Inversion method of adaptive time-frequency data correction based on apparent resistivity average slope

Abstract

The application discloses an inversion method based on apparent resistivity average slope adaptive time-frequency data correction, and comprises the following steps: acquiring time-frequency data and obtaining apparent resistivity average slope; correcting the time-frequency data by using the apparent resistivity average slope and an adaptive time-frequency data correction factor to obtain corrected time-frequency data; constructing an inversion data target function and inverting the corrected time-frequency data. Through the above scheme, the application has the advantages of simple logic, accuracy and reliability, and has high practical value and popularization value in the field of geophysical electromagnetic prospecting technology.

Description

Technical Field

[0001] This invention relates to the field of geophysical electromagnetic exploration technology, and in particular to an inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity. Background Technology

[0002] Currently, electromagnetic methods for exploring medium-to-deep resources face a bottleneck in detecting weak signals, especially in engineering geophysical exploration. Due to the complex geological environment, electromagnetic signals decay exponentially with depth, making it difficult for existing technologies to effectively capture deep electromagnetic anomalies. The electromagnetic response signals of targets such as concealed ore bodies and geothermal reservoirs are often weak and easily masked by background noise. For the exploration of these deep resources, existing electromagnetic methods cannot adequately address the problems of weak signals and ambiguous anomaly characteristics, leading to difficulties in signal capture and analysis. Particularly under high temperature and high pressure environments, the electromagnetic signal anomalies in geothermal reservoirs exhibit even more complex characteristics, further exacerbating the difficulty of signal identification and analysis. Therefore, improving the sensitivity of deep weak electromagnetic signal detection technology, developing more efficient weak electromagnetic anomaly signal identification algorithms, and solving the problem of multiple solutions in weak signal inversion modeling have become critical technical challenges that urgently need to be addressed.

[0003] To address the problem of weak and difficult-to-detect deep and medium-depth anomaly signals, traditional geophysical electrical methods generally suffer from low accuracy, weak effective information, and insufficient inversion resolution and reliability. For example, the disclosed technology, "Publication No. CN117270072A, entitled 'A Gravity and Magnetic Potential Field Imaging Inversion Method and System Based on an Improved Differential Evolution Algorithm,'" denoises and separates regional fields in gravity and magnetic potential field data to obtain residual field data, generates an initial model and property parameter range constraints for the subsurface medium, reads in the residual field data and initial model, generates inversion grid parameters and a priori information matrix, establishes an inversion objective function for imaging inversion, optimizes the inversion objective function using an improved differential evolution algorithm, and finally outputs the model vector with the minimum data objective function as the final inversion result. This patent may have the following shortcomings: the success of this method heavily relies on the design of the population model; for complex models, the reliability of the results cannot be guaranteed.

[0004] Furthermore, in the patent application CN109828307A, entitled "A Detection Method and Application of Transient Electromagnetic Multi-Frequency Fusion," multiple sets of transmission frequencies are used for data acquisition to obtain experimental data with different induced electromotive forces and different time-channel distributions. The data obtained by fusing multiple frequencies in the overlapping time domain is combined with the time-domain data specific to each frequency in chronological order to obtain multi-channel time-domain data, resulting in multi-frequency fused data. The combined data is then partitioned in the time domain to achieve an average time-channel distribution, yielding an induced electromotive force decay curve. The boundary between the fused data and the time-domain data specific to multiple frequencies is smoothed, and the average time-channel distribution is used to substitute the smoothed data into the original data for two-dimensional imaging inversion to obtain a resistivity profile. The problem with this patent is its emphasis on smoothing the data, which could potentially eliminate weak anomalous signals in complex real-world conditions, failing to improve inversion resolution and possibly further reducing it.

[0005] Currently, in the research of medium-to-deep resource exploration, a unified and efficient technical solution for the effective inversion of weak anomaly signals has not yet been formed. In engineering geophysical exploration, due to the influence of complex underground media, the electromagnetic signal of the target body gradually attenuates with increasing exploration depth, and the difference in physical properties between the ore body and the surrounding rock is small, making it easy for weak electromagnetic anomaly signals to be submerged by background noise, which is difficult for existing technologies to effectively identify. In addition, in mineral resource exploration, the electromagnetic signal of concealed ore bodies gradually attenuates with increasing depth, and existing technologies face significant challenges in capturing subtle differences between the ore body and the surrounding rock.

