Resistivity value energy ray estimation method for termite cave of reservoir dam

Abstract

The invention provides a resistivity value energy ray estimation method for a termite ant cave position of a reservoir dam, and the method comprises the steps: calibrating different types of reservoir dams, and taking a corresponding determined termite ant cave of the reservoir dam as a calibration position; detecting all calibration positions by using a transient electromagnetic detector to obtain sampling data, and taking the sampling data as a background value; detecting the whole dam body of the target reservoir dam by using a high-resolution transient electromagnetic detector, and calculating the net block-shaped apparent resistivity value of the whole dam body; and determining the termite cave position of the target reservoir dam according to the net block apparent resistivity value and the background value. After the specific reservoir dam is calibrated, the termite cave position of the corresponding dam can be determined based on the sampling data and the background value of the high-resolution transient electromagnetic detector of the reservoir dam, so that the precision and the efficiency of detecting the termite cave position of the reservoir dam can be improved under the condition that the reservoir dam is not damaged.

Description

Technical Field

[0001] The invention relates to the technical field of termite nest detection in a reservoir dam in a water conservancy project, and in particular to a resistivity value energy line estimation method for detecting the position of termite nests in a reservoir dam. Background Art

[0002] As an important water conservancy infrastructure, reservoir dams are responsible for urban and rural flood control, water storage, breeding, power generation and irrigation, and play an important role in promoting urban and rural economic development and improving the ecological environment. Termites are extremely destructive to reservoir dams. Termites build nests and reproduce in the soil reservoir dams, and build extensive ant tunnels, which can easily lead to reservoir dam leakage, pitting and other dangerous situations, and even cause dam collapse and collapse accidents. Termites are a major biological hazard that affects the safety of reservoir dam projects.

[0003] Detection of termite nests in reservoir dams is a world-class problem. The drilling method commonly used in engineering projects damages the dam body and affects the safety of the reservoir dam. Therefore, there is an urgent need for an accurate non-destructive detection method to accurately find the location of termite nests in reservoir dams, deal with the nests in a timely and rapid manner, and reduce the operating risks of reservoir dams. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a method for estimating the resistivity value energy line of the position of termite nests in a reservoir dam, so as to realize the detection of the position of termite nests.

[0005] To achieve the above objectives, the present invention provides the following solutions:

[0006] A method for estimating the resistivity value energy line of termite nest locations in a reservoir dam, comprising:

[0007] Calibrate different types of reservoirs and dams, and use the corresponding determined termite nest locations of reservoirs and dams as calibration locations;

[0008] Using a transient electromagnetic detector to detect all calibration positions to obtain sampling signals, preprocessing the sampling signals to obtain sampling data, and using the sampling data as the current low value;

[0009] Use a high-resolution transient electromagnetic detector to detect the entire dam of the target reservoir and calculate the apparent resistivity value of the mesh blocks of the entire dam;

[0010] The location of the termite nest of the target reservoir dam is determined according to the apparent resistivity value of the mesh block and the background value.

[0011] Preferably, preprocessing the sampled signal to obtain sampled data includes:

[0012] Performing wavelet decomposition on the sampled signal to obtain wavelet coefficients;

[0013] Conduct likelihood estimation on the wavelet coefficients, and determine the denoising model based on the likelihood estimation value;

[0014] Using the denoising model to process the wavelet coefficients to obtain denoised wavelet coefficients;

[0015] Reconstructing the denoised wavelet coefficients to obtain a denoised sampling signal;

[0016] The denoised sampling signal is subjected to analog-to-digital conversion to obtain preprocessing to obtain sampling data.

[0017] Preferably, performing likelihood estimation on the wavelet coefficients and determining a denoising model based on the likelihood estimation value comprises:

[0018] The wavelet coefficient corresponding to the minimum likelihood estimate is taken to determine the denoising threshold; the denoising threshold calculation formula is:

[0019]

[0020] In the formula, r i represents the likelihood estimate, w i represents the i-th value in the wavelet coefficient sequence, i = 1, 2, ... n, w k represents the kth value in the wavelet coefficient sequence, w m i n represents the square of the wavelet coefficient value corresponding to the minimum likelihood estimate, σ represents the standard deviation of Gaussian noise, and T1 represents the threshold;

[0021] The denoising model is determined using the denoising threshold; wherein the denoising model is:

[0022]

[0023] Among them, y ij represents the wavelet coefficient after denoising, x ij represents the jth wavelet coefficient under the i-th decomposition scale, α represents the preset coefficient, and sgn() represents the sign function.

