An ocean drilling stratigraphic structure detection method and related apparatus

Abstract

This invention discloses a method and related equipment for detecting geological structures during ocean drilling. The method includes: acquiring preliminary data of a candidate area and collecting seabed background noise in the candidate area using a concentric ring array to obtain raw noise data; preprocessing and segmenting the raw noise data to obtain segmented noise data; performing azimuth-weighted averaging and Bessel function fitting on the segmented noise data based on the principal noise direction of the concentric ring array to obtain a target dispersion curve; using the target dispersion curve as a constraint, performing nonlinear inversion using a genetic algorithm to obtain a shear wave velocity structure model; jointly verifying the shear wave velocity structure model with the preliminary data, and selecting drilling sites based on the joint verification results. This invention provides independent, quantitative, and traceable geophysical evidence for pre-drilling site selection, effectively reducing drilling risks, and can be widely applied in the field of seismic structure detection technology.

Description

Technical Field

[0001] This invention relates to the field of seismic structure detection technology, and in particular to a method and related equipment for detecting geological structures during ocean drilling. Background Technology

[0002] Ocean drilling is the largest, most internationally participated, and longest-running international collaborative scientific program in the field of contemporary Earth sciences. The currently accepted international pre-drilling site selection system uses active-source multichannel seismic reflection exploration as its core method. This involves obtaining subsurface reflection profiles through air gun array excitation and towed cable reception, thereby delineating candidate borehole locations. This method has significant advantages in identifying large tectonic interfaces and has played a crucial role in the history of ocean drilling.

[0003] However, active-source multichannel seismic reflection exploration has several limitations: 1. Seismic reflection profiles primarily reflect the geometry of wave impedance interfaces, and are not sensitive to information such as stratigraphic lithology, porosity, and fluid saturation, making it difficult to distinguish different geological properties of interfaces with the same impedance. 2. Vertical resolution is limited by the dominant source frequency, typically on the order of tens of meters, and is insufficient for identifying key geological bodies such as thin interbedded layers, fracture zones, and alteration zones, easily leading to deviations in drilling target positioning. 3. This method relies on artificial source excitation, resulting in high operating costs and long cycles, making it difficult to conduct high-density exploration of multiple candidate areas within a limited flight window.

[0004] In recent years, some studies have attempted to apply the terrestrial passive source background noise method to the marine environment, using natural noise recorded by seabed seismometers to extract surface wave dispersion curves and invert shear wave velocity structures, providing new physical constraints for lithological identification. However, existing marine background noise research mainly focuses on large-scale crustal structure detection, with array spans of tens to hundreds of kilometers and resolutions only at the kilometer level. When the relevant technical processes are directly applied to pre-drilling target area-scale detection, problems such as resolution mismatch, lack of array deployment schemes, severe directional interference from marine noise, and the lack of a complete criterion system from velocity structure to site selection decisions exist, making it difficult to meet the engineering needs of pre-drilling site selection. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for detecting geological structures in ocean drilling, aiming to solve at least one problem of the prior art.

[0006] To achieve the above objectives, one aspect of the present invention proposes a method for detecting geological structures during ocean drilling, the method comprising: Preliminary data of the candidate region is obtained, and the seabed background noise of the candidate region is collected using a concentric ring array to obtain the raw noise data; The original noise data is preprocessed and segmented to obtain segmented noise data; Based on the main noise direction of the concentric ring array, the segmented noise data is subjected to azimuth weighted averaging and Bessel function fitting to obtain the target dispersion curve; Using the target dispersion curve as a constraint, a genetic algorithm is used for nonlinear inversion to obtain the shear wave velocity structure model; Based on the shear wave velocity structure model and previous data, joint verification was conducted, and drilling locations were selected according to the results of the joint verification.

[0007] Among some alternative methods, the concentric ring array uses seabed seismometers as stations. The concentric ring array includes multiple seabed seismometers. The background noise of the seabed in the candidate area is collected using the concentric ring array to obtain raw noise data, including the following steps: The vertical components of all seabed seismometers in the concentric ring array are activated, and the seabed background noise in the candidate area for a preset duration is continuously collected as recorded data at a preset sampling rate. Among them, in the concentric ring array: a submarine seismograph is set up at the center of the candidate area, and multiple concentric rings are preset with the center of the candidate area as the center. Multiple submarine seismographs are arranged in an equilateral triangle on each ring. Perform validity checks on the recorded data; these checks include data type checks, data integrity checks, and data duration checks. If all validity checks pass, the recorded data will be output as raw noise data. If the validity check results show any failures, redeploy the concentric ring array, and / or adjust at least one of the preset sampling rate and preset duration, then return to the step of activating the vertical components of all seafloor seismometers in the concentric ring array to update the recorded data until the original noise data is obtained.

[0008] Some alternative methods involve preprocessing and segmenting the raw noise data to obtain segmented noise data, including the following steps: The signal in the preset frequency band is extracted from the original noise data as the first noise data; The first noise data is detrended and mean-removed to eliminate instrument baseline drift and obtain the second noise data. The second noise data is filtered using a bandpass filter to obtain the third noise data; Time-frequency analysis is performed on the third noise data, and abnormal data segments are removed based on the results of the time-frequency analysis to obtain the fourth noise data; among them, abnormal data segments include data segments that characterize ship interference, biological noise, impulse interference, or interrupted recordings; The fourth noise data is segmented to obtain segmented noise data.

[0009] In some alternative approaches, the segmented noise data includes the vertical component noise of each station in the concentric ring array. Based on the principal noise direction of the concentric ring array, the segmented noise data is subjected to azimuth-weighted averaging and Bessel function fitting to obtain the target dispersion curve, including the following steps: The vertical component noise of each station in the concentric ring array is segmented in the time domain and whitened in the spectrum to obtain the fifth noise data. Plane wave beamforming quantization is performed on each frequency point in the fifth noise data to obtain the noise response power at different azimuth angles; Integrating all noise response power within the target frequency band yields the full-band azimuth energy spectrum; The azimuth angle corresponding to the global maximum value in the full-band azimuth energy spectrum is taken as the main noise direction; Based on the vertical component noise of each station in the concentric ring array, spatial autocorrelation analysis is performed on the stations in the concentric ring array to obtain the spatial autocorrelation coefficient of each station pair in the concentric ring array. Based on the main noise direction, the spatial autocorrelation coefficients of each station pair are averaged using a cosine square weighting function to obtain the target spatial autocorrelation coefficient. By fitting the target space autocorrelation coefficient with the zero-order Bessel function, the dispersion curve of the Rayleigh wave phase velocity as a function of frequency is obtained and used as the target dispersion curve.

