CN115437024A - Double-parameter processing method for non-planar wave data of electromagnetic field signal of artificial source frequency domain - Google Patents
Double-parameter processing method for non-planar wave data of electromagnetic field signal of artificial source frequency domain Download PDFInfo
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Abstract
The invention discloses a double-parameter processing method of artificial source frequency domain electromagnetic field signal non-plane wave data, which comprises the following steps: according to the characteristics of the non-plane wave data, identifying and obtaining the frequency range of the non-plane wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase; independently training the first apparent resistivity and the first apparent phase by adopting a convolutional neural network, and respectively outputting a second apparent resistivity, a second apparent phase and a first error of the independently trained convolutional neural network; carrying out inverse calculation on the second apparent phase to obtain a corresponding third apparent resistivity; obtaining a second error between the second apparent resistivity and the third apparent resistivity; if the second error is smaller than the preset threshold value, taking the average value of the second apparent resistivity and the third apparent resistivity as the final apparent resistivity; and if the second error is larger than the preset threshold, obtaining corresponding weight according to the first error, and carrying out weighted average on the second apparent resistivity and the third apparent resistivity to obtain the final apparent resistivity.
Description
Technical Field
The invention relates to the technical field of electrical data signal processing, in particular to a double-parameter processing method for artificial source frequency domain electromagnetic field signal non-plane wave data.
Background
In geophysical exploration, non-planar wave effects of electromagnetic field signals exist in artificial source frequency domain electromagnetic methods (such as a controllable source audio frequency magnetotelluric method (CSAMT), a time-frequency electromagnetic method (TFEM) and the like), so that the signals of the artificial source frequency domain electromagnetic methods are seriously distorted, the actual situation of an underground electrical structure is further distorted, and deep information cannot be obtained. Therefore, it is necessary to process the artificial source frequency domain electromagnetic data to make the curve as close as possible to the natural source electromagnetic data without the non-planar wave effect. However, the non-plane wave effect processing method in the prior art mainly includes an under-mountain correction method and a transition region triangle method, and it should be noted that the under-mountain correction method is insufficient in correction in the transition region and excessive in correction in the near field region, which may also cause distortion of the depth measurement curve. The transition region triangle method has low correction accuracy due to its proximity.
For example, the patent publication No. CN108169800A, the chinese invention patent named "controllable source audio magnetotelluric method apparent resistivity near field correction method", includes: step A, converting current data, electric field data and magnetic field data in a time domain into frequency domain data by utilizing Fourier transform; b, calculating according to electric field data and magnetic field data in the frequency domain data to obtain the Carniya apparent resistivity and the phase; c, judging whether a near field effect exists according to the Charpy resistivity and the phase characteristics, and if so, judging whether the near field effect exists; the correction parameters are obtained by the ratio of the normalized theoretical electric field data and the current normalized electric field data.
However, it should be noted that the method disclosed in the above patent firstly performs normalization through complex calculation, and then obtains a series of correction coefficients by using simple ratios, but the theoretical premise of the calculation method of the correction coefficients is not sufficient; secondly, in the step c of the patent, the judgment and identification of the near field effect are carried out according to the apparent resistivity and the apparent phase characteristics, the dependence degree of manual experience is high, and time and labor are wasted when the data volume is large, namely, the efficiency is low and the intelligent degree is low; thirdly, the method calculates the apparent phase by using the corrected apparent resistivity, thereby obtaining the corrected apparent phase, namely the correction of the apparent phase depends on the correction of the apparent resistivity, the apparent phase is not corrected, namely the processing parameter is single; finally, the method mainly aims at the controllable source audio frequency magnetotelluric method in the artificial source frequency domain electromagnetic method, namely the artificial source frequency domain electromagnetic method applicable to the method is single.
