Methods and devices for underground space exploration based on natural source surface waves and drilling.

Abstract

This invention relates to the field of underground exploration technology, providing a method and apparatus for underground space exploration based on natural source surface waves and drilling. The method includes: constructing a preliminary geological structure model based on prior geological constraints of the area to be explored and a first surface wave signal; obtaining first geological profile data based on the preliminary geological structure model; performing drilling operations at borehole locations determined in the preliminary geological structure model and acquiring second geological profile data; correcting the preliminary geological structure model based on the difference between the first and second geological profile data to obtain a corrected geological structure model; resetting the prior geological constraints based on the second geological profile data; correcting the initial parameters of the array deployment and signal acquisition based on the corrected geological structure model; and constructing a target geological structure model based on the reset prior geological constraints and the second surface wave signal. This invention achieves more accurate, efficient, and comprehensive exploration of underground space.

Description

Technical Field

[0001] This invention relates to the field of underground exploration technology, and in particular to an underground space exploration method and apparatus based on natural source surface waves and drilling. Background Technology

[0002] Natural source surface wave detection is a geophysical exploration method based on the propagation characteristics of seismic waves. Its principle is to utilize the differences in propagation speed of surface waves excited by natural sources (such as environmental noise) in different strata. By acquiring and analyzing surface wave signals, structural information of the subsurface strata can be obtained through inversion. However, due to the ambiguity of the surface wave inversion process, relying solely on surface wave detection data is often insufficient to accurately determine the detailed lithology and specific structural characteristics of subsurface strata. Furthermore, it is easily affected by interference factors, leading to uncertainties in the detection results.

[0003] Therefore, developing an accurate and comprehensive method for underground space exploration is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This invention provides a method and apparatus for underground space exploration based on natural source surface waves and drilling, in order to solve the aforementioned technical problems existing in the prior art. This invention provides a method for underground space exploration based on natural source surface waves and drilling, 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected. The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the drilling. 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data. Based on the modified geological structure model, the initial parameters of the array deployment and the initial parameters of signal acquisition are modified to obtain the modified array deployment parameters and the modified signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed. The second surface wave signal is obtained by the natural source surface wave detection method based on the modified array deployment parameters and the modified signal acquisition parameters to detect the area to be detected.

[0005] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided. Based on the prior geological constraints of the area to be explored and the first surface wave signal, a preliminary geological structure model is constructed, including: Obtain the first surface wave signal of the region to be detected by the natural source surface wave detection method; Extract the surface wave dispersion curve from the first surface wave signal; An initial model for constructing the preliminary geological structure model based on the aforementioned prior geological constraints; Based on the initial model and geophysical laws, a theoretical dispersion curve is generated using forward modeling. The initial model is then inverted and optimized using an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve. The model parameters of the initial model are continuously adjusted. When the objective function converges, the inversion optimization ends, and the preliminary geological structure model is obtained.

[0006] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided, which performs inversion optimization on the initial model based on an objective function characterizing the difference between the surface wave dispersion curve and the theoretical dispersion curve, including: S1: Calculate the likelihood of the theoretical dispersion curve obtained by each inversion algorithm after each inversion and the surface wave dispersion curve. S2: Assign weights to each inversion algorithm after each inversion based on the likelihood. The higher the likelihood of the inversion algorithm, the higher the weight assigned. S3: Based on the weights of the inversion algorithm, the parameters of the respective inverted initial models are weighted and summed to obtain the merging parameters. Based on the merging parameters, the inverted and merged initial models are obtained. S4: Based on the initial model after inversion and merging, calculate the objective function. If the objective function converges, the inversion ends; otherwise, proceed to step S1.

[0007] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided, which controls the drilling system to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and acquires second geological profile data obtained from the drilling, including: Based on the preliminary geological structure model, the borehole locations were determined; The drilling system is controlled to drill at the borehole location and drilling data is acquired. The drilling data is denoised to obtain denoised drilling data; The noise-reduced drilling data was correlated with geological features, and the core logging data of the drilling system was combined to mark the strata, the thickness and depth of each layer, so as to obtain the second geological profile data.

[0008] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided, which controls the drilling system to drill at the borehole location, comprising: During the drilling process, a trajectory measurement system is used to measure the borehole trajectory once every s for each drill rod. When the deviation rate of the borehole trajectory is greater than the preset deviation rate threshold, a correction procedure is triggered to replace the drill bit and / or adjust the drilling data to correct the borehole trajectory. Here, s is greater than or equal to 1.

[0009] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided, wherein the drilling data is denoised to obtain denoised drilling data, including: The crest coefficient of the drilling data is calculated in segments, using the length of a single drill pipe as the unit. Based on the distribution range of the peak coefficient, different fluctuation stages are divided according to different ranges of the peak coefficient. Different fluctuation stages reflect different degrees of fluctuation in the drilling data. The drilling data is denoised by selecting a denoising algorithm and denoising threshold corresponding to different fluctuation stages to obtain denoised drilling data.