[0010] Among some alternative methods, based on the main direction of noise, a cosine square weighting function is used to perform azimuth-weighted averaging of the spatial autocorrelation coefficients of each station pair to obtain the target spatial autocorrelation coefficient, including the following steps: The azimuth angles of each station pair are obtained by transforming the position of each station in the concentric ring array; The main direction of noise is used as the reference azimuth angle, and the azimuth angle difference for each station is obtained by combining the azimuth angle calculation; the azimuth angle difference is constrained within a preset angle range. Based on the azimuth difference and combined with the preset effective influence half angle, the weight parameters of each station pair are obtained by using the cosine square weighting function. The expression for the weight parameter is: ; In the formula, Represents the weight parameters. Indicates azimuth difference. Indicates the effective influence of half-width characters; The target spatial autocorrelation coefficient is obtained by azimuth-weighted averaging of the spatial autocorrelation coefficients of each station pair using weight parameters. The expression for the target space autocorrelation coefficient is as follows: ; In the formula, Represents the autocorrelation coefficient of the target space. This represents the weight parameter of the i-th station pair. Let represent the spatial autocorrelation coefficient of the i-th station pair.

[0011] In some alternative approaches, the target dispersion curve is used as a constraint, and a genetic algorithm is employed for nonlinear inversion to obtain the shear wave velocity structure model, including the following steps: Obtain prior geological information for candidate regions; Based on prior geological information, the strata within a preset depth range below the seabed are constructed into a horizontal layered medium model with a preset number of layers. Then, a preset number of strata models are randomly generated as the initial population based on a reasonable search range according to preset parameters. The initial population is taken as the first population; Based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each formation model in the first group, the first fitness corresponding to each formation model in the first group is obtained by using root mean square error quantization. Using the first fitness as the genetic basis, genetic operations are performed on the first population to generate the second population; among which, genetic operations include selection, crossover and mutation. Based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each formation model in the second population, the second fitness corresponding to each formation model in the second population is obtained by using root mean square error quantization. The descent value is determined based on the difference between the first fitness and the second fitness; The second population is used as the first population, and the second fitness is used as the first fitness. The process is then repeated until the decrease value is less than the preset threshold for a continuous preset number of generations. The formation model with the lowest fitness in the second population of the last generation was used as the shear wave velocity structure model.

[0012] In some optional approaches, preliminary data, including seismic reflection profiles and gravity / magnetic data, are used for joint verification based on a shear wave velocity structure model combined with the preliminary data. Drilling locations are then selected based on the results of this joint verification, including the following steps: If the depth error between the bottom boundary depth of the segment with shear wave velocity below the preset velocity threshold in the shear wave velocity structure model and the depth of the sediment-basement interface corresponding to the seismic reflection profile does not exceed the first threshold, the sediment layer thickness is determined to be consistent. The lithology at each location is determined by mapping the basement shear wave velocity values ​​at each location in the candidate region in the shear wave velocity structure model. Velocity anomalies are determined based on the ratio of the difference between the shear wave velocity in a local segment and the background velocity in the shear wave velocity structure model, and the type of velocity anomaly indicates the structure type; among them, the anomalies of velocity anomalies include low-velocity anomalies and high-velocity anomalies. The shear wave velocity structure models obtained by inversion of concentric ring arrays in different orientations are compared. If the comparison results do not exceed the second threshold, the candidate area is determined as the priority drilling location. The pre-drilling velocity structure verification report is then formed by combining lithology, velocity anomaly and structural type. If the depth error exceeds the first threshold and / or the difference comparison result exceeds the second threshold, the process is backtracked to re-invert the shear wave velocity structure model to update the shear wave velocity structure model. Then, the process is returned to execute the step where the depth error between the bottom boundary depth of the segment where the shear wave velocity in the shear wave velocity structure model is lower than the preset velocity threshold and the sediment-basement interface depth corresponding to the seismic reflection profile does not exceed the first threshold. If the depth error still exceeds the first threshold and / or the difference comparison result still exceeds the second threshold after updating the shear wave velocity structure model a preset number of times, the candidate region will be downgraded or excluded. During the process of backtracking, gravity and magnetic data are also introduced for joint determination to update lithology type, velocity anomaly and tectonic type.

[0013] To achieve the above objectives, another aspect of the present invention provides an ocean drilling geological structure detection device, the device comprising: The first module is used to acquire preliminary data of the candidate region and use a concentric ring array to collect seabed background noise in the candidate region to obtain raw noise data. The second module is used to preprocess the raw noise data and segment the data to obtain segmented noise data; The third module is used to perform azimuth weighted averaging and Bessel function fitting on the segmented noise data based on the main noise direction of the concentric ring array to obtain the target dispersion curve. The fourth module is used to perform nonlinear inversion using a genetic algorithm, taking the target dispersion curve as a constraint, to obtain the shear wave velocity structure model; The fifth module is used for joint verification based on the shear wave velocity structure model and previous data, and to select drilling locations based on the joint verification results.

[0014] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0015] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0016] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0017] The present invention includes at least the following beneficial effects: The present invention provides a method, device, electronic equipment, storage medium, and program product for detecting the stratigraphic structure in ocean drilling. This scheme obtains preliminary data of candidate areas and collects seabed background noise of candidate areas using a concentric ring array to obtain raw noise data; the raw noise data is preprocessed and segmented to obtain segmented noise data; based on the main noise direction of the concentric ring array, the segmented noise data is subjected to azimuth weighted averaging and Bessel function fitting to obtain the target dispersion curve; the target dispersion curve is used as a constraint, and a genetic algorithm is used for nonlinear inversion to obtain a shear wave velocity structure model; the shear wave velocity structure model is combined with the preliminary data for joint verification, and drilling sites are selected based on the judgment results of the joint verification. This invention employs a concentric ring array to collect seabed background noise, enabling rapid detection of multiple candidate areas within a limited cruise window and facilitating low-cost batch operations. By performing azimuth-weighted averaging on segmented noise data followed by Bessel function fitting, the influence of strong directional ocean noise on the calculation of spatial autocorrelation functions is effectively suppressed, significantly improving the stability and signal-to-noise ratio of the dispersion curve. Furthermore, using the dispersion curve as a constraint, this invention employs a genetic algorithm for nonlinear inversion, enabling stable and high-resolution acquisition of shallow subsea shear wave velocity structures, solving the industry challenge of conventional reflection seismic studies' inability to systematically obtain shear wave velocity information. This invention jointly validates the shear wave velocity structure model with previous data and outputs borehole location selection results, constructing a complete technical chain from velocity structure inversion to drilling site selection decisions. This provides independent, quantitative, and traceable geophysical evidence for pre-drilling site selection, effectively reducing drilling risks. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an implementation environment for a method for detecting geological structures during ocean drilling, provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the ocean drilling geological structure detection method provided in this embodiment of the invention. Figure 3 This is a schematic diagram illustrating the overall process of the ocean drilling stratigraphic structure detection method provided in this embodiment of the invention; Figure 4 This is a schematic diagram of an example of the concentric circular array layout of candidate region OBS provided in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating an example of SPAC dispersion curve extraction provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of a one-dimensional shear wave velocity inversion example provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of the ocean drilling geological structure detection device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention.

[0020] It is understood that the terms "first," "second," etc., used in this invention may be used to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of embodiments of this invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" or "when" as used herein may be interpreted as "when," "in response to determination," or "in the event of a determination."

[0021] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0022] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this invention is for descriptive purposes only and is not intended to limit the invention.