Therefore, a dual-parameter processing method for artificial source frequency domain electromagnetic field signal non-plane wave data, which has the advantages of simple logic, high processing precision and high processing efficiency, is urgently needed.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method for processing nonplanar wave data of an electromagnetic field signal in an artificial source frequency domain by using two parameters, and the technical scheme adopted by the present invention is as follows:
the double-parameter processing method of the artificial source frequency domain electromagnetic field signal non-plane wave data comprises the following steps:
acquiring a curve image of original artificial source frequency domain electromagnetic data;
according to the characteristics of the non-plane wave data, acquiring the frequency range of the non-plane wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase by adopting image recognition;
building a convolutional neural network, and presetting parameters;
independently training the first apparent resistivity and the first apparent phase by adopting a convolutional neural network, respectively outputting a second apparent resistivity and a second apparent phase after independent training, and outputting a first error of the convolutional neural network independently trained by adopting the first apparent resistivity and the first apparent phase;
carrying out inverse calculation on the second apparent phase to obtain a corresponding third apparent resistivity;
obtaining a second error between the second apparent resistivity and the third apparent resistivity;
if the second error is smaller than the preset threshold value, taking the average value of the second apparent resistivity and the third apparent resistivity as the final apparent resistivity;
and if the second error is larger than a preset threshold value, obtaining corresponding weight according to the first error, and carrying out weighted average on the second apparent resistivity and the third apparent resistivity to obtain the final apparent resistivity.
Further, according to the characteristics of the non-plane wave data, the frequency range of the non-plane wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase are obtained by image recognition, and the method comprises the following steps:
the method comprises the steps of converting curve images of original artificial source frequency domain electromagnetic data into numerical information, obtaining numerical characteristics of a first apparent resistivity and a first apparent phase subjected to a non-planar wave effect by adopting preprocessing, characteristic extraction and classification decision, and identifying the frequency ranges of the first apparent resistivity and the first apparent phase subjected to the non-planar wave effect according to the characteristics.
Further, independently training the first apparent resistivity and the first apparent phase by adopting a convolutional neural network respectively, comprising the following steps of:
establishing a sample data set; the sample data set comprises input data and output data; the input data is first apparent resistivity and first apparent phase; the output data is fourth apparent resistivity and fourth apparent phase which are not influenced by the non-plane wave effect.
Preferably, the second apparent phase is inversely calculated to obtain a corresponding third apparent resistivity, and the expression is:
wherein the content of the first and second substances,
the third apparent resistivity is represented by the third resistivity,
represents a period;
the angular frequency is represented by the angular frequency,
representing a second apparent phase;
represents a differential;
a logarithmic function with base 10 is shown.
Preferably, a second error between the second apparent resistivity and the third apparent resistivity is obtained, and the expression is:
wherein, the first and the second end of the pipe are connected with each other,
representing a second apparent resistivity;
representing the third apparent resistivity.
Preferably, the corresponding weight is obtained according to the first error; the first error comprises a first network error of the first apparent resistivity after independent training processing of the convolutional neural network
And the second network error of the first visual phase after independent training processing of the convolutional neural network
(ii) a The expression of the weight is:
wherein the content of the first and second substances,
a weight coefficient representing the second apparent resistivity;
a weight coefficient representing the second apparent phase.
Preferably, the final apparent resistivity expression of the weighted average is:
wherein, the first and the second end of the pipe are connected with each other,
representing a second apparent resistivity;
representing a third apparent resistivity.
Preferably, the determining a second error between the second apparent resistivity and the third apparent resistivity further comprises:
when a plurality of groups of second apparent resistivity and third apparent resistivity exist, obtaining an average value of second errors of the corresponding second apparent resistivity and third apparent resistivity;
and judging by using the average value and a preset threshold value, and obtaining the final apparent resistivity.
Further, still include: according to the characteristics, the frequency range where the first apparent resistivity and the first apparent phase subjected to the non-plane wave effect are located is identified, and the first apparent resistivity and the first apparent phase corresponding to the frequency range are extracted from the curve image of the original artificial source frequency domain electromagnetic data.
