CN112666618A - Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium - Google Patents
Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium Download PDFInfo
- Publication number
- CN112666618A CN112666618A CN202011484866.6A CN202011484866A CN112666618A CN 112666618 A CN112666618 A CN 112666618A CN 202011484866 A CN202011484866 A CN 202011484866A CN 112666618 A CN112666618 A CN 112666618A
- Authority
- CN
- China
- Prior art keywords
- medium
- parameters
- magnetic field
- phase medium
- polarization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Geophysics And Detection Of Objects (AREA)
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 f1Radius of strongly polarized particle 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%; step 4 extractionTaking the dimension as 6 dimensions, and taking the extraction dimension of the step 5) as 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, 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)；
IV, solving local attraction factors
Wherein
Is [0,1 ]]The random number of (2);
v calculating the average best position
And 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 invention 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 of the surrounding rock by utilizing early dataσ0Based 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 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 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 the fitness fv (i) of each particle, and initializing the individual optimal solution piD(t) and the global optimal solution giD(t)；
IV, solving local attraction factors
Wherein
Is [0,1 ]]The random number of (2);
v calculating the average best position
And 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 strong surface polarization systemNumber 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.
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. The method of claim 1, wherein the conductivity σ of the surrounding rock in step 4 is constrained by step 30Extent, by step 4 constraining the conductivity σ of the surrounding rock in step 50And 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.
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 isF, (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) when the parameters are extracted;
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)；
IV, solving local attraction factors
Wherein
Is [0,1 ]]The random number of (2);
v calculating the average best position
And 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)。
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011484866.6A CN112666618B (en) | 2020-12-16 | 2020-12-16 | Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium |
| CA3122828A CA3122828C (en) | 2020-12-16 | 2021-06-21 | Squid-based electromagnetic detection method for induction-polarization symbiotic effect of two-phase coducting medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011484866.6A CN112666618B (en) | 2020-12-16 | 2020-12-16 | Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112666618A true CN112666618A (en) | 2021-04-16 |
| CN112666618B CN112666618B (en) | 2022-04-19 |
Family
ID=75405337
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011484866.6A Active CN112666618B (en) | 2020-12-16 | 2020-12-16 | Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112666618B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104570101A (en) * | 2013-10-09 | 2015-04-29 | 中国石油化工股份有限公司 | AVO (amplitude versus offset) three-parameter inversion method based on particle swarm optimization |
| CN105044771A (en) * | 2015-08-05 | 2015-11-11 | 北京多分量地震技术研究院 | 3D TTI double-phase medium seismic wave field value simulation method based on finite difference method |
| EP3018502A2 (en) * | 2014-11-07 | 2016-05-11 | Services Petroliers Schlumberger | Modeling fluid-conducting fractures in reservoir simulation grids |
| US20180128933A1 (en) * | 2016-11-07 | 2018-05-10 | The Climate Corporation | Work layer imaging and analysis for implement monitoring, control and operator feedback |
| CN108169802A (en) * | 2018-03-02 | 2018-06-15 | 吉林大学 | A kind of time domain electromagnetic data slow diffusion imaging method of harsh media model |
| CN110133733A (en) * | 2019-04-28 | 2019-08-16 | 吉林大学 | A kind of conductance based on particle swarm optimization algorithm-polarizability multi-parameter imaging method |
| CN111399044A (en) * | 2020-04-13 | 2020-07-10 | 中国石油大学(北京) | Reservoir permeability prediction method and device and storage medium |
| US20200229206A1 (en) * | 2017-12-30 | 2020-07-16 | Intel Corporation | Methods and devices for wireless communications |