[0023] To facilitate understanding of the technical solution of this invention, the following explanations are provided for the "industry-specific terms," ​​"vocabularies with special meanings," or "English or letter abbreviations" used in the embodiments of this invention: SPAC method: Spatial Autocorrelation Method, a technique for extracting surface wave dispersion curves from seismic background noise.

[0024] Ocean drilling: refers to engineering activities that use drilling vessels or platforms to conduct scientific drilling on the deep seabed, such as the International Ocean Discovery Program (IODP).

[0025] Pre-drilling site selection: The process of determining the final borehole location based on the results of geological and geophysical preliminary studies before the formal commencement of ocean drilling.

[0026] OBS: Ocean Bottom Seismometer, an instrument deployed on the seabed to record seismic waves or background noise.

[0027] Dispersion curve: A curve that describes the relationship between the propagation speed of a surface wave (such as a Rayleigh wave) and its frequency (or period).

[0028] Shear wave velocity: The propagation speed of seismic shear waves (S-waves) in strata is a key parameter characterizing the physical properties of rocks and the structure of strata.

[0029] Surface waves: Seismic waves that propagate along the Earth's surface or interface, including Rayleigh waves and Love waves, whose propagation speed varies with frequency (dispersion characteristics).

[0030] Background noise refers to weak, continuous vibration signals that exist in nature, originating from sources such as ocean waves, wind, atmospheric disturbances, and human activities.

[0031] Active source seismic exploration: a method of detecting underground structures by using artificial seismic sources (such as air guns, explosives, and vibrating vehicles) to generate seismic waves and receiving reflected or refracted signals.

[0032] Genetic Algorithm: A global optimization inversion algorithm based on the principle of biological evolution, used for shear wave velocity structure fitting calculation.

[0033] MCMC: Markov Chain Monte Carlo, a high-precision probabilistic inversion algorithm used for velocity modeling constrained by dispersion curves.

[0034] Rayleigh waves: Coupled surface waves of longitudinal and transverse waves propagating along the Earth's surface, are the main wave type used in the SPAC method for ocean background noise.

[0035] In related technologies, existing research on marine background noise mainly focuses on large-scale crustal structure detection, with array spans of tens to hundreds of kilometers and resolutions of only kilometers. When the relevant technical processes are directly applied to pre-drilling target area-scale detection, there are problems such as resolution mismatch, lack of array deployment schemes, severe directional interference from marine noise, and lack of a complete criterion system from velocity structure to site selection decision, which makes it difficult to meet the engineering needs of pre-drilling site selection.

[0036] In view of this, this invention provides a method and related equipment for detecting the stratigraphic structure in ocean drilling. This method obtains preliminary data of a candidate region and collects seabed background noise in the candidate region using a concentric ring array to obtain raw noise data. The raw noise data is preprocessed and segmented to obtain segmented noise data. Based on the main noise direction of the concentric ring array, the segmented noise data is subjected to azimuth-weighted averaging and Bessel function fitting to obtain the target dispersion curve. Using the target dispersion curve as a constraint, a genetic algorithm is used for nonlinear inversion to obtain a shear wave velocity structure model. The shear wave velocity structure model is combined with preliminary data for joint verification, and drilling sites are selected based on the joint verification results. This invention employs a concentric ring array to collect seabed background noise, enabling rapid detection of multiple candidate areas within a limited cruise window and facilitating low-cost batch operations. By performing azimuth-weighted averaging on segmented noise data followed by Bessel function fitting, the influence of strong directional ocean noise on the calculation of spatial autocorrelation functions is effectively suppressed, significantly improving the stability and signal-to-noise ratio of the dispersion curve. Furthermore, using the dispersion curve as a constraint, this invention employs a genetic algorithm for nonlinear inversion, enabling stable and high-resolution acquisition of shallow subsea shear wave velocity structures, solving the industry challenge of conventional reflection seismic studies failing to systematically obtain shear wave velocity information. This invention jointly validates the shear wave velocity structure model with previous data and outputs borehole location selection results, constructing a complete technical chain from velocity structure inversion to drilling site selection decisions. This provides independent, quantitative, and traceable geophysical evidence for pre-drilling site selection, effectively reducing drilling risks.

[0037] It is understood that the ocean drilling stratigraphic structure detection method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0038] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0039] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0040] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0041] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0042] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a method for detecting the geological structure in ocean drilling. The following description uses the application of this method in server 101 as an example. It can be understood that this method can also be applied in terminal 102.

[0043] Reference Figure 2 , Figure 2 This is an optional flowchart of the ocean drilling stratigraphic structure detection method provided in the embodiment of the present invention. The execution subject of the ocean drilling stratigraphic structure detection method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S500.

[0044] Step S100: Obtain preliminary data of the candidate region and collect seabed background noise of the candidate region using a concentric ring array to obtain raw noise data; It should be noted that the concentric ring array uses seabed seismometers as stations. The concentric ring array includes multiple seabed seismometers. In some embodiments, the process of collecting seabed background noise in candidate areas using the concentric ring array to obtain raw noise data may include the following steps: activating the vertical components of all seabed seismometers in the concentric ring array, and continuously collecting seabed background noise in the candidate area for a preset duration at a preset sampling rate as recorded data; wherein, in the concentric ring array: one seabed seismometer is deployed at the center of the candidate area, and multiple concentric rings are preset with the center of the candidate area as the center, with multiple seabed seismometers arranged in an equilateral triangle on each ring; performing a validity check on the recorded data; wherein, the validity check includes data type check, data integrity check, and data duration check; if the validity check results are all passed, the recorded data is output as raw noise data; if the validity check results are failed, the concentric ring array is redeployed, and / or at least one of the preset sampling rate and preset duration is adjusted, and the step of activating the vertical components of all seabed seismometers in the concentric ring array is returned to update the recorded data until the raw noise data is obtained.

[0045] For example, in some specific implementations, the deployment of the OBS array in the candidate region and the acquisition of background noise data can be achieved as follows: A concentric ring OBS array was deployed directly above the candidate anomaly area delineated by previous multi-channel seismic reflection profiles and gravity and magnetic data: 1. Deploy one OBS unit at the center; 2. Consider 2-3 concentric rings, preferably with radii R = 300m and 600m; 3. Three OBS units are evenly distributed in each ring, forming an equilateral triangle. 4. A single array consists of 7 OBS units, continuously recording background noise for 120-168 hours (5-7 days). 5. OBS working mode: Vertical component acquisition, sampling rate ≥100Hz.

[0046] In this embodiment of the invention, a quality control node ① is configured in this step: raw data validity check (such as data type check, data integrity check, and data duration check). If it fails, it is re-executed: the array is redeployed or the observation time is extended, and additional data is collected. If it passes, it proceeds to the subsequent processing steps.

