CN116699724B - Time domain induced polarization data quality evaluation method, system and system - Google Patents
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Abstract
The application discloses a time domain induced polarization data quality evaluation method, a time domain induced polarization data quality evaluation system and a time domain induced polarization data quality evaluation system, which are used for providing polarity parameters, shape parameters and fitting parameters of an attenuation curve, comprehensively considering data characteristics of each period of induced polarization curve attenuation, further evaluating the time-frequency conversion method from the aspect of data interpretation, quantifying all indexes, and comprehensively evaluating the induced polarization characteristics according to importance degree, establishing a complete data evaluation system, accurately evaluating the time domain induced polarization data quality from different angles, providing reliable technical support for subsequent data processing and interpretation, and improving the reliability and accuracy of detection and monitoring results.
Description
Technical Field
The application relates to the technical field of geophysics, in particular to a time domain induced polarization data quality evaluation method, system and system.
Background
The time domain induced polarization method is used as a geophysical exploration method, and is widely applied to the field of mineral exploration based on the difference of the conductivity and the electric storage capacity of underground media. In recent years, the soil and groundwater pollution detection and monitoring industry in the environmental field of China develops rapidly, and new requirements of rapid, fine and nondestructive measurement and the like are put forward for detection and monitoring technology. The induced polarization method has high measurement efficiency, can be used for describing the three-dimensional distribution of the underground medium, is a nondestructive detection method, has sufficient obtained parameter types and high result accuracy, and is gradually applied to the field of soil and groundwater pollution detection and monitoring. However, unlike mineral exploration, the induced polarization response characteristic of the polluted medium is weak, and the background conductivity of the polluted site is high and the data quality is poor, so that the interpretation result is greatly influenced by the data quality. Thus, accurate evaluation of the quality of the time domain induced polarization data determines the credibility and accuracy of the interpretation result.
At present, no unified standard and complete method system exists for evaluating the quality of time domain induced polarization data, and the integrated polarization rate is usually evaluated manually by judging the positive and negative of the integrated polarization rate or the positive and negative, form and mutation characteristics of a visual attenuation curve. Existing evaluation means only consider visual characterization of the data and do not integrate the decay characteristics of the induced polarization curve at each time period. In addition, when the data quality evaluation is carried out, all indexes are qualitative evaluation, and a unified quantization standard is not provided, so that the data quality of all characteristic indexes of the attenuation curve is not comprehensively considered. Finally, the evaluation of the data quality only considers the characteristics of the decay curve itself, and the original data is not evaluated from the data interpretation point of view. Therefore, the current evaluation method is easy to cause misjudgment of data quality, further influences data processing and interpretation, causes the precision reduction of detection and monitoring results, even causes misinterpretation, and does not have credibility and popularization value. Therefore, a complete method for evaluating the quality of time domain induced polarization data is needed.
Disclosure of Invention
The application overcomes the defects of the prior art and provides a time domain induced polarization data quality evaluation method, a system and a system.
The first aspect of the application provides a time domain induced polarization data quality evaluation method, which comprises the following steps:
windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
Evaluating monotonicity of adjacent window data and difference value monotonicity thereof, and calculating shape parameters of time domain induced polarization data；
Evaluating the fitting degree of window data and negative exponential function, and calculating the fitting parameters of time domain induced polarization data；
Performing time-frequency conversion on the full-waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
Synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
Establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if it isThe profile data is poor in quality, induced polarization data is not available, and the inversion result is not credible.
In this scheme, windowed full waveform induced polarization data specifically is:
the obtained original full-waveform induced polarization data is subjected to windowing, and the windowing process comprises harmonic denoising, drift correction, spike removal and window data geometric averaging.
