CN112666618B - Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium - Google Patents
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Abstract
The invention relates to a geometric-physical property multi-feature parameter extraction method for a multi-phase medium, and aims to simultaneously extract geometric features and physical property parameters of an underground multi-phase medium and improve the accuracy of geological identification. The method is based on a generalized equivalent polarization model, and defines that an underground medium is composed of surrounding rocks, a strong polarization medium and a weak polarization medium; calculating time domain magnetic field responses of the two-phase medium and the multi-phase medium under the excitation of trapezoidal waves, and forming a target function with magnetic field data acquired by the superconducting quantum sensor; calculating the conductivity of the surrounding rock according to the measured magnetic field early data to serve as a constraint condition; aiming at the condition of multi-dimension and few-constraint of the extracted parameters, the dimensions are set to respectively correspond to a two-phase medium and a multi-phase medium, and the parameters are respectively extracted by adopting a quantum particle swarm optimization algorithm, wherein the two-phase medium extraction result is used as a constraint condition during the multi-phase medium extraction. The invention is more in line with the essential characteristics of underground multiphase media, effectively realizes the global extraction of multidimensional parameters and improves the extraction speed and accuracy.
Description
Technical Field
The invention relates to the field of transient electromagnetic exploration, in particular to a geometric-physical property multi-characteristic parameter extraction method for a multi-phase medium.
Background
In the field of transient electromagnetic exploration, the apparent resistivity functions of different time reflecting the electrical distribution rule of an underground conductive medium are obtained by preprocessing and inverting received data, and then apparent conductivity-depth imaging is realized. At present, domestic mineral exploitation and exploration gradually develop towards targets such as complex mines, blind mines and the like. For complex geological structures, conventional methods of conductivity interpretation have been inadequate to accurately identify the composition and distribution of subsurface media. Besides the conductivity characteristics, the actual geological body containing the ore also comprises geometric characteristics such as ore radius and volume fraction, and physical parameters such as conductivity, polarizability, frequency correlation coefficient and the like of each ore. Therefore, the geometric characteristics and physical parameters need to be subjected to parameter extraction, so that the inversion accuracy is improved. Therefore, in view of the above circumstances, it is necessary to study a model and a method for simultaneously and accurately extracting geometric-physical multi-feature parameters suitable for a multi-phase polarized medium.
In the field of transient electromagnetic exploration, the most widely applied method is the Occam inversion method proposed by Constable (1987) in the current main parameter extraction and inversion methods, but the problems of a large number of matrix operations and multiple forward modeling exist. Yan nations auspicious (2015) applies a least squares method to perform distribution extraction on conductance and polarization parameters, but depends too much on the inversion result of the first step and is easy to fall into a local optimal solution. In addition, the particle swarm method is applied to the extraction of the polarization parameters by the Zhao skill (2019), but the particle swarm method cannot perform global search, and the adopted Cole-Cole model is simple and cannot analyze the geometric characteristics and the physicochemical characteristics of the underground medium.
CN110133733A discloses a conductance-polarizability multi-parameter imaging method based on a particle swarm optimization algorithm, and particularly relates to a method for extracting polarization parameters of measured data in two sections respectively based on a Cole-Cole polarization model, but the method is simple in model (only comprising conductivity and polarizability parameters), and generalizes geometrical characteristics and physicochemical characteristics of underground multi-phase media in parameter extraction, so that the media of all phases cannot be distinguished, and the accuracy of inversion of a complex target body is low due to the limitation of the particle swarm optimization algorithm.
CN101706587A discloses a method for extracting parameters of an electrical prospecting polarization model, in particular to a method for directly extracting parameters of a single Cole-Cole model, which is based on a random statistical algorithm and a least square method to extract Cole-Cole model parameters from a relative phase spectrum and an amplitude spectrum, thereby ensuring that the selection of initial values does not influence the extraction result and retaining the characteristic of faster extraction speed of the least square method. However, the method can only be used for extraction through complex conductivity, and cannot analyze the geometric characteristics and physicochemical characteristics of the underground medium, obviously complex conductivity data cannot be directly measured under actual conditions, and more electromagnetic response data is obtained, so that the method is difficult to apply to field exploration.