[0047] Step S200: Preprocess and segment the original noise data to obtain segmented noise data; It should be noted that in some embodiments, step S200 may include the following steps: extracting a signal of a preset frequency band from the original noise data as first noise data; performing detrending and mean-removing processing on the first noise data to eliminate instrument baseline drift and obtain second noise data; filtering the second noise data using a bandpass filter to obtain third noise data; performing time-frequency analysis on the third noise data, and removing abnormal data segments based on the results of the time-frequency analysis to obtain fourth noise data; wherein, abnormal data segments include data segments characterizing ship interference, biological noise, impulse interference, or interrupted recording; segmenting the fourth noise data to obtain segmented noise data.

[0048] For example, in some specific implementations, background noise data preprocessing—detrending, denoising, filtering, data segmentation, etc.—can be specifically implemented as follows: 1. Extract the dominant ocean noise frequency band of 0.05-2Hz to highlight long-period surface wave signals; 2. Detrend, mean, and bandpass filtering are applied to eliminate instrument baseline drift; 3. Time-frequency analysis and anomaly removal, eliminating ship interference, biological noise, impulse interference, and interrupted data; 4. Data segmentation and quality control to ensure noise stability.

[0049] In this embodiment of the invention, a quality control node ② is configured in this step: if the preprocessing effect is deemed unqualified, the filter frequency band, segment duration and other parameters are adjusted in step S200 and the preprocessing is repeated; if qualified, the subsequent processing steps are entered.

[0050] Step S300: Based on the main noise direction of the concentric ring array, perform azimuth weighted averaging and Bessel function fitting on the segmented noise data to obtain the target dispersion curve. It should be noted that the segmented noise data includes the vertical component noise of each station in the concentric ring array. In some embodiments, step S300 may include the following steps: performing time-domain segmentation and spectral whitening processing on the vertical component noise of each station in the concentric ring array to obtain the fifth noise data; performing plane wave beamforming quantization on each frequency point in the fifth noise data to obtain the noise response power at different azimuth angles; integrating all noise response power within the target frequency band to obtain the full-band azimuth energy spectrum; and extracting the global maximum from the full-band azimuth energy spectrum. The azimuth angle corresponding to the value is taken as the main noise direction; based on the vertical component noise of each station in the concentric ring array, spatial autocorrelation analysis is performed on the stations in the concentric ring array to obtain the spatial autocorrelation coefficient of each station pair in the concentric ring array; based on the main noise direction, the spatial autocorrelation coefficient of each station pair is azimuthally weighted by a cosine square weighting function to obtain the target spatial autocorrelation coefficient; the target spatial autocorrelation coefficient is fitted with a zero-order Bessel function to obtain the dispersion curve of Rayleigh wave phase velocity as a function of frequency, which is taken as the target dispersion curve.

[0051] For example, in some specific implementations, the SPAC method for dispersion curve extraction can be implemented as follows: 1. Calculate the spatial autocorrelation coefficients of vertical component data for all stations within the array; 2. By performing azimuth-weighted averaging based on the main direction of ocean noise, the influence of non-uniform noise fields is weakened, and the stability of the dispersion curve and signal-to-noise ratio are improved; 3. By fitting the curve to the zeroth-order Bessel function, the Rayleigh wave phase velocity-frequency dispersion curve is obtained, and the error range is given.

[0052] In this embodiment of the invention, a quality control node ③ is configured in this step: dispersion curve quality judgment. If the curve is scattered, has many breakpoints, or exceeds the error limit, return to step S200 to adjust the preprocessing parameters, and repeat steps S200 to S300; if qualified, proceed to the subsequent processing steps.

[0053] In some preferred embodiments, the method for determining the principal direction of noise can be implemented as follows: The main noise direction is obtained through beamforming and combined with azimuth energy statistics. The main direction is finally determined by the maximum value of the azimuth energy spectrum. The specific process is as follows: Noise main direction determination process: 1. Perform time-domain segmentation and spectral whitening on the vertical component noise of each station in the OBS array.

[0054] 2. For each frequency point f, perform plane wave beamforming calculations to obtain the noise response power at different azimuth angles θ. .

[0055] 3. In the target frequency band ( Integrating over all frequencies within the target frequency band, we obtain the full-band azimuth energy spectrum: ; 4. Search The global maximum azimuth angle is the main direction of the noise: ; 5. If there are multiple similar peak values, the direction of the energy weighting center should be taken to avoid directional jumps caused by local interference.

[0056] Specifically, the noise main direction is equal to the peak direction of the beamforming azimuth energy spectrum. This is a comprehensive method of "beamforming first, then statistically analyzing the energy, and finally determining the main direction", rather than simply real-time statistics.

[0057] It should be noted that in some embodiments, based on the main noise direction, a cosine square weighted function is used to perform an azimuth-weighted average of the spatial autocorrelation coefficients of each station pair to obtain the target spatial autocorrelation coefficient. This may include the following steps: The azimuth angle of each station pair is obtained by transforming the position of each station in the concentric ring array; the main noise direction is used as the reference azimuth angle, and the azimuth angle is combined to calculate the corresponding azimuth angle difference for each station pair; wherein the azimuth angle difference is constrained within a preset angle range; based on the azimuth angle difference and combined with a preset effective influence half angle, a cosine square weighted function is used to calculate the weight parameter of each station pair; the spatial autocorrelation coefficient of each station pair is then averaged using the weight parameter to obtain the target spatial autocorrelation coefficient.

[0058] For example, in some specific implementations, the mathematical model and algorithm for azimuth-weighted averaging can be implemented as follows: Using a continuous cosine square weighting function instead of a simple ±30° hard threshold ensures a smooth transition of weights, which is more in line with the coherence characteristics of the SPAC method.

[0059] 1. Definition of weighting function: Let the azimuth angle of any pair of stations be θ, and the main noise direction be... Define azimuth difference: ; And constrained within [0°, 180°].

[0060] The weighting function uses continuous cosine square weighting: ; In the formula, Represents the weight parameters. Indicates azimuth difference. Indicates the effective influence of half-width characters; in: (The main direction effectively affects half the angle); when When the weights change continuously in the range [0,1], when hour, This station does not participate in the averaging.

[0061] 2. Physical meaning: main direction The position with the largest weight, The greater the deviation, the smaller the weight, resulting in a smooth decrease; values ​​beyond 60° are completely eliminated to avoid side noise interfering with SPAC coherence.

[0062] 3. Final formula for azimuth-weighted average: SPAC coefficients for all station pairs Perform a weighted average: ; That is: Azimuth-weighted average SPAC coherence function = sum of SPAC values ​​of each station × azimuth weights / total weight; where, Represents the autocorrelation coefficient of the target space. This represents the weight parameter of the i-th station pair. Let represent the spatial autocorrelation coefficient of the i-th station pair.

[0063] Step S400: Using the target dispersion curve as a constraint, a genetic algorithm is used for nonlinear inversion to obtain the shear wave velocity structure model. It should be noted that, in some embodiments, step S400 may include the following steps: obtaining geological prior information of the candidate region; based on the geological prior information, constructing a horizontal layered medium model with a preset number of layers within a preset depth range below the seabed, and then randomly generating a preset number of strata models as an initial population according to a reasonable search range based on preset parameters; using the initial population as the first population; obtaining the first fitness corresponding to each strata model in the first population using root mean square error quantization based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each strata model in the first population; using the first fitness as the genetic basis, performing genetic operations on the first population to generate a second population; The genetic operations include selection, crossover, and mutation. Based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each formation model in the second population, the second fitness of each formation model in the second population is obtained using root mean square error quantization. The decrease value is determined based on the difference between the first fitness and the second fitness. The second population is used as the first population, and the second fitness is used as the first fitness. The process is repeated, using the first fitness as the genetic basis to perform genetic operations on the first population to generate the second population, until the decrease value is consistently less than a preset threshold for a preset number of generations. The formation model with the smallest second fitness in the last generation of the second population is selected as the shear wave velocity structure model.

[0064] For example, in some specific embodiments, the nonlinear inversion of the shallow subsurface shear wave velocity structure can be achieved as follows: 1. Using the dispersion curve as a constraint, a genetic algorithm is employed to perform the inversion. 2. Inversion depth: 0-1500m below the seabed; 3. Layering accuracy: ≤10m; 4. Output a one-dimensional shear wave velocity-depth structure model, providing velocity, depth, and error range.

[0065] In this embodiment of the invention, a quality control node ④ is configured in this step: Inversion convergence and rationality judgment. If the inversion does not converge or the result is abnormal, return to step S200 to optimize the preprocessing, or return to step S300 to re-extract the dispersion curve and repeat steps S200 to S400; if qualified, proceed to the subsequent processing steps.

[0066] In some preferred embodiments, the inversion process can be implemented as follows: Step 1: Model parameterization and initial model construction; Using a horizontally layered viscoelastic strata as the initial model, and based on the exploration depth range and prior information on seismic interpretation of stratification, the shallow strata are divided into 4–6 layers. The parameters for each layer include: layer thickness h. i (Represents the thickness of the i-th layer); Transverse wave velocity V s,i (The subscript 's' corresponds to the transverse wave, indicating the transverse wave velocity of the i-th layer); the longitudinal wave velocity V p The density ρ is fixed using typical empirical ratios for the sea area (the parameters for each layer are obtained by converting the shear wave velocity of the corresponding layer; subsequent formulas use a unified calculation model as an example, without distinguishing specific layers in the examples): V P / V s ≈1.7~2.0; density increases linearly with velocity to reduce unknowns in the inversion and reduce ambiguity.

[0067] Based on prior regional geological knowledge, given the shear wave velocity V s Reasonable search range for layer thickness h: Loose sedimentary layer: V s ∈[0.3, 1.0] km / s; sedimentary substrate: V s ∈ [1.5, 2.5] km / s; Volcanic / metamorphic basement: V s ∈ [2.5, 4.0] km / s; Single layer thickness: h ∈ [5, 100] m.

[0068] Step 2: Establish the objective function; Using the measured dispersion curve extracted by the SPAC method as the sole hard constraint, the objective function is the root mean square error between the observed and theoretical dispersion curves: ; in: Representing the The specific values ​​of each effective frequency point (in Hz); This refers to the measured phase velocity of the SPAC. The theoretical phase velocity calculated for the current stratigraphic model; The number of effective frequency points participating in the inversion; The inversion objective is to minimize the objective function E.

[0069] Step 3: Select an inversion algorithm and perform iterations; Genetic algorithm inversion process: 1. Randomly generate an initial population (50-100 models) that meets the parameter range.

[0070] 2. Calculate the theoretical dispersion curve for each model to obtain the objective function value, which is used as the fitness.

[0071] 3. Perform selection, crossover, and mutation operations to generate a new generation of models.

[0072] 4. Retain the best individual in each generation and iterate and optimize generation by generation.

[0073] 5. The objective function decreases less than a threshold of 10 for 30 consecutive generations. -3 Convergence is determined at km / s.

[0074] 6. Take the last optimal model as the inversion result.

[0075] Step 4: Convergence assessment and anomaly handling; The following conditions must be met for the signal to be considered convergent: 1. The objective function tends to stabilize and no longer decreases significantly; 2. The standard deviation of the model parameters is less than the set threshold; 3. Reach the maximum number of iterations.

[0076] If the function does not converge, the objective function oscillates, or the velocity exhibits unreasonable values: The issue was determined to be due to insufficient dispersion curve quality or inappropriate preprocessing parameters. Immediately feed back to the dispersion extraction step to remove abnormal frequency points and refit; If convergence is still not achieved, return to the data preprocessing stage to adjust the filtering and segmentation parameters.

[0077] Step 5: Output the final shear wave velocity structure model; Output after convergence: 1. Layered transverse wave velocity V s1. Profile; 2. Total thickness of sedimentary layer; 3. Basement velocity and burial depth; 4. Model uncertainty; The results were directly used to conduct joint verification of sedimentary thickness, basement lithology, anomalies, and lateral homogeneity with multiple seismic reflection profiles.

[0078] Step S500: Based on the shear wave velocity structure model and previous data, a joint verification is performed, and the drilling locations are selected according to the judgment results of the joint verification. It should be noted that the preliminary data includes seismic reflection profiles and gravity and magnetic data. In some embodiments, step S500 may include the following steps: if the depth error between the bottom boundary depth of the segment with a shear wave velocity lower than a preset velocity threshold in the shear wave velocity structure model and the sediment-basement interface depth corresponding to the seismic reflection profile does not exceed a first threshold, the sedimentary layer thickness is determined to be consistent; the lithology type at the corresponding location is determined by mapping the basement shear wave velocity values ​​at each location in the candidate area in the shear wave velocity structure model; the velocity anomaly is determined based on the difference ratio between the shear wave velocity of a local segment and the background velocity in the shear wave velocity structure model, and the structural type is indicated based on the type of the velocity anomaly; wherein, the velocity anomaly includes low-velocity anomalies and high-velocity anomalies; the shear wave velocity structure models obtained by inversion from concentric ring arrays in different orientations are compared, and if the difference comparison results are consistent... If the depth error does not exceed the second threshold, the candidate area is identified as a priority drilling location, and a pre-drilling velocity structure verification report is generated by integrating lithology, velocity anomalies, and structural types. If the depth error exceeds the first threshold and / or the difference comparison result exceeds the second threshold, the shear wave velocity structure model is updated by re-inverting the process. The process is then repeated to execute the step where the depth error between the bottom boundary depth of the layer with shear wave velocity below the preset velocity threshold in the shear wave velocity structure model and the sediment-basement interface depth corresponding to the seismic reflection profile does not exceed the first threshold. If the depth error still exceeds the first threshold and / or the difference comparison result still exceeds the second threshold after updating the shear wave velocity structure model a preset number of times, the candidate area is downgraded or excluded. During the process of re-inverting the process, gravity and magnetic data are also introduced for joint judgment to update the lithology, velocity anomalies, and structural types.

[0079] For example, in some specific implementations, the inversion results are jointly verified with multichannel seismic reflection profiles and gravity and magnetic data, which can achieve the following: The SPAC inversion results were jointly verified with the interpretation of multichannel seismic reflection profiles, and four key judgments were completed: 1. Verification of sediment layer thickness: Shear wave velocity < 1.0 km / s indicates a loose sediment layer. The bottom boundary depth is consistent with the sediment-basement interface depth interpreted by seismic analysis if the error is ≤ 10%.