In this scheme, the polarity parameter specifically is:
for the induced polarization data of n windows, the polarity parametersCalculated according to the following formula:
；
in the above formula, P is a polarity parameter,is->The polarity coefficient of the window, when->The induced polarization data of the windows are positive, +.>When->When the induced polarization data for each window is negative,/>。
in this aspect, the shape parameterThe method specifically comprises the following steps:
shape parameters for the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,is a monotonic coefficient between the jth window and the (j+1) th window, when the (j)>The window is greater than->When the window is opened, the user can get the window->When the jth window is smaller than the j + i window, and (2)>；/>Is a difference monotonic coefficient, when->Windows and->When the difference between the windows is larger than the difference between the j+1th window and the j+2th window, the window is left and right>On the contrary, the->S is a shape parameter;
for the induced polarization data of n windows, the least square method and the Bayesian method are respectively used for evaluating the induced polarization data and the negative exponential functionLeast square employs a more stable LM algorithmThe Bayesian method adopts a Markov chain Monte Carlo sampling method to respectively calculate the determinable coefficient +.> and />。
In this scheme, the fitting parametersThe method specifically comprises the following steps:
fitting parameters to the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,a determinable coefficient obtained for the least square method, < ->The determinable coefficients are obtained by a Bayesian method;
for full waveform induced polarization data, pass timeThe frequency conversion method converts the time domain data into amplitude and phase difference data of a frequency domain; fitting the frequency domain data with the Cole-Cole model by using a least square method and a Bayesian method respectively, calculating the determinable coefficients of amplitude and phase difference, and calculating the transformation coefficient of each method according to the determinable coefficients：
；
In the above-mentioned method, the step of,and->The determinable coefficients of amplitude and phase, respectively.
In this scheme, the transformation parametersThe method specifically comprises the following steps:
transformation parameters for full waveform induced polarization dataCalculated according to the following formula:
；
in the above-mentioned method, the step of,and->The transformation coefficients obtained by the least square method and the Bayesian method are respectively.
In this scheme, the quality parameterThe depth-of-view coefficient is specifically:
integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data according to the following formula：
；
in the formula The polarity weight, the shape weight, the fitting weight and the transformation weight are respectively required to satisfy:、/>、/>、/>、/>；
synthesizing single time domain induced polarization data quality parametersAfter the depth of view coefficient, calculating the data quality parameter of the induced polarization full section>The specific formula is as follows:
；
wherein ,is in cross sectionInduced polarization data volume, < >>Is->Data quality parameters of the individual data,/->Is->Depth of view coefficient of the data,>，/>is->Depth of view of the data->Is the minimum of the depth of view of the full section, < ->Is a data quality parameter of the induced polarization full section.
The second aspect of the present application also provides a time domain induced polarization data quality evaluation system, the system comprising: the device comprises a memory and a processor, wherein the memory comprises a time domain induced polarization data quality evaluation program, and the time domain induced polarization data quality evaluation program realizes the following steps when being executed by the processor:
windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
Evaluating monotonicity of adjacent window data and difference value thereofTonality, calculating shape parameters of time domain induced polarization data；
Evaluating the fitting degree of window data and negative exponential function, and calculating the fitting parameters of time domain induced polarization data；
Performing time-frequency conversion on the full-waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
Synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
Establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if it isThe profile data is poor in quality, induced polarization data is not available, and the inversion result is not credible.
In this scheme, the system includes computer control module, computer control module is used for carrying out automatic evaluation, screening and judgement after the windowed induced polarization data of leading-in.
In this scheme, the computer control module is used for deriving the decision result of each attenuation curve.
By the scheme of the application, the following beneficial effects can be realized:
the polar parameter, the shape parameter and the fitting parameter of the attenuation curve are provided, the data characteristics of each period of the attenuation of the induced polarization curve are comprehensively considered, and the data quality is evaluated by a richer angle and a more scientific means.
The time-frequency conversion method is provided, the data quality is further evaluated according to the rationality of data interpretation, so that the data evaluation method focuses on the data and focuses on the kernel of the data.
And quantifying all indexes, carrying out comprehensive evaluation according to the importance degree and the induced polarization characteristics, establishing a complete data evaluation system, providing reliable technical support for subsequent data processing and interpretation, and improving the credibility and accuracy of detection and monitoring results.
The application discloses a time domain induced polarization data quality evaluation method, a time domain induced polarization data quality evaluation system and a time domain induced polarization data quality evaluation system, which are used for providing polarity parameters, shape parameters and fitting parameters of an attenuation curve, comprehensively considering data characteristics of each period of induced polarization curve attenuation, further evaluating the time-frequency conversion method from the aspect of data interpretation, quantifying all indexes, and comprehensively evaluating the induced polarization characteristics according to importance degree, establishing a complete data evaluation system, accurately evaluating the time domain induced polarization data quality from different angles, providing reliable technical support for subsequent data processing and interpretation, and improving the reliability and accuracy of detection and monitoring results.
