Underground space detection method and device based on natural source surface wave and drilling

Abstract

The invention relates to the technical field of underground exploration, and provides an underground space detection method and device based on natural source surface waves and drilling, and the method comprises the steps: constructing an initial geological structure model based on a prior geological constraint condition and a first surface wave signal of a to-be-detected region, and obtaining first geological profile data based on the initial geological structure model, drilling operation is carried out on a drilling point position determined in the primary geological structure model, and second geological profile data are obtained; on the basis of the difference between the first geological profile data and the second geological profile data, correcting the primarily judged geological structure model to obtain a corrected geological structure model, and resetting a prior geological constraint condition on the basis of the second geological profile data; and correcting the array layout initial parameters and the signal acquisition initial parameters based on the corrected geologic structure model, and constructing a target geologic structure model based on the reset prior geologic constraint condition and the second surface wave signal. According to the invention, more accurate, efficient and comprehensive detection of the underground space is realized.

Description

Technical Field

[0001] The present invention relates to the field of underground exploration technology, and in particular to an underground space detection method and device based on natural source surface waves and drilling. Background Art

[0002] Natural source surface wave detection is a geophysical exploration method based on the propagation characteristics of seismic waves. It exploits the differences in propagation velocities of surface waves generated by natural sources (such as ambient noise) in different strata. By collecting and analyzing surface wave signals, the structural information of the subsurface strata can be inverted. However, due to the multi-solution nature of surface wave inversion, it is often difficult to accurately determine the detailed lithology and structural characteristics of the subsurface strata based solely on surface wave detection data. Furthermore, surface wave detection data are easily affected by interference factors, resulting in uncertainty in the detection results.

[0003] Therefore, realizing an underground space detection method with accurate and comprehensive detection structure is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The present invention provides a method and device for underground space detection based on natural source surface waves and drilling, which is used to solve the above technical problems existing in the prior art. The present invention provides an underground space detection method based on natural source surface waves and drilling, comprising: Constructing a preliminary geological structure model based on a priori geological constraints of the area to be detected and a first surface wave signal, and obtaining first geological profile data based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected using a natural source surface wave detection method based on initial array layout parameters and initial signal acquisition parameters; Controlling the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model, and obtaining second geological profile data obtained by drilling; Based on the difference between the first geological section data and the second geological section data, the preliminary geological structure model is revised to obtain a revised geological structure model, and the prior geological constraint conditions are reset based on the second geological section data; Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the signal acquisition revised parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed, wherein the second surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the revised array layout parameters and the signal acquisition revised parameters.

[0005] According to the present invention, a method for underground space detection based on natural source surface waves and drilling is provided, which constructs a preliminary geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, including: Acquiring a first surface wave signal of the area to be detected detected by the natural source surface wave detection method; extracting a surface wave dispersion curve from the first surface wave signal; Constructing an initial model of the preliminary geological structure model based on the prior geological constraints; Based on the initial model and geophysical laws, a theoretical dispersion curve is generated by forward simulation. The initial model is inversely optimized based on an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve to continuously adjust the model parameters of the initial model. When the objective function converges, the inversion optimization is completed and the preliminary geological structure model is obtained.

[0006] According to a method for underground space detection based on natural source surface waves and drilling provided by the present invention, inversion optimization of the initial model is performed based on an objective function representing the difference between the surface wave dispersion curve and the theoretical dispersion curve, comprising: S1: Calculate the likelihood between the theoretical dispersion curve obtained by the initial model of each inversion algorithm after each inversion and the surface wave dispersion curve; S2: assigning a weight to each inversion algorithm after each inversion according to the likelihood, wherein the higher the likelihood, the higher the weight assigned to the inversion algorithm; S3: Based on the weight of the inversion algorithm, the parameters of the initial models after each inversion are weighted and summed to obtain the merged parameters, and the merged initial model after inversion is obtained based on the merged parameters; S4: Based on the initial model merged after inversion, the objective function is calculated. If the objective function converges, the inversion ends; otherwise, step S1 is executed.

[0007] According to a method for underground space detection based on natural source surface waves and drilling provided by the present invention, a drilling system is controlled to perform a drilling operation at a drilling point determined in the preliminary geological structure model, and second geological profile data obtained by drilling is obtained, including: Determining the drilling locations based on the preliminary geological structure model; Controlling the drilling system to drill at the drilling point and acquiring drilling data; performing noise reduction processing on the drilling data to obtain noise-reduced drilling data; The noise-reduced drilling data is correlated with the geological characteristics and combined with the core logging data of the drilling system to mark the stratification, thickness and depth of each layer to obtain the second geological profile data.

[0008] According to a method for detecting underground space based on natural source surface waves and drilling provided by the present invention, controlling a drilling system to drill at the drilling point includes: During the drilling process, the trajectory measurement system is used to measure the drilling trajectory once every s drill rods. When the deviation rate of the drilling trajectory is greater than the preset deviation rate threshold, the correction program is triggered to replace the drill bit and / or adjust the drilling data to correct the drilling trajectory, where s is greater than or equal to 1.

[0009] According to a method for detecting underground space based on natural source surface waves and drilling provided by the present invention, the drilling data is subjected to noise reduction processing to obtain the noise-reduced drilling data, including: Calculating the crest factor of the drilling data in sections, taking the length of a single drill pipe as a unit; Based on the distribution range of the crest coefficient, different fluctuation stages corresponding to different ranges of the crest coefficient are divided, and different fluctuation stages reflect different fluctuation degrees of drilling data; A noise reduction algorithm and a noise reduction threshold corresponding to different fluctuation stages are selected to perform noise reduction processing on the drilling data to obtain noise-reduced drilling data.

[0010] According to the present invention, a method for underground space detection based on natural source surface waves and drilling is provided. The noise-reduced drilling data is correlated with geological features and combined with the core logging data of the drilling system to mark the stratification of the strata, the thickness and depth of each layer, so as to obtain second geological profile data, including: Expanding lithologic characteristic quantitative indicators based on the noise-reduced drilling data, the lithologic characteristic quantitative indicators including: drilling specific work and torque fluctuation coefficient; In combination with the core logging data, a quantitative mapping relationship between drilling data and stratigraphic stratification is established; The stratification of the stratum, the thickness and depth of each layer are marked by the real-time drilling data in the hole and the quantitative mapping relationship to obtain the second geological profile data.

