CN107578068A - A kind of satellite-derived gravity data data and Gravity Satellite data fusion method - Google Patents
A kind of satellite-derived gravity data data and Gravity Satellite data fusion method Download PDFInfo
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Abstract
The invention discloses a kind of satellite-derived gravity data data and Gravity Satellite data fusion method, basic step is：Gravity Satellite data are treated as the data matrix with satellite-derived gravity data data formed objects by the way of interpolation；2 layers of wavelet decomposition are carried out to two kinds of data using bior2.2 small echos respectively, obtain a low frequency coefficient component and six high frequency coefficient components；Extract low frequency coefficient component of the low frequency coefficient component of satellite-derived gravity data data as fused data；Two layers of high frequency coefficient component of two kinds of data is weighted fusion respectively, the high frequency coefficient component as fused data；The low frequency of fused data and high frequency coefficient component are reconstructed using bior2.2 small echos, obtain gravity fused data.Method provided by the invention takes into full account satellite-derived gravity data data and precision feature of the Gravity Satellite data in frequency domain, and the data precision for improving marine gravity field is merged by Develop Data.
Description
Technical field
The present invention relates to digital convergence techniques field, more particularly to the application field of ocean multi-source gravimetric data fusion, tool
It is a kind of satellite-derived gravity data data and Gravity Satellite data fusion method that body, which is said,.
Background technology
It is two kinds of important ways that marine gravity field is established using remote sensing mode to survey high satellite and Gravity Satellite.At present, base
1 point is can reach in the marine gravity data spatial resolution for surveying high satellite acquisition, and the spatial resolution of Gravity Satellite data is then
For 100 kilometers, the spatial resolution difference of two class data is larger, different in the frequency information precision that frequency domain represents, and how will
Different spatial resolutions, the remote sensing gravimetric data of different accuracy carry out fusion treatment, are improved while spatial resolution is ensured
Data precision, it is significant for establishing marine gravity field.
Wavelet transformation can transform to the frequency of different size frequency values by the data of spatial domain according to different yardsticks
Domain, the height frequency under different scale can be handled respectively, realize the purpose of data fusion.
The content of the invention
(1) technical problems to be solved
The invention provides a kind of satellite-derived gravity data data and Gravity Satellite data fusion method, take into full account that satellite is surveyed
High gravimetric data and precision feature of the Gravity Satellite data in frequency domain, are merged by Develop Data and improve marine gravity field
Data precision.
(2) technical scheme
The present invention comprises the steps of：
(1) Gravity Satellite data are subjected to interpolation processing, keep the spatial resolution of itself and satellite-derived gravity data data
Unanimously；
(2) 2 layers of wavelet decomposition are carried out to satellite-derived gravity data data, obtains a low frequency coefficient component aLL, two levels
High frequency coefficient component aHL1 and aHL2, two vertical high frequency coefficient component aLH1 and aLH2 and two diagonal high frequency coefficient point
Measure aHH1 and aHH2；
(3) 2 layers of wavelet decomposition are carried out to gravity satellite data, obtains a low frequency coefficient component sLL, two horizontal high-frequents
Coefficient component sHL1 and sHL2, two vertical high frequency coefficient component sLH1 and sLH2 and two diagonal high frequency coefficient component
SHH1 and sHH2；
(4) the low frequency coefficient component aLL in satellite-derived gravity data data decomposition result, the low frequency as fused data are extracted
Coefficient component fLL, i.e. fLL=aLL；
(5) by decomposition result, first layer high frequency coefficient component aHL1, aLH1, aHH1 of satellite-derived gravity data data and
First layer high frequency coefficient component sHL1, sLH1, sHH1 of Gravity Satellite data are weighted fusion respectively, obtain fused data
First layer high frequency coefficient component fHL1, fLH1, fHH1, i.e.,
FHL1=x1·aHL1+y1SHL1, fLH1=x1·aLH1+y1SLH1, fHH1=x1·aHH1+y1·
SHH1,
Wherein, x1And y1For satellite-derived gravity data data and the weight coefficient of Gravity Satellite data first layer；
(6) by decomposition result, second layer high frequency coefficient component aHL2, aLH2, aHH2 of satellite-derived gravity data data and
The second layer high frequency coefficient component sHL2, sLH2, sHH2 of Gravity Satellite data are weighted fusion respectively, obtain fused data
The second layer high frequency coefficient component fHL2, fLH2, fHH2, i.e.,
FHL2=x2·aHL2+y2SHL2, fLH2=x2·aLH2+y2SLH2, fHH2=x2·aHH2+y2·sHH2
Wherein, x2And y2For satellite-derived gravity data data and the weight coefficient of the Gravity Satellite data second layer；
(7) small echo is carried out using the low frequency coefficient component in step (4) and the high frequency coefficient component of step (5) and (6)
Reconstruct, obtains marine gravity fused data；
Wherein, aLL represents the low frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL1 represents the first layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH1 represents the first layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH1 represents the diagonal high frequency coefficient component of first layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL2 represents the second layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH2 represents the second layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH2 represents the diagonal high frequency coefficient component of the second layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
SLL represents the low frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHL1 represents the first layer horizontal high-frequent coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SLH1 represents the first layer vertical high frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHH1 represents the diagonal high frequency coefficient component of first layer of Gravity Satellite two layers of wavelet decomposition result of data；
FLL represents the low frequency coefficient component of fused data；
FHL1 represents the first layer horizontal high-frequent coefficient component of fused data；
FLH1 represents the first layer vertical high frequency coefficient component of fused data；
FHH1 represents the diagonal high frequency coefficient component of first layer of fused data；
FHL2 represents the second layer horizontal high-frequent coefficient component of fused data；
FLH2 represents the second layer vertical high frequency coefficient component of fused data；
FHH2 represents the diagonal high frequency coefficient component of the second layer of fused data.