Drawings
FIG. 1 is a flow chart of a method for evaluating quality of time domain induced polarization data according to the present application;
FIG. 2 shows a full waveform induced polarization raw data plot in an embodiment of the application;
FIG. 3 is a diagram of window data and a fitted curve in an embodiment of the present application;
FIG. 4 is a diagram of window data and a fitted curve in an embodiment of the present application;
FIG. 5 is a schematic diagram of time-frequency conversion of full waveform data in an embodiment of the application;
FIG. 6 shows a schematic view of a depth profile in an embodiment of the application;
fig. 7 shows a block diagram of a time domain induced polarization data quality evaluation system of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a method for evaluating the quality of time domain induced polarization data according to the present application.
As shown in fig. 1, according to a first embodiment of the present application, a first aspect of the present application provides a time domain induced polarization data quality evaluation method, including:
s102, windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
S104, evaluating the monotonicity of adjacent window data and the difference value monotonicity thereof, and calculating the shape parameters of the time domain induced polarization data；
S106, evaluating the fitting degree of window data and negative exponential function, and calculating fitting parameters of time domain induced polarization data；
S108, performing time-frequency conversion on the full waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
S110, integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
S112, synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
S114, establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if it isThe profile data is poor in quality, induced polarization data is not available, and the inversion result is not credible.
FIG. 2 shows a full waveform induced polarization raw data plot in an embodiment of the application;
as shown in FIG. 2, for the full waveform time domain induced polarization raw data collected in this embodiment, power is supplied by A, B two power supply electrodes for 4s with a power supply current of 20mA, and the method adoptsThe data acquisition modes of the system are positive power supply, power failure, negative power supply and power failure, so that data noise is reduced. And power is supplied while a M, N electrode is used as a measuring electrode for measurement, a voltage signal between M, N is measured, and the sampling rate is 5000Hz. When power is supplied, the voltage instantaneously reaches a primary voltage value, then slowly rises, and the rising speed gradually drops until the voltage is stable; after power failure, the voltage does not drop to zero immediately, but drops to a secondary voltage, and then gradually drops to approach zero. The secondary voltage attenuation process is an induced polarization process, and the full waveform primary data is affected by noise and has large data volume, so that the windowing process is needed first.
FIG. 3 is a diagram of window data and a fitted curve in an embodiment of the present application;
windowing schematic of full waveform data as shown in fig. 3, the measured voltage signal includes injection current induced voltage response, background drift voltage, tip voltage, harmonic noise, and random noise. The method comprises the steps of firstly, carrying out drift removal on original data, including linear drift removal and model drift removal, then, tip noise removal, and finally, harmonic noise removal and filtering denoising. And windowing the denoised data, removing the data in the 100 ms delay time after power failure, converting the data in the residual time into 10 time windows, and gradually increasing the time window size according to the exponential distribution. After dividing the time window, taking the geometric average value of all the data in each window to obtain the voltage of each window, wherein the above is the whole process of windowing the original full-waveform data.
After windowing the data, judging the positive and negative of the first 9 window data to obtain the polarity coefficient of each window, and when the window data is positive, judging the polarity coefficient1 when the number of windows is 1When the data is negative, the polarity coefficient is +.>Is 0. Calculating the polarity parameters of the windowed attenuation curve according to the polarity coefficients, which are the polarity parameters of the present example +.>Calculated according to the following formula:
；
in the above-mentioned method, the step of,is->The polarity coefficients of the windows. In this example, polarity parameter->The minimum value is 0.045, and the maximum value is 1.