[0010] According to the present invention, a method for underground space exploration based on natural source surface waves and drilling is provided. This method performs correlation analysis between noise-reduced drilling data and geological features, and combines this with core logging data from the drilling system to label the strata, thickness, and depth of each layer, in order to obtain second geological profile data, including: The lithological characteristic quantification index is extended based on the noise-reduced drilling data. The lithological characteristic quantification index includes: drilling specific power and torque fluctuation coefficient. Based on the core logging data, a quantitative mapping relationship between drilling data and formation stratification is established; By using real-time drilling data from the borehole and the aforementioned quantitative mapping relationship, the strata are labeled with their layering, thickness, and depth, thus obtaining second geological profile data.

[0011] The present invention also provides an underground space exploration device based on natural source surface waves and drilling, comprising the following modules: The preliminary judgment model construction module is used to construct a preliminary judgment geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, and to obtain the first geological profile data based on the preliminary judgment geological structure model. The first surface wave signal is obtained 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 in the area to be detected.

[0012] The profile data acquisition module is used to control the drilling system to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the 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 to reset the prior geological constraints based on the second geological profile data.

[0014] The target model construction module is used to correct the initial parameters of the array layout and the initial parameters of signal acquisition based on the modified geological structure model, so as to obtain the corrected parameters of the array layout and the corrected parameters of signal acquisition. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed. The natural source surface wave detection method is used to detect the second surface wave signal based on the corrected parameters of the array layout and the corrected parameters of signal acquisition.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the underground space exploration method based on natural source surface waves and drilling as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the underground space exploration method based on natural source surface waves and drilling as described above.

[0017] The present invention provides a method and apparatus for underground space exploration based on natural source surface waves and drilling. This method constructs a preliminary geological structure model to obtain first geological profile data, then drills to obtain second geological profile data. Based on the differences between the first and second geological profile data, the preliminary geological structure model is corrected to obtain a corrected geological structure model. This corrected geological structure model then guides a second surface wave probe to obtain the target geological structure model. Because this exploration process utilizes drilling data and core samples to accurately deduce the stratification, thickness, and depth of each layer, and dynamically feeds this data back to correct the preliminary geological structure model, it effectively solves the inherent multi-solution problem of traditional natural source surface wave probe methods. This enables more accurate, efficient, and comprehensive exploration of underground space, meeting the urgent need for high-precision underground geological structure information in various engineering construction, resource exploration, and other application scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this 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 this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

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

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

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

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

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

[0024] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The underground space exploration method based on natural source surface waves and drilling, as described in this invention embodiment, is as follows: Figure 1 As shown, the procedure includes 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected.

[0028] Specifically, before employing the natural source surface wave detection method, a seismic array needs to be deployed. The array layout is crucial to data quality and inversion interpretation results. Array layout parameters include: array shape (e.g., linear and circular) and observation point placement (e.g., spacing between points). For example, based on the size of the area to be detected, topographical conditions, and target detection depth (e.g., 300 meters), a circular array (observation points arranged on a circle) is selected. Circular arrays offer significant advantages in omnidirectional signal reception and data processing, making them suitable for detecting complex geological structures. In array design, the relationship between observation point spacing and detection depth needs to be clearly defined. The array radius, based on empirical formulas for surface wave exploration, can be set to 450-900 meters. The spacing between observation points should be set to half the surface wave wavelength (λ), and the surface wave wavelength must be determined in advance through pre-detection and dispersion analysis. Simultaneously, seismic detectors must be precisely installed at each observation point to ensure close coupling with the ground, efficiently acquiring the first surface wave signal from natural surface waves, thus ensuring the accuracy and reliability of the detection work.

[0029] In this step, a high-precision seismic detector can be used to continuously record the first surface wave signal at each observation point for an extended period. The signal acquisition parameters include sampling frequency and recording duration. During acquisition, the sampling frequency is flexibly adjusted according to the target detection depth and the surface wave frequency range. For example, if the estimated surface wave frequency range is 0.1~100Hz, a sampling frequency of 200Hz or higher is set to acquire high-frequency signals, ensuring that the sampling frequency is at least twice the highest expected surface wave frequency. The recording duration depends on the geological complexity of the area to be detected and the signal stability. For example, 8~12 hours is set for areas with simple geological structures and stable signals, while 24~48 hours is extended for areas with complex geological structures or large fluctuations.

[0030] The prior geological constraints can be obtained based on the geological map of the area to be explored and historical exploration results (specific forms of prior geological constraints include: a certain stratum thickness range: 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; a surface wave velocity range: combined with core test data, the shear wave velocity of sandstone is set to 800-1800 m / s, and that of granite to 1500-3000 m / s). These prior geological constraints are used to construct the initial model for the preliminary geological structure model. The preliminary geological structure model is obtained through forward and inverse simulations based on the first surface wave signal and the initial model. This preliminary geological structure model includes strata stratification (different rock layers, each with different lithology), the thickness and depth of each layer, and other model parameters. These model parameters characterize the first geological profile data.

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

[0032] In this step, drilling exploration is used to obtain second geological profile data of the area to be explored. Drilling exploration involves directly drilling into the ground to obtain rock core samples for direct observation. Combined with the drilling data, the strata's layering, thickness, and depth are accurately deduced. Therefore, accurate and detailed second geological profile data can be obtained through drilling exploration. This drilling data includes: drilling depth, drilling speed (which can be calculated from drilling depth and time), drilling pressure, and torque. The relationship between drilling speed and drilling depth is presented in the form of a graph, 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 explored 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data.