[0006] Therefore, there is an urgent need to propose a simple, accurate and reliable inversion method based on the average slope of apparent resistivity for adaptive time-frequency data correction. Summary of the Invention

[0007] To address the aforementioned problems, the present invention aims to provide an inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity. The technical solution adopted by the present invention is as follows:

[0008] An inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity includes the following steps:

[0009] Acquire time-frequency data and calculate the average slope of apparent resistivity;

[0010] An adaptive time-frequency data correction factor is preset, and the time-frequency data is corrected using the average slope of apparent resistivity and the adaptive time-frequency data correction factor to obtain the corrected time-frequency data.

[0011] Construct an objective function for the inversion data and perform inversion on the corrected time-frequency data.

[0012] Furthermore, the time-frequency data is corrected using the apparent resistivity average slope and an adaptive time-frequency data correction factor to obtain the corrected time-frequency data, the expression of which is: in, This represents the observation data before the adaptive time-frequency data correction factor; This represents the data after adaptive time-frequency data correction; Indicates the correction factor; This represents the adaptive time-frequency data correction factor.

[0013] Furthermore, the correction coefficient The expression is: in, This represents the average slope of the apparent resistivity.

[0014] Furthermore, the correction coefficient The expression is: in, represents the average slope of the apparent resistivity; e represents the base of the natural logarithm.

[0015] Furthermore, the correction coefficient The expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates frequency.

[0016] Furthermore, the correction coefficient The expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates time. Further, the average slope of the apparent resistivity. The average of the absolute values ​​of the slopes of the current data point and its adjacent data points is calculated using the following steps:

[0017] If the time-frequency data is one-dimensional, it is obtained from the two adjacent time-frequency data points before and after the time-frequency data point;

[0018] If the time-frequency data is two-dimensional, it is obtained from the eight adjacent time-frequency data points around the time-frequency data point;

[0019] If the time-frequency data is three-dimensional, it is obtained from the 26 adjacent time-frequency data points around the time-frequency data point.

[0020] Furthermore, the adaptive time-frequency data correction factor The inflection point method or the lowest point method can be used to obtain the value.

[0021] Furthermore, the expression for the objective function of the inverted data is: in, This represents the frequency domain adaptive time-frequency data correction coefficient matrix; Represents a weighted matrix of frequency domain data; Represents the frequency domain observation data matrix; Represents the frequency domain forward modeling data matrix; Represents the frequency domain control matrix; This represents the time-domain adaptive time-frequency data correction coefficient matrix; Represents a weighted matrix of time-domain data; Represents the time-domain observation data matrix; Represents the forward-modeled data matrix in the time domain; This is the time-domain control matrix; T represents the transpose of the matrix.

[0022] The frequency domain adaptive time-frequency data correction coefficient matrix The expression is: in, This represents the adaptive time-frequency data correction coefficient matrix corresponding to the nth frequency observation data; This represents the number of matrix units corresponding to all the data. This represents the adaptive time-frequency data correction coefficient matrix corresponding to the i-th frequency observation data; This represents the adaptive time-frequency data correction coefficient matrix corresponding to the first frequency observation data; where n is a natural number greater than 1; and i is a natural number less than or equal to n.

[0023] The adaptive time-frequency data correction coefficient matrix corresponding to the i-th frequency observation data The expression is: in, This represents the adaptive time-frequency data correction coefficient for the i-th frequency observation data; This represents the number of matrix elements for all frequency observation data at the i-th frequency.

[0024] The frequency domain control matrix The expression is: in, This represents the block matrix corresponding to the first frequency forward modeling data; This represents the block matrix corresponding to the i-th frequency forward modeling data; This represents the block matrix corresponding to the nth frequency forward modeling data; where n represents a natural number greater than 1.