Disclosure of Invention
The invention aims to provide a geometric-physical property multi-feature parameter extraction method for a multi-phase medium, aiming at the problems that the existing method cannot distinguish geometric features and physical property parameters of the multi-phase medium, a generalized equivalent polarization model is established according to the complex characteristics of the actual underground medium, and aiming at the condition that the model parameters are more.
The present invention is achieved in such a way that,
a method for geometric-physical multi-feature parameter extraction for a multi-phase medium, the method comprising:
1) defining that the underground multi-phase medium is composed of surrounding rocks, a strong polarization medium and a weak polarization medium based on a generalized equivalent polarization model of the underground multi-phase medium, and respectively calculating time domain magnetic field response of the two-phase medium and time domain magnetic field response of the multi-phase medium under trapezoidal wave excitation;
2) a superconducting quantum sensor time domain electromagnetic detection system is adopted to observe field magnetic field data, and the measured data is subjected to superposition sampling, filtering and primary magnetic field elimination;
3) directly calculating the conductivity sigma of the surrounding rock by using early data in the step 2)0;
4) Constructing a target function by using the actually measured magnetic field data and the time domain magnetic field response of the two-phase medium in the step 1), and according to the conductivity sigma of the surrounding rock in the step 3)0Setting a value constraint range, and extracting the conductivity sigma of the surrounding rock based on a quantum particle swarm optimization algorithm0And 5 parameters of the strongly polarized medium, wherein the parameters of the strongly polarized medium comprise the conductivity sigma of the strongly polarized medium1Strong polarization dispersion coefficient C1Strong polarization volume fraction f1Strongly polarized particlesRadius a1And a strong surface polarization coefficient alpha1;
5) Constructing an objective function by using the actually measured magnetic field data and the time domain magnetic field response of the multi-phase medium in the step 1), setting a new constraint range according to the parameter extraction result in the step 4), and simultaneously extracting the conductivity sigma of the surrounding rock based on the quantum-behaved particle swarm optimization algorithm05 parameters of the strongly polarized medium and 5 parameters of the weakly polarized medium, wherein the parameters of the weakly polarized medium comprise conductivity sigma of the weakly polarized medium2Weak polarization dispersion coefficient C2Weak polarization volume fraction f2Radius of weakly polarized particles a2And weak surface polarization coefficient alpha2;
6) And (5) storing the geometric parameters and physical parameters extracted in the step 5), and analyzing the geometric characteristics and physicochemical characteristics of the underground medium.
Further, in step 1, in the case of a multi-phase medium, the generalized equivalent polarization model includes 11 geometric-physical parameters, and in order to improve the accuracy and speed of parameter extraction, 11 parameters to be extracted are divided into the conductivity σ of the surrounding rock05 parameters of a strongly polarized medium and 5 parameters of a weakly polarized medium; in a two-phase medium, including the conductivity σ of the surrounding rock0And 5 strongly polarized medium parameters.
Further, constraining the conductivity sigma of the surrounding rock in the step 4) through the step 3)0Range, constraining the conductivity σ of the surrounding rock in step 5) by step 4)0And 5 strong polarized medium parameters, wherein the upper limit and the lower limit of the particle swarm algorithm search range are +/-25%; the extraction dimension of step 4 is 6 dimensions, and the extraction dimension of step 5) is 11 dimensions.
Further, the quantum particle swarm optimization algorithm in the steps 4) and 5) comprises the following steps:
i, constructing an objective function:wherein X is a parameter to be extracted, Ft (X) is the time domain magnetic field response of the step 1), N is the effective sampling point number of the magnetic field data of the step 2), and Bt is the magnetic field data of the step 2);
II initializing the number M of population individuals and randomly generating the population within a constraint rangePosition x of particle yielding uniform distributioni(t)=(xi1(t),xi2(t),…,xiD(t)), wherein i is 1,2, …, M, the spatial dimension D of step 4) is 6, the spatial dimension D of step 5) is 11;
III, calculating the fitness fv (i) of each particle, and initializing the individual optimal solution piD(t) and the global optimal solution giD(t);
v calculating the average best positionAnd updating the position of the particle according to the following formula:
in the formula uiD(t) is [0,1 ]]Is a random number ofiDIf (t) > 0.5, it is "-", otherwise it is "+".