[0034] In this step, since the data of the second geological profile is different from the data of the first geological profile in the preliminary 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 combined with the data of the second geological profile, and reset the prior geological constraints based on the data of the second geological profile.

[0035] Using the borehole locations as reference points, the second geological profile data is used to map the stratigraphic layering, thickness, and depth of each layer revealed by the second geological profile data to the corresponding locations in the first geological profile data obtained from the surface wave preliminary geological structure model, thus identifying areas of difference. For example, if drilling reveals that the actual thickness of a certain stratum is 5 meters thicker than that in the preliminary geological structure model obtained from surface wave detection, the thickness of that stratum is corrected in the preliminary geological structure model. Since the second geological profile data obtained from drilling represents data from a single point, while the preliminary geological structure model represents the first geological profile data of the entire surface of the area to be explored, the revised prior geological constraints must also be considered during the correction process. The first geological profile data outside of borehole locations must still satisfy the revised prior geological constraints.

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

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

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

[0039] The optimization process for the initial signal acquisition parameters is as follows: Based on the in-depth analysis of the characteristics of the first surface wave signal during the data correction process, the signal acquisition parameters for this surface wave detection are adjusted. For example, for areas with complex geological structures and significant loss of high-frequency surface wave signals during the initial acquisition, the sampling frequency is further increased to 300Hz or higher during this acquisition to ensure effective acquisition of surface wave signals over a wider frequency range. Simultaneously, the recording duration is reasonably extended according to the geological complexity of the area to be detected. For example, for areas where data quality was unstable during the initial detection, 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 reduce it to no more than 3%. Once data anomalies are detected, the cause is promptly investigated and re-acquisition is performed.

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

[0041] This embodiment of the underground space exploration method based on natural source surface waves and drilling constructs a preliminary geological structure model to obtain first geological profile data. Drilling then yields second geological profile data. Based on the differences between the first and second geological profile data, the preliminary geological structure model is corrected to obtain a corrected geological structure model. This corrected geological structure model then guides a second surface wave exploration to obtain the target geological structure model. Because this exploration process utilizes drilling data and core samples from the drilling process to accurately deduce the stratification, thickness, and depth of each layer, and dynamically feeds this data back to correct the preliminary geological structure model, it effectively solves the inherent multi-solution problem of traditional natural source surface wave exploration methods. This enables more accurate, efficient, and comprehensive exploration of underground space, meeting the urgent need for high-precision underground geological structure information in various engineering construction, resource exploration, and other application scenarios.

[0042] In some embodiments, in step S110, while acquiring the first surface wave signal, the quality of the first surface wave signal is also checked in real time. A quantitative index is established by calculating the waveform variance of the first surface wave signal, requiring that the proportion of abnormal waveforms does not exceed 5%, and judging whether there is interference signal. Once the proportion of abnormal waveforms exceeds 5%, the first surface wave signal is reacquired. Before acquisition, the coupling between the seismic detector and the ground is carefully checked to ensure tight coupling. The position deviation of the seismic detector is adjusted to not exceed ±5 cm, so as to ensure that rich, high-quality first surface wave signals covering different frequency bands can be acquired.

[0043] In some embodiments, such as Figure 2 As shown, step S110 specifically includes: Step S210: Obtain the first surface wave signal of the area to be detected by the natural source surface wave detection method. Specifically, the first surface wave signal can be obtained through a seismic detector. Preferably, in this step, the obtained first surface wave signal can also be denoised and filtered to remove high-frequency noise interference. Bandpass filtering can be used, and the filtering parameters are determined by combining 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 0.1~50Hz. The lower cutoff frequency of the bandpass filter is set to 0.05Hz, and the upper cutoff frequency is set to 60Hz to retain the effective surface wave signal frequency band while suppressing low-frequency trend terms and high-frequency interference.

[0044] Step S220: Extract the surface wave dispersion curve from the first surface wave signal. Specifically, algorithms such as spatial autocorrelation (SPAC) and frequency-wavenumber (FK) methods can be used to analyze and process the first surface wave signal to extract the surface wave dispersion curve, which reflects the relationship between the surface wave velocity and the frequency or wavelength.

[0045] Step S230: Construct 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 the initial model includes initial estimates of model parameters such as the strata layering, the thickness and depth of each layer.

[0046] Step S240: Based on the initial model and geophysical laws, a theoretical dispersion curve is generated using forward modeling. The initial model is then inverted and optimized based on an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve. The model parameters of the initial model are continuously adjusted. When the objective function converges, the inversion optimization ends, and the preliminary geological structure model is obtained.

[0047] For example, the least squares error can be used as the objective function Φ(m)=||d obs -d calc (m)|| 2 Among them, d obs The surface wave dispersion curve represents information about the actual underground geological structure, d calc (m) represents the theoretical dispersion curve generated using forward modeling based on the initial model m and geological and physical 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 to minimize the value of the objective function Φ(m), so that the simulation results are as close as possible to the actual detection results of surface wave detection, and a model that is more consistent with the real geological conditions is obtained, i.e., the preliminary geological structure model.

[0049] In some embodiments, 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 characterizing the difference between the surface wave dispersion curve and the theoretical dispersion curve, including: Step S241: Calculate the likelihood of the theoretical dispersion curve obtained from the initial model after each inversion algorithm and the surface wave dispersion curve. The likelihood is calculated as follows: .