Preferably, the fourth apparent resistivity and the fourth apparent phase are extracted from a curve image of raw artificial source frequency domain electromagnetic data that is not affected by the non-plane wave effect.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention skillfully adopts an image recognition technology, and in the non-plane wave effect recognition, the invention recognizes and extracts non-plane wave data from the image characteristics of different parameters, thereby reducing the degree of dependence on manual experience while realizing automatic extraction;
(2) In the processing of the non-plane wave data, the invention avoids calculating complex correction coefficients, realizes intelligent processing by training and learning the characteristics of the non-plane wave data and the plane wave data, and practically improves the processing precision;
(3) The invention skillfully realizes independent processing and mutual verification of two parameters (apparent resistivity and apparent phase) in non-plane wave data processing, reversely calculates the apparent resistivity by using the processed apparent phase through formula derivation, and calculates the final apparent resistivity value in different ways under different conditions, thereby accurately and reliably obtaining the processed data;
in conclusion, the method has the advantages of simple logic, high processing precision, high processing efficiency and the like, and has high practical value and popularization value in the technical field of electric data signal processing.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of protection, and it is obvious for those skilled in the art that other related drawings can be obtained according to these drawings without inventive efforts.
FIG. 1 is a logic flow diagram of the present invention.
FIG. 2 is a graph of apparent resistivity of the raw data of the present invention.
FIG. 3 is a graph of apparent phase of the raw data of the present invention.
Fig. 4 is a graph of a second apparent resistivity of the present invention.
Fig. 5 is a graph of a second apparent phase of the present invention.
FIG. 6 is a graph of the error of the network model (apparent resistivity, apparent phase) of the present invention.
Fig. 7 is a graph of a third apparent resistivity of the present invention.
Fig. 8 is a final apparent resistivity plot of the present invention.
Detailed Description
To further clarify the objects, technical solutions and advantages of the present application, the present invention will be further described with reference to the accompanying drawings and examples, and embodiments of the present invention include, but are not limited to, the following examples. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.
In this embodiment, the term "and/or" is only one kind of association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and claims of the present embodiment are used for distinguishing different objects, and are not used for describing a specific order of the objects. For example, the first target object and the second target object, etc. are specific sequences for distinguishing different target objects, rather than describing target objects.
In the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "such as" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion.
In the description of the embodiments of the present application, the meaning of "a plurality" means two or more unless otherwise specified. For example, a plurality of processing units refers to two or more processing units; the plurality of systems refers to two or more systems.
As shown in fig. 1 to fig. 8, the present embodiment provides a method for processing dual parameters of non-plane wave data of an artificial source frequency domain electromagnetic field signal, which includes the following steps:
firstly, obtaining a curve image of original electromagnetic data of an artificial source frequency domain.
And secondly, according to the characteristics of the non-plane wave data, acquiring the frequency range of the non-plane wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase by adopting image recognition, as shown in fig. 2 to 3. The method comprises the steps of converting curve images of original artificial source frequency domain electromagnetic data into numerical information, obtaining numerical characteristics of a first apparent resistivity and a first apparent phase subjected to a non-planar wave effect by adopting preprocessing, characteristic extraction and classification decision, and identifying the frequency ranges of the first apparent resistivity and the first apparent phase subjected to the non-planar wave effect according to the characteristics. In this embodiment, the preprocessing of image recognition, feature extraction, classification decision, etc. belong to the conventional technologies, and detailed steps thereof are not described herein.
In this embodiment, the frequency range where the first apparent resistivity and the first apparent phase subjected to the non-plane wave effect are located is identified according to the features, and the first apparent resistivity and the first apparent phase corresponding to the frequency range are extracted from the original curve image of the electromagnetic data in the artificial source frequency domain.
And thirdly, building a convolutional neural network, and presetting parameters, wherein the parameters comprise training times, a ReLU function and the like.
And fourthly, independently training the first apparent resistivity and the first apparent phase by adopting a convolutional neural network, respectively outputting the independently trained second apparent resistivity and second apparent phase, and outputting a first error of the convolutional neural network independently trained by adopting the first apparent resistivity and the first apparent phase. Wherein, a sample data set is established before independent training; the sample data set comprises input data and output data; the input data are first apparent resistivity and first apparent phase; the output data is fourth apparent resistivity and fourth apparent phase which are not influenced by the non-plane wave effect. Wherein the fourth apparent resistivity and the fourth apparent phase are extracted from a curve image of original artificial source frequency domain electromagnetic data which is not affected by the non-plane wave effect.
And fifthly, carrying out inverse calculation on the second apparent phase to obtain a corresponding third apparent resistivity, wherein the expression is as follows:
wherein the content of the first and second substances,
a third apparent resistivity is represented by the third resistivity,
represents a period;
the angular frequency is represented by the angular frequency,
representing a second apparent phase;
represents a differential;
a logarithmic function with base 10 is shown.