[0025] The block matrix corresponding to the i-th frequency forward modeling data The expression is: The time-domain adaptive time-frequency data correction coefficient matrix The expression is: in, This represents the adaptive time-frequency data correction coefficient matrix corresponding to the first time observation data; This represents the adaptive time-frequency data correction coefficient matrix corresponding to the i-th time observation data; This represents the adaptive time-frequency data correction coefficient matrix corresponding to the nth time observation data; where i is a natural number less than or equal to n.

[0026] The adaptive time-frequency data correction coefficient matrix corresponding to the i-th time observation data The expression is: in, This represents the adaptive time-frequency data correction coefficient for the i-th time data. This represents the number of matrix cells for all time observation data at the i-th time.

[0027] The time-domain control matrix The expression is: Compared with the prior art, the present invention has the following beneficial effects:

[0028] This invention effectively improves the inversion accuracy of weak anomaly signals in deep resource exploration by adaptive time-frequency data correction processing of both time-domain and frequency-domain signals. In engineering geophysical exploration, complex underground media result in weak target signals that are easily interfered with by noise. Signal attenuation intensifies with increasing depth, making it difficult for traditional methods to identify subtle differences. Adaptive time-frequency data correction processing of signals in different frequency bands enhances detection capabilities and improves positioning accuracy. In mineral and geothermal resource exploration, signal attenuation and multi-frequency superposition affect the identification effectiveness of traditional techniques. Adaptive time-frequency data correction processing strengthens signal processing and extracts key anomaly signals. This invention employs adaptive time-frequency data correction processing of both time-domain and frequency-domain signals, helping to reduce interference, improve the identification capability of weak anomaly signals, provide an efficient and accurate inversion scheme for deep resource exploration, overcome technical bottlenecks, and achieve precise assessment and development.

[0029] This invention employs an adaptive time-frequency data correction method based on the average slope of apparent resistivity in both frequency and time domain signal processing. This method optimizes signal characteristics to improve the inversion effect of weak signals. In frequency domain signal processing, enhancing the signal response in low-frequency or specific frequency bands effectively improves the positioning accuracy of concealed targets and helps extract relevant signal features. In time domain signal processing, the method primarily enhances the response intensity of weak signals, particularly in engineering geophysical exploration, by amplifying weak anomaly signals in complex underground media and adjusting the signal intensity within a time window, thereby improving the response of the acquired signals. This invention can also optimize based on the waveform characteristics and background noise of the signal, reducing misjudgments caused by noise interference and ensuring accurate signal identification.

[0030] In summary, this invention has the advantages of simple logic and high accuracy and reliability, and has high practical and promotional value in the field of geophysical electromagnetic exploration technology. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a logic flowchart of the present invention.

[0033] Figure 2 This is a schematic diagram of the one-dimensional slope calculation in this invention.

[0034] Figure 3 This is a schematic diagram of the two-dimensional slope calculation in this invention.

[0035] Figure 4 This is a schematic diagram of the three-dimensional slope calculation in this invention.

[0036] Figure 5 This is a schematic diagram illustrating the search for adaptive time-frequency data correction factors using the inflection point method in this invention.

[0037] Figure 6 This is a schematic diagram of the search for adaptive time-frequency data correction factor using the lowest point method in this invention.

[0038] Figure 7 A schematic diagram of the observation system provided by this invention.

[0039] Figure 8 A three-dimensional geoelectric model diagram provided in this embodiment of the invention.

[0040] Figure 9 The full-frequency inversion result diagram provided in this embodiment of the invention.

[0041] Figure 10 The inversion result diagram with an adaptive time-frequency data correction factor of 3 provided in this embodiment of the invention.

[0042] Figure 11 The inversion result diagram with an adaptive time-frequency data correction factor of 5 provided in this embodiment of the invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the present invention will be further described below with reference to the accompanying drawings and embodiments. The embodiments of the present invention include, but are not limited to, the following embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0044] In this embodiment, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0045] The terms "first" and "second," etc., used in the specification and claims of this embodiment are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0046] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0047] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units means two or more processing units; multiple systems means two or more systems.