[0024] Preferably, a high-resolution transient electromagnetic detector is used to detect the entire dam of the target reservoir, and the grid-shaped apparent resistivity value of the entire dam is calculated, including:

[0025] Using the formula:

[0026]

[0027] The calculated apparent resistivity value of the whole dam body; where Φ i (m) represents the difference between the i-th and i-1-th inversions, α0 represents the smoothness weight, dH It represents the derivative of the thickness of the transient electrical detection line in the depth direction, d z represents the derivative in the depth direction on the transient electrical detection line, N represents the regularized norm, when N is 2, it means that the dam filling material at the target location is concrete, when N is 4, it means that the dam filling material at the target location is clay, when N is 6, it means that the dam filling material at the target location is miscellaneous soil, when N is 8, it means that the dam filling material at the target location is stone, α1 represents the regression weight, m i represents the i-th inversion value, m i-1 represents the i-1th inversion value.

[0028] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] The present invention provides a resistivity value energy line estimation method for the location of a leakage channel of a reservoir dam. Compared with the prior art, after calibrating a specific reservoir dam, the present invention can determine the location of termite nests in the corresponding dam based on the sampling data and background value of a high-resolution transient electromagnetic detector of the reservoir dam, thereby improving the accuracy and efficiency of detecting the location of the leakage channel of the reservoir dam without damaging the reservoir dam. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0031] Figure 1 A flow chart of a method for estimating the resistivity value energy line of termite nest locations in a reservoir dam provided by the present invention;

[0032] Figure 2 The background value of apparent resistivity of the termite nest position provided by the present invention;

[0033] Figure 3 A survey line diagram of the location of termite nests in the first reservoir dam provided by the present invention;

[0034] Figure 4 The survey line diagram of the location of termite nests in the second reservoir dam provided by the present invention;

[0035] Figure 5 The survey line diagram of the location of termite nests in the third reservoir dam provided by the present invention;

[0036] Figure 6 This is the survey line map of the location of termite nests in the fourth reservoir dam provided by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] The terms "first", "second", "third" and "fourth" in the specification and claims of the present application and the drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a series of steps, processes, methods, etc. are not limited to the listed steps, but may optionally include steps that are not listed, or may optionally include other step elements inherent to these processes, methods, products or devices.

[0040] In order to make the above-mentioned objectives, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] See also Figure 1 , a method for estimating the resistivity value energy line of the termite nest position in a reservoir dam, comprising:

[0042] Step 1: Calibrate different types of reservoirs and dams, and use the corresponding determined termite nest locations of the reservoirs and dams as calibration locations;

[0043] Step 2: Use a transient electromagnetic detector to detect all calibration positions to obtain sampling signals, pre-process the sampling signals to obtain sampling data, and use the sampling data as the current low value;

[0044] It should be noted that when using a high-resolution transient electromagnetic detector to scan all calibration positions, if the apparent resistivity values ​​of the reservoir dam detected after two scans are consistent, the currently collected sampling data will be used as the background value.

[0045] Further, the sampling signal is preprocessed to obtain sampling data, including:

[0046] Performing wavelet decomposition on the sampled signal to obtain wavelet coefficients;

[0047] Conduct likelihood estimation on the wavelet coefficients, and determine the denoising model based on the likelihood estimation value;

[0048] Using the denoising model to process the wavelet coefficients to obtain denoised wavelet coefficients;

[0049] Reconstructing the denoised wavelet coefficients to obtain a denoised sampling signal;

[0050] The denoised sampling signal is subjected to analog-to-digital conversion to obtain preprocessing to obtain sampling data.

[0051] Among them, the likelihood estimation is performed on the wavelet coefficients, and the denoising model is determined based on the likelihood estimation value, including:

[0052] The wavelet coefficient corresponding to the minimum likelihood estimate is taken to determine the denoising threshold; the denoising threshold calculation formula is:

[0053]

[0054] In the formula, r i represents the likelihood estimate, w i represents the i-th value in the wavelet coefficient sequence, i = 1, 2, ... n, w k represents the kth value in the wavelet coefficient sequence, w min represents the square of the wavelet coefficient value corresponding to the minimum likelihood estimate, σ represents the standard deviation of Gaussian noise, and T1 represents the threshold;

[0055] The denoising model is determined using the denoising threshold; wherein the denoising model is:

[0056]

[0057] Among them, y ij represents the wavelet coefficient after denoising, x ij represents the jth wavelet coefficient under the i-th decomposition scale, α represents the preset coefficient, and sgn() represents the sign function.