[0050] Specifically, for the first i The initial model of each inversion algorithm after each inversion. m i Calculate the likelihood using the formula above. σThis represents the noise standard deviation. Likelihood calculation is based on statistical principles and reflects the degree of fit between the inversion results (theoretical dispersion curve) and the observed data (surface wave dispersion curve) at a given noise level. The higher the likelihood, the better the inversion results match the actual observations.

[0051] Step S242: Assign weights to each inversion algorithm after each inversion based on the likelihood. The higher the likelihood of the inversion algorithm, the higher the weight is assigned. That is, the higher the weight, the higher the credibility of the inversion algorithm.

[0052] Specifically, based on the algorithmic characteristics of different inversion algorithms, initial weights are assigned to each algorithm. For example, based on extensive real-world case analysis and theoretical research, it has been found that in layered structural regions, 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 regions, the nonlinear inversion algorithm exhibits stronger 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 reasonably according to specific geological conditions and likelihood to more accurately reflect the effectiveness of each algorithm in the current scenario.

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

[0054] Step S244: Based on the initial model after inversion and merging, calculate the objective function. If the objective function converges, the inversion ends; otherwise, proceed to step S241.

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

[0056] In this embodiment, multiple inversion algorithms are combined with an objective function to invert and optimize the initial model, avoiding the shortcomings of a single inversion algorithm, so that the final preliminary geological structure model is closer to the actual geological conditions.

[0057] Furthermore, the prior geological constraints are incorporated into the inversion optimization process, and the model parameters are adjusted to minimize the objective function while satisfying the prior geological constraints.

[0058] Specifically, in actual inversion calculations, the adjustment of model parameters must not only minimize the objective function but also satisfy prior geological constraints. For example, in a certain area to be explored, if the thickness of a certain stratum is known to be between 50 and 80 meters, and the inverted thickness of this stratum exceeds this range, the model parameters corresponding to the initial model are adjusted to bring it closer to a reasonable range, while taking into account other constraints and the optimization of the objective function, thereby obtaining a preliminary geological structure model that better reflects the actual geological conditions.

[0059] In some embodiments, such as Figure 3 As shown, step S120 specifically includes: Step S310: Determine borehole locations based on the preliminary geological structure model. Specifically, select representative locations from the preliminary geological structure model as borehole locations. These locations can cover the boundaries between different strata or suspected anomaly areas in the model, so as to obtain the most valuable geological information through drilling to verify and correct the preliminary geological structure model. Alternatively, borehole locations can be determined by combining the preliminary geological structure model with actual task requirements. For example, if the preliminary geological structure model determines that there is a longitudinal interface between soil and rock layers in the area to be explored, and the actual task is to build a highway in the area, to ensure that the highway does not collapse, then it is only necessary to determine the ground points corresponding to the longitudinal interface as borehole locations to determine the accurate location and depth of the longitudinal interface between the soil and rock layers, providing data support for road construction.

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

[0061] Specifically, the drilling system is equipped with sensors at corresponding locations to measure drilling speed, drilling pressure, and torque, thereby acquiring drilling data. For example, the drilling pressure sensor is installed on the hydraulic feed system's pressure oil circuit, with a range covering 0~50MPa; the torque sensor is integrated into the power head drive shaft and equipped with a temperature compensation module to eliminate the effects of frictional heat. A 16-channel data acquisition box is also configured, with a sampling frequency of no less than 10Hz. Drilling data collected by each sensor is transmitted in real time to the ground industrial control computer via a CAN bus, ensuring complete recording of drilling data waveforms throughout the entire drilling process.

[0062] Step S330: Denoise the drilling data to obtain denoised drilling data. Due to various factors such as equipment vibration, sensor error, and formation heterogeneity, drilling data inevitably contains high-frequency vibration noise. If effective noise removal is not performed, it will affect the reliability of subsequent data analysis and may even lead to incorrect geological survey results. Therefore, it is necessary to denoise the drilling data.

[0063] Step S340: Perform correlation analysis between the noise-reduced drilling data and geological features, and combine the core logging data of the drilling system to mark the strata, thickness and depth of each layer, so as to obtain the second geological profile data.

[0064] In some embodiments, in step S320, during the drilling process of the controlled drilling system at the borehole location, a trajectory measurement system measures the borehole trajectory every second (e.g., 2 drill rods, approximately 6 meters). When the deviation rate of the borehole trajectory exceeds a preset deviation rate threshold, a correction procedure is triggered to replace the drill bit and / or adjust the drilling data to correct the borehole trajectory. Here, s is greater than or equal to 1. In this embodiment, through correction, it is ensured that the borehole can reach the designated borehole location vertically. Subsequently, after drilling is completed, the extracted rock core is systematically cataloged to more accurately describe the stratification, thickness, and depth of the strata at the borehole location.

[0065] In some embodiments, step S330 specifically includes: Step S331: Using the length of a single drill pipe as a unit, the peak coefficient of the drilling data is calculated in segments, thereby realizing refined segmented analysis of the drilling data.

[0066] To quantify the volatility of drilling data, the crest factor (C) is used to characterize the volatility features in the time domain. The formula for calculating the crest factor is as follows: ; .

