Stratum layering interface depth detection method based on spatial frequency domain features

Abstract

This invention discloses a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics. The specific steps are as follows: Step 1, collect data sequences of subsurface physical parameters varying with depth and process each data sequence into a one-dimensional discrete depth sequence; Step 2, decompose the one-dimensional discrete depth sequence processed in Step 1 into several modes using the VMD method; Step 3, calculate the energy of each mode at each depth and extract the center frequency of each mode; Step 4, determine potential stratigraphic interfaces based on the energy and center frequency obtained in Step 3. This invention achieves highly sensitive identification of stratigraphic boundaries through spatial abrupt changes in modal energy and center frequency, demonstrating strong and accurate stratigraphic interface identification capabilities.

Description

Technical Field

[0001] This invention belongs to the technical field of geological exploration and underground property identification methods, specifically involving a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics. Background Technology

[0002] Currently, the identification and depth detection of underground stratigraphic boundaries are of great significance in fields such as engineering geology, geological disaster prevention and control, and resource exploration. Traditional stratigraphic identification methods mostly rely on drilling sampling, seismic exploration, and resistivity imaging to determine the boundaries between different strata by acquiring physical or geological information. However, these methods often suffer from problems such as long operation cycles, high costs, limited spatial resolution, and significant environmental disturbances, making them difficult to widely apply in large-scale or high-resolution monitoring.

[0003] To achieve continuous and precise identification of underground structures, researchers have recently attempted to utilize sensor technology to acquire depth-varying underground physical parameters (such as soil moisture, resistivity, and seismic wave velocity), and then combine this with signal processing techniques to analyze these sequence data to identify stratigraphic boundaries. However, traditional wavelet analysis methods suffer from problems such as mode aliasing, strong boundary effects, and fixed decomposition frequency bands when processing stratigraphic property sequences, leading to some ambiguity in the identification of interface abrupt changes and making it difficult to accurately extract interface features.

[0004] Therefore, there is an urgent need for a method to detect the burial depth of stratigraphic interfaces, so as to achieve more accurate identification of stratigraphic interfaces and provide data support for subsequent subsidence analysis and intelligent modeling. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, thereby solving the problem of poor accuracy in extracting interface features in existing methods.

[0006] The technical solution adopted in this invention is a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, and the specific steps are as follows:

[0007] Step 1: Collect data sequences of underground physical parameters as a function of depth and process each data sequence into a one-dimensional discrete depth sequence;

[0008] Step 2: Decompose the one-dimensional discrete depth sequence processed in Step 1 into several modes using the VMD method;

[0009] Step 3: Calculate the energy of each mode at each depth and extract the center frequency of each mode;

[0010] Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries.

[0011] The invention is further characterized by:

[0012] In step 1, the physical parameters are one or more of humidity, resistivity, density, and seismic wave velocity.

[0013] In step 2, the objective function for decomposition is:

[0014] (1)

[0015] In equation (1), This represents the k-th mode of the decomposition. The center frequency of the k-th mode; It is a convolution operator; For Dirac functions; This is the Hilbert kernel, used to extract the analytic signal of the modalities; j represents the imaginary unit. ;

[0016] The problem is then transformed into an unconstrained optimization problem using the Lagrange multiplier method, and solved it iteratively using the alternating direction multiplier method (ADMM) to obtain several modes. .

[0017] In step 3, the energy of each mode at each depth The expression is:

[0018] (2).

[0019] The specific process of step 4 is as follows:

[0020] Based on the energy of each mode at each depth obtained in step 3 Calculate its first-order difference sequence across the entire depth range. The expression is:

[0021] (3)

[0022] In equation (3), The distance between adjacent depth sampling points; based on the center frequency of each mode obtained in step 3. Calculate its first-order difference sequence across the entire depth range. The expression is:

[0023] (4)

[0024] when Greater than the energy mutation threshold or Greater than the frequency mutation threshold If so, then that depth is determined to be a potential stratigraphic boundary.

[0025] If at the same depth, there exist at least 3 modes that simultaneously satisfy or If the depth is determined to be a strong abrupt change point, it will be preferentially identified as a potential stratigraphic boundary.

[0026] Energy mutation threshold The expression is:

[0027] (5)

[0028] In equation (5), for The mean; for Standard deviation; This is an empirical coefficient.

[0029] Frequency mutation threshold The expression is:

[0030] (6)

[0031] In equation (6), for The mean; for Standard deviation; It serves as a sensitivity control factor for frequency mutations.

[0032] The beneficial effects of this invention are:

[0033] (1) The method for detecting the burial depth of the stratigraphic interface based on spatial frequency domain characteristics of the present invention decomposes the data using the VMD method to obtain multiple modes. It can adaptively extract the most representative frequency modes in the signal, avoid the problems of mode mixing and fixed frequency band, and has stronger resolution.

