dimensions, avoiding phase information interference, and having good robustness to random noise; (2) compared with the simple arithmetic mean, the square root operation is more in line with the principle of energy superposition and can effectively eliminate horizontal azimuth deviation.

[0038] S32. The calculated spectral ratio is smoothed using an adaptive logarithmic frequency smoothing algorithm.

[0039] Since the spectral amplitude of actual data often varies drastically, direct division can easily produce singular values. Therefore, adaptive logarithmic frequency smoothing is applied to the calculated spectral ratio to make the spectral ratio data more robust and reliable.

[0040] The adaptive logarithmic frequency smoothing algorithm is a smoothing algorithm in the logarithmic coordinates of the frequency domain, mainly used for smoothing spectral ratio data. The formula for the smoothing algorithm is as follows:

[0041] in, It is a robust iterative operator. It is a variable bandwidth smoothing operator. It is the spectral ratio before smoothing; It is the smoothed spectral ratio; It is a robust iteration count; It is the local bandwidth threshold.

[0042] The advantage of smoothing the spectral ratio using the adaptive logarithmic frequency smoothing algorithm is that: (1) Adaptive local smoothing. For example... Figure 2 As shown, Figure 2 The horizontal axis represents frequency (Hz), ranging from 0 to 5 Hz, covering the main frequency band of micro-motion signals. The vertical axis represents HVSR (horizontal / vertical spectral ratio), using a logarithmic scale (10⁻⁶). -1 ~10 1 This reflects the amplitude ratio of the horizontal and vertical components at different frequencies, and the peak position usually corresponds to the natural resonant frequency of the site.

[0043] The gray dots represent the actual data (HVSR spectral ratios calculated from the original micro-motion data, which have obvious noise and fluctuations), and the black solid line is the fitted curve before adaptive smoothing (without smoothing, retaining the high-frequency fluctuations of the original data).

[0044] The black dashed line represents the fitted curve after adaptive smoothing (logarithmic frequency domain adaptive smoothing effectively suppresses noise, resulting in a smoother curve). Before smoothing, the original HVSR data (solid line) contained a large number of high-frequency spikes and fluctuations. After smoothing, as can be seen from the adaptively smoothed curve (dashed line), the overall trend is highly consistent with the original data, the peak positions and shapes are completely preserved, high-frequency spikes and local fluctuations are effectively suppressed, and the curve is smoother and more continuous; noise in the low-frequency (0~1Hz) and high-frequency (3~5Hz) segments is significantly reduced, and the overall outline of the curve is clearer, which facilitates subsequent analysis.

[0045] By comparing the fitted curves before and after smoothing, it can be seen that using different degrees of smoothing in different frequency regions optimizes the balance between bias and variance; it can handle situations where the frequency function exhibits different levels of detail in different frequency bands. (2) Automatic parameter selection. The optimal resolution parameters are determined automatically, which reduces the need for manual parameter tuning and improves the practicality of the algorithm.

[0046] S33. Obtain the frequency value corresponding to the largest amplitude from the smoothed spectral ratio value as the peak frequency.

[0047] S4. Invert the shear wave velocity structure from the micro-motion signal. Based on the shear wave velocity structure, convert the peak frequency below each station into the interface depth to obtain the interface depth information of the entire work area. This includes the following steps: S41. The transverse wave velocity structure is obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm. This algorithm has high computational efficiency and strong stability, ensuring that reliable transverse wave velocities are obtained for frequency depth conversion.

[0048] like Figure 3 As shown, for example, the shear wave velocity structure can be obtained from a three-component micro-motion signal by inverting the BFGS quasi-Newton gradient inversion algorithm, including the following steps: recovering the micro-motion signal (three-component noise record) into a passive source surface wave signal; constructing an initial shear wave velocity model based on the passive source surface wave signal, constructing an approximate inverse matrix of the Hessian matrix using the BFGS quasi-Newton method to avoid directly calculating the second derivative, and updating the shear wave velocity model through an iterative formula; updating the approximate inverse matrix using the BFGS correction rule during the iteration process to ensure that the matrix is ​​positive definite and maintains a stable descent direction; and terminating the iteration and outputting the optimal shear wave velocity structure when the objective function converges to a preset threshold.