[0048] like Figures 1 to 11 As shown, this embodiment provides an inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity. It primarily addresses the bottleneck of weak signal detection in existing technologies for medium-deep resource exploration, particularly in engineering geophysical exploration, mineral resource exploration, and geothermal resource exploration. This is mainly due to the susceptibility of complex underground media to noise masking, the attenuation of concealed ore bodies due to depth, and the weak and multi-frequency superposition of signals in geothermal reservoirs under high temperature and pressure environments. Common problems include a lack of high-sensitivity sensing technology, insufficient intelligent recognition algorithms, and limitations in multi-solution inversion modeling, necessitating the development of weak signal enhancement acquisition and multimodal data fusion technologies. Therefore, this embodiment aims to improve the inversion quality of weak anomaly signals in medium-deep resource exploration, overcoming the limitations of traditional technologies and providing efficient and accurate technical support for the precise exploration and development of engineering geophysical exploration, mineral resources, and geothermal resources.

[0049] This embodiment uses the apparent resistivity average slope k, the adaptive time-frequency data correction coefficient β, and the adaptive time-frequency data correction factor. The modified expression is as follows:

[0050]

[0051] in, This represents the observation data before the adaptive time-frequency data correction factor; This represents the data after adaptive time-frequency data correction; Indicates the correction factor; This represents the adaptive time-frequency data correction factor.

[0052] In this embodiment, three methods are used to adjust the correction coefficient. Make corrections:

[0053] The first method involves directly applying adaptive time-frequency data correction to the data, and its expression is as follows:

[0054]

[0055] in, This represents the average slope of the apparent resistivity.

[0056] The second method involves exponentially adaptive time-frequency data correction of the data, the expression of which is:

[0057]

[0058] in, represents the average slope of the apparent resistivity; e represents the base of the natural logarithm.

[0059] The third method involves adaptive time-frequency data correction based on skin depth:

[0060] Here, the skin depth calculation formula in the frequency domain is used. Angular frequency, Let be the magnetic permeability of the conductor. Let be the conductivity of the conductor, which undergoes adaptive time-frequency data correction with frequency. Its expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates frequency.

[0061] In addition, according to the formula for calculating skin depth in the time domain ( Let be the magnetic permeability of the conductor. (where is the conductivity of the conductor) is used to adaptively correct the time-frequency data over time, and its expression is:

[0062]

[0063] in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates time.

[0064] In this embodiment, the determination of the average slope of apparent resistivity includes the following steps:

[0065] like Figures 2 to 4 As shown, if the time-frequency data is one-dimensional, it is obtained from the two adjacent time-frequency data points (i.e., through...). (The average slope is calculated from the two sets of data). If the time-frequency data is two-dimensional, it is calculated from the eight adjacent time-frequency data points around the time-frequency data point; if the time-frequency data is three-dimensional, it is calculated from the 26 adjacent time-frequency data points around the time-frequency data point. When the average slope of apparent resistivity k < q (q is a preset threshold), the data is corrected; when the average slope k > q, it is skipped and no correction is performed.

[0066] In addition, the adaptive time-frequency data correction factor in this embodiment The inflection point method or the minimum point method are used to obtain the value. Both methods are based on different adaptive time-frequency data correction factors. The corresponding iteration termination fitting difference is used for selection.

[0067] In this embodiment, the time-frequency data is corrected using the apparent resistivity average slope and an adaptive time-frequency data correction factor to obtain the corrected time-frequency data; an inversion data objective function is constructed, and the corrected time-frequency data is inverted.

[0068] The following is a real-world example to demonstrate the feasibility of this embodiment:

[0069] The first step is to establish a three-dimensional geoelectric model.

[0070] like Figure 8 As shown, this embodiment designs an anomaly model with a top burial depth of 500 m, where the resistivity of the black anomaly is 10 Ω·m and the resistivity of the uniform half-space is 500 Ω·m. In reality, this burial depth is considered medium to deep, and the anomaly generated by the anomaly is weak. Full-frequency joint inversion reveals that the inversion effect is very poor. The inversion results indicate that the anomaly response signal generated by this model is extremely weak. This embodiment uses this model for adaptive time-frequency data correction factor inversion to fully verify the feasibility and effectiveness of the adaptive time-frequency data correction factor scheme.

[0071] The second step is to establish an observation system.