[0067] in, x peak The peak value of the signal corresponding to the drilling data is taken as the maximum absolute value. x rms The root mean square of the signal corresponding to the drilling data represents the effective value of the data. x i Indicates the first [number] before noise reduction i Drilling data. A high crest coefficient indicates the presence of significant abnormal peaks in the signal. By calculating the crest coefficient, the fluctuation characteristics of different drilling stages can be quantified, providing a basis for the selection of noise reduction methods.

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

[0069] Step S332: Based on the distribution range of the crest coefficient, divide the data into different fluctuation stages corresponding to different ranges of the crest coefficient. Different fluctuation stages reflect different degrees of fluctuation in the drilling data.

[0070] Specifically, based on the calculated crest coefficients, fluctuation characteristic curves for each fluctuation stage are plotted. These curves visually reflect the distribution range of the crest coefficients. Based on this distribution range, a correlation is established between the C-value and the noise level. Specifically, the fluctuation stages are divided into the following three phases: Low fluctuation phase (C<2): The signal is stable, and the noise is mainly high-frequency with small fluctuations.

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

[0072] High volatility phase (C≥5): Extremely abnormal peak values, which may be caused by equipment failure or strong geological changes.

[0073] Step S333: Select the denoising algorithm and denoising threshold corresponding to different fluctuation stages to denoise the drilling data and obtain the denoised 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 the low-fluctuation stage (C<2), the db4 wavelet basis is selected, and the decomposition level is 3-4. Wavelet transform is a time-frequency analysis method. When dealing with this type of high-frequency small-amplitude noise, the db4 wavelet basis can adapt well to the local characteristics of the signal. By reasonably setting the number of decomposition levels, high-frequency small-amplitude noise can be effectively removed while retaining the characteristics of the stationary signal.

[0075] For the moderate fluctuation stage (2 ≤ C < 5), the sym5 wavelet basis is selected, and soft thresholding is applied. The sym5 wavelet basis has certain symmetry and regularity, which can suppress obvious spikes while preserving the effective signal caused by stratigraphic changes to the greatest extent. Soft thresholding sets the portion of the wavelet coefficients below the threshold to zero and subtracts the threshold from the portion above the threshold, thereby reducing the impact on the effective signal while removing noise. For example, if the soft threshold is set to 5, then the portion of the wavelet coefficients with an absolute value less than 5 is set to zero, and the portion with an absolute value greater than 5 is subtracted by 5.

[0076] For high-fluctuation phases (C ≥ 5), set reasonable sensor data fluctuation thresholds to truncate outliers. In this phase, outliers can severely interfere with drilling data. Setting fluctuation thresholds can effectively prevent equipment malfunctions or strong geological changes from interfering with the drilling data. The fluctuation threshold setting needs to be determined based on the equipment's normal operating range and historical data on the geological characteristics of the area. For example, if historically the normal drilling speed range for this area to be explored was 1 mm / s to 5 mm / s, then the fluctuation threshold can be set to a lower limit of 1 mm / s and an upper limit of 5 mm / s.

[0077] Furthermore, after denoising the drilling data, the process also includes verifying the denoising effect. Specifically, the denoising effect can be verified by calculating 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 The signal power of the signal corresponding to the drilling data. P noi For noise power, x peak max This represents the maximum peak value of the signal corresponding to the drilling data before noise reduction. and These represent the first and second denoising values, respectively. i Drilling data. 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, different drilling stages can be identified based on core logging data, and the noise reduction effect can be verified according to different indicators for different drilling stages.

[0079] During the stable drilling phase, the signal is relatively stable, and the main focus is on noise suppression. Therefore, in this phase, SNR is the primary metric for verifying the noise reduction effect. The SNR before and after noise reduction is calculated separately, and the smoothness of the time-domain waveform is checked. By comparing the spectrum, if the SNR after noise reduction is greater than 5dB compared to the SNR before noise reduction, high-frequency noise is confirmed to be suppressed.

[0080] During the formation change phase, the signal may exhibit spikes or abrupt changes. These characteristics reflect the changes in the formation. Therefore, during this phase, RMSE is the preferred method to verify the noise reduction effect. RMSE is calculated; if RMSE < 0.1, it is checked whether the spikes are fully preserved. The data is then compared with the core data from drilling. If the spikes are fully preserved, the formation characteristics are confirmed to be consistent.

[0081] During equipment vibration, the signal exhibits periodic fluctuations. PSNR primarily focuses on the relationship between the signal peak value and the root mean square error of the noise. Therefore, during equipment vibration, PSNR is the preferred method to verify the noise reduction effect. Calculate the PSNR, check whether the periodic fluctuations are reasonably suppressed, and compare it with the equipment status record. If PSNR > 30dB, it confirms that the vibration characteristics have been correctly noise-reduced.

[0082] In this embodiment, noise reduction can yield more accurate drilling data and avoid interference from 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: Expand the lithological characteristic quantification index based on the noise-reduced drilling data. The lithological characteristic quantification index includes: drilling specific power and torque fluctuation coefficient.

[0085] Drilling specific work reflects the energy consumed in breaking a unit volume of rock. Different lithologies require different amounts of energy during drilling due to their varying physical and mechanical properties. Therefore, drilling specific work serves as an important quantitative indicator for distinguishing lithologies. The formula for calculating drilling specific work is as follows: .