[0080] 2. Basement velocity and lithology determination: Sedimentary basement: 1.5-2.5 km / s; Volcanic basement: 2.5-3.5 km / s; Metamorphic / Dense igneous basement: >3.5 km / s; 3. Anomaly identification: Local shear wave velocities 20%-30% lower than the background velocity are identified as low-velocity anomalies, indicating fluid enrichment zones, fracture zones, alteration zones, gas chimneys, etc.; high-velocity anomalies indicate intrusive bodies, bedrock uplifts, dikes, etc.

[0081] 4. Lateral uniformity assessment: If the difference between the inversion results of arrays in different orientations is ≤15%, it is considered laterally uniform and the borehole can be directly determined; if the difference is >15%, the borehole position needs to be fine-tuned in the target area to the most stable section.

[0082] The final result is a pre-drilling velocity structure verification report, which provides an independent and quantitative basis for drilling site selection.

[0083] In this embodiment of the invention, a quality control node ⑤ is configured in this step: geological interpretation consistency judgment; if there is a significant contradiction with the seismic interpretation → first return to step S300 to correct the dispersion curve; if it is still abnormal, return to step S200 to optimize the preprocessing; if it is still mismatched, return to step S100 to verify the array layout and the original data; verify consistency → output the final stratigraphic structure model and complete the pre-drilling stratigraphic exploration.

[0084] The subsequent steps when one or more criteria fail to meet the threshold are as follows: 1. First step: Anomaly marking and classification; Items that do not meet the criteria are marked, and a distinction is made between minor anomalies (error 10%–20%) and significant conflicts (error > 20%).

[0085] (1) Minor anomalies: judged as method differences / resolution differences, acceptable but risk needs to be marked; (2) Significant conflict: If the geological model is determined to be contradictory, it must be subject to comprehensive arbitration.

[0086] 2. Second step: Initiate multi-geophysical joint assessment; A comprehensive assessment was made by incorporating data on gravity and magnetic anomalies, seafloor topography, shallow seismic profiles, and regional lithological patterns. (1) If the gravity and magnetic field show a high-density / high-magnetic substrate bulge, then the shallower substrate revealed by SPAC should be accepted; (2) If gravity and magnetic fields are stable and the region is dominated by thick deposits, then the larger sedimentary thickness interpreted by the seismic profile should be accepted. (3) If there is anomaly of volcanic / metamorphic rock, the SPAC shear wave velocity lithology determination shall take precedence.

[0087] 3. Third step: Retrospectively correct the data processing flow (quality control closed loop); If a reasonable explanation cannot be provided after arbitration, the process will be retroactively applied according to priority: (1) Re-examine the picking and fitting of the dispersion curve and remove abnormal dispersion points; (2) Return to step S200 to adjust the filter frequency band, data segment length, and azimuth weighting range; (3) If necessary, verify the OBS array geometry, main noise direction, and observation duration in step S100.

[0088] 4. Fourth step: Re-invert and verify together again; After correcting the parameters, SPAC inversion is re-executed, and four key criteria are re-evaluated against the seismic profile until the consistency criteria are met.

[0089] 5. Fifth step: Final site selection decision; (1) Consistency is achieved after iteration: the candidate area is retained and can be listed as a priority drilling location; (2) If there is still significant conflict after multiple iterations and there is no reasonable explanation: if the geological structure of the area is complex and the lateral changes are drastic, the candidate area should be downgraded or abandoned, and the uncertainty risk should be noted in the site selection report.

[0090] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0091] To address the shortcomings of existing technologies, this invention provides a method for detecting geological structures in ocean drilling based on the seismic background noise SPAC method. This method is used to rapidly obtain shallow, high-resolution shear wave velocity structures during the pre-drilling site selection stage, assisting in determining the optimal borehole location. For example... Figure 3 The image shows an optional process example of the ocean drilling formation structure detection method according to an embodiment of the present invention; in some specific application scenarios, the specific implementation steps of the method of the present invention are as follows: Step 1: Deployment of OBS arrays in candidate areas; A concentric ring OBS array was deployed directly above the candidate anomaly area delineated by previous multi-channel seismic reflection profiles and gravity and magnetic data: 1. Deploy one OBS unit at the center; 2. Consider 2-3 concentric rings, preferably with radii R = 300m and 600m; 3. Three OBS units are evenly distributed in each ring, forming an equilateral triangle. 4. A single array consists of 7 OBS units, continuously recording background noise for 120-168 hours (5-7 days); in high signal-to-noise ratio sea areas, the recording time can be shortened to 3 days; the array radius can be adjusted between 200-1000m to adapt to different detection depth requirements.

[0092] 5. OBS working mode: Vertical component acquisition, sampling rate ≥100Hz.

[0093] like Figure 4 The diagram shows an example of a concentric circular array of OBS in a candidate area, consisting of 7 OBS: a central array S0, 3 inner ring arrays (S1-S3), and 3 outer ring arrays (S4-S6); the circular arrays are evenly arranged in an equilateral triangle; the detection depth is approximately 3-5 times the array radius; the central array S0 is the pre-drilling verification hole.

[0094] Step 2: Background noise data preprocessing; 1. Extract the dominant ocean noise frequency band of 0.05-2Hz to highlight long-period surface wave signals; 2. Detrend, mean, and bandpass filtering are applied to eliminate instrument baseline drift; 3. Time-frequency analysis and anomaly removal, eliminating ship interference, biological noise, impulse interference, and interrupted data; 4. Data segmentation and quality control to ensure noise stability.

[0095] like Figure 5 The image shown is a schematic diagram illustrating an example of dispersion curve extraction using the SPAC method. Figure 5 In the middle: the left side shows the background noise cross-correlation function waveform; the right side shows the Rayleigh wave dispersion curve (phase velocity-frequency), including data points and error bars.

[0096] Step 3: Extraction of dispersion curves using the SPAC method; 1. Calculate the spatial autocorrelation coefficients of vertical component data for all stations within the array; 2. By performing azimuth-weighted averaging based on the main direction of ocean noise, the influence of non-uniform noise fields is weakened, and the stability of the dispersion curve and signal-to-noise ratio are improved; 3. By fitting the curve to the zeroth-order Bessel function, the Rayleigh wave phase velocity-frequency dispersion curve is obtained, and the error range is given.

[0097] Step 4: Transverse wave velocity structure inversion; 1. Using the dispersion curve as a constraint, invert the frequency response using a genetic algorithm or the MCMC method: 2. Inversion depth: 0-1500m below the seabed; 3. Layering accuracy: ≤10m; 4. Output a one-dimensional shear wave velocity-depth structure model, providing velocity, depth, and error range; for example... Figure 6 The diagram shown is a schematic representation of a one-dimensional transverse wave velocity inversion example.