Further, the method for interpolation processing is the mode of cubic algebraic curves in the step (1).
Further, the wavelet decomposition number of plies is equal to 2 in the step (2) and (3), and wavelet basis function is bior2.2 small echos.
Further, in the step (5) Weighted Fusion weight factor, satellite-derived gravity data data and Gravity Satellite data
Respectively x1=0.5, y1=0.5.
Further, in the step (6) Weighted Fusion weight factor, satellite-derived gravity data data and Gravity Satellite data
Respectively x2=0.5 and y2=0.5.
Further, the basic function of wavelet reconstruction is bior2.2 small echos in the step (7).
(3) beneficial effect
Advantages of the present invention is embodied in：
Because the spatial resolution between satellite-derived gravity data data and Gravity Satellite data and precision are inconsistent, it is difficult to logical
The method Develop Data processing of spatial domain fusion is crossed, the present invention considers from signal transacting angle, carries out two kinds of numbers in frequency domain
According to fusion, to improve data precision.The process handled by wavelet decomposition, frequency domain, reconstructed is by satellite-derived gravity data data and again
The power satellite data information useful to data precision is merged, and the data precision for improving marine gravity field has important
Meaning.
Brief description of the drawings
Fig. 1 is the step flow chart that the present invention is implemented,
Fig. 2 is that first layer high frequency coefficient merges schematic diagram,
Fig. 3 is that second layer high frequency coefficient merges schematic diagram.
Embodiment
To make the purpose of the present invention, content and advantage clearer, with reference to the accompanying drawings and examples, to the present invention's
Embodiment is described in further detail：
Reference picture 1, specific implementation step of the invention are：
(1) Gravity Satellite data are subjected to interpolation processing, keep the spatial resolution of itself and satellite-derived gravity data data
Unanimously；
Wherein, used preprocess method is cubic algebraic curves, obtains pretreated and satellite-derived gravity data
The consistent Gravity Satellite data of size of data.
(2) 2 layers of wavelet decomposition are carried out to satellite-derived gravity data data, obtains a low frequency coefficient component aLL, two levels
High frequency coefficient component aHL1 and aHL2, two vertical high frequency coefficient component aLH1 and aLH2 and two diagonal high frequency coefficient point
Measure aHH1 and aHH2；
Wherein, the wavelet decomposition number of plies is 2 layers, and wavelet function is bior2.2 small echos.
(3) 2 layers of wavelet decomposition are carried out to gravity satellite data, obtains a low frequency coefficient component sLL, two horizontal high-frequents
Coefficient component sHL1 and sHL2, two vertical high frequency coefficient component sLH1 and sLH2 and two diagonal high frequency coefficient component
SHH1 and sHH2；
Wherein, the wavelet decomposition number of plies is 2 layers, and wavelet function is bior2.2 small echos.
(4) the low frequency coefficient component aLL in satellite-derived gravity data data decomposition result, the low frequency as fused data are extracted
Coefficient component fLL, i.e. fLL=aLL；The low-frequency component of the strong satellite-derived gravity data data of Reliability comparotive is selected, as melting
Close the low frequency of result.
(5) referring to the drawings 2, by decomposition result, the first layer high frequency coefficient component aHL1 of satellite-derived gravity data data,
ALH1, aHH1 and first layer high frequency coefficient component sHL1, sLH1, sHH1 of Gravity Satellite data are weighted fusion respectively, obtain
To first layer high frequency coefficient component fHL1, fLH1, fHH1 of fused data, i.e.,
FHL1=x1·aHL1+y1SHL1, fLH1=x1·aLH1+y1SLH1, fHH1=x1·aHH1+y1·sHH1
Wherein, x1And y1For satellite-derived gravity data data and the weight coefficient of Gravity Satellite data first layer, its size difference
For 0.5 and 0.5.