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

[0087] The torque fluctuation coefficient reflects the stability of torque during drilling. Different lithologies and formation structures cause variations in the resistance experienced by the drill bit, resulting in torque fluctuations. Therefore, this torque fluctuation coefficient is of great significance for identifying lithology and formation characteristics. The formula for calculating the torque fluctuation coefficient is as follows: .

[0088] Where A represents the cross-sectional area of ​​the borehole (cm²). The standard deviation of torque; This represents the average torque.

[0089] Step S342: Based on the core logging data, establish a quantitative mapping relationship between drilling data and formation stratification. Since stratification mainly characterizes the lithology of each layer, this quantitative mapping relationship is also a quantitative mapping relationship between drilling data and lithology.

[0090] In this step, through big data statistical analysis and combined with the core logging data obtained from this drilling operation, a quantitative mapping relationship between drilling data (mainly drilling speed), lithological characteristic quantitative indicators, and lithology is established, as shown in Table 1: Table 1. Mapping Relationship between Drilling Data, Quantitative Indicators of Lithological Characteristics, and Lithology

[0091] It should be noted that while the mapping relationship, as shown in Table 1 above, can be directly obtained through statistical analysis of a large amount of actual engineering data, it may be affected by various factors in practical applications, 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. Thus, in this embodiment, the mapping relationship is derived based on statistical analysis of a large amount of actual engineering data and verification using core logging data obtained from this drilling operation. This makes the mapping relationship more consistent with the actual situation of the area to be explored, resulting in more accurate second geological profile data.

[0092] Step S343: Using real-time drilling data and the quantitative mapping relationship, the strata are labeled with their layering, thickness, and depth to obtain the second geological profile data. Specifically, the lithology of the current stratum can be determined by real-time drilling speed, drilling specific power, and torque fluctuation coefficient. Figure 4 By analyzing the relationship between drilling speed and drilling depth, the thickness and depth of the current rock strata can be obtained, thereby indicating the stratification of the formation and the thickness and depth of each layer.

[0093] Furthermore, in this embodiment, to facilitate observation by researchers, the noise-reduced drilling data undergoes multi-dimensional visualization analysis. Using borehole depth as the horizontal axis, the characteristic curves of key drilling data such as weight on drill bit (WOB), drill rod rotation speed (RPM), torque, and drilling speed (ROP) are simultaneously displayed with depth using dual vertical axes or multiple subplots, enabling comparative analysis of multiple drilling data. This visualization method can intuitively present the changing trends of different parameters at different depths, facilitating researchers to discover the interrelationships between drilling data and potential correlations 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 in the prior geological constraints corresponding to the borehole locations are changed. For example, the stratigraphic thickness range at the corresponding borehole location in the prior geological constraints.

[0095] Using the borehole locations as reference points, the stratigraphic layering, thickness, and depth of each layer in the area to be explored, as revealed by the second geological profile data, are mapped to the corresponding locations in the first geological profile data. If there are discrepancies, 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 with the objective function as the goal, the adjusted preliminary geological structure model is inverted and optimized. When the objective function converges, the corrected geological structure model is obtained. Furthermore, during the inversion and optimization process, it is ensured that the adjustments to the model parameters satisfy the reset prior geological constraints.

[0096] Specifically, the inversion optimization process is basically the same as step S240 above. The difference is that in step S130, the adjusted preliminary geological structure model is used as the initial model for inversion optimization, and finally the corrected geological structure model is obtained.

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

[0098] In some embodiments, in step S140, the dispersion curve extraction method is optimized in constructing the target geological structure model based on the reset prior geological constraints and the second surface wave signal. Besides using the spatial autocorrelation (SPAC) method and the frequency-wavenumber (F-K) method from the initial detection, the phase-shifting method can also be used, which has higher accuracy in extracting dispersion curves for complex geological structures. After extracting a new surface wave dispersion curve from the second surface wave signal using the phase-shifting method, steps S230 and S240 are executed. 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. The new initial model is then inverted and optimized based on an objective function characterizing the difference between the new surface wave dispersion curve and the new theoretical dispersion curve, continuously adjusting the model parameters. When the objective function converges, the inversion optimization ends, and the target geological structure model is obtained.

[0099] In this embodiment, since the modified geological structure model is closer to the actual underground geological structure, the initial parameters of the array deployment and the initial parameters of signal acquisition are modified based on the modified geological structure model to obtain the modified array deployment parameters and the modified signal acquisition parameters. Then, a second surface wave detection is performed based on the modified array deployment parameters and the modified signal acquisition parameters to obtain the 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 derived by inverting the objective function of the dispersion curve corresponding to the second surface wave signal and the theoretical dispersion curve of 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 will be described below. The underground space detection device based on natural source surface waves and drilling described below can be referred to in correspondence with the underground space detection method based on natural source surface waves and drilling described above.

[0101] The underground space exploration device based on natural source surface waves and drilling, as described in this embodiment of the invention, is as follows: Figure 5 As shown, it includes: The preliminary judgment model construction module 510 is used to construct a preliminary judgment geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, and to obtain the first geological profile data based on the preliminary judgment geological structure model. The first surface wave signal is obtained 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 of the area to be detected.