The induced polarization data needs to satisfy monotonic decrease, the decreasing speed is slower and slower, and the shape parameters of the window data are evaluated according to the two conditions. The shape parameters comprise a monotonic coefficient and a difference monotonic coefficient, and for any two window data, when the data of the previous window is larger than the data of the next window, the monotonic coefficient is +.>1, when the data of the previous window is smaller than the data of the next window, monotonic coefficient +.>Is 0. For any three window data, when the data difference value of the current two windows is larger than the data difference value of the next two windows, the difference value monotonic coefficient +.>1, when the data difference value of the current two windows is smaller than the data difference value of the next two windows, the difference value monotonic coefficient +.>Is the following. After obtaining the monotonic coefficient and the difference monotonic coefficient, the shape parameter of the embodiment is calculated according to the following formula:
；
，/>is->First->And->A monotonic coefficient of difference between them. The range of the shape parameter is 0-1. FIG. 4 is a diagram of window data and a fitted curve in an embodiment of the present application;
as shown in FIG. 4, for the 10 windows of induced polarization data, the least squares and Bayesian methods were used to evaluate their and negative exponential functions, respectivelyLeast square employs a more stable LM algorithmThe Bayesian method adopts a Markov chain Monte Carlo sampling method to respectively calculate the determinable coefficient +.> and />. The induced polarization decay curve satisfies the time domain Cole-Cole model, but the gamma function of the model contains infinite integral, which is very difficult to calculate, and the simplified resulting Debye model is an exponential function when the Cole-Cole index is 1. +.>As unknown parameter a, < >>As an unknown parameter b, the induced polarization measured data should satisfy the following exponential function:
；
the data quality can be quantified by curve fitting the window data based on the exponential function described above and evaluating the fit. The fitting degree of the measured data is evaluated by firstly estimating two unknown parameters of an exponential function, the parameter estimation problem of a nonlinear function is solved in the process, and MATLAB codes are written based on a nonlinear least square method and a Bayesian method to solve the problem. In the least square method, the convergence rate of parameter estimation by the Gaussian Newton method is high, but the stability is to be improved, the improved LM method improves the algorithm stability by adding a diagonal matrix, and the parameter updating amount of each iteration of the method is as follows:
；
in the formula Updating the parameter for each iteration; />The matrix is a jacobian matrix, namely a partial derivative matrix of window data on unknown parameters; />Is a unit matrix; />Is a damping coefficient; />The difference between the window visual polarization rate and the fitting visual polarization rate. The parameter is given an initial value, the parameter updated by each iteration is closer to the real parameter until the convergence condition is met, and the iteration is stopped.
Bayesian methods provide a large amount of global information on unknown parameters and do not depend on the choice of initial values. The parameter estimation process of the Bayesian method comprises the following steps:
；
in the above-mentioned method, the step of,for the parameters to be estimated->Is the known data +.>For likelihood functions, a given parameter +.>Post dataProbability of->For a priori distribution, it is shown that one is about +.>Is a function of the known information of the (c),for normalizing constant, ++>For posterior distribution, the measurement data are integrated +.>A priori distribution->The related information provided.
Since posterior distribution needs to solve the problem of integration of high-dimensional functions, the Markov chain Monte Carlo method needs to be adopted for sampling. Firstly, constructing a Markov chain to make the Markov chain smoothly distributed into posterior distribution of parameters to be estimated, and then making the posterior distribution of parameters be estimated from a certain pointStarting from the Markov chain, a sampling simulation is performed, yielding a sequence of points: />,/>Finally, discretizing the continuous posterior distribution probability density function through Monte Carlo sampling to convert the complex function integration problem into the discretized accumulation problem.
After the windowed data are fitted by the two methods, the determinable coefficient of each group of data is calculated according to the fitting result, and the determinable coefficient calculation formula is as follows:
；
in the formula The sum of squares of the residual errors is used for representing the fitting degree, and the smaller the value is, the higher the fitting degree is;is the sum of the total squares. Coefficient of determinable->Between 0 and 1, the closer to 1, the better the model fit. Obtaining the determinable coefficient of the two methods> and />After that, the fitting parameters of the window data can be calculated +.>Fitting parameters were calculated according to the following formula:
；
in the above-mentioned method, the step of,a determinable coefficient obtained for the least square method, < ->The determined coefficients are obtained by a Bayesian method. The value range of the fitting parameter is 0-1.