-
2020
- 2020-12-16 CN CN202011484866.6A patent/CN112666618B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104570101A (en) * | 2013-10-09 | 2015-04-29 | 中国石油化工股份有限公司 | AVO (amplitude versus offset) three-parameter inversion method based on particle swarm optimization |
| EP3018502A2 (en) * | 2014-11-07 | 2016-05-11 | Services Petroliers Schlumberger | Modeling fluid-conducting fractures in reservoir simulation grids |
| CN105044771A (en) * | 2015-08-05 | 2015-11-11 | 北京多分量地震技术研究院 | 3D TTI double-phase medium seismic wave field value simulation method based on finite difference method |
| US20180128933A1 (en) * | 2016-11-07 | 2018-05-10 | The Climate Corporation | Work layer imaging and analysis for implement monitoring, control and operator feedback |
| US20200229206A1 (en) * | 2017-12-30 | 2020-07-16 | Intel Corporation | Methods and devices for wireless communications |
| CN108169802A (en) * | 2018-03-02 | 2018-06-15 | 吉林大学 | A kind of time domain electromagnetic data slow diffusion imaging method of harsh media model |
| CN110133733A (en) * | 2019-04-28 | 2019-08-16 | 吉林大学 | A kind of conductance based on particle swarm optimization algorithm-polarizability multi-parameter imaging method |
| CN111399044A (en) * | 2020-04-13 | 2020-07-10 | 中国石油大学(北京) | Reservoir permeability prediction method and device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 桂俊川等: "横观各向同性页岩岩石物理模型建立-以龙马溪组页岩为例", 《地球物理学报》 * |
| 王国权等: "粒子群优化的等效基质模量提取和横波预测方法", 《石油科学通报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112666618B (en) | 2022-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| West et al. | Interactive seismic facies classification using textural attributes and neural networks | |
| He et al. | Transition probability‐based stochastic geological modeling using airborne geophysical data and borehole data | |
| Beiki et al. | Eigenvector analysis of gravity gradient tensor to locate geologic bodies | |
| Tronicke et al. | Crosshole traveltime tomography using particle swarm optimization: A near-surface field example | |
| CN110133733B (en) | Conductance-polarizability multi-parameter imaging method based on particle swarm optimization algorithm | |
| Ekinci et al. | Parameter estimations from gravity and magnetic anomalies due to deep-seated faults: differential evolution versus particle swarm optimization | |
| Christensen et al. | Combining airborne electromagnetic and geotechnical data for automated depth to bedrock tracking | |
| Cassidy et al. | The application of finite-difference time-domain modelling for the assessment of GPR in magnetically lossy materials | |
| Madsen et al. | Time-domain induced polarization–an analysis of Cole–Cole parameter resolution and correlation using Markov Chain Monte Carlo inversion | |
| CN111221048B (en) | Boulder boundary identification and imaging method based on cross-hole resistivity CT multi-scale inversion | |
| Biswas | Inversion of amplitude from the 2-D analytic signal of self-potential anomalies | |
| Liu et al. | Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes | |
| Alperovich et al. | A new combined wavelet methodology: Implementation to GPR and ERT data obtained in the Montagnole experiment | |
| Piro et al. | Beyond image analysis in processing archaeomagnetic geophysical data: case studies of chamber tombs with dromos | |
| CN112666618B (en) | Geometric-physical property multi-characteristic parameter extraction method for multi-phase medium | |
| Zhao et al. | 2D and 3D imaging of a buried prehistoric canoe using GPR attributes: a case study | |
| Tronicke et al. | GPR attribute analysis: There is more than amplitudes | |
| Singha et al. | Accounting for tomographic resolution in estimating hydrologic properties from geophysical data | |
| Lopera et al. | Clutter reduction in GPR measurements for detecting shallow buried landmines: a Colombian case study | |
| Abdulrazzaq et al. | Performance of GPR attribute analysis to detect and characterise buried archaeological targets near Ukhaidir palace, Iraq. | |
| Li et al. | Integrated geophysical study in the cemetery of Marquis of Haihun | |
| AU2016247148A1 (en) | Bipole source modeling | |
| Göktürkler et al. | Uygulamalı jeofizikte metasezgiseller | |
| Liu et al. | Combined geophysical surveys using a novel approach to characterize ancient burnt soils: A field experiment at Liangzhu city site in Hangzhou, China | |
| Koster et al. | Identifying sedimentary structures and spatial distribution of tsunami deposits with GPR-examples from Spain and Greece |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |