Polarization anomaly information extraction method based on resistivity inversion results

Abstract

The application discloses a polarization anomaly information extraction method based on resistivity inversion results, and comprises the following steps: introducing a Cole-Cole model into controllable source one-dimensional forward calculation, and deriving one-dimensional frequency domain forward calculation formula containing induced polarization effect; simulating frequency domain electromagnetic field response under the condition of layered induced polarization medium, and analyzing the influence of induced polarization parameters in the Cole-Cole model on the electromagnetic field response; adopting a step-by-step processing strategy to carry out inversion processing on the electrical property parameters of the measured electromagnetic field data, so as to obtain geoelectric section resistivity data; and adopting mapping imaging and sensitive optimal layer recursive imaging to extract the geoelectric section resistivity data, so as to obtain induced polarization anomaly information contained in the electromagnetic field response. 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 exploration technology.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method for extracting polarization anomaly information based on resistivity inversion results. Background Technology

[0002] Currently, numerous scholars have conducted extensive research on induced polarization (IP) anomaly information in artificial source electromagnetic methods, mainly divided into transient electromagnetic methods (TEM) and controlled-source audio-frequency electromagnetic methods (CSAMT). Among these, research on IP information in CSAMT started relatively late both domestically and internationally, with limited research abroad and only beginning in China in the last decade or so. In 2007, Yue Anping et al., in their paper "Discussion on Extracting IP Information from CSAMT Signals," described the basic steps for extracting IP anomalies from electromagnetic field signals of controlled-source audio-frequency electromagnetic methods and outlined related research directions. In 2008, Liu Lei et al., in their paper "Research on CSAMT-Induced Polarization Effects on Polarizable Earth," described the use of a generalized inverse inversion method to invert the geoelectric model of the intermediate layer polarisphere, and based on the inversion results, preliminarily concluded that extracting IP anomaly information from CSAMT signals is feasible. However, only the polarization parameters of the intermediate layer were inverted, while other strata were not involved in the inversion. In the same year, Tang Jingtian et al., in their paper "Apparent Resistivity Response of Polarized Layers in CSAMT Method," discussed the apparent resistivity response of the CSAMT method containing intermediate polarized layers, analyzing theoretical numerical curves to show that the influence of induced polarization (IP) is not negligible. In 2009, Huang Lei, in "IP Effect Extraction in CSAMT," mentioned analyzing the electromagnetic field components of CSAMT containing excitation effects and proposed the idea of ​​using Ey for induced polarization information extraction. Li Yong et al., in their paper "Research on Excitation Polarization Effect of Electromagnetic Sounding at Electric Couple Source Frequency," explained the analysis of induced polarization anomalies from electric couple sources, performed resolution analysis on each component of the electromagnetic field, proposed using Ex to extract induced polarization information, and provided corresponding theoretical calculation formulas. Similarly, Xu Wendong, in "Research on the Influence and Application of IP Effect in CSAMT," also believed that the induced polarization effect has the greatest impact on Ex, especially at low and medium frequencies, and provided relevant inversion objective functions and the idea of ​​prioritizing induced polarization parameters with significant influence. In 2012, Yu Chuantao proposed the idea of ​​using CSAMT data and IP data for joint inversion in his paper "Application Research of Two-Dimensional Forward and Joint Inversion of CSAMT with Induced Electrostatic Effect," which was applied to actual exploration and achieved good results. In 2013, Feng Bing et al. mentioned using Ex and Hy component data to invert the induced electrostatic parameters of the subsurface medium in a step-by-step manner in their paper "A Preliminary Study on the Method of Extracting Induced Electrostatic Effect from CSAMT Electromagnetic Field Response." In 2015, Wang Junlu further studied the forward electromagnetic field response of a two-dimensional geoelectric model containing a Cole-Cole model in her paper "Finite Element Numerical Simulation of Line Source Controllable Source Based on Cole-Cole Complex Resistivity Model." In 2016, Xu Yucong mentioned using an adaptive regularized inversion method to conduct one-dimensional inversion research on CSAMT with induced electrostatic effect in his paper "One-Dimensional Forward and Inversion Research of CSAMT with Induced Electrostatic Effect."In 2017, Huang Yiwei mentioned the three-dimensional forward modeling calculation of a controllable source with induced polarization in his paper "Research on Three-Dimensional Forward Modeling and Response of Controllable Source with Induced Polarization Effect", which provided a forward modeling basis for three-dimensional induced polarization inversion.