[0011] The present invention also provides an underground space detection device based on natural source surface waves and drilling, comprising the following modules: A preliminary model construction module is used to construct a preliminary geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, and to obtain first geological profile data based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0012] The profile data acquisition module is used to control the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model and to acquire the second geological profile data obtained by drilling.

[0013] The model correction module is used to correct the preliminary geological structure model based on the difference between the first geological profile data and the second geological profile data to obtain a corrected geological structure model, and reset the prior geological constraints based on the second geological profile data.

[0014] A target model construction module is used to correct the initial parameters of the array layout and the initial parameters of the signal acquisition based on the corrected geological structure model to obtain the corrected array layout parameters and the corrected signal acquisition parameters, and to construct a target geological structure model based on the reset prior geological constraints and the second surface wave signal, wherein the natural source surface wave detection method detects the second surface wave signal based on the corrected array layout parameters and the corrected signal acquisition parameters.

[0015] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the program, it implements any of the above-described underground space detection methods based on natural source surface waves and drilling.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements any of the above-described underground space detection methods based on natural source surface waves and drilling.

[0017] The present invention provides an underground space detection method and device based on natural source surface waves and drilling. By constructing a preliminary geological structure model, first geological profile data is obtained, and second geological profile data is obtained by drilling. According to the difference between the first geological profile data and the second geological profile data, the preliminary geological structure model is corrected to obtain a corrected geological structure model. The corrected geological structure model is then used to guide a second surface wave detection to obtain a target geological structure model. Since the detection process utilizes the drilling data and core samples during the drilling process to accurately deduce the stratification of the strata, the thickness and depth of each layer, and its dynamic feedback is used to correct the preliminary geological structure model, the inherent multi-solution problem of the traditional natural source surface wave detection method is effectively solved, thereby realizing more accurate, efficient and comprehensive detection of underground space, and meeting the urgent needs of various application scenarios such as engineering construction and resource exploration for high-precision underground geological structure information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is one of the flow charts of the underground space detection method based on natural source surface waves and drilling provided by the present invention.

[0020] Figure 2 This is the second flow chart of the underground space detection method based on natural source surface waves and drilling provided by the present invention.

[0021] Figure 3 This is the third flow chart of the underground space detection method based on natural source surface waves and drilling provided by the present invention.

[0022] Figure 4 This is a relationship diagram between drilling speed and drilling depth in the underground space detection method based on natural source surface waves and drilling provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the underground space detection device based on natural source surface waves and drilling provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0026] The underground space detection method based on natural source surface waves and drilling in the embodiment of the present invention is as follows: Figure 1 As shown, the process includes the following steps S110 to S140.

[0027] Step S110: Based on the prior geological constraints of the area to be detected and the first surface wave signal, a preliminary geological structure model is constructed, and first geological profile data is obtained based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0028] Specifically, before employing the natural surface wave detection method, an array must be deployed. Array layout is crucial for data quality and inversion interpretation. Array layout parameters include array shape (e.g., linear or circular) and observation point layout (e.g., spacing between points). For example, a circular array (with observation points arranged along a circle) is selected based on the size of the area to be surveyed, the terrain, and the target detection depth (e.g., 300 meters). Circular arrays offer significant advantages in omnidirectional signal reception and data processing, making them suitable for detecting complex geological structures. Array design requires a clear relationship between observation point spacing and detection depth. The array radius can be set between 450 and 900 meters based on empirical formulas for surface wave exploration. The spacing between observation points is set to half the surface wave wavelength (λ), which must be determined in advance through pre-survey and dispersion analysis. Furthermore, geophones must be precisely placed at each observation point to ensure close coupling with the ground surface, efficiently capturing the first surface wave signal from natural surface waves and ensuring accurate and reliable detection.

[0029] In this step, high-precision geophones can be used to continuously record the first surface roll signal from each observation point over a long period of time. Signal acquisition parameters include sampling frequency and recording duration. During the acquisition process, the sampling frequency is flexibly adjusted based on the target detection depth and the surface roll frequency range. For example, when the estimated surface roll frequency range is 0.1-100 Hz, the sampling frequency is set to 200 Hz or above to acquire high-frequency signals, ensuring that the sampling frequency is at least twice the highest expected surface roll frequency. The recording duration is determined by the geological complexity of the area to be detected and the stability of the signal. For example, in areas with simple geological structures and stable signals, the recording duration is set to 8-12 hours, while in areas with complex geological structures or large fluctuations, the recording duration is extended to 24-48 hours.

[0030] Among them, the prior geological constraints can be obtained based on the geological map of the area to be explored and the historical exploration results (the specific forms of the prior geological constraints are, for example: the thickness range of a certain stratum: based on the geological data of the area to be explored, the thickness of a certain stratum is limited to between 50 and 80 meters; the surface wave velocity range: combined with the core test data, the shear wave velocity of sandstone is set to 800-1800 m / s, and that of granite is set to 1500-3000 m / s). The prior geological constraints are used to construct the initial model of the preliminary geological structure model. The preliminary geological structure model is obtained by forward and inversion simulation based on the first surface wave signal and the initial model. The preliminary geological structure model includes model parameters such as the stratification of the strata (different rock layers, each layer has different lithology), the thickness and depth of each layer, and the first geological profile data are characterized by these model parameters.

[0031] Step S120: Control the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model, and obtain second geological profile data obtained by drilling.

[0032] In this step, drilling detection is used to obtain the second geological profile data of the area to be detected. Drilling detection is to directly drill into the ground, obtain core samples, observe visually, and accurately deduce the stratification of the strata, the thickness and depth of each layer by combining the drilling data. Therefore, accurate and detailed second geological profile data can be obtained through drilling detection. Among them, the drilling data includes: drilling depth, drilling speed (which can be calculated by drilling depth and time), drilling pressure, and torque. The relationship between drilling speed and drilling depth is presented in the form of a relationship diagram, such as Figure 4 As shown, the horizontal axis represents the drilling speed, and the vertical axis represents the drilling depth. Different areas to be detected will present different relationship diagrams.

[0033] Step S130: Based on the difference between the first geological profile data and the second geological profile data, the preliminary geological structure model is corrected to obtain a corrected geological structure model, and the priori geological constraints are reset based on the second geological profile data.

[0034] In this step, since it is found that the second geological profile data is different from the first geological profile data of the initial geological structure model, it is necessary to reconsider the prior geological constraints of the geological map of the area to be explored and the historical exploration results previously combined, and reset the prior geological constraints based on the second geological profile data.