FIG. 5 is a schematic diagram of time-frequency conversion of full waveform data in an embodiment of the application;
as shown in fig. 5, for the full waveform induced polarization raw data, the time domain data is converted into amplitude and phase difference data in the frequency domain by a time-frequency conversion method. Windowing frequency data, increasing window size according to exponential distribution of frequency, fitting frequency domain data and frequency domain Cole-Cole model by using least square method and Bayesian method, calculating the determinable coefficient of amplitude and phase difference, selecting the best fitting mode of two methods as final determinable coefficient, and calculating transformation coefficient of each method according to the determinable coefficient：
；
In the above-mentioned method, the step of,and->The determinable coefficients of amplitude and phase, respectively.
Transformation parameters for full waveform induced polarization dataCalculated according to the following formula:
；
in the above-mentioned method, the step of,and->The transformation coefficients obtained by the least square method and the Bayesian method are respectively.
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter obtained by calculation, and calculating the quality parameter of the single attenuation curve time domain induced polarization data according to the following formula：
；
in the formula The polarity weight, the shape weight, the fitting weight and the transformation weight are respectively required to satisfy:、/>、/>、/>、/>. In this embodiment, the background conductivity is low, the induced polarization itself has better data quality, however, the response characteristics between the detection target and the background target are weak, and the decision weight of data interpretation needs to be increased, so the value of this embodiment is as follows:。
according to a second embodiment of the present application, there is provided:
as shown in fig. 1, the present embodiment provides a method, a system and a system for evaluating quality of time domain induced polarization data, for a section including a plurality of induced polarization attenuation curves, including the following steps:
the single decay curve time domain induced polarization data quality parameters for each induced polarization decay curve are calculated according to the method steps of the first embodiment, respectively；
Data quality parameters of induced polarization full profileComprehensive single time domain induced polarization data quality parameter +.>And obtaining the depth-of-view coefficient:
；
in the formula For the amount of induced polarization data on the section, +.>Is->The data quality parameter of the individual data,，/>，/>is->Depth of view of the data->Is the minimum value of the depth of view of the full section;
full profile time domain induced polarization data quality passEvaluation was performed: if->The section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if it isThe profile data is poor in quality, induced polarization data is not available, and the inversion result is not credible.
FIG. 6 shows a schematic view of a depth profile in an embodiment of the application;
as shown in fig. 6, the full-section induced polarization data collected in this embodiment is obtained by a single cable method, the data collection array is obtained by a gradient method, two total measuring lines are connected end to end, 32 electrodes are arranged on each measuring line, 64 electrodes are arranged on each measuring line, the electrode distance is 2 meters, and the total length of the measuring lines is 126 meters. 1372 induced polarization attenuation curves are collected in total, the supply time is 2 seconds, the measurement repetition number is 1-2, and the supply current is 200 mA.
After 1372 induced polarization attenuation curves are obtained, the quality parameters of each attenuation curve are calculated according to the method and steps of the first embodimentNumber->To->. Calculating the apparent depth +.of each decay curve based on the positional relationship between the powered electrode A, B and the measuring electrode M, N of each decay curve>The apparent depth calculation formula is as follows:
；
in the above-mentioned method, the step of,for measuring the abscissa at the midpoint of the electrode M, N, < >>、/>The abscissa of the supply electrodes a and B, respectively. After obtaining the apparent depth of all data points, calculating the apparent depth coefficient of each data point>The apparent depth coefficient calculation formula is:
；
in the above-mentioned method, the step of,is the minimum of the depth of view of the full section, in this example +.>1.071 meters. Comprehensive single time domain induced polarization data quality parameter +.>And depth of view coefficient->The data quality parameter of the full profile in this embodiment can be obtained +.>：
；
in the formula Is->Data quality parameters of the individual data,/->. Data quality parameter of this embodiment +.>0.73%>The section data has better quality, and the inversion can be performed after the data is processed. The final inversion result can delineate the range of the target area, and the reliability of the data quality evaluation method is verified through drilling verification.
Fig. 7 shows a block diagram of a time domain induced polarization data quality evaluation system of the present application.