[0003] In addition, time-frequency electromagnetic (TFEM) is a new electromagnetic exploration technology that has emerged in the last decade or so. This method was proposed by Davydycheva in "Electrical Prospecting Method for Oil Search Using the Induced Polarization Effect" and further improved by Zhang Ruifeng in "Extraction and Application of Time-Domain Induced Polarization Parameters in TFEM." It has now been successfully applied in oil and gas exploration and development both domestically and internationally. In 2020, He Zhanxiang, in "Time-Frequency Electromagnetic (TFEM) Technology: Data Joint Constraint Inversion," introduced the artificial fish swarm intelligent optimization algorithm into TFEM data processing, employing a step-by-step constraint inversion strategy to achieve polarization inversion through constraint interfaces and resistivity. Currently, in Jiang Wenbo's "BGP Time-Frequency Electromagnetic Technology Introduction PPT," the processing of polarization anomaly information involves inverting the polarization inversion in the frequency domain based on the time-domain resistivity inversion results.

[0004] Overall, the current processing of polarization anomaly information is basically done through iterative inversion. In addition to the long computation time and inability to provide imaging results in real time, the resolution is often poor due to the linear approximation of the iterative format and the increase in the scale of the measured data.

[0005] Therefore, there is an urgent need to propose a simple, accurate, and reliable method for extracting polarization anomaly information based on resistivity inversion results. Summary of the Invention

[0006] To address the above problems, the purpose of this invention is to provide a method for extracting polarization anomaly information based on resistivity inversion results. The technical solution adopted by this invention is as follows:

[0007] A method for extracting polarization anomaly information based on resistivity inversion results includes the following steps:

[0008] The Cole-Cole model is introduced into the one-dimensional forward modeling of controllable sources, and the one-dimensional frequency domain forward modeling formula containing the induced polarization effect is derived.

[0009] The frequency domain electromagnetic field response under the condition of layered excited polarized medium is simulated, and the influence of the induced polarization parameters in the Cole-Cole model on the electromagnetic field response is analyzed.

[0010] A step-by-step processing strategy was adopted to invert the electrical parameters of the measured electromagnetic field data and obtain the geoelectric cross-sectional resistivity data.

[0011] Mapping imaging and sensitive optimal tomographic imaging were used to extract resistivity data of geoelectric cross sections to obtain induced polarization anomaly information contained in the electromagnetic field response.

[0012] Furthermore, the step-by-step processing strategy for inverting the electrical parameters of the measured electromagnetic field data to obtain the geoelectric resistivity data includes the following steps:

[0013] The apparent resistivity of the entire region was obtained using electromagnetic field amplitude data;

[0014] The resistivity data of the geoelectric cross section are obtained by using the OCAAM or NLCG inversion algorithm to fit the apparent resistivity of the whole area.

[0015] Furthermore, the method for extracting polarization anomaly information based on resistivity inversion results also includes:

[0016] The inversion data corresponding to the forward model incorporating the Cole-Cole model were compared with the geoelectric resistivity data obtained by inverting the electrical parameters of the measured electromagnetic field data, and corrections were made accordingly.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] This invention extracts polarization anomaly information from frequency-domain electromagnetic sounding data for prediction of geological hazards, such as fractured water-bearing structures, during the construction of deep-buried long tunnels. This is a novel approach proposed in this research. By combining and comprehensively analyzing the electrical distribution characteristics of polarizability and resistivity, it is expected to improve the success rate of geological hazard prediction. In summary, this invention has the advantages of simple logic, accuracy, and reliability, and has high practical and promotional value in the field of geophysical exploration technology. Attached Figure Description

[0019] 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.

[0020] Figure 1 This is a comparison of the ExA simulation curves with and without the induced polarization effect in this invention.

[0021] Figure 2 This is a comparison chart of the polarizability model and the extracted polarization anomaly curve in this invention.

[0022] Figure 3 This is the curve used to determine the frequency-depth relationship of the high-resistivity marker layer in this invention.