[0058] Common wavelet threshold functions mainly include: hard threshold function (HTF) and soft threshold function (STF). The hard threshold function and the soft threshold function are discontinuous at the threshold, so they cannot remove noise very well. The present invention analyzes from the perspective of mathematical properties and estimates the parameters of the model according to the probability distribution (likelihood estimate) of the data, so as to better adapt to the data characteristics. In this way, the accuracy and efficiency of the wavelet denoising model can be improved, making it better suitable for signals collected under different circumstances.

[0059] The present invention preferably selects 10 determined termite nest locations on reservoir dams as calibration locations, and performs two scans with a high-resolution transient electromagnetic detector. There is no obvious abnormality in the two scans, and the apparent resistivity values ​​measured by the high-resolution transient electromagnetic detector are consistent, and the collected data is used as the background value.

[0060] Step 3: Use a high-resolution transient electromagnetic detector to detect the entire dam of the target reservoir and calculate the grid-shaped apparent resistivity value of the entire dam.

[0061] Furthermore, step 3 includes:

[0062] (1) Preprocessing of apparent resistivity data detected by high-resolution transient electromagnetic detector

[0063] Data cleaning of the original recorded data can eliminate strong signal interference and reduce the impact of the errors caused by it on the measured data.

[0064] (2) Calculation model of apparent resistivity value detected by high-resolution transient electromagnetic detector:

[0065] The apparent resistivity value of transient electromagnetic method adopts the smoothness constraint OCCAM optimization inversion method. That is, under the condition that the fitting error between the observed data and the model data is minimized, the roughness of the inversion model is also minimized. The objective function of the inversion is:

[0066]

[0067] in:

[0068] N is the regularization norm. It can be 2, 4, 6 or 8. The specific value is determined according to the dam filling material at the test location: 2 (concrete), 4 (clay), 6 (miscellaneous soil) and 8 (stone).

[0069] A - measured data of any measuring point within the transient electromagnetic frame;

[0070] F - transient electromagnetic one-dimensional forward operator;

[0071] m – inversion value;

[0072] - the fit required for inversion;

[0073] W=diag{1 / ε1,1 / ε2,L1 / ε j ,L1 / ε M}, ε j is the standard deviation of the jth data, is the roughness function of the inversion model, and is written in the form of a matrix:

[0074] R=||P·m|| 2 (3)

[0075] Where P is the roughness matrix, expressed as

[0076]

[0077] P represents the smoothness of the change of the parameter vector of the data calculation model in the vertical depth direction, ||P|| 2 It represents the sum of the squares of the change rates of N parameters of the model in the vertical depth direction.

[0078] The following Occam method inversion target equation is established:

[0079] Φ(m)=λ||R|| 2 +||Af(m)|| 2 (5)

[0080] Where:

[0081] Φ(m) is the difference between each inversion

[0082] λ——Lagrangian factor, used to balance the proportion of model smoothness term in data inversion;

[0083] ||R|| 2 ——Model smoothness term;

[0084] A——Measured data of any measuring point in the transient electromagnetic frame;

[0085] ||df(m)|| 2 ——Model parameter fitting item.

[0086] The ultimate goal of data inversion is to find the optimal model parameter m so that the deviation between the field measured data A and the inverted value / (m) is minimized.

[0087] Using the above least squares solution method, we can get the iterative calculation formula of the inversion method:

[0088]

[0089] in:

[0090] Φ i (m)——the difference between the i-th and i-1-th inversions

[0091] α0——smoothness weight, 0.5, 1, 1.5, 2, 0.5 (stone), 1 (miscellaneous soil), 1.5 (clay), 2 (concrete)

[0092] d H ——On the transient electrical detection line, the derivative of the line thickness in the depth direction

[0093] d z——On the transient electrical test line, the derivative in the depth direction

[0094] N is the regularization norm. It can be 2, 4, 6 or 8. The specific value is determined according to the dam filling material at the test location: 2 (concrete), 4 (clay), 6 (miscellaneous soil), 8 (stone)

[0095] α1 - regression weight, 1, 2, 3, 4, the specific value is determined according to the dam filling material at the test location, 1 (stone), 2 (miscellaneous soil), 3 (clay), 4 (concrete)

[0096] m i ——The i-th inversion value

[0097] m i-1 ——The i-1th inversion value

[0098] Step 4: Determine the location of the termite nest of the target reservoir dam according to the grid block apparent resistivity value and the background value.