[0035] The second geological profile data is based on the drilling point as the reference point, and the stratification, thickness and depth of each layer revealed by the second geological profile data are matched to the corresponding position of the first geological profile data obtained by the surface wave preliminary geological structure model, and the difference area is marked. For example: if drilling finds that the actual thickness of a certain stratum is 5 meters thicker than the preliminary geological structure model detected by surface wave, the thickness of the stratum is corrected in the preliminary geological structure model. Since the second geological profile data obtained by drilling is the data of a certain point, and the preliminary geological structure model represents the first geological profile data of the entire surface of the area to be detected, the redefined prior geological constraints also need to be considered during the correction. The first geological profile data at non-drilling points must still meet the redefined prior geological constraints.

[0036] Step S140: Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the revised signal acquisition parameters; and based on the reset prior geological constraints and the second surface wave signal, a target geological structure model is constructed, wherein the second surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the revised array layout parameters and the revised signal acquisition parameters.

[0037] In this step, the initial parameters of the array deployment and the initial parameters of the signal acquisition are modified based on the modified geological structure model and the experience feedback of the array deployment and signal acquisition in step S110.

[0038] Specifically, the optimization process for the initial array deployment parameters is as follows: If the initial survey reveals poor signal reception in certain areas, the array radius, observation point spacing, and distribution are adjusted for the current survey. Based on the modified geological structure model, the complexity of the geological structure can be understood. For areas with complex geological structures and weak initial detection signals, the array radius is appropriately reduced to 300-600 meters. The observation points are also more densely spaced, with the spacing between observation points adjusted to 1 / 3 of the surface wave wavelength. In areas with complex geological structures, the number of observation points is increased, and the distribution of observation points is modified to better capture the surface wave signal characteristics generated by the complex geological structure, thereby improving the comprehensiveness and accuracy of signal acquisition.

[0039] The optimization process for initial signal acquisition parameters is as follows: Based on an in-depth analysis of the first surface roll signal characteristics during the data correction process, the signal acquisition parameters for the current surface roll survey are adjusted. For example, for areas with complex geological structures and significant high-frequency surface roll signal loss during the initial acquisition, the sampling frequency is further increased to 300Hz or above during the current acquisition to ensure effective acquisition of surface roll signals across a wider frequency range. Furthermore, the recording duration is appropriately extended based on the geological complexity of the area being surveyed. For example, for areas with unstable data quality during the initial acquisition, the recording duration is extended to 36-72 hours to increase signal stability and reliability. During the acquisition process, data quality is monitored in real time, and the control standard for the proportion of abnormal waveforms is improved to no more than 3%. Any data anomalies are promptly identified and the cause is re-acquired.

[0040] After correcting the initial parameters of the array layout and the initial parameters of signal acquisition, the target geological structure model is constructed based on the reset prior geological constraints and the second surface wave signal. The target geological structure model can more accurately reflect the underground space structure.

[0041] The underground space detection method based on natural source surface waves and drilling in this embodiment obtains first geological profile data by constructing a preliminary geological structure model, obtains second geological profile data by drilling, and corrects the preliminary geological structure model according to the difference between the first geological profile data and the second geological profile data to obtain a corrected geological structure model. The corrected geological structure model is then used to guide the second surface wave detection to obtain the target geological structure model. Since the detection process utilizes the drilling data and core samples during the drilling process to accurately deduce the stratification of the strata, the thickness and depth of each layer, and its dynamic feedback is used to correct the preliminary geological structure model, it effectively solves the multi-solution problem inherent in the traditional natural source surface wave detection method, thereby realizing more accurate, efficient and comprehensive detection of underground space, and meeting the urgent needs of various application scenarios such as engineering construction and resource exploration for high-precision underground geological structure information.

[0042] In some embodiments, in step S110, while obtaining the first surface roll signal, the quality of the first surface roll signal is also checked in real time, and a quantitative index is established by calculating the waveform variance of the first surface roll signal, requiring that the proportion of abnormal waveforms does not exceed 5%, to determine whether there is an interference signal. Once it is found that the proportion of abnormal waveforms exceeds 5%, the first surface roll signal is acquired again. Before acquisition, the coupling between the seismic detector and the ground should be carefully checked to ensure that the coupling is tight, and the position deviation of the seismic detector is adjusted to no more than ±5 cm, so as to ensure that rich, high-quality first surface roll signals covering different frequency bands can be acquired.

[0043] In some embodiments, as Figure 2 As shown, step S110 specifically includes: Step S210: Obtain a first surface wave signal from the target area detected by the natural source surface wave detection method. Specifically, the first surface wave signal can be obtained by a seismic detector. Preferably, in this step, the obtained first surface wave signal can also be subjected to denoising and filtering to remove high-frequency noise interference. Bandpass filtering can be used for filtering, and the filtering parameters are determined based on the surface wave frequency range corresponding to the target detection depth. For example, for a detection depth of 300 meters, the surface wave frequency range is approximately 0.1-50 Hz. The lower cutoff frequency of the bandpass filter is set to 0.05 Hz and the upper cutoff frequency is set to 60 Hz to retain the effective surface wave signal frequency band while suppressing low-frequency trend items and high-frequency interference.

[0044] Step S220: Extracting a surface-roll dispersion curve from the first surface-roll signal. Specifically, the first surface-roll signal can be analyzed and processed using algorithms such as the spatial autocorrelation (SPAC) method and the frequency-wavenumber (FK) method to extract a surface-roll dispersion curve. The curve reflects how surface-roll velocity varies with frequency or wavelength.

[0045] Step S230: constructing an initial model of the preliminary geological structure model based on the prior geological constraints. Specifically, the initial model is constructed using a layered parameterization method, and includes initial estimates of model parameters such as the stratification of the strata, thickness and depth of each layer, etc.

[0046] Step S240: Based on the initial model and geophysical laws, a theoretical dispersion curve is generated by forward simulation, and the initial model is inversely optimized based on an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve to continuously adjust the model parameters of the initial model. When the objective function converges, the inversion optimization is terminated to obtain the preliminary geological structure model.

[0047] For example, the least square error can be used as the objective function Φ(m)=||d obs −d calc (m)|| 2 Among them, d obs represents the surface wave dispersion curve, which contains the information of the real underground geological structure, d calc (m) represents the theoretical dispersion curve generated by forward modeling based on the initial model m and geophysical laws, || d obs −d calc (m)|| represents d obs −d calc The norm of (m).

[0048] In this embodiment, the goal of inversion optimization is to continuously adjust the parameters of the initial model m so as to minimize the value of the objective function Φ(m), thereby making the simulation results as close as possible to the actual detection results of surface wave detection, and obtaining a model that is more consistent with the actual geological conditions, that is, the preliminary geological structure model.