And sixthly, solving a second error between the second apparent resistivity and the third apparent resistivity, wherein the expression is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing a second apparent resistivity;
representing the third apparent resistivity.
In this embodiment, when there are multiple sets of the second apparent resistivity and the third apparent resistivity, an average value of second errors of the corresponding second apparent resistivity and the third apparent resistivity is obtained; and judging by using the average value and a preset threshold value, and obtaining the final apparent resistivity.
And (3) judging:
(1) And if the second error is smaller than the preset threshold value, taking the average value of the second apparent resistivity and the third apparent resistivity as the final apparent resistivity.
(2) And if the second error is larger than a preset threshold value, obtaining corresponding weight according to the first error, and carrying out weighted average on the second apparent resistivity and the third apparent resistivity to obtain the final apparent resistivity.
Specifically, the corresponding weight is obtained according to the first error; the first error comprises a first network error of the first apparent resistivity after independent training processing of the convolutional neural network
And the second network error of the first visual phase after independent training processing by the convolutional neural network
(ii) a The expression of the weight is:
wherein the content of the first and second substances,
a weight coefficient representing the second apparent resistivity;
a weight coefficient representing the second apparent phase.
In addition, the final apparent resistivity expression of the weighted average is:
wherein the content of the first and second substances,
representing a second apparent resistivity;
representing a third apparent resistivity.
Example 2
The embodiment provides a method for processing the non-plane wave data of the artificial source frequency domain electromagnetic field signal by two parameters,
in this embodiment, theoretical model data is used for further explanation, so that the generation process of theoretical data is explained here, and if actual measurement data is used, the data can be directly processed by the present invention after being preprocessed.
Firstly, a geological model is established, wherein the geological model is a three-layer layered geological model, and each layer of stratum has two parameters of resistivity and layer thickness. Here, the resistivity of each layer is set to 10, the index is changed from 0 to 4, and the step size is 0.2, so that each layer of stratum has 21 resistivity parameter changes. The layer thickness is set to vary between 100 and 1000m in steps of 100m, so that there are 10 layer thickness parameters per layer of formation. The total number of geological models is 21 × 21 × 21 × 10 × 10 = 926100.
Furthermore, each geological model is subjected to forward modeling of artificial source frequency domain electromagnetism, in the embodiment, the transmitting-receiving distance is fixed to be 10km, and the modeling frequency is
Hz, and 88 frequency points in total. 926100 apparent resistivity and apparent phase curve data influenced by the non-plane wave effect are simulated to be used as input data of the network model trained in the step 3.
Further, each geological model is subjected to forward modeling of natural source frequency domain electromagnetism, and the simulation frequency is
Hz, and 88 frequency points in total. 926100 apparent resistivity and apparent phase curve data which are not influenced by the non-plane wave effect are simulated to be used as output data of the training network model in the step 3.
The specific processing procedure of this embodiment is as follows:
firstly, 1852200 total curve images of original artificial source frequency domain electromagnetic data corresponding to 926100 data are identified and extracted according to distortion characteristics of an apparent resistivity curve and an apparent phase curve.
And secondly, converting the curve image of the original artificial source frequency domain electromagnetic data into numerical information, obtaining numerical characteristics of the first apparent resistivity and the first apparent phase subjected to the non-plane wave effect by adopting preprocessing, characteristic extraction and classification decision, and identifying the frequency range of the first apparent resistivity and the first apparent phase subjected to the non-plane wave effect according to the characteristics, wherein the frequency range subjected to the non-plane wave effect is identified to be 0.125-100Hz through an image identification technology as shown in figures 2-3.
According to the frequency range of the identified non-plane wave effect, extracting first apparent resistivity and first apparent phase data corresponding to the frequency range from the original data. It should be noted that the frequency ranges of the non-plane wave effect on the first apparent resistivity and the first apparent phase should be consistent, so that in practical processing, if the data volume is large, it is preferable to use one of the parameters.
And thirdly, building a convolutional neural network, and independently training and learning the extracted first apparent resistivity and first apparent phase data by using the neural network respectively. In this embodiment, a convolutional neural network model (CNN) is selected for training and learning, and the convolutional network model is composed of an input layer, a convolutional layer, a pooling layer, a full-link layer, and an output layer.