[0098] Step 5: Pre-drilling auxiliary verification and comprehensive site selection (quantitative criteria); The SPAC inversion results were jointly verified with the interpretation of multichannel seismic reflection profiles, and four key judgments were completed: 1. Verification of sediment layer thickness: Shear wave velocities < 1.0 km / s indicate loose sedimentary layers. The depth of the bottom boundary of these layers is consistent with the depth of the sediment-basement interface interpreted by seismic analysis, with an error of ≤ 10%.

[0099] 2. Basement velocity and lithology determination: Depositional basement: 1.5-2.5 km / s Volcanic rock base: 2.5-3.5 km / s Metamorphic / dense igneous basement: >3.5km / s 3. Anomaly identification: Local shear wave velocities 20%-30% lower than the background velocity are identified as low-velocity anomalies, indicating fluid enrichment zones, fracture zones, alteration zones, gas chimneys, etc.; high-velocity anomalies indicate intrusive bodies, bedrock uplifts, dikes, etc.

[0100] 4. Lateral uniformity assessment: If the difference in the inversion results of arrays in different orientations is ≤15%, it is considered to be laterally uniform and can be directly determined by hole positioning; if the difference is >15%, the hole position needs to be finely adjusted within the target area to the most stable section.

[0101] The final result is a pre-drilling velocity structure verification report, which provides an independent and quantitative basis for drilling site selection.

[0102] In summary, compared with the prior art, the present invention has at least the following beneficial effects: (1) Improve drilling success rate: Provides meter-level resolution shear wave velocity structure, which can accurately identify lithological interfaces, fluid layers, fracture zones and alteration zones, upgrading site selection from structural speculation to quantitative verification by velocity science, and greatly reducing drilling risks.

[0103] (2) Low cost, high efficiency, and batch operation: It makes full use of natural background noise and does not require artificial seismic sources; a single array can complete the acquisition in 5-7 days and can quickly scan multiple candidate areas during the interval between flights; the unit area cost is only less than 1 / 10 of that of active source high-resolution earthquakes.

[0104] (3) Filling the gap in pre-drilling shear wave velocity information: Conventional reflection seismic data cannot directly provide shear wave velocity. The present invention systematically obtains shear wave velocity structure, significantly improving the ability to identify lithology, pores, and fluids, and improving geophysical constraints.

[0105] (4) Achieve quantitative and repeatable site selection: Establish clear quantitative indicators such as speed threshold, anomaly judgment, and lateral uniformity to make the site selection process more scientific, objective, traceable, and repeatable.

[0106] (5) Improve the pre-drilling exploration technology system for ocean drilling: focus on high-precision detection in shallow areas of 0-1500m, complement the existing deep area exploration, and fill the gap in special detection methods with "kilometer-level depth and meter-level resolution".

[0107] like Figure 7 As shown, this embodiment of the invention also provides an ocean drilling formation structure detection device 900, which can implement the above-described method. This device may include: The first module 901 is used to acquire preliminary data of the candidate region and collect seabed background noise of the candidate region using a concentric ring array to obtain raw noise data. The second module 902 is used to preprocess the raw noise data and segment the data to obtain segmented noise data; The third module 903 is used to perform azimuth weighted averaging and Bessel function fitting on the segmented noise data based on the main noise direction of the concentric ring array to obtain the target dispersion curve; The fourth module, 904, is used to perform nonlinear inversion using a genetic algorithm, taking the target dispersion curve as a constraint, to obtain the shear wave velocity structure model. The fifth module, 905, is used for joint verification based on the shear wave velocity structure model and previous data, and for selecting drilling locations based on the joint verification results.

[0108] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0109] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0110] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0111] like Figure 8 As shown, Figure 8 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0112] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0113] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0114] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0115] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0116] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0117] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0118] The ocean drilling stratigraphic structure detection method, apparatus, electronic equipment, storage medium, and program products provided in this invention acquire preliminary data of candidate areas and collect seabed background noise of candidate areas using a concentric ring array to obtain raw noise data; preprocess and segment the raw noise data to obtain segmented noise data; perform azimuth-weighted averaging and Bessel function fitting on the segmented noise data based on the main noise direction of the concentric ring array to obtain the target dispersion curve; use the target dispersion curve as a constraint and perform nonlinear inversion using a genetic algorithm to obtain a shear wave velocity structure model; perform joint verification based on the shear wave velocity structure model and preliminary data, and select drilling sites based on the joint verification results. This invention employs a concentric ring array to collect seabed background noise, enabling rapid detection of multiple candidate areas within a limited cruise window and facilitating low-cost batch operations. By performing azimuth-weighted averaging on segmented noise data followed by Bessel function fitting, the influence of strong directional ocean noise on the calculation of spatial autocorrelation functions is effectively suppressed, significantly improving the stability and signal-to-noise ratio of the dispersion curve. Furthermore, using the dispersion curve as a constraint, this invention employs a genetic algorithm for nonlinear inversion, enabling stable and high-resolution acquisition of shallow subsea shear wave velocity structures, solving the industry challenge of conventional reflection seismic studies failing to systematically obtain shear wave velocity information. This invention jointly validates the shear wave velocity structure model with previous data and outputs borehole location selection results, constructing a complete technical chain from velocity structure inversion to drilling site selection decisions. This provides independent, quantitative, and traceable geophysical evidence for pre-drilling site selection, effectively reducing drilling risks.

[0119] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0120] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0122] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0123] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for detecting geological structures during ocean drilling, characterized in that, The method includes the following steps: Preliminary data of the candidate region is obtained, and the seabed background noise of the candidate region is collected using a concentric ring array to obtain the raw noise data; The original noise data is preprocessed and segmented to obtain segmented noise data; Based on the main noise direction of the concentric ring array, the segmented noise data is subjected to azimuth weighted averaging and Bessel function fitting to obtain the target dispersion curve; Using the target dispersion curve as a constraint, a genetic algorithm is used for nonlinear inversion to obtain the shear wave velocity structure model. Based on the shear wave velocity structure model and the previous data, a joint verification was performed, and drilling locations were selected according to the judgment results of the joint verification.

2. The method according to claim 1, characterized in that, The concentric ring array uses seabed seismometers as stations, and the concentric ring array includes multiple seabed seismometers. The process of collecting seabed background noise in the candidate area using the concentric ring array to obtain raw noise data includes the following steps: The vertical components of all the seabed seismometers in the concentric ring array are activated, and the seabed background noise in the candidate area for a preset duration is continuously collected as recorded data at a preset sampling rate. In the concentric ring array: a submarine seismograph is set up at the center of the candidate area, and multiple concentric rings are preset with the center of the candidate area as the center. Multiple submarine seismographs are arranged in an equilateral triangle on each ring. The recorded data is subjected to a validity check; wherein the validity check includes a data type check, a data integrity check, and a data duration check. If all validity checks pass, the recorded data is output as the original noise data. If any of the validity checks fail, the concentric ring array is redeployed, and / or at least one of the preset sampling rate and the preset duration is adjusted. The process then returns to the step of activating the vertical components of all the seafloor seismometers in the concentric ring array to update the recorded data until the original noise data is obtained.