VI, comparing the fitness value of each particle and updating the individual optimal solution piD(t) and the global optimal solution giD(t);
VII repeating the steps III-VI until an optimal value is found or the maximum iteration number is reached, and outputting an overall optimal solution giD(t)。
Compared with the prior art, the invention has the beneficial effects that:
compared with the traditional conductivity parameter extraction method, the geometric-physical property multi-characteristic parameter extraction method for the multiphase medium better conforms to the physicochemical characteristics and the electromagnetic field propagation diffusion rule of the underground multiphase medium, and the extracted parameters can accurately distinguish the geometric characteristics and the physical property parameters of the multiphase medium instead of only comprehensively approximating a target area as a uniform whole, so that the interpretation precision of the conductivity detection depth and the comprehensiveness of geological information analysis of the detected underground area are improved; and aiming at the multi-parameter problem of the generalized equivalent polarization model, the multi-phase medium is divided into a strong polarization medium and a weak polarization medium according to the polarization degree, and the dimension of the multi-phase medium in steps is variable by carrying out the geometric-physical parameters of the multi-phase medium on the basis of the quantum particle swarm optimization algorithm, so that the global search capability and accuracy of parameter extraction are effectively improved. The method is beneficial to detecting resources such as complex ores, polymetallic ores, blind ores and the like by adopting a transient electromagnetic method, and provides a new theoretical basis and a new technical means for developing mineral resources.
Drawings
FIG. 1 is a flow chart of a geometric-physical multi-feature parameter extraction method for a multi-phase medium;
FIG. 2 is a flow chart of a quantum-behaved particle swarm optimization algorithm;
FIG. 3 is a three-layered earth model built from geological data;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method adopts a superconducting quantum sensor time domain electromagnetic detection system to observe field magnetic field data after the transmission current is cut off, and calculates the conductivity sigma of the surrounding rock by utilizing early data0Based on a generalized equivalent polarization model, time domain magnetic field responses under trapezoidal wave excitation of a two-phase medium and a multi-phase medium are respectively calculated, an objective function is constructed with actually measured magnetic field data, parameter extraction based on a quantum particle swarm optimization algorithm is carried out, step-by-step dimension variable parameter extraction is carried out on geometric-physical parameters of the multi-phase medium, and finally, the generalized equivalent polarization model is utilized to realize geometric-physical multi-feature parameter extraction of the multi-phase medium, wherein a flow chart is shown in fig. 1.
A geometric-physical multi-feature parameter extraction method for a multi-phase medium comprises the following steps:
1) defining the underground multi-phase medium to be composed of surrounding rocks, a strong polarization medium and a weak polarization medium based on a generalized equivalent polarization model of the underground multi-phase medium, and respectively calculating time domain magnetic field responses of the two-phase medium and the multi-phase medium under trapezoidal wave excitation;
when the generalized equivalent polarization model is used in a multi-phase medium, the complex conductivity expression is as follows,
in the formula (I), the compound is shown in the specification, the total of 11 parameters are included, and the parameters are divided into the conductivity sigma of the surrounding rock05 parameters of strongly polarized medium and 5 parameters of weakly polarized medium. Wherein the parameters of the strongly polarized medium include the conductivity σ of the strongly polarized medium1Strong polarization dispersion coefficient C1Strong polarization volume fraction f1Radius of strongly polarized particle a1And a strong surface polarization coefficient alpha1(ii) a Wherein the parameters of the weakly polarized medium include conductivity σ of the weakly polarized medium2Weak polarization dispersion coefficient C2Weak polarization volume fraction f2Radius of weakly polarized particles a2And weak surface polarization coefficient alpha2。
Volume fraction of weak polarization f in two-phase medium 20, i.e. the effective parameter contains only the conductivity σ of the surrounding rock0And 5 strongly polarized medium parameters.