According to the result of the training test, the parameters of the network model are continuously adjusted, and in the embodiment, the finally determined main parameters of the network model comprise: the training round number is 100 theory, the number of samples in single training is 8, the optimizer is selected to be an adam optimizer, the convolution kernel of the convolution layer is 32, a ReLU function is adopted as an activation function, and the ReLU function is used for accelerating convergence in a convolutional neural network due to the value characteristics of the ReLU function.
The training test results are shown in fig. 4 to 5, the processed artificial source frequency domain electromagnetic data and the natural source frequency domain electromagnetic data have high consistency regardless of the apparent resistivity curve or the apparent phase curve, the processing effect is obvious, but the processing effect of the apparent phase is slightly better than the apparent resistivity. As shown in fig. 6, it can be seen from the training error graph of the network model that, in the present embodiment, the error curve of the apparent phase network model decreases faster and the error is smaller, so that the apparent phase network model is also relatively better than the apparent resistivity network model.
And fourthly, carrying out inverse calculation on the second apparent phase to obtain corresponding third apparent resistivity, and solving a second error between the second apparent resistivity and the third apparent resistivity. When the data volume is large, the average value of errors of all frequency points can be taken as the error magnitude of the whole data, and then the same calculation mode is selected for all frequency points according to the relation between the error magnitude and the set threshold value to carry out final apparent resistivity calculation so as to reduce the calculation amount. When the data volume is small, the final apparent resistivity calculation can be respectively carried out on each frequency point according to the relation between the relative error magnitude of each frequency point and the threshold value.
And fifthly, in the embodiment, the set threshold is 30%, the average value of errors of all frequency points is taken as the error size of the data, the error of the data is calculated to be 23.76%, the error is smaller than the set threshold, and the final apparent resistivity is calculated. As shown in fig. 8, it can be seen that after the apparent resistivity curve originally affected by the non-plane wave effect is processed by the method of the present invention, the final apparent resistivity curve substantially coincides with the apparent resistivity curve not affected by the non-plane wave effect, so that the effect of the non-plane wave effect is substantially eliminated, and the processing effect is substantially improved.
In conclusion, the invention has the advantages of simple logic, high processing precision, high processing efficiency and the like, has outstanding substantive characteristics and obvious progress compared with the prior art, and has very high practical value and popularization value in the technical field of electrical data signal processing.
The above-mentioned embodiments are only preferred embodiments of the present invention, and do not limit the scope of the present invention, but all the modifications made by the principles of the present invention and the non-inventive efforts based on the above-mentioned embodiments shall fall within the scope of the present invention.
Claims (10)
1. The double-parameter processing method of the artificial source frequency domain electromagnetic field signal non-plane wave data is characterized by comprising the following steps of:
acquiring a curve image of original artificial source frequency domain electromagnetic data;
according to the characteristics of the non-plane wave data, acquiring the frequency range of the non-plane wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase by adopting image recognition;
building a convolutional neural network, and presetting parameters;
independently training the first apparent resistivity and the first apparent phase by adopting a convolutional neural network, respectively outputting a second apparent resistivity and a second apparent phase after independent training, and outputting a first error of the convolutional neural network independently trained by adopting the first apparent resistivity and the first apparent phase;
carrying out inverse calculation on the second apparent phase to obtain a corresponding third apparent resistivity;
obtaining a second error between the second apparent resistivity and the third apparent resistivity;
if the second error is smaller than the preset threshold value, taking the average value of the second apparent resistivity and the third apparent resistivity as the final apparent resistivity;
and if the second error is larger than a preset threshold value, obtaining corresponding weight according to the first error, and carrying out weighted average on the second apparent resistivity and the third apparent resistivity to obtain the final apparent resistivity.
2. The dual-parameter processing method of the artificial source frequency domain electromagnetic field signal non-planar wave data according to claim 1, wherein the frequency range of the non-planar wave data and the corresponding numerical characteristics of the first apparent resistivity and the first apparent phase are obtained by image recognition according to the characteristics of the non-planar wave data, and the method comprises the following steps:
the method comprises the steps of converting curve images of original artificial source frequency domain electromagnetic data into numerical information, obtaining numerical characteristics of a first apparent resistivity and a first apparent phase subjected to a non-planar wave effect by adopting preprocessing, characteristic extraction and classification decision, and identifying the frequency ranges of the first apparent resistivity and the first apparent phase subjected to the non-planar wave effect according to the characteristics.
3. The dual-parameter processing method of artificial source frequency domain electromagnetic field signal non-plane wave data according to claim 1, wherein the independent training of the first apparent resistivity and the first apparent phase by the convolutional neural network comprises:
establishing a sample data set; the sample data set comprises input data and output data; the input data is first apparent resistivity and first apparent phase; the output data is fourth apparent resistivity and fourth apparent phase which are not influenced by the non-plane wave effect.
4. The dual-parameter processing method of artificial source frequency domain electromagnetic field signal non-plane wave data according to claim 1, 2 or 3, wherein the second apparent phase is inversely calculated to obtain a corresponding third apparent resistivity, and the expression is as follows:
wherein the content of the first and second substances,
the third apparent resistivity is represented by the third resistivity,
represents a period;
the angular frequency is represented by the angular frequency,
representing a second apparent phase;
represents a differential;
a logarithmic function with base 10 is shown.
5. The method for dual-parameter processing of artificial source frequency domain electromagnetic field signal non-plane wave data according to claim 1, 2 or 3, wherein a second error between the second apparent resistivity and the third apparent resistivity is obtained, which is expressed as:
wherein the content of the first and second substances,
representing a second apparent resistivity;
representing the third apparent resistivity.
6. The method for dual-parameter processing of artificial source frequency domain electromagnetic field signal non-planar wave data according to claim 1, 2 or 3, wherein the corresponding weight is obtained according to the first error; the first error comprises a first network error of the first apparent resistivity after independent training processing of the convolutional neural network
And the second network error of the first visual phase after independent training processing of the convolutional neural network
(ii) a The expression of the weight is:
wherein the content of the first and second substances,
a weight coefficient representing the second apparent resistivity;
a weight coefficient representing the second apparent phase.
7. The method for dual-parameter processing of artificial source frequency domain electromagnetic field signal non-plane wave data according to claim 6, wherein the final apparent resistivity expression of the weighted average is:
wherein the content of the first and second substances,
representing a second apparent resistivity;
representing the third apparent resistivity.
8. The dual-parameter processing method of artificial source frequency domain electromagnetic field signal non-planar wave data of claim 1, wherein obtaining a second error between a second apparent resistivity and a third apparent resistivity, further comprises:
when a plurality of groups of second apparent resistivity and third apparent resistivity exist, obtaining an average value of second errors of the corresponding second apparent resistivity and third apparent resistivity;
and judging by using the average value and a preset threshold value, and obtaining the final apparent resistivity.
9. The dual-parameter processing method of non-planar wave data of an artificial source frequency domain electromagnetic field signal according to claim 2, wherein obtaining numerical characteristics of a first apparent resistivity and a first apparent phase corresponding to the non-planar wave data comprises the following steps: according to the characteristics, the frequency range where the first apparent resistivity and the first apparent phase subjected to the non-plane wave effect are located is identified, and the first apparent resistivity and the first apparent phase corresponding to the frequency range are extracted from the curve image of the original artificial source frequency domain electromagnetic data.