[0034] (2) The method for detecting the burial depth of the stratigraphic interface based on the spatial frequency domain characteristics of the present invention achieves high sensitivity identification of stratigraphic boundaries by detecting spatial abrupt changes in modal energy and center frequency, and has strong stratigraphic interface identification capability and high accuracy.

[0035] (3) The method for detecting the burial depth of the stratigraphic interface based on the spatial frequency domain characteristics of the present invention can be used to extract the deep stratigraphic structure characteristics in the mining-affected zone, improve the ability to interpret the mechanism of the subsidence process, and enhance the accuracy of safety early warning;

[0036] (4) The method for detecting the burial depth of the strata layer interface based on the spatial frequency domain characteristics of the present invention can be used in combination with a small amount of actual drilling data to extrapolate the frequency domain characteristics and identify the layers throughout the entire mining area. Under the condition of sparse data, it can greatly improve the accuracy and spatial coverage of the three-dimensional geological model and provide a high-quality basic model for resource assessment, disaster prediction and intelligent mining. Detailed Implementation

[0037] The present invention will now be described in detail with reference to specific embodiments.

[0038] Example 1

[0039] This invention relates to a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, which is implemented according to the following steps:

[0040] Step 1: Collect data sequences of underground physical parameters as a function of depth and process each data sequence into a one-dimensional discrete depth sequence;

[0041] Step 2: Decompose the one-dimensional discrete depth sequence processed in Step 1 into several modes using the VMD method;

[0042] Step 3: Calculate the energy of each mode at each depth and extract the center frequency of each mode;

[0043] Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries.

[0044] Example 2

[0045] This invention relates to a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, which is implemented according to the following steps:

[0046] Step 1: Collect data sequences of underground physical parameters varying with depth. , Representing depth and processing the data sequence into a one-dimensional discrete depth sequence. The corresponding depth is ;

[0047] The physical parameters include one or more of the following: humidity, resistivity, density, and seismic wave velocity.

[0048] Step 2, process each data sequence after Step 1. The mode is decomposed into several modes using the VMD (Variational Mode Decomposition) method. ;

[0049] The specific process is as follows:

[0050] The objective function for decomposition is:

[0051] (1)

[0052] In equation (1), This represents the k-th mode of the decomposition. The center frequency of the k-th mode; It is a convolution operator; For Dirac functions; This is the Hilbert kernel, used to extract the analytic signal of the modalities; j represents the imaginary unit. ;

[0053] The problem is then transformed into an unconstrained optimization problem using the Lagrange multiplier method, and solved it iteratively using the alternating direction multiplier method (ADMM) to obtain several modes. ;

[0054] Step 3: Calculate the energy of each mode at each depth. And extract the center frequency of each mode. ;

[0055] Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries;

[0056] In test areas with known stratification depths, the threshold ranges corresponding to different types of strata can be established through back calibration, serving as empirical support for subsequent engineering applications.

[0057] When there is a combination of several physical parameters, the accuracy of the judgment result is higher.

[0058] Example 3

[0059] This invention relates to a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, which is implemented according to the following steps:

[0060] Step 1: Collect data sequences of underground physical parameters varying with depth. , Representing depth and processing the data sequence into a one-dimensional discrete depth sequence. The corresponding depth is ;

[0061] The physical parameters include one or more of the following: humidity, resistivity, density, and seismic wave velocity.

[0062] Step 2, process each data sequence after Step 1. The mode is decomposed into several modes using the VMD (Variational Mode Decomposition) method. ;

[0063] The specific process is as follows:

[0064] The objective function for decomposition is:

[0065] (1)

[0066] In equation (1), This represents the k-th mode of the decomposition. The center frequency of the k-th mode; It is a convolution operator; For Dirac functions; This is the Hilbert kernel, used to extract the analytic signal of the modalities; j represents the imaginary unit. ;

[0067] The problem is then transformed into an unconstrained optimization problem using the Lagrange multiplier method, and solved it iteratively using the alternating direction multiplier method (ADMM) to obtain several modes. ;

[0068] Step 3: Calculate the energy of each mode at each depth. And extract the center frequency of each mode. ;

[0069] Energy of each mode at each depth The expression is:

[0070] (2)

[0071] Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries;

[0072] In test areas with known stratification depths, the threshold ranges corresponding to different types of strata can be established through back calibration, serving as empirical support for subsequent engineering applications.

[0073] When there is a combination of several physical parameters, the accuracy of the judgment result is higher.

[0074] Example 4

[0075] This invention relates to a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics, which is implemented according to the following steps:

[0076] Step 1: Collect data sequences of underground physical parameters varying with depth. , Representing depth and processing the data sequence into a one-dimensional discrete depth sequence. The corresponding depth is ;

[0077] The physical parameters include one or more of the following: humidity, resistivity, density, and seismic wave velocity.

[0078] Step 2, process each data sequence after Step 1. The mode is decomposed into several modes using the VMD (Variational Mode Decomposition) method. ;

[0079] The specific process is as follows:

[0080] The objective function for decomposition is:

[0081] (1)

[0082] In equation (1), This represents the k-th mode of the decomposition. The center frequency of the k-th mode; It is a convolution operator; For Dirac functions; This is the Hilbert kernel, used to extract the analytic signal of the modalities; j represents the imaginary unit. ;

[0083] The problem is then transformed into an unconstrained optimization problem using the Lagrange multiplier method, and solved it iteratively using the alternating direction multiplier method (ADMM) to obtain several modes. ;

[0084] Step 3: Calculate the energy of each mode at each depth. And extract the center frequency of each mode. ;

[0085] Energy of each mode at each depth The expression is:

[0086] (2)

[0087] Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries;

[0088] The specific process is as follows:

[0089] Based on the energy of each mode at each depth obtained in step 3 Calculate its first-order difference sequence across the entire depth range. The expression is:

[0090] (3)

[0091] In equation (3), This is the distance between adjacent depth sampling points, i.e., the depth interval, in meters;

[0092] Based on the center frequency of each mode obtained in step 3 Calculate its first-order difference sequence across the entire depth range. The expression is:

[0093] (4)

[0094] when Greater than the energy mutation threshold or Greater than the frequency mutation threshold If so, then that depth is determined to be a potential stratigraphic boundary;

[0095] In test areas with known stratification depths, the threshold ranges corresponding to different types of strata can be established through back calibration, serving as empirical support for subsequent engineering applications.

[0096] When there is a combination of several physical parameters, the accuracy of the judgment result is higher.

[0097] Example 5

[0098] Based on Example 4,

[0099] If at the same depth, there exist at least 3 modes that simultaneously satisfy or If the depth is determined to be a strong abrupt change point, it will be preferentially identified as a potential stratigraphic boundary.

[0100] Example 6

[0101] Based on Example 5, the energy mutation threshold The expression is:

[0102] (5)

[0103] In equation (5), for The mean; for Standard deviation; This is an empirical coefficient, usually between 1.5 and 3, and can be dynamically adjusted based on engineering experience or data noise levels.

[0104] Frequency mutation threshold The expression is:

[0105] (6)

[0106] In equation (6), for The mean; for Standard deviation; It serves as a sensitivity control factor for frequency mutations.

[0107] This invention provides a method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics. It is applicable to any one-dimensional depth sequence or a combination of several one-dimensional sequences, including any one of humidity, resistivity, and acoustic velocity, or a combination of humidity and resistivity, etc., which facilitates its widespread application. Furthermore, this invention does not rely on destructive drilling and can be combined with existing or indirectly obtained geological parameters for analysis, making it suitable for engineering needs such as stratigraphic modeling and geological hazard analysis.

Claims

1. A method for detecting the depth of a stratigraphic layering interface based on spatial frequency domain features, characterized in that, The specific steps are as follows: Step 1: Collect data sequences of underground physical parameters as a function of depth and process each data sequence into a one-dimensional discrete depth sequence; Step 2: Decompose the one-dimensional discrete depth sequence processed in Step 1 into several modes using the VMD method; In step 2, the objective function for decomposition is: （1） In formula (1), is the kth mode of decomposition; is the center frequency of the kth mode; is a convolution operator; is a Dirac function; is a Hilbert kernel for extracting the analytic signal of the mode; j represents the imaginary unit, ; denotes the depth; The problem is then transformed into an unconstrained optimization problem using the Lagrange multiplier method, and solved it iteratively using the alternating direction multiplier method to obtain several modes. ; Step 3: Calculate the energy of each mode at each depth and extract the center frequency of each mode; In step 3, the energy of each mode at each depth The expression is: （2）； Step 4: Based on the energy and center frequency obtained in Step 3, determine the potential stratigraphic boundaries; The specific process of step 4 is as follows: Based on the energy of each mode at each depth obtained in step 3 Calculate its first-order difference sequence across the entire depth range. The expression is: （3） In equation (3), The distance between adjacent depth sampling points; Based on the center frequency of each mode obtained in step 3 Calculate its first-order difference sequence across the entire depth range. The expression is: （4） when Greater than the energy mutation threshold or Greater than the frequency mutation threshold If so, then that depth is determined to be a potential stratigraphic boundary.

2. The method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics according to claim 1, characterized in that, In step 1, the physical parameters are one or more of humidity, resistivity, density, and seismic wave velocity.

3. The method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics according to claim 1, characterized in that, If at the same depth, there exist at least 3 modes that simultaneously satisfy or If the depth is determined to be a strong abrupt change point, it will be preferentially identified as a potential stratigraphic boundary.

4. The method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics according to claim 1, characterized in that, Energy mutation threshold The expression is: （5） In equation (5), for The mean; for Standard deviation; This is an empirical coefficient.

5. The method for detecting the burial depth of stratigraphic interfaces based on spatial frequency domain characteristics according to claim 1, characterized in that, Frequency mutation threshold The expression is: （6） In equation (6), for The mean; for Standard deviation; It serves as a sensitivity control factor for frequency mutations.

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