The second aspect of the present application also provides a time domain induced polarization data quality evaluation system 7, comprising: a memory 71, a processor 72, said memory comprising a time domain induced polarization data quality assessment program which when executed by said processor performs the steps of:
windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
Evaluating monotonicity of adjacent window data and difference value monotonicity thereof, and calculating shape parameters of time domain induced polarization data；
Evaluating the fitting degree of window data and negative exponential function, and calculating the fitting parameters of time domain induced polarization data；
Performing time-frequency conversion on the full-waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
Synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
Establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if it isThe profile data is poor in quality, induced polarization data is not available, and the inversion result is not credible.
According to the embodiment of the application, for one or more sections containing a plurality of induced polarization attenuation curves, all data of the sections can be imported into the system at one time, the system can automatically evaluate, screen and judge all the section data, accurate and rapid data evaluation is realized, and the judgment result of each attenuation curve is derived, so that rapid processing and interpretation guidance of the data are facilitated.
The application discloses a time domain induced polarization data quality evaluation method, a time domain induced polarization data quality evaluation system and a time domain induced polarization data quality evaluation system, which are used for providing polarity parameters, shape parameters and fitting parameters of an attenuation curve, comprehensively considering data characteristics of each period of induced polarization curve attenuation, further evaluating the time-frequency conversion method from the aspect of data interpretation, quantifying all indexes, and comprehensively evaluating the induced polarization characteristics according to importance degree, establishing a complete data evaluation system, accurately evaluating the time domain induced polarization data quality from different angles, providing reliable technical support for subsequent data processing and interpretation, and improving the reliability and accuracy of detection and monitoring results.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present application may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. A method for evaluating quality of time domain induced polarization data, comprising:
windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
Evaluating monotonicity of adjacent window data and difference value monotonicity thereof, and calculating shape parameters of time domain induced polarization data；
Evaluating the fitting degree of window data and negative exponential function, and calculating the fitting parameters of time domain induced polarization data；
Performing time-frequency conversion on the full-waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
Synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
Establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if->The section data is poor in quality, induced polarization data is unavailable, and the inversion result is not credible;
wherein, the polarity parameter specifically is:
for the induced polarization data of n windows, the polarity parametersCalculated according to the following formula:
；
in the above formula, P is a polarity parameter,is->The polarity coefficient of the window, when->The induced polarization data of the windows are positive, +.>When->When the induced polarization data of the individual windows are negative, < >>；
Wherein the shape parameterThe method specifically comprises the following steps:
shape parameters for the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,is a monotonic coefficient between the jth window and the (j+1) th window, when the (j)>The window is greater than->Personal windowWhen in use, the wearer is on the mouth>When the jth window is smaller than the j + i window, and (2)>；/>Is a difference monotonic coefficient, when->Windows and->When the difference between the windows is larger than the difference between the j+1th window and the j+2th window, the window is left and right>On the contrary, the->S is a shape parameter;
for the induced polarization data of n windows, the least square method and the Bayesian method are respectively used for evaluating the induced polarization data and the negative exponential functionLeast square employs a more stable LM algorithm +.>The Bayesian method adopts a Markov chain Monte Carlo sampling method to respectively calculate the determinable coefficient +.> and />；
in the formula Updating the parameter for each iteration; />The matrix is a jacobian matrix, namely a partial derivative matrix of window data on unknown parameters; />Is a unit matrix; />Is a damping coefficient; />The difference value between the window visual polarization rate and the fitting visual polarization rate is obtained;
wherein the fitting parametersThe method specifically comprises the following steps:
fitting parameters to the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,a determinable coefficient obtained for the least square method, < ->The determinable coefficients are obtained by a Bayesian method;
for full waveform induced polarization data, converting time domain data into amplitude and phase of frequency domain by using time-frequency conversion methodBit difference data; fitting the frequency domain data with the Cole-Cole model by using a least square method and a Bayesian method respectively, calculating the determinable coefficients of amplitude and phase difference, and calculating the transformation coefficient of each method according to the determinable coefficients：
；
In the above-mentioned method, the step of,and->The determinable coefficients of amplitude and phase respectively;
wherein the transformation parametersThe method specifically comprises the following steps:
transformation parameters for full waveform induced polarization dataCalculated according to the following formula:
；
in the above-mentioned method, the step of,and->Transform coefficients obtained by a least square method and a Bayesian method respectively;
wherein the quality parameterThe depth-of-view coefficient is specifically:
integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data according to the following formula：
；
in the formula The polarity weight, the shape weight, the fitting weight and the transformation weight are respectively required to satisfy:、/>、/>、/>、/>；
synthesizing single time domain induced polarization data quality parametersAfter the depth of view coefficient, calculating the data quality parameter of the induced polarization full section>The specific formula is as follows:
；
wherein ,for the amount of induced polarization data on the section, +.>Is->Data quality parameters of the individual data,/->Is->Depth of view coefficient of the data,>，/>is->Depth of view of the data->Is the minimum of the depth of view of the full section, < ->Is a data quality parameter of the induced polarization full section.
2. The method for evaluating the quality of time domain induced polarization data according to claim 1, wherein the windowed full waveform induced polarization data specifically comprises:
the obtained original full-waveform induced polarization data is subjected to windowing, and the windowing process comprises harmonic denoising, drift correction, spike removal and window data geometric averaging.
3. A time domain induced polarization data quality assessment system, the system comprising: the device comprises a memory and a processor, wherein the memory comprises a time domain induced polarization data quality evaluation program, and the time domain induced polarization data quality evaluation program realizes the following steps when being executed by the processor:
windowing full waveform induced polarization data to obtain n windows of induced polarization data, evaluating the positive and negative of the induced polarization data of all n windows and calculating the time domain induced polarization data polarity parameters；
Evaluating monotonicity of adjacent window data and difference value monotonicity thereof, and calculating shape parameters of time domain induced polarization data；
Evaluating the fitting degree of window data and negative exponential function, and calculating the fitting parameters of time domain induced polarization data；
Performing time-frequency conversion on the full-waveform induced polarization data, and calculating a time domain induced polarization data conversion parameter;
Integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data；
Synthesizing single time domain induced polarization data quality parametersCalculating data quality parameters of induced polarization full section according to depth of view coefficient>；
Establishing an evaluation standard of the quality of the time domain induced polarization data of the full profile: if it isThe section data has good quality and can be directly inverted; if->The section data has better quality, and inversion can be performed after the data is processed; if->The section data is poor in quality, induced polarization data is unavailable, and the inversion result is not credible;
wherein, the polarity parameter specifically is:
for the induced polarization data of n windows, the polarity parametersCalculated according to the following formula:
；
in the above formula, P is a polarity parameter,is->The polarity coefficient of the window, when->The induced polarization data of the windows are positive, +.>When->When the induced polarization data of the individual windows are negative, < >>；
Wherein the shape parameterThe method specifically comprises the following steps:
shape parameters for the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,is a monotonic coefficient between the jth window and the (j+1) th window, when the (j)>The window is greater than->When the window is opened, the user can get the window->When the jth window is smaller than the j + i window, and (2)>；/>Is a difference monotonic coefficient, when->Windows and->When the difference between the windows is larger than the difference between the j+1th window and the j+2th window, the window is left and right>On the contrary, the->S is a shape parameter;
for the induced polarization data of n windows, the least square method and the Bayesian method are respectively used for evaluating the induced polarization data and the negative exponential functionLeast square employs a more stable LM algorithm +.>The Bayesian method adopts a Markov chain Monte Carlo sampling method to respectively calculate the determinable coefficient +.> and />；
in the formula Updating the parameter for each iteration; />The matrix is a jacobian matrix, namely a partial derivative matrix of window data on unknown parameters; />Is a unit matrix; />Is a damping coefficient; />The difference value between the window visual polarization rate and the fitting visual polarization rate is obtained;
wherein the fitting parametersThe method specifically comprises the following steps:
fitting parameters to the induced polarization data for n windowsCalculated according to the following formula:
；
in the above-mentioned method, the step of,a determinable coefficient obtained for the least square method, < ->The determinable coefficients are obtained by a Bayesian method;
for full waveform induced polarization data, converting time domain data into amplitude and phase difference data of a frequency domain by a time-frequency conversion method; fitting the frequency domain data with the Cole-Cole model by using a least square method and a Bayesian method respectively, calculating the determinable coefficients of amplitude and phase difference, and calculating the transformation coefficient of each method according to the determinable coefficients：
；
In the above-mentioned method, the step of,and->The determinable coefficients of amplitude and phase respectively;
wherein the transformation parametersThe method specifically comprises the following steps:
transformation parameters for full waveform induced polarization dataCalculated according to the following formula:
；
in the above-mentioned method, the step of,and->Transform coefficients obtained by a least square method and a Bayesian method respectively;
wherein the quality parameterThe depth-of-view coefficient is specifically:
integrating the polarity parameter, the shape parameter, the fitting parameter and the transformation parameter, and calculating the quality parameter of the single attenuation curve time domain induced polarization data according to the following formula：
；
in the formula The polarity weight, the shape weight, the fitting weight and the transformation weight are respectively required to satisfy:、/>、/>、/>、/>；
synthesizing single time domain induced polarization data quality parametersAfter the depth of view coefficient, calculating the data quality parameter of the induced polarization full section>The specific formula is as follows:
；
wherein ,for the amount of induced polarization data on the section, +.>Is->Data quality parameters of the individual data,/->Is->Depth of view coefficient of the data,>，/>is->Depth of view of the data->Is the minimum of the depth of view of the full section, < ->Is a data quality parameter of the induced polarization full section.
4. A time domain induced polarization data quality evaluation system according to claim 3, wherein the system comprises a computer control module for automatically evaluating, screening and determining the imported windowed induced polarization data.
5. The time domain induced polarization data quality evaluation system of claim 4, wherein the computer control module is configured to derive a determination of each decay curve.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101520517A (en) * | 2008-02-25 | 2009-09-02 | 中国石油集团东方地球物理勘探有限责任公司 | Method for accurately evaluating targets containing oil gas in clastic rock basin |
| CN101592029A (en) * | 2009-07-02 | 2009-12-02 | 大庆油田有限责任公司 | A kind of method for well logging with induced polarization spectrum |
| EP2352041A1 (en) * | 2008-10-23 | 2011-08-03 | Obshchestvo S Ogranichennoy Otvetstvennostyu "Sibirskaya Geofizicheskaya Nauchno-Proizvodstvennaya Kompaniya" | Method for quantitatively separating the effects of electromagnetic induction and induced polarization |
| CN113447992A (en) * | 2021-06-22 | 2021-09-28 | 陕西地矿第二综合物探大队有限公司 | Method and system for prospecting ore body by using time domain induced polarization method |
| CN114814952A (en) * | 2022-05-12 | 2022-07-29 | 山东大学 | Three-dimensional time domain-based induced polarization data acquisition method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160282498A1 (en) * | 2015-03-27 | 2016-09-29 | Cgg Services Sa | Apparatus and method for calculating earth's polarization properties from airborne time-domain electromagnetic data |
-
2023
- 2023-08-01 CN CN202310953115.1A patent/CN116699724B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101520517A (en) * | 2008-02-25 | 2009-09-02 | 中国石油集团东方地球物理勘探有限责任公司 | Method for accurately evaluating targets containing oil gas in clastic rock basin |
| EP2352041A1 (en) * | 2008-10-23 | 2011-08-03 | Obshchestvo S Ogranichennoy Otvetstvennostyu "Sibirskaya Geofizicheskaya Nauchno-Proizvodstvennaya Kompaniya" | Method for quantitatively separating the effects of electromagnetic induction and induced polarization |
| CN101592029A (en) * | 2009-07-02 | 2009-12-02 | 大庆油田有限责任公司 | A kind of method for well logging with induced polarization spectrum |
| CN113447992A (en) * | 2021-06-22 | 2021-09-28 | 陕西地矿第二综合物探大队有限公司 | Method and system for prospecting ore body by using time domain induced polarization method |
| CN114814952A (en) * | 2022-05-12 | 2022-07-29 | 山东大学 | Three-dimensional time domain-based induced polarization data acquisition method and device |
Non-Patent Citations (3)
| Title |
|---|
| 地下水资源勘查中时间域激发极化法的应用;戴国水;中国资源综合利用;第38卷(第08期);24-26 * |
| 基于时域激电的层状大地视谱参数测深曲线反演;高柱 等;工程地球物理学报;第11卷(第01期);18-23 * |
| 时间域激发极化法的二维正演研究;李桂花 等;科学技术创新(第25期);38-39 * |
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