[0102] The profile data acquisition module 520 is used to control the drilling system to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the 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 to 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 signal acquisition based on the modified geological structure model, so as to obtain the corrected array layout parameters and the corrected signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed. The second surface wave signal is obtained by the natural source surface wave detection method based on the corrected array layout parameters and the corrected signal acquisition parameters to detect the area to be detected.

[0105] The underground space detection device based on natural source surface waves and drilling in this invention constructs a preliminary geological structure model to obtain first geological profile data, and then drills to obtain second geological profile data. Based on the difference between the first and second geological profile data, the preliminary geological structure model is corrected to obtain a corrected geological structure model. The corrected geological structure model then guides a second surface wave detection to obtain the target geological structure model. Because this detection process utilizes drilling data and core samples from the drilling process to accurately deduce the stratification, thickness, and depth of each layer, and dynamically feeds this data back to correct the preliminary geological structure model, it effectively solves the inherent multi-solution problem of traditional natural source surface wave detection methods. This enables more accurate, efficient, and comprehensive detection of underground space, meeting the urgent need for high-precision underground geological structure information in various engineering construction, resource exploration, and other application scenarios.

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

[0107] The dispersion curve extraction module is used to extract the surface wave dispersion curve from the first surface wave signal.

[0108] The initial model building module is used to build the initial model of the preliminary geological structure model based on the prior geological constraints.

[0109] The model optimization module is used to generate a theoretical dispersion curve based on the initial model and geological and physical laws using forward modeling. Based on the objective function characterizing the difference between the surface wave dispersion curve and the theoretical dispersion curve, the initial model is inverted and optimized to continuously adjust the model parameters of the initial model. When the objective function converges, the inversion optimization ends, and the preliminary geological structure model is obtained.

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

[0111] S2: Assign weights to each inversion algorithm after each inversion based on the likelihood. The higher the likelihood of the inversion algorithm, the higher the weight assigned.

[0112] S3: Based on the weights of the inversion algorithm, the parameters of the respective inverted initial models are weighted and summed to obtain the merging parameters. Based on the merging parameters, the merged initial model after inversion is obtained.

[0113] S4: Based on the initial model after inversion and merging, calculate the objective function. If the objective function converges, the inversion ends; otherwise, proceed to step S1.

[0114] In some embodiments, the profile data acquisition module 520 includes: The borehole location determination module is used to determine the borehole location based on the preliminary geological structure model.

[0115] The drilling control module is used to control the drilling system to drill at the borehole location and to acquire drilling data.

[0116] The drilling data noise reduction module is used to perform noise reduction processing on the drilling data to obtain noise-reduced drilling data.

[0117] The correlation analysis module is used to perform correlation analysis between the noise-reduced drilling data and geological features, and, in conjunction with the core logging data of the drilling system, to mark the strata, the thickness and depth of each layer, so as to obtain the second geological profile data.

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

[0119] In some embodiments, the drilling data noise reduction module includes: The crest coefficient calculation module is used to calculate the crest coefficient of the drilling data in segments, using the length of a single drill pipe as the unit.

[0120] The fluctuation stage division module is used to divide the fluctuation stage into different fluctuation stages corresponding to different ranges of the peak coefficient based on the distribution range of the peak coefficient. Different fluctuation stages reflect different fluctuation degrees of drilling data.

[0121] The noise reduction 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, so as to obtain noise-reduced drilling data.

[0122] In some embodiments, the association analysis module includes: The index extension module is used to extend the lithological characteristic quantification index based on the noise-reduced drilling data. The lithological characteristic quantification index includes: drilling specific power and torque fluctuation coefficient.

[0123] The mapping relationship establishment module is used to establish a quantitative mapping relationship between drilling data and formation stratification by combining the core logging data.

[0124] The strata labeling module is used to label the strata layering, thickness and depth of each layer using real-time drilling data and the quantitative mapping relationship to obtain second geological profile data.

[0125] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... 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 can call logical instructions in the memory 630 to execute a subsurface space exploration 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected.

[0126] The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to obtain the second geological profile data obtained from the drilling.

[0127] 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data.

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

[0129] Furthermore, the logical 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, in essence, or the part that contributes to the prior art, or a part 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] On the other hand, the present invention also provides a computer program product, which includes 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 is able to execute the underground space exploration 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected.

[0131] The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to obtain the second geological profile data obtained from the drilling.

[0132] 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data.

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

[0134] In another aspect, the present invention also 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 exploration method based on natural source surface waves and drilling provided by the methods described above, 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected.

[0135] The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to obtain the second geological profile data obtained from the drilling.

[0136] 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data.