[0049] Figure 3 The horizontal axis of the micro-motion signal (three-component noise record) section represents time (s), ranging from 0 to 2.5 s, and the vertical axis represents amplitude. It shows the micro-motion noise waveforms of the three components (Z, N, E). The micro-motion signal (three-component noise record) is the raw micro-motion noise data collected. The signal may seem chaotic, but it contains surface wave propagation information of the underground medium, which is the basis for subsequent analysis.

[0050] Figure 3 The recovered passive source surface wave signal is represented by time (s) on the horizontal axis and trace number on the vertical axis. Random noise is transformed into surface wave records similar to active source earthquakes, and the travel time and dispersion characteristics of the surface waves are extracted to provide data for subsequent inversion.

[0051] Figure 3 The section on the inversion results of the transverse wave velocity structure shows that the horizontal axis represents the transverse wave velocity (km / s) and the vertical axis represents the depth (km). The light gray dashed line in the figure represents the initial model, and the black solid line represents the optimal inversion model (the true velocity structure obtained by inversion through the quasi-Newton gradient method).

[0052] The BFGS quasi-Newton gradient algorithm is primarily used to solve unconstrained nonlinear optimization problems. It avoids directly calculating the second derivative by approximating the Hessian matrix, while maintaining superlinear convergence speed. The formula for the conventional quasi-Newton gradient algorithm is as follows:

[0053] in, This is the model data after the (k+1)th iteration update. It is the model data after the kth iteration; It is the approximate inverse of the Hessian matrix in the k-th iteration; It is the gradient operator. It is the objective function.

[0054] The conventional quasi-Newton gradient algorithm can be rewritten using the BFGS formula as follows:

[0055] in, It is the updated approximate inverse matrix. It is the step size vector. It is the model gradient difference. T It is the transpose of the matrix.

[0056] The BFGS quasi-Newton gradient algorithm for inverting the transverse wave velocity structure from a micro-motion signal has the following advantages: (1) Numerical stability. The rounding error of the algorithm will not be amplified, and any disturbance that deviates from the minimum change direction will be constrained by the norm term, ensuring strong numerical stability of the algorithm; (2) Maintaining positive definiteness. The algorithm ensures positive definiteness through the outer product of two terms. The algorithm is positive definite for any non-zero vector, ensuring fast and stable convergence.

[0057] S42. After obtaining the shear wave velocity structure below each station, combine the peak frequency below each station and convert the peak frequency into interface depth using a frequency-depth conversion algorithm to obtain the interface depth information of the entire work area, which can be used as a constraint condition in the subsequent first arrival tomography inversion.

[0058] like Figure 4 As shown, the process of converting peak frequency to interface depth using a frequency-depth conversion algorithm includes: determining the average shear wave velocity of the entire work area based on the shear wave velocity structure beneath each station; and determining the interface depth by the ratio of the average shear wave velocity to the product of an empirical constant and the peak frequency, thereby achieving interface imaging based on the micro-motion spectral ratio method, as detailed below:

[0059] in, It is the average shear wave velocity of the formation. It is the peak frequency. 4 represents the interface depth, and 4 is an empirical constant.

[0060] Figure 4 The top left corner shows the shear wave velocity structure, with the horizontal axis representing the shear wave velocity (km / s) and the vertical axis representing the depth (m). The light gray dashed line represents the initial model, and the black solid line represents the optimal inversion model (obtained by inversion using the quasi-Newton gradient method).

[0061] Figure 4 The upper right corner shows the High-Voltage Spectral Ratio (HVSR) curve, with the horizontal axis representing frequency (Hz) and the vertical axis representing amplitude (HVSR value). The key information is the peak frequency of the curve, which corresponds to the natural resonant frequency of the site and is a crucial parameter for subsequent frequency depth conversion.

[0062] Figure 4Below is a two-dimensional near-surface shear wave velocity profile, with the horizontal axis representing horizontal distance and the vertical axis representing depth (m). The single-point shear wave velocity model is combined with the peak frequency of the micromotion spectral ratio through frequency-depth conversion, expanding it into a two-dimensional profile. Micrologging data from both sides of the profile (micrologging 1 and micrologging 2) serve as hard constraints to calibrate the model, verifying the reliability of the inversion results. For example, the micrologging data on the left side shows velocities of 1682 m / s and 2947 m / s for the two layers, respectively, which highly match the stratigraphic characteristics in the profile.