[0049] In some embodiments, the inversion optimization may be performed by, but is not limited to, fusing the inversion results of multiple inversion algorithms. Specifically, in step S240, the initial model is inverted and optimized based on an objective function representing the difference between the surface wave dispersion curve and the theoretical dispersion curve, including: Step S241: Calculate the likelihood between the theoretical dispersion curve obtained by each inversion algorithm in the initial model after each inversion and the surface wave dispersion curve. The likelihood calculation method is as follows: .

[0050] Specifically, for the i The initial model of the inversion algorithm after each inversion m i , calculate the likelihood according to the above formula, σRepresents the standard deviation of noise. Likelihood calculation is based on statistical principles and reflects the degree of fit between the inversion result (theoretical dispersion curve) and the observed data (surface wave dispersion curve) under a given noise level. The higher the likelihood, the more consistent the inversion result is with the actual observation.

[0051] Step S242: assigning a weight to each inversion algorithm after each inversion according to the likelihood. The higher the likelihood of the inversion algorithm, the higher the weight assigned. That is, the higher the weight, the higher the credibility of the inversion algorithm.

[0052] Specifically, initial weights are assigned to different inversion algorithms based on their algorithmic characteristics. For example, based on extensive case analysis and theoretical research, it was found that in layered structural areas, the linear inversion algorithm has a higher likelihood and can be assigned a weight of 0.8, while the nonlinear inversion algorithm has a weight of 0.2. In complex structural areas, the nonlinear inversion algorithm exhibits greater adaptability and is assigned a weight of 0.9, while the linear inversion algorithm has a weight of 0.1. These weights are not fixed and need to be adjusted appropriately based on specific geological conditions and likelihoods to more accurately reflect the effectiveness of each algorithm in the current scenario.

[0053] Step S243: Based on the weight of the inversion algorithm, the parameters of the inverted initial models are weighted and summed to obtain a merged parameter, and the inverted merged initial model is obtained based on the merged parameter.

[0054] Step S244: Based on the initial model merged after inversion, the objective function is calculated. If the objective function converges, the inversion ends; otherwise, step S241 is executed.

[0055] Specifically, based on the theoretical dispersion curve of the initial model merged after inversion and the surface wave dispersion curve, the objective function value is calculated according to the objective function formula.

[0056] In this embodiment, multiple inversion algorithms are used in combination with the objective function to perform inversion optimization on the initial model, thereby avoiding the defects of a single inversion algorithm and making the final preliminary geological structure model closer to the actual geological conditions.

[0057] Furthermore, the a priori geological constraints are integrated into the inversion optimization process, and the model parameters are adjusted to minimize the objective function under the premise of satisfying the a priori geological constraints.

[0058] Specifically, during actual inversion calculations, model parameter adjustments must not only minimize the objective function but also meet a priori geological constraints. For example, in a certain area to be explored, the thickness of a certain stratum is known to be between 50 and 80 meters. If the inversion-derived thickness of the stratum exceeds this range, the corresponding model parameters of the initial model are adjusted to bring them closer to a reasonable range, while taking into account other constraints and optimizing the objective function, thereby obtaining a preliminary geological structure model that better conforms to the actual geological conditions.

[0059] In some embodiments, as Figure 3 As shown, step S120 specifically includes: Step S310: Determine the drilling points based on the preliminary geological structure model. Specifically, select representative locations in the preliminary geological structure model as drilling points. These points can cover the boundaries of different strata in the model or suspected abnormal areas, so as to obtain the most valuable geological information through drilling to verify and correct the preliminary geological structure model. The drilling points can also be determined in combination with the preliminary geological structure model and actual task requirements. For example, the preliminary geological structure model is used to determine that there is a longitudinal interface between the soil layer and the rock layer somewhere underground in the area to be detected. The actual task is to build a highway in the area to be detected. In order to ensure that the highway does not collapse, at this time, it is only necessary to determine the ground point corresponding to the longitudinal interface as the drilling point to determine the exact position and depth of the longitudinal interface between the soil layer and the rock layer, and provide data support for road construction.

[0060] Step S320: Control the drilling system to drill at the drilling point and obtain drilling data. After obtaining the drilling point, the drilling system can be controlled to perform drilling operations at the drilling point.

[0061] Specifically, sensors measuring drilling speed, weight on bit, and torque are installed at appropriate locations within the drilling system. These sensors capture drilling data. For example, a weight on bit sensor is installed in the hydraulic feed system's pressure line, with a range of 0 to 50 MPa. A torque sensor is integrated into the drive shaft of the power unit and equipped with a temperature compensation module to mitigate the effects of frictional heat. A 16-channel data acquisition box with a sampling frequency of at least 10 Hz transmits drilling data collected by each sensor to a surface industrial computer in real time via the CAN bus, ensuring a complete record of drilling data waveforms throughout the entire drilling process.

[0062] Step S330: De-noise the drilling data to obtain de-noised drilling data. Due to various factors, such as equipment vibration, sensor errors, and formation heterogeneity, drilling data inevitably contains high-frequency vibration noise. Failure to effectively remove this noise can affect the reliability of subsequent data analysis and even lead to erroneous geological survey results. Therefore, de-noising the drilling data is necessary.

[0063] Step S340: performing correlation analysis on the drilling data after noise reduction and the geological characteristics, and combining the core logging data of the drilling system to mark the stratification of the strata, the thickness and depth of each layer, so as to obtain the second geological profile data.

[0064] In some embodiments, in step S320, while controlling the drilling system to drill at the specified drilling location, a trajectory measurement system is used to measure the drilling trajectory every s (e.g., 2 drill rods, approximately 6 meters). When the deviation of the drilling trajectory exceeds a preset deviation threshold, a deviation correction program is triggered to replace the drill bit and / or adjust the drilling data to correct the drilling trajectory, where s is greater than or equal to 1. In this embodiment, deviation correction ensures that the borehole reaches the specified drilling location vertically. After drilling is completed, the extracted core is systematically cataloged to more accurately describe the stratification of the strata at the drilling location, the thickness and depth of each layer, and other characteristics.

[0065] In some embodiments, step S330 specifically includes: Step S331: Calculate the crest factor of the drilling data in segments based on the length of a single drill rod, thereby achieving refined segmented analysis of the drilling data.

[0066] In order to quantify the fluctuation degree of drilling data, the crest factor (Crest Factor, C) is used to characterize the fluctuation characteristics in the time domain. The calculation formula of the crest factor is: ; .