[0023] Figure 4 This is the curve used to determine the frequency-depth relationship of the low-resistivity marker layer in this invention.

[0024] Figure 5 This is the theoretical model of polarizability in this invention.

[0025] Figure 6 This is a cross-sectional view of the polarizability mapping imaging in this invention. Detailed Implementation

[0026] 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.

[0027] Example

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] like Figures 1 to 6As shown, this embodiment provides a method for extracting polarization anomaly information based on resistivity inversion results. It only requires one forward modeling step and can achieve the mapping imaging from induced polarization anomaly to polarizability through a simple frequency-depth conversion, which greatly improves the calculation speed. At the same time, since the induced polarization anomaly information is extracted directly from the measured data, the resolution potential of the exploration method itself is fully explored, ensuring the high resolution of the imaging results.

[0033] The specific plan is as follows:

[0034] First, verification of the correctness of the forward modeling results:

[0035] To investigate the influence of induced polarization (IP) on measured electromagnetic field data, this embodiment, building upon previous work, introduces the Cole-Cole model into one-dimensional forward modeling of a controllable source, deriving a one-dimensional frequency domain forward modeling formula incorporating IPA. Then, it simulates the frequency domain electromagnetic field response under layered excited polarized media conditions, analyzing the influence of IPA parameters in the Cole-Cole model on the electromagnetic field response. To verify the accuracy of the forward modeling results, an identical geoelectric model is designed, and the simulation results are compared with those obtained using the Dipole1D program published by Kerry Key of the Scripps Institution of Oceanography, to determine the correctness of the program's forward modeling results.

[0036] Second, verification of the reliability of the inversion results:

[0037] A step-by-step processing strategy is proposed to invert the electrical parameters of the measured electromagnetic field data. First, resistivity parameters are inverted. The apparent resistivity of the entire area is calculated from data such as electromagnetic field amplitude. Then, inversion algorithms such as OCAAM and NLCG are used to perform resistivity inversion by fitting the apparent resistivity of the entire area. After obtaining the resistivity distribution results of the geoelectric cross-section, techniques such as mapping imaging and sensitive optimal tomographic imaging are used to further extract the induced polarization anomaly information contained in the electromagnetic field response. In this embodiment, the effectiveness and reliability of the inversion imaging method are judged by the consistency between the resistivity and polarizability cross-sectional imaging results and the designed theoretical model through the inversion processing of the forward modeling data of the theoretical model.

[0038] This embodiment uses the technology of processing measured data for tunnel geological disaster prediction to demonstrate its effectiveness.

[0039] After the inversion imaging method has been validated through theoretical model testing, research has begun on strategies for processing measured data. Since measured data contains various interferences, eliminating these noises or distortions to ensure effective data processing becomes a crucial research focus. First, representative frequency-domain electromagnetic sounding data reflecting lithologically intact and water-bearing fractured areas were collected within the study area, and analysis and preprocessing of the data were conducted. Then, the qualified preprocessed results underwent inversion imaging processing based on electrical parameters. Through experiments setting various control parameters, the processed results were optimized to achieve the best fit with known geological conditions, thereby exploring and developing a measured data processing technology model that can significantly improve the success rate of tunnel geological hazard prediction.

[0040] Specific examples:

[0041] (1) Extraction of polarization anomaly information

[0042] Based on the influence characteristics of the induced polarization effect on the electric field component Ex, a mapping imaging data processing method is proposed. Through the study of accurate identification of high-resistivity and low-resistivity marker layers, a relatively accurate frequency-depth conversion relationship is established. Simultaneously, by mapping the extracted polarization anomaly information to a reasonable initial guessed polarizability solution space (based on statistical results of physical properties), a semi-quantitative or quantitative polarizability imaging method is essentially realized. The reliability of the method has been preliminarily verified through theoretical model experiments, but further testing and improvement are needed in the processing of measured data. The theoretical geoelectric model parameters are shown in Table 1.

[0043] Table 1. Parameters of Theoretical Geoelectric Model

[0044]

[0045] Table 1 shows the geoelectric parameters of the designed theoretical model. Simulation results for the model with and without induced polarization are shown in Table 1. Figure 1 The curves in the figure are compared. As can be seen from the figure, when the induced polarization effect is present, the ExA curve from short period to long period (or from high frequency to low frequency) basically shows the cumulative superposition characteristics of the induced polarization effect caused by each polarization anomalous electrical layer. That is, as the frequency decreases, the deviation between the two shows a clear trend of gradually increasing.

[0046] Based on the above analysis and understanding, the variation of the amplitude difference of ExA between the two with the observation period was studied, and the calculated results are shown in [reference needed]. Figure 2As shown in the figure, the variation characteristics of the amplitude difference with the period correspond well with the distribution curve of the polarizability in the theoretical model—however, no effective information was extracted from the fourth weak anomaly layer. Based on this theoretical and experimental result, a mapping imaging approach was developed. This involves directly mapping the extracted polarization anomaly information to the polarizability solution space based on physical property statistics through frequency-depth conversion, thereby quickly completing the extraction and imaging of polarization anomaly information.

[0047] (2) Frequency domain to depth conversion

[0048] like Figures 3 to 4 As shown, the conversion from frequency domain to depth is also based on the analysis and research of the simulation results of the theoretical model. For high-resistivity marker layers, by adjusting their resistivity, the frequency-depth conversion relationship can be determined by observing the changes in simulated values, such as subtracting the simulated result with lower resistivity from the original simulation result. The frequency corresponding to the maximum difference between the two can be used to establish the conversion relationship with the center depth of the marker layer. For low-resistivity marker layers, by slightly increasing the resistivity of the overlying and underlying strata, the frequency corresponding to the minimum difference between the increased and original model simulation results can be calculated, thus establishing the conversion relationship with the center depth of the marker layer.

[0049] (3) Mapping from polarization anomaly to solution space

[0050] After the polarization anomaly information is extracted and the frequency-depth conversion relationship is determined, mapping imaging can be performed. Mapping imaging is to establish a simple linear transformation relationship between the polarization anomaly information and the polarizability solution space (this data is formed based on the analysis and statistics of rock and mineral samples or electrical logging data in the study area. The closer the statistical results are to the actual geoelectric distribution, the better the mapping imaging effect). That is, the minimum and maximum values ​​of the polarization anomaly are mapped to the minimum and maximum values ​​of the polarizability solution space to determine the conversion relationship between the two, thereby completing the conversion of the extracted polarization anomaly information to the depth domain polarizability distribution. Figure 5 It is a polarizability theoretical model. Figure 6 The polarizability mapping imaging results obtained through the above processing are shown in the figure. As can be seen from the figure, except for the fourth weak polarization anomalous layer, the imaging effects of the other layers are good.

[0051] 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. A method for extracting polarization anomaly information based on resistivity inversion results, characterized in that, Includes the following steps: The Cole-Cole model is introduced into the one-dimensional forward modeling of controllable sources, and the one-dimensional frequency domain forward modeling formula containing the induced polarization effect is derived. The frequency domain electromagnetic field response under the condition of layered excited polarized medium is simulated, and the influence of the induced polarization parameters in the Cole-Cole model on the electromagnetic field response is analyzed. A step-by-step processing strategy was adopted to invert the electrical parameters of the measured electromagnetic field data and obtain the geoelectric cross-sectional resistivity data. Mapping imaging and sensitive optimal tomographic imaging were used to extract resistivity data of geoelectric cross sections to obtain induced polarization anomaly information contained in the electromagnetic field response.

2. The method for extracting polarization anomaly information based on resistivity inversion results according to claim 1, characterized in that, The step-by-step processing strategy for inverting the electrical parameters of the measured electromagnetic field data to obtain the geoelectric resistivity data includes the following steps: The apparent resistivity of the entire region was obtained using electromagnetic field amplitude data; The resistivity data of the geoelectric cross section are obtained by using the OCAAM or NLCG inversion algorithm to fit the apparent resistivity of the whole area.

3. The method for extracting polarization anomaly information based on resistivity inversion results according to claim 1, characterized in that, Also includes: The inversion data corresponding to the forward model with the Cole-Cole model is compared with the geoelectric resistivity data obtained by inverting the electrical parameters of the measured electromagnetic field data, and then corrections are made.

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