[0099] Furthermore, step 4 includes:

[0100] Use the apparent resistivity value capability line method to draw the apparent resistivity value capability line of the entire dam. Draw an apparent resistivity warning map according to the background value to determine the location of the termite nest. Figure 3-6 As shown, the apparent resistivity of termite nests is generally between 100-250. This is because there is moisture inside the termite nests.

[0101] The present invention converts sample characteristic signals such as leakage channels into corresponding apparent resistivity values, thereby achieving intuitiveness and accuracy in the location characteristics and positioning of termite nests, and can be specifically used to identify the locations of termite nests in reservoir dams. This can improve the accuracy and efficiency of termite nest location detection in reservoir dams without destroying the reservoir dams.

[0102] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the method disclosed in the embodiment, since it corresponds to the device disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the device part description.

[0103] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for estimating the resistivity value energy line of termite nest locations in reservoir dams, characterized in that: include: Calibrate different types of reservoirs and dams, and use the corresponding determined termite nest locations of reservoirs and dams as calibration locations; Using a transient electromagnetic detector to detect all calibration positions to obtain sampling signals, preprocessing the sampling signals to obtain sampling data, and using the sampling data as the current low value; Use transient electromagnetic detector to detect the entire dam of the target reservoir and calculate the apparent resistivity value of the mesh blocks of the entire dam; The location of the termite nest of the target reservoir dam is determined according to the apparent resistivity value of the mesh block and the background value.

2. The method for estimating the resistivity value energy line of the termite nest position in a reservoir dam according to claim 1, characterized in that: Preprocessing the sampled signal to obtain sampled data includes: Performing wavelet decomposition on the sampled signal to obtain wavelet coefficients; Conduct likelihood estimation on the wavelet coefficients, and determine the denoising model based on the likelihood estimation value; Using the denoising model to process the wavelet coefficients to obtain denoised wavelet coefficients; Reconstructing the denoised wavelet coefficients to obtain a denoised sampling signal; The denoised sampling signal is subjected to analog-to-digital conversion to obtain preprocessing to obtain sampling data.

3. The method for estimating the resistivity value energy line of the termite nest position in a reservoir dam according to claim 2, characterized in that: The likelihood of the wavelet coefficients is estimated, and the denoising model is determined based on the likelihood estimate, including: The wavelet coefficient corresponding to the minimum likelihood estimate is taken to determine the denoising threshold; the denoising threshold calculation formula is: In the formula, r i represents the likelihood estimate, w i Represents the i-th value in the wavelet coefficient sequence, i = 1, 2, ... n, w k represents the kth value in the wavelet coefficient sequence, w min represents the square of the wavelet coefficient value corresponding to the minimum likelihood estimate, σ represents the standard deviation of Gaussian noise, and T1 represents the threshold; The denoising model is determined using the denoising threshold; wherein the denoising model is: Among them, y ij represents the wavelet coefficient after denoising, x ij represents the jth wavelet coefficient under the i-th decomposition scale, α represents the preset coefficient, and sgn( ) represents the sign function.

4. The method for estimating the resistivity value energy line of the termite nest position in a reservoir dam according to claim 3, characterized in that: Use a high-resolution transient electromagnetic detector to detect the entire dam of the target reservoir, and calculate the grid-shaped apparent resistivity value of the entire dam, including: Using the formula: Φ i (m)=α0{[d H / d Z ] N d Z } 1 / N +α1{∫[(m i -m i-1 ) / m i-1 ]Nd z } 1 / N The calculated apparent resistivity value of the whole dam body; where Φ i (m) represents the difference between the i-th and i-1-th inversions, α0 represents the smoothness weight, d H It represents the derivative of the thickness of the transient electrical detection line in the depth direction, d z represents the derivative in the depth direction on the transient electrical detection line, N represents the regularized norm, when N is 2, it means that the dam filling material at the target location is concrete, when N is 4, it means that the dam filling material at the target location is clay, when N is 6, it means that the dam filling material at the target location is miscellaneous soil, when N is 8, it means that the dam filling material at the target location is stone, α1 represents the regression weight, m i represents the i-th inversion value, m i-1 represents the i-1th inversion value.

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