[0067] in, x peak is the peak value of the signal corresponding to the drilling data, and takes the maximum absolute value. x rms is the root mean square of the signal corresponding to the drilling data, indicating the effective value of the data, x i Indicates the first i A high crest factor indicates the presence of significant abnormal peaks in the signal. By calculating the crest factor, the fluctuation characteristics of different drilling stages can be quantified, providing a basis for selecting noise reduction methods.

[0068] It should be noted that before calculating the crest factor, the drilling data obtained should be formed into a standardized data table or database. First, the timestamp should be unified: all sensor data should be synchronized to the same time base (accuracy 0.1s), and then the units should be unified to the international standard units. Finally, the extreme data points should be 3 σ The criteria are identified and filtered to form a unified data storage format, such as CSV file format.

[0069] Step S332: Based on the distribution range of the crest coefficient, different fluctuation stages corresponding to different ranges of the crest coefficient are divided, and different fluctuation stages reflect different fluctuation degrees of drilling data.

[0070] Specifically, the fluctuation characteristic curve of each fluctuation stage is drawn according to the calculated crest coefficient. The fluctuation characteristic curve can intuitively reflect the distribution range of the crest coefficient. According to the distribution range of the crest coefficient, the corresponding relationship between the C value and the noise level is established. Specifically, it is divided into the following three fluctuation stages: Low fluctuation stage (C<2): The signal is stable and the noise is mainly high-frequency small fluctuations.

[0071] Moderate fluctuation stage (2≤C<5): There are obvious peaks, which may be caused by formation changes or equipment vibration.

[0072] High fluctuation stage (C≥5): Extreme abnormal peaks, which may be caused by equipment failure or strong formation changes.

[0073] Step S333: selecting a noise reduction algorithm and a noise reduction threshold corresponding to different fluctuation stages to perform noise reduction processing on the drilling data to obtain noise-reduced drilling data.

[0074] Specifically, based on the three fluctuation stages mentioned above, different noise reduction algorithms and noise reduction thresholds are selected as follows: For low-volatility phases (C < 2), the db4 wavelet basis is selected, with a decomposition level of 3-4. The wavelet transform is a time-frequency analysis method, and the db4 wavelet basis is well suited to the local characteristics of the signal when processing this type of high-frequency, small-amplitude noise. By properly setting the number of decomposition levels, it can effectively remove high-frequency, small-amplitude noise while preserving stationary signal characteristics.

[0075] For moderate fluctuations (2 ≤ C < 5), the sym5 wavelet basis is selected, along with soft thresholding. The sym5 wavelet basis exhibits certain symmetry and regularity, suppressing significant spikes while preserving the most effective signal from stratum changes. Soft thresholding zeroes any wavelet coefficients below a threshold and subtracts the threshold from any coefficients above it, thereby removing noise while minimizing the impact on the effective signal. For example, if the soft threshold is set to 5, then any wavelet coefficients with an absolute value less than 5 are zeroed, while any coefficients above 5 are subtracted by 5.

[0076] During high-volatility periods (C ≥ 5), set reasonable sensor data floating thresholds to truncate outliers. During this period, outliers can severely interfere with drilling data. Setting a floating threshold effectively prevents interference from equipment failures or significant formation changes. The floating threshold setting should be determined based on the equipment's normal operating range and historical geological data for the area. For example, if historically, the normal drilling speed in the area being explored ranged from 1 mm / s to 5 mm / s, the floating threshold could be set with a lower limit of 1 mm / s and an upper limit of 5 mm / s.

[0077] Furthermore, after the drilling data is denoised, the process of verifying the denoising effect is also included. Specifically, the denoising effect can be verified by calculating indicators such as the signal-to-noise ratio (SNR), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) of the signals before and after denoising. The calculation formulas for each indicator are as follows: ; ; .

[0078] in, P sig is the signal power of the drilling data corresponding signal, P noi is the noise power, x peak max is the maximum peak value of the corresponding signal of the drilling data before noise reduction, and Represents the first i Drilling data is collected. After noise reduction, if the SNR is significantly improved, the smaller the RMSE, and the higher the PSNR, the expected noise reduction effect has been achieved. Specifically, the core logging data can be used to identify different drilling stages and verify the noise reduction effect based on different indicators for each drilling stage.

[0079] During the steady drilling phase, the signal is relatively stable, and the primary focus is on noise suppression. Therefore, the SNR is the preferred method for verifying the effectiveness of noise reduction. Calculate the SNR before and after noise reduction to check for time domain waveform smoothness and compare the spectrograms. If the SNR after noise reduction improves by more than 5dB compared to the pre-noise reduction SNR, high-frequency noise has been suppressed.

[0080] During the formation change phase, the signal will experience some spikes or mutations. These features reflect the formation changes. Therefore, during this phase, RMSE is preferably used to verify the noise reduction effect. Calculate RMSE. If RMSE is < 0.1, check whether the spikes are intact and compare them with the core data. If the spikes are intact, confirm that the formation characteristics are consistent.

[0081] During device vibration, the signal exhibits periodic fluctuations. PSNR primarily focuses on the relationship between the peak value of the signal and the root mean square error of the noise. Therefore, PSNR is the preferred method for verifying noise reduction effectiveness during this period. Calculate PSNR to check whether periodic fluctuations are being adequately suppressed and compare it to device status records. If PSNR > 30dB, the vibration signature is confirmed to be effectively noise-reduced.

[0082] In this embodiment, more accurate drilling data can be obtained after noise reduction, avoiding interference of high-frequency vibration data on the drilling data.

[0083] It should be noted that the drilling stage and the fluctuation stage describe the drilling process from different perspectives, and each drilling stage can be divided into the three different fluctuation stages mentioned above.

[0084] In some embodiments, step S340 specifically includes: Step S341: Expanding lithologic characteristic quantitative indices based on the drilling data after noise reduction, wherein the lithologic characteristic quantitative indices include: drilling specific work and torque fluctuation coefficient.

[0085] Drilling specific work reflects the energy consumed in breaking a unit volume of rock. Different rock types require different energies during drilling due to their different physical and mechanical properties. Therefore, drilling specific work can be used as an important quantitative indicator to distinguish rock types. The calculation formula for drilling specific work is as follows: .

[0086] in, WOB Indicates bit pressure, RPM Indicates the drill pipe speed, ROP Indicates drilling speed.