3. The method according to claim 1, characterized in that, The process of preprocessing and segmenting the original noise data to obtain segmented noise data includes the following steps: The signal of a preset frequency band is extracted from the original noise data as the first noise data; The first noise data is detrended and mean-removed to eliminate instrument baseline drift and obtain the second noise data; The second noise data is filtered using a bandpass filter to obtain the third noise data; The third noise data is subjected to time-frequency analysis, and abnormal data segments are removed based on the results of the time-frequency analysis to obtain the fourth noise data; wherein, the abnormal data segments include data segments that characterize ship interference, biological noise, impulse interference, or interrupted recording; The fourth noise data is segmented to obtain the segmented noise data.

4. The method according to claim 1, characterized in that, The segmented noise data includes the vertical component noise of each station in the concentric ring array. The step of performing azimuth-weighted averaging and Bessel function fitting on the segmented noise data based on the principal noise direction of the concentric ring array to obtain the target dispersion curve includes the following steps: The vertical component noise of each station in the concentric ring array is subjected to time-domain segmentation and spectral whitening to obtain the fifth noise data; Plane wave beamforming quantization is performed on each frequency point in the fifth noise data to obtain the noise response power at different azimuth angles; Integrate all the noise response power within the target frequency band to obtain the full-band azimuth energy spectrum; The azimuth angle corresponding to the global maximum value in the full-band azimuth energy spectrum is taken as the main direction of the noise. Based on the vertical component noise of each station in the concentric ring array, spatial autocorrelation analysis is performed on the stations in the concentric ring array to obtain the spatial autocorrelation coefficient of each station pair in the concentric ring array. Based on the main direction of the noise, the spatial autocorrelation coefficients of each station pair are averaged using a cosine square weighting function to obtain the target spatial autocorrelation coefficient. The target spatial autocorrelation coefficient is fitted with a zero-order Bessel function to obtain the dispersion curve of Rayleigh wave phase velocity as a function of frequency, which is then used as the target dispersion curve.

5. The method according to claim 4, characterized in that, The step of obtaining the target spatial autocorrelation coefficient by performing azimuth-weighted averaging of the spatial autocorrelation coefficients of each station pair based on the main noise direction using a cosine square weighting function includes the following steps: The azimuth angles of each station pair are obtained by converting the position of each station in the concentric ring array. Using the main noise direction as the reference azimuth angle, the azimuth angle difference for each station is calculated by combining the azimuth angle; wherein, the azimuth angle difference is constrained within a preset angle range. Based on the azimuth difference and combined with the preset effective influence half angle, the weight parameters of each station pair are obtained by cosine square weighting function calculation. The expression for the weight parameter is as follows: ； In the formula, Represents the weight parameters. Indicates azimuth difference. Indicates the effective influence of half-width characters; The target spatial autocorrelation coefficient is obtained by performing an azimuth-weighted average of the spatial autocorrelation coefficients of each station pair using the weight parameters. The expression for the target spatial autocorrelation coefficient is as follows: ； In the formula, Represents the autocorrelation coefficient of the target space. This represents the weight parameter of the i-th station pair. Let represent the spatial autocorrelation coefficient of the i-th station pair.

6. The method according to claim 1, characterized in that, The step of using the target dispersion curve as a constraint and performing nonlinear inversion using a genetic algorithm to obtain the shear wave velocity structure model includes the following steps: Obtain the geological prior information of the candidate region; Based on the aforementioned geological prior information, the strata within a preset depth range below the seabed are constructed into a horizontal layered medium model with a preset number of layers. Then, a preset number of strata models are randomly generated as the initial population based on a reasonable search range according to preset parameters. The initial population is taken as the first population; Based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each formation model in the first population, the first fitness corresponding to each formation model in the first population is obtained by using root mean square error quantization. Using the first fitness as a genetic basis, genetic operations are performed on the first population to generate a second population; wherein, the genetic operations include selection, crossover, and mutation. Based on the measured phase velocity corresponding to the target dispersion curve and the theoretical phase velocity corresponding to each formation model in the second population, the second fitness corresponding to each formation model in the second population is obtained by using root mean square error quantization. The descent value is determined based on the difference between the first fitness and the second fitness; The second population is used as the first population, and the second fitness is used as the first fitness. The process returns to the step of using the first fitness as the genetic basis to perform genetic operations on the first population to generate the second population, until the decrease value is less than a preset threshold for a consecutive preset number of generations. The formation model with the lowest second fitness in the last generation of the second population is used as the shear wave velocity structure model.

7. The method according to claim 1, characterized in that, The preliminary data includes seismic reflection profiles and gravity and magnetic data. The joint verification based on the shear wave velocity structure model and the preliminary data, and the selection of drilling locations based on the joint verification results, includes the following steps: If the depth error between the bottom boundary depth of the segment with shear wave velocity below the preset velocity threshold in the shear wave velocity structure model and the depth of the sediment-basement interface corresponding to the seismic reflection profile does not exceed the first threshold, the sediment layer thickness is determined to be consistent. The lithology at each location is determined by mapping the basement shear wave velocity values ​​at each location in the candidate region of the shear wave velocity structure model. The velocity anomaly is determined based on the ratio of the difference between the local segment shear wave velocity and the background velocity in the shear wave velocity structure model, and the construction type is indicated based on the type of the velocity anomaly; wherein, the anomaly of the velocity anomaly includes low-velocity anomaly and high-velocity anomaly. The shear wave velocity structure models obtained by inversion from concentric ring arrays in different orientations are compared. If the comparison results do not exceed the second threshold, the candidate region is determined as the priority drilling location. The lithology, velocity anomaly and structural type are then integrated to form a pre-drilling velocity structure verification report. If the depth error exceeds the first threshold and / or the result of the difference comparison exceeds the second threshold, the shear wave velocity structure model is updated by re-inverting the process through process backtracking, and the step of returning to execute the step of if the depth error between the bottom boundary depth of the segment where the shear wave velocity in the shear wave velocity structure model is lower than the preset velocity threshold and the depth of the sediment-basement interface corresponding to the seismic reflection profile does not exceed the first threshold is performed. If the depth error still exceeds the first threshold and / or the difference comparison result still exceeds the second threshold after updating the shear wave velocity structure model a preset number of times, the candidate region will be downgraded or excluded. In the process of backtracking, the gravity and magnetic data are also introduced for joint determination to update the lithology type, the velocity anomaly, and the structural type.

8. A geological structure detection device for ocean drilling, characterized in that, The apparatus, applicable to the method according to any one of claims 1 to 7, comprises: The first module is used to acquire preliminary data of the candidate region and collect the seabed background noise of the candidate region using a concentric ring array to obtain the raw noise data. The second module is used to preprocess and segment the original noise data to obtain segmented noise data. The third module is used to perform azimuth weighted averaging and Bessel function fitting on the segmented noise data based on the main noise direction of the concentric ring array to obtain the target dispersion curve; The fourth module is used to perform nonlinear inversion using a genetic algorithm, taking the target dispersion curve as a constraint, to obtain the shear wave velocity structure model. The fifth module is used to perform joint verification based on the shear wave velocity structure model and the previous data, and to select drilling locations based on the judgment results of the joint verification.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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