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

[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part 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, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts 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, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some 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 method for underground space exploration based on natural source surface waves and drilling, characterized in that, include: 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 the first geological profile data is obtained based on the preliminary geological structure model. The first surface wave signal is obtained 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 in the area to be detected. The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the drilling. 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 the corrected geological structure model, and the prior geological constraints are reset based on the second geological profile data. Based on the modified geological structure model, the initial parameters of the array deployment and the initial parameters of signal acquisition are modified to obtain the modified array deployment parameters and the modified signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed. The second surface wave signal is obtained by the natural source surface wave detection method based on the modified array deployment parameters and the modified signal acquisition parameters to detect the area to be detected. The drilling system is controlled to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the drilling, including: Based on the preliminary geological structure model, the borehole locations were determined; The drilling system is controlled to drill at the borehole location and drilling data is acquired. The drilling data is denoised to obtain denoised drilling data; The lithological characteristic quantification index is extended based on the noise-reduced drilling data. The lithological characteristic quantification index includes: drilling specific power and torque fluctuation coefficient. By combining core logging data, a quantitative mapping relationship between drilling data and stratigraphic stratification is established; By using real-time drilling data from the borehole and the aforementioned quantitative mapping relationship, the strata are labeled with their layering, thickness, and depth, thus obtaining second geological profile data.

2. The underground space exploration method based on natural source surface waves and drilling according to claim 1, characterized in that, Based on the prior geological constraints and the first surface wave signal of the area to be explored, a preliminary geological structure model is constructed, including: Obtain the first surface wave signal of the region to be detected by the natural source surface wave detection method; Extract the surface wave dispersion curve from the first surface wave signal; An initial model for constructing the preliminary geological structure model based on the aforementioned prior geological constraints; Based on the initial model and geophysical laws, a theoretical dispersion curve is generated using forward modeling. The initial model is then inverted and optimized using an objective function that characterizes the difference between the surface wave dispersion curve and the theoretical dispersion curve. The model parameters of the initial model are continuously adjusted. When the objective function converges, the inversion optimization ends, and the preliminary geological structure model is obtained.

3. The underground space exploration method based on natural source surface waves and drilling according to claim 2, characterized in that, The initial model is inverted and optimized based on an objective function characterizing the difference between the surface wave dispersion curve and the theoretical dispersion curve, including: S1: Calculate the likelihood of the theoretical dispersion curve obtained by each inversion algorithm after each inversion and the surface wave dispersion curve. S2: Assign weights to each inversion algorithm after each inversion based on the likelihood. The higher the likelihood of the inversion algorithm, the higher the weight assigned. S3: Based on the weights of the inversion algorithm, the parameters of the respective inverted initial models are weighted and summed to obtain the merging parameters. Based on the merging parameters, the inverted and merged initial models are obtained. S4: Based on the initial model after inversion and merging, calculate the objective function. If the objective function converges, the inversion ends; otherwise, proceed to step S1.

4. The underground space exploration method based on natural source surface waves and drilling according to claim 1, characterized in that, Controlling the drilling system to drill at the borehole location includes: During the drilling process, a trajectory measurement system is used to measure the borehole trajectory once every s for each drill rod. When the deviation rate of the borehole trajectory is greater than the preset deviation rate threshold, a correction procedure is triggered to replace the drill bit and / or adjust the drilling data to correct the borehole trajectory. Here, s is greater than or equal to 1.

5. The underground space exploration method based on natural source surface waves and drilling according to claim 1, characterized in that, The drilling data is denoised to obtain denoised drilling data, including: The crest coefficient of the drilling data is calculated in segments, using the length of a single drill pipe as the unit. Based on the distribution range of the peak coefficient, different fluctuation stages are divided according to different ranges of the peak coefficient. Different fluctuation stages reflect different degrees of fluctuation in the drilling data. The drilling data is denoised by selecting a denoising algorithm and denoising threshold corresponding to different fluctuation stages to obtain denoised drilling data.

6. A subsurface space exploration device based on natural source surface waves and drilling, characterized in that, include: The preliminary judgment model construction module is used to construct a preliminary judgment geological structure model based on the prior geological constraints of the area to be detected and the first surface wave signal, and to obtain the first geological profile data based on the preliminary judgment geological structure model. The first surface wave signal is obtained 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 in the area to be detected. The profile data acquisition module is used to control the drilling system to perform drilling operations at the borehole locations determined in the preliminary geological structure model, and to acquire the second geological profile data obtained from the drilling. 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 the corrected geological structure model, and to reset the prior geological constraints based on the second geological profile data. The target model construction module is used to correct the initial parameters of the array deployment and the initial parameters of signal acquisition based on the modified geological structure model, so as to obtain the modified array deployment parameters and the modified signal acquisition parameters. Based on the reset prior geological constraints and the second surface wave signal, the target geological structure model is constructed. The second surface wave signal is obtained by the natural source surface wave detection method based on the modified array deployment parameters and the modified signal acquisition parameters to detect the area to be detected. The profile data acquisition module includes: The borehole location determination module is used to determine the borehole location based on the preliminary geological structure model. The drilling control module is used to control the drilling system to drill at the borehole location and to acquire drilling data. A drilling data noise reduction module is used to perform noise reduction processing on the drilling data to obtain noise-reduced drilling data; The index extension module is used to extend the lithological characteristic quantification index based on the noise-reduced drilling data. The lithological characteristic quantification index includes: drilling specific power and torque fluctuation coefficient. The mapping relationship establishment module is used to establish a quantitative mapping relationship between drilling data and formation stratification by combining core logging data; The strata labeling module is used to label the strata layering, thickness and depth of each layer using real-time drilling data and the quantitative mapping relationship to obtain second geological profile data.

7. 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, it implements the underground space exploration method based on natural source surface waves and drilling as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the underground space exploration method based on natural source surface waves and drilling as described in any one of claims 1 to 5.

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