[0087] The torque fluctuation coefficient reflects the stability of the torque during drilling. Different lithology and formation structure will cause the resistance of the drill bit to change, which in turn causes torque fluctuation. Therefore, the torque fluctuation coefficient is of great significance for identifying lithology and formation characteristics. The torque fluctuation coefficient calculation formula is as follows: .

[0088] Where A represents the cross-sectional area of the drill hole (cm²), is the torque standard deviation; is the mean torque.

[0089] Step S342: In combination with the core logging data, a quantitative mapping relationship between the drilling data and the stratum stratification is established. Since the stratification mainly characterizes the lithology of each layer, the quantitative mapping relationship is also a quantitative mapping relationship between the drilling data and the lithology.

[0090] In this step, through big data statistical analysis and combined with the core logging data obtained from this drilling, a quantitative mapping relationship between drilling data (mainly drilling speed), lithologic characteristic quantitative indicators, and lithology is established, as shown in Table 1: Table 1 Mapping relationship between drilling data, lithologic characteristics quantitative indicators and lithology

[0091] It should be noted that this mapping relationship, i.e., Table 1 above, can be directly obtained through statistical analysis of a large amount of actual engineering data. However, in actual application, it may be affected by various factors, such as differences in drilling systems and changes in drilling direction. Therefore, when using this mapping relationship, appropriate adjustments should be made based on specific circumstances. Therefore, in this embodiment, this mapping relationship is derived based on statistical analysis of a large amount of actual engineering data and verification of the core logging data obtained in this drilling. This makes the mapping relationship more consistent with the actual conditions of the area to be explored, and the resulting second geological profile data is also more accurate.

[0092] Step S343: Mark the stratification, thickness and depth of each layer by using the real-time drilling data in the hole and the quantitative mapping relationship to obtain the second geological profile data. Specifically, the lithology of the current rock layer can be determined by the real-time drilling speed, drilling power and torque fluctuation coefficient. Figure 4 The relationship between drilling speed and drilling depth is used to obtain the thickness and depth of the current rock layer, thereby marking the stratification of the stratum and the thickness and depth of each layer.

[0093] Furthermore, to facilitate observation by researchers, this embodiment uses multi-dimensional visualization analysis of the noise-reduced drilling data. Using borehole depth as the horizontal axis, dual vertical axes or multiple subgraphs are used to simultaneously display characteristic curves of key drilling parameters such as weight on bit (WOB), drill pipe speed (RPM), torque, and rate of penetration (ROP) as they change with depth, enabling comparative analysis of multiple drilling data sets. This visualization approach intuitively presents the changing trends of different parameters at different depths, helping researchers identify interrelationships between drilling data and potential associations with geological features.

[0094] In some embodiments, step S130 specifically includes: Based on the second geological profile data, the prior geological constraints are reset, and the constraints at positions corresponding to the drilling points in the prior geological constraints are changed, for example, the stratum thickness range at positions corresponding to the drilling points in the prior geological constraints.

[0095] The second geological profile data is used with the drilling point as the reference point, and the stratification of the strata in the area to be detected, the thickness and depth of each layer revealed by the second geological profile data are matched to the corresponding positions of the first geological profile data. If there is any difference, the preliminary geological structure model is adjusted based on the second geological profile data; Using the adjusted preliminary geological structure model as the initial model and minimizing the above objective function as the goal, the adjusted preliminary geological structure model is inversely optimized. When the objective function converges, the revised geological structure model is obtained. During the inversion optimization process, the adjustment of the model parameters is ensured to meet the reset prior geological constraints.

[0096] Specifically, the inversion optimization process is substantially the same as the above step S240 , except that in step S130 , the adjusted preliminary geological structure model is used as the initial model for inversion optimization, and finally a revised geological structure model is obtained.

[0097] Since the revised geological structure model is obtained by reference to the second geological profile data obtained by drilling, it is closer to the actual underground geological structure than the initial geological structure model.

[0098] In some embodiments, in step S140, during the construction of the target geological structure model based on the reset prior geological constraints and the second surface roll signal, the dispersion curve extraction method is optimized. In addition to the spatial autocorrelation (SPAC) method and the frequency-wavenumber (F-K) method used in the initial detection, the phase shift method can also be used, which offers higher accuracy in extracting dispersion curves for complex geological structures. After extracting a new surface roll dispersion curve from the second surface roll signal using the phase shift method, steps S230 and S240 are performed. The difference is that in step S230, a new initial model is constructed based on the reset prior geological constraints. In step S240, a new theoretical dispersion curve is generated using forward modeling based on the new initial model and geophysical laws. Inverse optimization is then performed on the new initial model based on an objective function representing the difference between the new surface roll dispersion curve and the new theoretical dispersion curve, continuously adjusting the model parameters of the new initial model. When the objective function converges, the inversion optimization concludes, resulting in the target geological structure model.

[0099] In this embodiment, since the revised geological structure model is closer to the actual underground geological structure, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised based on the revised geological structure model to obtain the revised array layout parameters and the signal acquisition revised parameters. Then, secondary surface wave detection is performed based on the revised array layout parameters and the signal acquisition revised parameters to obtain a second surface wave signal. The second surface wave signal has higher reliability and authenticity than the first surface wave signal, and the dispersion curve corresponding to the second surface wave signal also has higher reliability and authenticity. Therefore, the target geological structure model inverted by the objective function of the dispersion curve corresponding to the second surface wave signal and the theoretical dispersion curve forward modeled by the new initial model also has higher reliability, accuracy and authenticity.

[0100] The underground space detection device based on natural source surface waves and drilling provided by the present invention is described below. The underground space detection device based on natural source surface waves and drilling described below and the underground space detection method based on natural source surface waves and drilling described above can be referenced to each other.

[0101] The underground space detection device based on natural source surface waves and drilling in the embodiment of the present invention is as follows: Figure 5 Shown, including: The preliminary model construction module 510 is used to construct a preliminary geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, and obtain first geological profile data based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0102] The profile data acquisition module 520 is used to control the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model, and to acquire second geological profile data obtained by drilling.

[0103] The model correction module 530 is used to correct the preliminary geological structure model based on the difference between the first geological profile data and the second geological profile data to obtain a corrected geological structure model, and reset the prior geological constraints based on the second geological profile data.

[0104] The target model construction module 540 is used to correct the initial parameters of the array layout and the initial parameters of the signal acquisition based on the modified geological structure model to obtain the corrected array layout parameters and the corrected signal acquisition parameters, and to construct the target geological structure model based on the reset prior geological constraints and the second surface wave signal, wherein the second surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the corrected array layout parameters and the corrected signal acquisition parameters.