10. The method for dual-parameter processing of artificial source frequency domain electromagnetic field signal non-planar wave data according to claim 3, wherein said fourth apparent resistivity and fourth apparent phase are extracted from a curved image of the original artificial source frequency domain electromagnetic data unaffected by the non-planar wave effect.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103267980A (en) * | 2013-05-29 | 2013-08-28 | 上海艾都能源科技有限公司 | Geophysical prospecting method and measuring device for earth electromagnetic field source correction |
| CN108169800A (en) * | 2017-12-27 | 2018-06-15 | 江苏省有色金属华东地质勘查局地球化学勘查与海洋地质调查研究院 | Controlled-source audiomagnetotellurics method apparent resistivity near-field calibrating method |
| CN108873083A (en) * | 2018-05-06 | 2018-11-23 | 东华理工大学 | A kind of artificial field source frequency domain electromagnetism apparent resistivity measurement method |
| CN109188536A (en) * | 2018-09-20 | 2019-01-11 | 成都理工大学 | Time-frequency electromagnetism and magnetotelluric joint inversion method based on deep learning |
| CN109884714A (en) * | 2019-03-05 | 2019-06-14 | 中国地质科学院地球物理地球化学勘查研究所 | A kind of controllable source method for electromagnetically measuring, device and its storage medium |
| US20210123343A1 (en) * | 2019-10-28 | 2021-04-29 | Conocophillips Company | Integrated machine learning framework for optimizing unconventional resource development |
| AU2020256395B1 (en) * | 2020-03-09 | 2021-06-03 | Institute Of Geology And Geophysics, Chinese Academy Of Sciences | Deep-resource electromagnetic exploration method combining movable source and fixed source |
| CN113156520A (en) * | 2021-04-25 | 2021-07-23 | 中国科学院地质与地球物理研究所 | Non-planar wave identification method and system for controllable source audio magnetotelluric sounding |
| CN113962133A (en) * | 2021-11-13 | 2022-01-21 | 中国地质科学院地球物理地球化学勘查研究所 | Controllable source audio magnetotelluric method three-dimensional finite difference forward modeling method and system |
| US20220350049A1 (en) * | 2021-04-26 | 2022-11-03 | Institute Of Geology And Geophysics, Chinese Academy Of Sciences | Magnetotelluric inversion method based on fully convolutional neural network |
-
2022
- 2022-11-07 CN CN202211386323.XA patent/CN115437024B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103267980A (en) * | 2013-05-29 | 2013-08-28 | 上海艾都能源科技有限公司 | Geophysical prospecting method and measuring device for earth electromagnetic field source correction |
| CN108169800A (en) * | 2017-12-27 | 2018-06-15 | 江苏省有色金属华东地质勘查局地球化学勘查与海洋地质调查研究院 | Controlled-source audiomagnetotellurics method apparent resistivity near-field calibrating method |
| CN108873083A (en) * | 2018-05-06 | 2018-11-23 | 东华理工大学 | A kind of artificial field source frequency domain electromagnetism apparent resistivity measurement method |
| CN109188536A (en) * | 2018-09-20 | 2019-01-11 | 成都理工大学 | Time-frequency electromagnetism and magnetotelluric joint inversion method based on deep learning |
| CN109884714A (en) * | 2019-03-05 | 2019-06-14 | 中国地质科学院地球物理地球化学勘查研究所 | A kind of controllable source method for electromagnetically measuring, device and its storage medium |
| US20210123343A1 (en) * | 2019-10-28 | 2021-04-29 | Conocophillips Company | Integrated machine learning framework for optimizing unconventional resource development |
| AU2020256395B1 (en) * | 2020-03-09 | 2021-06-03 | Institute Of Geology And Geophysics, Chinese Academy Of Sciences | Deep-resource electromagnetic exploration method combining movable source and fixed source |
| CN113156520A (en) * | 2021-04-25 | 2021-07-23 | 中国科学院地质与地球物理研究所 | Non-planar wave identification method and system for controllable source audio magnetotelluric sounding |
| US20220350049A1 (en) * | 2021-04-26 | 2022-11-03 | Institute Of Geology And Geophysics, Chinese Academy Of Sciences | Magnetotelluric inversion method based on fully convolutional neural network |
| CN113962133A (en) * | 2021-11-13 | 2022-01-21 | 中国地质科学院地球物理地球化学勘查研究所 | Controllable source audio magnetotelluric method three-dimensional finite difference forward modeling method and system |
Non-Patent Citations (5)
| Title |
|---|
| TAO SHAN 等: "Application of multitask learning for 2-D modeling of magnetotelluric surveys:TE Case", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 * |
| 罗威 等: "大地电磁场源效应特征分析及其校正研究", 《地球物理学报》 * |
| 肖鹏飞 等: "基于可控源音频大地电磁法的场源影响校正新方法", 《科学技术与工程》 * |
| 许伟: "CSAMT在深埋长隧道勘察中的研究与应用", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 谢林涛 等: "基于神经网络的视电阻率快速算法", 《地球物理学进展》 * |
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