2) A superconducting quantum sensor time domain electromagnetic detection system is adopted to observe field magnetic field data, and the measured data is subjected to superposition sampling, filtering and primary magnetic field elimination;
3) directly calculating the conductivity sigma of the surrounding rock by using early data (generally data within 1ms after the emission current is cut off) in the data in the step 20;
4) Constructing an objective function by using the actually measured magnetic field data and the time domain magnetic field response of the two-phase medium in the step 1), and constructing a surrounding rock according to the step 3)Electrical conductivity sigma0Setting a constraint range for the value, setting the upper limit and the lower limit of a particle swarm search range to be +/-25%, and extracting the conductivity sigma of the surrounding rock based on a quantum particle swarm optimization algorithm0And 5 parameters of the strongly polarized medium, wherein the parameters of the strongly polarized medium comprise the conductivity sigma of the strongly polarized medium1Strong polarization dispersion coefficient C1Strong polarization volume fraction f1Radius of strongly polarized particle a1And a strong surface polarization coefficient alpha1;
5) Constructing an objective function by using actually-measured magnetic field data and time domain magnetic field response of the multi-phase medium in the step 1), setting a new restriction range according to the parameter extraction result in the step 4, setting the upper limit and the lower limit of a particle swarm search range to be +/-25%, and simultaneously extracting the conductivity sigma of the surrounding rock based on a quantum particle swarm optimization algorithm05 parameters of a strongly polarized medium and 5 parameters of a weakly polarized medium; wherein the parameters of the strongly polarized medium include the conductivity σ of the strongly polarized medium1Strong polarization dispersion coefficient C1Strong polarization volume fraction f1Radius of strongly polarized particle a1And a strong surface polarization coefficient alpha1(ii) a Wherein the parameters of the weakly polarized medium include conductivity σ of the weakly polarized medium2Weak polarization dispersion coefficient C2Weak polarization volume fraction f2Radius of weakly polarized particles a2And weak surface polarization coefficient alpha2;
The quantum particle swarm optimization algorithm in the steps 4) and 5) is shown in fig. 2, and specifically comprises the following steps:
i, constructing an objective function:wherein X is a parameter to be extracted, Ft (X) is time domain magnetic field response of the step 1, N is the effective sampling point number of the magnetic field data of the step 2, and Bt is the magnetic field data of the step 2;
II initializing the number M of population individuals, randomly generating the positions x of particles which are subject to uniform distribution in a constraint rangei(t)=(xi1(t),xi2(t),…,xiD(t)), wherein i is 1,2, …, M, the spatial dimension D of step 4 is 6, the spatial dimension D of step 5 is 11;
III calculating eachFitness fv (i) of each particle, and initializing individual optimal solution piD(t) and the global optimal solution giD(t);
v calculating the average best positionAnd updating the position of the particle according to the following formula:
in the formula uiD(t) is [0,1 ]]Is a random number ofiDIf (t) > 0.5, it is "-", otherwise it is "+".
VI comparing the fitness value of each particle and updating piD(t) and giD(t);
VII repeating the steps III-VI until an optimal value is found or the maximum iteration number is reached, and outputting an overall optimal solution giD(t)。
6) And (5) storing the geometric parameters and physical parameters of the actual measurement data extracted in the step (5), and analyzing the geometric characteristics and physicochemical characteristics of the underground medium.
Based on the method, a three-layer layered earth model shown in fig. 3 is established according to geological data, and geometric-physical multi-characteristic parameter extraction of the multiphase medium is carried out on the model. The extraction results and relative errors are shown in table 1, illustrating the utility and accuracy of the invention.
TABLE 1 Utility and accuracy of the invention
Claims (4)
1. A method for extracting multi-characteristic geometric-physical parameters for a multi-phase medium, the method comprising:
1) defining that the underground multi-phase medium is composed of surrounding rocks, a strong polarization medium and a weak polarization medium based on a generalized equivalent polarization model of the underground multi-phase medium, and respectively calculating time domain magnetic field response of the two-phase medium and time domain magnetic field response of the multi-phase medium under trapezoidal wave excitation;
2) a superconducting quantum sensor time domain electromagnetic detection system is adopted to observe field magnetic field data, and the measured data is subjected to superposition sampling, filtering and primary magnetic field elimination;
3) directly calculating the conductivity sigma of the surrounding rock by using early data in the step 2)0;
4) Constructing a target function by using the actually measured magnetic field data and the time domain magnetic field response of the two-phase medium in the step 1), and according to the conductivity sigma of the surrounding rock in the step 3)0Setting a value constraint range, and extracting the conductivity sigma of the surrounding rock based on a quantum particle swarm optimization algorithm0And 5 parameters of the strongly polarized medium, wherein the parameters of the strongly polarized medium comprise the conductivity sigma of the strongly polarized medium1Strong polarization dispersion coefficient C1Strong polarization volume fraction f1Radius of strongly polarized particle a1And a strong surface polarization coefficient alpha1;
5) Constructing an objective function by using actually-measured magnetic field data and time domain magnetic field response of the multi-phase medium in the step 1), setting a new constraint range according to the parameter extraction result in the step 4), and simultaneously extracting the conductivity sigma of the surrounding rock based on a quantum particle swarm optimization algorithm05 parameters of the strongly polarized medium and 5 parameters of the weakly polarized medium, wherein the parameters of the weakly polarized medium comprise conductivity sigma of the weakly polarized medium2Weak polarization dispersion coefficient C2Weak polarization volume fraction f2Radius of weakly polarized particles a2And weak surface polarization coefficient alpha2;
6) And (5) storing the geometric parameters and physical parameters extracted in the step 5), and analyzing the geometric characteristics and physicochemical characteristics of the underground medium.
2. The method as claimed in claim 1, wherein in step 1), the generalized equivalent polarization model for the multi-phase medium comprises 11 geometric-physical parameters, and in order to improve the accuracy and speed of parameter extraction, the 11 parameters to be extracted are divided into the conductivity σ of the surrounding rock05 parameters of a strongly polarized medium and 5 parameters of a weakly polarized medium; in a two-phase medium, including the conductivity σ of the surrounding rock0And 5 strongly polarized medium parameters.
3. Method according to claim 1, characterized in that the conductivity σ of the surrounding rock in step 4) is constrained by step 3) to0Range, constraining the conductivity σ of the surrounding rock in step 5) by step 4)0And 5 strong polarization medium parameters, wherein the upper limit and the lower limit of the search range of the quantum particle swarm optimization algorithm are set to be +/-25%; the extraction dimension of step 4) is 6 dimensions, and the extraction dimension of step 5) is 11 dimensions.
4. The method according to claim 1, wherein the quantum-behaved particle swarm optimization algorithm in steps 4) and 5) comprises the steps of:
i, constructing an objective function:wherein X is a parameter to be extracted, Ft (X) is the time domain magnetic field response of the step 1), N is the effective sampling point number of the magnetic field data of the step 2), and Bt is the magnetic field data of the step 2);
II initializing the number M of population individuals, randomly generating the positions x of particles which are subject to uniform distribution in a constraint rangei(t)=(xi1(t),xi2(t),…,xiD(t)), wherein i is 1,2, …, M, the spatial dimension D of step 4) is 6, the spatial dimension D of step 5) is 11;
III, calculating the fitness fv (i) of each particle, and initializing the individual optimal solution piD(t) and the global optimal solution giD(t);
v calculating the average best positionAnd updating the position of the particle according to the following formula:
in the formula uiD(t) is [0,1 ]]Is a random number ofiD(t) > 0.5 is "-", otherwise "+";
VI, comparing the fitness value of each particle and updating the individual optimal solution piD(t) and the global optimal solution giD(t);
VII repeating the steps III-VI until an optimal value is found or the maximum iteration number is reached, and outputting an overall optimal solution giD(t)。
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