[0105] The underground space detection device based on natural source surface waves and drilling in an embodiment of the present invention obtains first geological profile data by constructing a preliminary geological structure model, obtains second geological profile data by drilling, and corrects the preliminary geological structure model according to the difference between the first geological profile data and the second geological profile data to obtain a corrected geological structure model. The second surface wave detection is then guided by the corrected geological structure model to obtain a target geological structure model. Since the detection process utilizes the drilling data and core samples in the drilling process to accurately deduce the stratification of the strata, the thickness and depth of each layer, and its dynamic feedback is used to correct the preliminary geological structure model, it effectively solves the multi-solution problem inherent in the traditional natural source surface wave detection method, thereby realizing more accurate, efficient and comprehensive detection of underground space, and meeting the urgent needs of various application scenarios such as engineering construction and resource exploration for high-precision underground geological structure information.

[0106] In some embodiments, the preliminary judgment model building module 510 includes: The surface wave signal acquisition module is used to acquire the first surface wave signal of the area to be detected by the natural source surface wave detection method.

[0107] The dispersion curve extraction module is configured to extract a surface roll dispersion curve from the first surface roll signal.

[0108] The initial model construction module is used to construct the initial model of the preliminary geological structure model based on the priori geological constraints.

[0109] The model optimization module is used to generate a theoretical dispersion curve by forward simulation based on the initial model and geophysical laws, and to perform inverse optimization on the initial model based on an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve, so as to continuously adjust the model parameters of the initial model. When the objective function converges, the inversion optimization is terminated to obtain the preliminary geological structure model.

[0110] In some embodiments, the model optimization module is specifically configured to perform the following steps: S1: Calculating the likelihood between the theoretical dispersion curve obtained by the initial model of each inversion algorithm after each inversion and the surface wave dispersion curve.

[0111] S2: assigning a weight to each inversion algorithm after each inversion according to the likelihood, wherein the higher the likelihood of the inversion algorithm, the higher the weight assigned to the algorithm.

[0112] S3: Based on the weight of the inversion algorithm, the parameters of the initial models after each inversion are weighted and summed to obtain the merged parameters, and the merged initial model after inversion is obtained based on the merged parameters.

[0113] S4: Based on the initial model merged after inversion, the objective function is calculated. If the objective function converges, the inversion ends; otherwise, step S1 is executed.

[0114] In some embodiments, the cross-sectional data acquisition module 520 includes: The drilling point determination module is used to determine the drilling point based on the preliminary geological structure model.

[0115] The drilling control module is used to control the drilling system to drill at the drilling point and obtain drilling data.

[0116] The drilling data denoising module is used to perform denoising processing on the drilling data to obtain denoised drilling data.

[0117] The correlation analysis module is used to perform correlation analysis on the drilling data after noise reduction and geological characteristics, and combine it with the core logging data of the drilling system to mark the stratification of the strata, the thickness and depth of each layer to obtain the second geological profile data.

[0118] In some embodiments, the drilling control module is specifically used to measure the drilling trajectory once every s drill rods using a trajectory measurement system during the drilling process. When the deviation rate of the drilling trajectory is greater than a preset deviation rate threshold, a correction program is triggered to replace the drill bit and / or adjust the drilling data to correct the drilling trajectory, where s is greater than or equal to 1.

[0119] In some embodiments, the drilling data noise reduction module includes: The crest factor calculation module is used to calculate the crest factor of the drilling data in sections based on the length of a single drill rod.

[0120] The fluctuation stage division module is used to divide the distribution range of the crest coefficient into different fluctuation stages corresponding to different ranges of the crest coefficient, and different fluctuation stages reflect different fluctuation degrees of drilling data.

[0121] The noise reduction processing module is used to select a noise reduction algorithm and a noise reduction threshold corresponding to different fluctuation stages to perform noise reduction processing on the drilling data to obtain the drilling data after noise reduction.

[0122] In some embodiments, the association analysis module includes: The index expansion module is used to expand the lithologic characteristic quantitative index based on the drilling data after noise reduction, and the lithologic characteristic quantitative index includes: drilling specific work and torque fluctuation coefficient.

[0123] The mapping relationship establishment module is used to establish a quantitative mapping relationship between the drilling data and the stratum stratification conditions in combination with the core logging data.

[0124] The stratum marking module is used to mark the stratification of the stratum, the thickness and depth of each layer through the real-time drilling data in the hole and the quantitative mapping relationship to obtain the second geological profile data.

[0125] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call logic instructions in the memory 630 to execute an underground space detection method based on natural source surface waves and drilling, the method including: Based on the prior geological constraints of the area to be detected and the first surface wave signal, a preliminary geological structure model is constructed, and first geological profile data is obtained based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0126] The drilling system is controlled to perform drilling operations at the drilling points determined in the preliminary geological structure model, and second geological profile data obtained by drilling is acquired.

[0127] Based on the difference between the first geological profile data and the second geological profile data, the preliminary geological structure model is revised to obtain a revised geological structure model, and the priori geological constraints are reset based on the second geological profile data.

[0128] Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the revised signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed, wherein the natural source surface wave detection method detects the second surface wave signal based on the revised array layout parameters and the revised signal acquisition parameters.

[0129] Furthermore, the logic instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] On the other hand, the present invention further provides a computer program product, comprising a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the underground space detection method based on natural source surface waves and drilling provided by the above methods, the method comprising: Based on the prior geological constraints of the area to be detected and the first surface wave signal, a preliminary geological structure model is constructed, and first geological profile data is obtained based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0131] The drilling system is controlled to perform drilling operations at the drilling points determined in the preliminary geological structure model, and second geological profile data obtained by drilling is acquired.

[0132] Based on the difference between the first geological profile data and the second geological profile data, the preliminary geological structure model is revised to obtain a revised geological structure model, and the priori geological constraints are reset based on the second geological profile data.

[0133] Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the revised signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed, wherein the natural source surface wave detection method detects the second surface wave signal based on the revised array layout parameters and the revised signal acquisition parameters.

[0134] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the underground space detection method based on natural source surface waves and drilling provided by the above methods, the method comprising: Based on the prior geological constraints of the area to be detected and the first surface wave signal, a preliminary geological structure model is constructed, and first geological profile data is obtained based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the initial parameters of the array layout and the initial parameters of the signal acquisition.

[0135] The drilling system is controlled to perform drilling operations at the drilling points determined in the preliminary geological structure model, and second geological profile data obtained by drilling is acquired.

