This application claims priority to Chinese Patent Application No. 202311508985.4, filed on Nov. 14, 2023, the contents of which are hereby incorporated by reference.
The application belongs to that technical field of geophysical exploration, and in particular relates to an estimation method of detecting elevation by a semi-airborne electromagnetic method.
Electromagnetic method (EM) is a widely used geophysical electromagnetic detection method, which obtains underground electrical information by observing the electromagnetic response excited by natural or artificial sources. In practical field work, it is difficult for conventional ground detection methods to be carried out quickly in desert, gobi, complex terrain and vegetation coverage areas. In order to overcome the above construction difficulties and give consideration to the larger detection depth, the researchers arranged the emission source on the ground and mounted the receiver on the flight platform, thus producing the semi-airborne electromagnetic method (SAEM), as shown in
The receiver of SAEM detection is in the air, so the height above the ground of the receiver is an important parameter for inversion when processing semi-airborne electromagnetic data. In practical exploration, in order to ensure the navigation safety of unmanned aerial vehicles, the mode of flying at a fixed altitude is usually adopted, so the height of the receiver from the ground may be obtained by subtracting the surface elevation from the GPS elevation of the receiver. However, for many areas, there is no ready-made high-precision surface elevation model, and it is often necessary to obtain surface elevation data through open channels or small low-cost equipment based on rangefinders. However, the data obtained by this method often has some data defects, which leads to the wrong points in the elevation model obtained along the survey line, as shown in
The data collected and measured by this kind of sensor carries noise interference, which is usually removed by filtering operation in engineering application. The conventional Kalman filtering method needs to be based on accurate model and known statistical characteristics of interference signals. However, for the problem of elevation estimation, the statistical characteristics of the wrong points in the measured data are unknown, which makes the conventional Kalman filtering algorithm lose its optimality and greatly reduces the estimation accuracy, as shown in
In order to solve the above technical problems, the application provides an estimation method of detecting elevation by a semi-airborne electromagnetic method, which realizes the effective estimation of surface elevation under the background of poor quality of original elevation data and few references, and lays a good data foundation for subsequent data processing and inversion interpretation.
In order to achieve the above objective, the application provides an estimation method of detecting elevation by a semi-airborne electromagnetic method, including the following steps:
Optionally, judging the original elevation data includes:
Optionally, obtaining the wrong point data sets includes:
Optionally, a judging index based on Mahalanobis distance is:
{tilde over (y)}k=(({tilde over (y)}k−ŷk−)T(Pŷ
where {tilde over (y)}k is the judging index, {tilde over (y)}k is an actual measured value of the elevation, {tilde over (y)}k− is a predicted measured value of the elevation, and Pŷ
Optionally, the improved Kalman filtering method is:
where
Optionally, fusing the filtering result of the wrong point data and the filtering result of the normal data includes:
Compared with the prior art, the application has the following advantages and technical effects.
The estimation method of elevation disclosed by the application realizes the effective estimation of the surface elevation under the background of poor quality of original elevation data and few references, and lays a good data foundation for subsequent data processing and inversion interpretation.
The accompanying drawings, which constitute a part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application, and do not constitute an improper limitation of this application. In the attached drawings:
It should be noted that the embodiments in this application and the features in the embodiments may be combined with each other without conflict. The present application will be described in detail with reference to the attached drawings and embodiments.
It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system with a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from here.
This embodiment puts forward an effective estimation method of detecting elevation by a semi-airborne electromagnetic method. The method mainly includes the following steps.
Step 1, the original elevation data are judged by using a judging index based on Mahalanobis distance, and the wrong point data set is obtained. The judging process is as follows:
then {tilde over (y)}k is compared with the preset threshold χ, and if {tilde over (y)}k is greater than χ, the point is marked as a wrong point.
Step 2, in order to improve the accuracy of judgment, the original data are judged from a first to a last and from the last to t
he first of the survey line, and the wrong point data sets Aforward and Ainverse are obtained respectively. The results of bilateral judgment are fused, and the intersection O of two data sets is taken and recorded as the real wrong point data set. Sometimes it is difficult to find an appropriate threshold. A reasonable means is to select a smaller threshold to make a preliminary judgment, and then prior information is used to further constrain the obtained data set (for example, the elevation of the wrong point is often lower, so an elevation threshold may be taken, and the one less than the elevation threshold is the wrong point). This means additionally incorporates prior information and has higher accuracy.
Step 3, the adjusted Kalman filtering method is used for wrong points, and the conventional Kalman filtering method is still used for normal data. The specific adjustment of the Kalman filtering method used for the wrong points is as follows:
Step 4, similarly, based on the analysis described in step 3, the original data (at this time, the wrong point and normal data have been distinguished) are filtered from the first to the last and from the last to the first of the survey line, and the filtering results {circumflex over (X)}forward (the set of the first filtering result and the third filtering result) and {circumflex over (X)}inverse (the set of the second filtering result and the fourth filtering result) are obtained respectively. The results of bilateral filtering are fused (generally averaged) to get {circumflex over (X)}avg, which will be used as the measured value sequence {tilde over (Y)} for the next working process.
Step 5, the above steps are repeated until the obtained result {circumflex over (X)}avg meets the requirements (for example, there are no more wrong points in the data), and the detailed process is shown in
Taking the measured elevation data in
As can be seen from
The application discloses an estimation method of detecting elevation by semi-airborne electromagnetic method, which mainly includes the following steps: (1) the original elevation data are judged by using a judging index based on Mahalanobis distance to obtain a wrong point data set; (2) bilateral judgment of the wrong point is carried out, and the results of bilateral judgment are fused, and the data set obtained by judgment may be further constrained by using the prior information; (3) the adjusted Kalman filtering method is used for wrong points, and the conventional Kalman filtering method is used for normal data; (4) the strategy of bilateral filtering is used, and the results of bilateral filtering are fused to improve the accuracy of estimation. The elevation estimation method disclosed in this embodiment realizes the effective estimation of surface elevation under the background of poor quality of original elevation data and few references, and lays a good data foundation for subsequent data processing and inversion interpretation.
The above is only the preferred embodiment of this application, but the protection scope of this application is not limited to this. Any change or replacement that may be easily thought of by a person familiar with this technical field within the technical scope disclosed in this application should be included in the protection scope of this application. Therefore, the protection scope of this application should be based on the protection scope of the claims.
Number | Date | Country | Kind |
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202311508985.4 | Nov 2023 | CN | national |
Number | Name | Date | Kind |
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20170060132 | Chung et al. | Mar 2017 | A1 |
20190204467 | Curt | Jul 2019 | A1 |
20220113443 | Cole | Apr 2022 | A1 |
20230383643 | Wu | Nov 2023 | A1 |
Number | Date | Country |
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2020101108 | Jul 2020 | AU |
109459705 | Mar 2019 | CN |
111766632 | Oct 2020 | CN |
116361616 | Jun 2023 | CN |
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