[0072] The observation system in this embodiment varies depending on the type of data collected and the methods used. For example, Figure 7As shown, when using the semi-airborne electromagnetic method, the system primarily measures Bz data. To acquire this data, artificial sources were deployed on the ground outside the measurement area, with the measuring points located in the air.

[0073] The third step is to establish the initial model.

[0074] Because inversion problems typically exhibit significant ambiguity, the absence of a reliable initial model can lead to a variety of different inversion results. Therefore, a reasonable and reliable initial model must be constructed before performing forward and inversion calculations. This initial model provides a suitable starting point for the inversion process, helps limit the range of solutions, and reduces uncertainties during the inversion process. In the theoretical three-dimensional geoelectric model, the initial model is set as a uniform half-space with a resistivity of 500 Ω·m. This assumption simplifies the problem, allowing the inversion calculations to begin under idealized conditions, facilitating further adjustments and optimizations.

[0075] Figure 8 The embodiment presents a uniform half-space three-dimensional geoelectric model with a background resistivity of 500 Ωm. The black anomaly in the figure represents a low-resistivity anomaly with a resistivity of 10 Ωm. This embodiment obtains Bz data through forward modeling and adds 2% random noise as inversion data. The full-frequency joint inversion results of this embodiment are as follows: Figure 9 As shown in the figure, there is essentially no inversion effect on low-resistivity anomalies. Furthermore, this embodiment employs a joint inversion method for analysis. Figure 10 , Figure 11 The figures show the inversion results for adaptive time-frequency data correction factors 3 and 5, respectively. It can be seen from the figures that although there are certain differences in the inversion effects with different adaptive time-frequency data correction factors, they are similar to the full-frequency and full-time inversion results. Figure 9 In comparison, this embodiment can effectively improve the inversion quality of weak anomalous signals. Therefore, the correctness of the adaptive time-frequency data correction factor calculation and inversion technology in uniform half-space in this embodiment is demonstrated.

[0076] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any changes made based on the design principles of the present invention, or any non-creative modifications made thereon, shall fall within the scope of protection of the present invention.

Claims

1. An inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity, characterized in that, Includes the following steps: Acquire electromagnetic time-frequency data and calculate the average slope of apparent resistivity; An adaptive time-frequency data correction factor is preset, and the time-frequency data is corrected using the apparent resistivity average slope and the adaptive time-frequency data correction factor to obtain the corrected time-frequency data, the expression of which is: in, This represents the observation data before the adaptive time-frequency data correction factor; This represents the data after adaptive time-frequency data correction; Indicates the correction factor; This represents the adaptive time-frequency data correction factor; Construct an objective function for inverting the data, and perform inversion on the corrected time-frequency data; the expression for the objective function for inverting the data is: ； in, This represents the frequency domain adaptive time-frequency data correction coefficient matrix; This represents a weighted matrix of frequency domain data. Represents the frequency domain observation data matrix; Represents the frequency domain forward modeling data matrix; Represents the frequency domain control matrix; This represents the time-domain adaptive time-frequency data correction coefficient matrix; Represents a weighted matrix of time-domain data; Represents the time-domain observation data matrix; Represents the forward-modeled data matrix in the time domain; This is the time-domain control matrix; T represents the transpose of the matrix. The correction coefficient The expression is: in, This represents the average slope of the apparent resistivity. or the correction factor The expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; or the correction factor The expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates frequency; or the correction factor The expression is: in, represents the average slope of apparent resistivity; e represents the base of the natural logarithm; Indicates time; The average slope of apparent resistivity It is the average of the absolute values ​​of the slopes of the current data point and its adjacent data points.

2. The inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity according to claim 1, characterized in that, The average slope of apparent resistivity is obtained through the following steps: If the time-frequency data is one-dimensional, it is obtained from the two adjacent time-frequency data points before and after the time-frequency data point; If the time-frequency data is two-dimensional, it is obtained from the eight adjacent time-frequency data points around the time-frequency data point; If the time-frequency data is three-dimensional, it is obtained from the 26 adjacent time-frequency data points around the time-frequency data point.

3. The inversion method based on adaptive time-frequency data correction using the average slope of apparent resistivity according to claim 1, characterized in that, The adaptive time-frequency data correction factor The inflection point method or the lowest point method can be used to obtain the value.

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