[0136] Based on the difference between the first geological profile data and the second geological profile data, the preliminary geological structure model is revised to obtain a revised geological structure model, and the priori geological constraints are reset based on the second geological profile data.

[0137] Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the revised signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed, wherein the natural source surface wave detection method detects the second surface wave signal based on the revised array layout parameters and the revised signal acquisition parameters.

[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0139] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for underground space detection based on natural source surface waves and drilling, characterized in that: include: Constructing a preliminary geological structure model based on a priori geological constraints of the area to be detected and a first surface wave signal, and obtaining first geological profile data based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected using a natural source surface wave detection method based on initial array layout parameters and initial signal acquisition parameters; Controlling the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model, and obtaining second geological profile data obtained by drilling; Based on the difference between the first geological section data and the second geological section data, the preliminary geological structure model is revised to obtain a revised geological structure model, and the prior geological constraint conditions are reset based on the second geological section data; Based on the revised geological structure model, the initial parameters of the array layout and the initial parameters of the signal acquisition are revised to obtain the revised array layout parameters and the signal acquisition revised parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed, wherein the second surface wave signal is obtained by detecting the area to be detected by the natural source surface wave detection method based on the revised array layout parameters and the signal acquisition revised parameters.

2. The underground space detection method based on natural source surface waves and drilling according to claim 1 is characterized in that: Based on the prior geological constraints of the area to be detected and the first surface wave signal, a preliminary geological structure model is constructed, including: Acquiring a first surface wave signal of the area to be detected detected by the natural source surface wave detection method; extracting a surface wave dispersion curve from the first surface wave signal; Constructing an initial model of the preliminary geological structure model based on the prior geological constraints; Based on the initial model and geophysical laws, a theoretical dispersion curve is generated by forward simulation. The initial model is inversely optimized based on an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve to continuously adjust the model parameters of the initial model. When the objective function converges, the inversion optimization is completed and the preliminary geological structure model is obtained.

3. The underground space detection method based on natural source surface waves and drilling according to claim 2, characterized in that: The initial model is inversely optimized based on an objective function representing a difference between the surface wave dispersion curve and the theoretical dispersion curve, comprising: S1: Calculate the likelihood between the theoretical dispersion curve obtained by the initial model of each inversion algorithm after each inversion and the surface wave dispersion curve; S2: assigning a weight to each inversion algorithm after each inversion according to the likelihood, wherein the higher the likelihood, the higher the weight assigned to the inversion algorithm; S3: Based on the weight of the inversion algorithm, the parameters of the initial models after each inversion are weighted and summed to obtain the merged parameters, and the merged initial model after inversion is obtained based on the merged parameters; S4: Based on the initial model merged after inversion, the objective function is calculated. If the objective function converges, the inversion ends; otherwise, step S1 is executed.

4. The underground space detection method based on natural source surface waves and drilling according to any one of claims 1 to 3, characterized in that: Controlling the drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model and obtaining second geological profile data obtained by drilling, including: Determining the drilling locations based on the preliminary geological structure model; Controlling the drilling system to drill at the drilling point and acquiring drilling data; performing noise reduction processing on the drilling data to obtain noise-reduced drilling data; The noise-reduced drilling data is correlated with the geological characteristics and combined with the core logging data of the drilling system to mark the stratification, thickness and depth of each layer to obtain the second geological profile data.

5. The underground space detection method based on natural source surface waves and drilling according to claim 4 is characterized in that: Controlling the drilling system to drill at the drilling point includes: During the drilling process, the trajectory measurement system is used to measure the drilling trajectory once every s drill rods. When the deviation rate of the drilling trajectory is greater than the preset deviation rate threshold, the correction program is triggered to replace the drill bit and / or adjust the drilling data to correct the drilling trajectory, where s is greater than or equal to 1.

6. The underground space detection method based on natural source surface waves and drilling according to claim 4, characterized in that: Performing noise reduction processing on the drilling data to obtain noise-reduced drilling data includes: Calculating the crest factor of the drilling data in sections, taking the length of a single drill pipe as a unit; Based on the distribution range of the crest coefficient, different fluctuation stages corresponding to different ranges of the crest coefficient are divided, and different fluctuation stages reflect different fluctuation degrees of drilling data; A noise reduction algorithm and a noise reduction threshold corresponding to different fluctuation stages are selected to perform noise reduction processing on the drilling data to obtain noise-reduced drilling data.

7. The underground space detection method based on natural source surface waves and drilling according to claim 4 is characterized in that: The noise-reduced drilling data is correlated with geological features and combined with the core logging data from the drilling system to annotate the strata, thickness, and depth of each layer to obtain the second geological profile data, including: Expanding lithologic characteristic quantitative indicators based on the noise-reduced drilling data, the lithologic characteristic quantitative indicators including: drilling specific work and torque fluctuation coefficient; In combination with the core logging data, a quantitative mapping relationship between drilling data and stratigraphic stratification is established; The stratification of the stratum, the thickness and depth of each layer are marked by the real-time drilling data in the hole and the quantitative mapping relationship to obtain the second geological profile data.

8. An underground space detection device based on natural source surface waves and drilling, characterized in that: include: a preliminary model construction module, configured to construct a preliminary geological structure model based on a priori geological constraints of the area to be detected and a first surface wave signal, and to obtain first geological profile data based on the preliminary geological structure model, wherein the first surface wave signal is obtained by detecting the area to be detected using a natural source surface wave detection method based on initial array layout parameters and initial signal acquisition parameters; a profile data acquisition module, configured to control a drilling system to perform drilling operations at the drilling points determined in the preliminary geological structure model, and to acquire second geological profile data obtained by drilling; a model correction module, configured to correct the preliminary geological structure model based on the difference between the first geological section data and the second geological section data to obtain a corrected geological structure model, and reset the prior geological constraints based on the second geological section data; A target model construction module is used to correct the initial parameters of the array layout and the initial parameters of the signal acquisition based on the corrected geological structure model to obtain the corrected array layout parameters and the corrected signal acquisition parameters, and to construct a target geological structure model based on the reset prior geological constraints and the second surface wave signal, wherein the second surface wave signal is obtained by detecting the area to be detected by a natural source surface wave detection method based on the corrected array layout parameters and the corrected signal acquisition parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the underground space detection method based on natural source surface waves and drilling is implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the underground space detection method based on natural source surface waves and drilling as described in any one of claims 1 to 7 is implemented.

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