[0063] The frequency-depth conversion algorithm has been verified by a large number of experiments and drilling data to be applicable. Using this formula for frequency-depth conversion is simple and efficient.

[0064] S5. Using the interface depth information as a constraint, incorporate the first arrival wave travel time tomography inversion to complete the near-surface velocity modeling.

[0065] The modeling method of this invention can provide accurate and reliable interface information as constraints for near-surface modeling in complex areas lacking micro-logging, thereby improving the near-surface modeling accuracy in these complex areas and providing reliable surface results for subsequent high-quality seismic imaging and exploration breakthroughs.

[0066] Based on the above near-surface modeling methods, such as Figure 5 As shown, this embodiment of the invention also provides a near-surface modeling system based on micro-motion spectral ratio interface imaging constraints, including a data acquisition module, a data preprocessing module, a calculation module, a frequency-depth conversion and interface imaging module, and a tomographic inversion modeling module.

[0067] The data acquisition module is used to collect continuous micro-motion data for a set duration across the entire work area. The data preprocessing module is used to preprocess the continuous micro-motion data.

[0068] The calculation module is used to calculate the spectral ratio of the preprocessed micro-motion data, smooth the spectral ratio, and obtain the peak frequency from the smoothed spectral ratio. The frequency-depth conversion and interface imaging module is used to invert and obtain the shear wave velocity structure from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area.

[0069] The tomographic inversion modeling module is used to incorporate interface depth information as a constraint, add first-arrival wave travel time tomographic inversion, and complete near-surface velocity modeling.

[0070] The near-surface modeling system of this invention is based on micro-motion detection technology, a geophysical method that uses signals extracted from natural sources or human-generated noise to detect subsurface structures. Micro-motion detection offers significant advantages such as eliminating the need for artificial sources, ease of construction, and environmental friendliness, making it particularly suitable for high-density data acquisition in areas with complex terrain and difficult access. This invention achieves high-precision imaging of subsurface interfaces through micro-motion spectral ratio analysis and incorporates this imaging result as a spatial constraint into the first-arrival tomography inversion framework.

[0071] The near-surface modeling method and system of this invention breaks through the technical bottleneck of near-surface modeling in areas with abrupt topographic changes, and provides an economical, efficient and environmentally friendly technical solution for oil and gas seismic exploration under complex surface conditions. It has important application value and broad industrialization prospects.

[0072] The near-surface modeling method and system of this invention effectively improves the strong dependence of existing near-surface modeling technologies on micro-logging data by integrating the micro-motion spectral ratio method into the near-surface modeling processing flow. Continuous micro-motion data acquisition is achieved throughout the entire work area using node instruments, reducing data acquisition difficulty and facilitating application in complex dual-area regions. An adaptive logarithmic frequency smoothing algorithm is used for accurate calculation of the spectral ratio, effectively improving the calculation accuracy of peak frequencies derived from the spectral ratio. Subsequently, the shear wave velocity is calculated using the BFGS quasi-Newton gradient method for frequency-depth conversion. This algorithm is highly efficient and stable, ensuring reliable shear wave velocity. Finally, the interface information of the entire work area is obtained through frequency-depth conversion. The overall technical solution has strong applicability in the field of high-resolution near-surface modeling in complex areas.

[0073] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A near-surface modeling method based on micro-motion spectral ratio interface imaging constraints, characterized in that, Includes the following steps: Collect continuous micro-motion data for a set duration across the entire work area; Preprocess the continuous micro-motion data; The spectral ratio of the preprocessed micro-motion data is calculated, the spectral ratio is smoothed, and the peak frequency is obtained from the smoothed spectral ratio. The calculation of the spectral ratio of the preprocessed micro-motion data includes the following steps: dividing the continuous micro-motion record into segments of fixed duration; transforming the micro-motion seismic data from the time domain to the frequency domain using Fourier transform for each segment; and then calculating the micro-motion spectral ratio based on the square root averaging algorithm, including: taking the square root of the power spectral density of the east-west and north-south component micro-motion signals to obtain the isotropic horizontal Fourier spectrum value; and comparing the horizontal Fourier spectrum value with the spectral density of the vertical component micro-motion signal to obtain the micro-motion spectral ratio. The shear wave velocity structure is obtained by inversion from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area. By using interface depth information as a constraint and incorporating first-arrival wave travel-time tomography inversion, near-surface velocity modeling is completed.

2. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to claim 1, characterized in that, Collect continuous micro-motion data for a set duration across the entire work area, including the following steps: Micro-motion data collection was conducted throughout the entire work area. In addition to the regular blasting data collection, continuous micro-motion data collection was carried out for a period of more than 10 hours.

3. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to claim 1, characterized in that, Preprocessing of continuous micro-motion data includes the following steps: After removing the mean, trend, and spikes from the continuous micro-motion data, the STA / LTA algorithm is applied to remove seismic signals, resulting in preprocessed micro-motion data.

4. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to claim 1, characterized in that, Smoothing the spectral ratio and obtaining the peak frequency from the smoothed spectral ratio includes the following steps: An adaptive logarithmic frequency smoothing algorithm is used to smooth the calculated spectral ratio values; The frequency value corresponding to the largest amplitude value is obtained from the smoothed spectral ratio and used as the peak frequency.

5. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to any one of claims 1-4, characterized in that, The shear wave velocity structure is obtained by inversion from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area. This includes the following steps: The transverse wave velocity structure was obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm. After obtaining the shear wave velocity structure below each station, the peak frequency below each station is combined and converted into interface depth using a frequency-depth conversion algorithm.

6. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to claim 5, characterized in that, The transverse wave velocity structure is obtained from a micro-motion signal using the BFGS quasi-Newton gradient inversion algorithm, including the following steps: The micro-motion signal is restored to a passive source surface wave signal; An initial shear wave velocity model is constructed based on a passive source surface wave signal; An approximate inverse matrix of the Hessian matrix is ​​constructed using the BFGS quasi-Newton method, and the shear wave velocity model is updated through an iterative formula. During the iteration process, the BFGS correction rule is used to update the approximate inverse matrix to ensure that the matrix is ​​positive definite and maintains a stable descent direction. When the objective function converges to a preset threshold, the iteration terminates and the optimal shear wave velocity structure is output.

7. The near-surface modeling method based on micro-motion spectral ratio interface imaging constraints according to claim 5, characterized in that, The peak frequency is converted into interface depth using a frequency-depth conversion algorithm, which includes the following steps: Based on the shear wave velocity structure beneath each station, the average shear wave velocity of the entire work area is determined. The ratio of the average shear wave velocity to the product of the empirical constant and the peak frequency is then used to determine the interface depth.

8. A near-surface modeling system based on micro-motion spectral ratio interface imaging constraints, characterized in that, include: The data acquisition module is used to collect continuous micro-motion data for a set duration across the entire work area; The data preprocessing module is used to preprocess continuous micro-motion data; The calculation module is used to calculate the spectral ratio of the preprocessed micro-motion data, smooth the spectral ratio, and obtain the peak frequency from the smoothed spectral ratio. The calculation of the spectral ratio of the preprocessed micro-motion data includes the following steps: dividing the continuous micro-motion record into segments of fixed duration; transforming the micro-motion seismic data from the time domain to the frequency domain using Fourier transform for each segment; and then calculating the micro-motion spectral ratio based on the square root averaging algorithm, including: taking the square root of the power spectral density of the east-west and north-south component micro-motion signals to obtain the isotropic horizontal Fourier spectrum value; and comparing the horizontal Fourier spectrum value with the spectral density of the vertical component micro-motion signal to obtain the micro-motion spectral ratio. The frequency-depth conversion and interface imaging module is used to invert and obtain the shear wave velocity structure from the micro-motion signal. Based on the shear wave velocity structure, the peak frequency below each station is converted into the interface depth to obtain the interface depth information of the entire work area. The tomographic inversion modeling module is used to incorporate interface depth information as a constraint, add first-arrival wave travel time tomographic inversion, and complete near-surface velocity modeling.

9. The near-surface modeling system based on micro-motion spectral ratio interface imaging constraints according to claim 8, characterized in that, The frequency depth conversion and interface imaging module is specifically used for: The transverse wave velocity structure was obtained from the micro-motion signal by using the BFGS quasi-Newton gradient inversion algorithm. After obtaining the shear wave velocity structure below each station, the peak frequency below each station is combined and converted into interface depth using a frequency-depth conversion algorithm.

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