(6) referring to the drawings 3, by decomposition result, the second layer high frequency coefficient component aHL2 of satellite-derived gravity data data,
ALH2, aHH2 and the second layer high frequency coefficient component sHL2, sLH2, sHH2 of Gravity Satellite data are weighted fusion respectively, obtain
To the second layer high frequency coefficient component fHL2, fLH2, fHH2 of fused data, i.e.,
FHL2=x2·aHL2+y2SHL2, fLH2=x2·aLH2+y2SLH2, fHH2=x2·aHH2+y2·sHH2
Wherein, x2And y2For satellite-derived gravity data data and the weight coefficient of the Gravity Satellite data second layer, its size difference
For 0.5 and 0.5.
(7) small echo is carried out using the low frequency coefficient component in step (4) and the high frequency coefficient component of step (5) and (6)
Reconstruct, obtains marine gravity fused data；
In above-mentioned steps：ALL represents the low frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL1 represents the first layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH1 represents the first layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH1 represents the diagonal high frequency coefficient component of first layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL2 represents the second layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH2 represents the second layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH2 represents the diagonal high frequency coefficient component of the second layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
SLL represents the low frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHL1 represents the first layer horizontal high-frequent coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SLH1 represents the first layer vertical high frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHH1 represents the diagonal high frequency coefficient component of first layer of Gravity Satellite two layers of wavelet decomposition result of data；
FLL represents the low frequency coefficient component of fused data；
FHL1 represents the first layer horizontal high-frequent coefficient component of fused data；
FLH1 represents the first layer vertical high frequency coefficient component of fused data；
FHH1 represents the diagonal high frequency coefficient component of first layer of fused data；
FHL2 represents the second layer horizontal high-frequent coefficient component of fused data；
FLH2 represents the second layer vertical high frequency coefficient component of fused data；
FHH2 represents the diagonal high frequency coefficient component of the second layer of fused data.
Claims (6)
1. a kind of satellite-derived gravity data data and Gravity Satellite data fusion method, it is characterised in that comprise the following steps：
(1) Gravity Satellite data are subjected to interpolation processing, are consistent the spatial resolution of itself and satellite-derived gravity data data；
(2) 2 layers of wavelet decomposition are carried out to satellite-derived gravity data data, obtains a low frequency coefficient component aLL, two horizontal high-frequents
Coefficient component aHL1 and aHL2, two vertical high frequency coefficient component aLH1 and aLH2 and two diagonal high frequency coefficient component
AHH1 and aHH2；
(3) 2 layers of wavelet decomposition are carried out to gravity satellite data, obtains a low frequency coefficient component sLL, two horizontal high-frequent coefficients
Component sHL1 and sHL2, two vertical high frequency coefficient component sLH1 and sLH2 and two diagonal high frequency coefficient component sHH1 and
sHH2；
(4) the low frequency coefficient component aLL in satellite-derived gravity data data decomposition result, the low frequency coefficient as fused data are extracted
Component fLL, i.e. fLL=aLL；
(5) by decomposition result, first layer high frequency coefficient component aHL1, aLH1, aHH1 and gravity of satellite-derived gravity data data
First layer high frequency coefficient component sHL1, sLH1, sHH1 of satellite data are weighted fusion respectively, obtain the first of fused data
Layer high frequency coefficient component fHL1, fLH1, fHH1, i.e.,
FHL1=x1·aHL1+y1SHL1, fLH1=x1·aLH1+y1SLH1, fHH1=x1·aHH1+y1SHH1,
Wherein, x1And y1For satellite-derived gravity data data and the weight coefficient of Gravity Satellite data first layer；
(6) by decomposition result, second layer high frequency coefficient component aHL2, aLH2, aHH2 and gravity of satellite-derived gravity data data
The second layer high frequency coefficient component sHL2, sLH2, sHH2 of satellite data are weighted fusion respectively, obtain the second of fused data
Layer high frequency coefficient component fHL2, fLH2, fHH2, i.e.,
FHL2=x2·aHL2+y2SHL2, fLH2=x2·aLH2+y2SLH2, fHH2=x2·aHH2+y2·sHH2
Wherein, x2And y2For satellite-derived gravity data data and the weight coefficient of the Gravity Satellite data second layer；
(7) wavelet reconstruction is carried out using the low frequency coefficient component in step (4) and the high frequency coefficient component of step (5) and (6),
Obtain marine gravity fused data；
Wherein, aLL represents the low frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL1 represents the first layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH1 represents the first layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH1 represents the diagonal high frequency coefficient component of first layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHL2 represents the second layer horizontal high-frequent coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
ALH2 represents the second layer vertical high frequency coefficient component of satellite-derived gravity data two layers of wavelet decomposition result of data；
AHH2 represents the diagonal high frequency coefficient component of the second layer of satellite-derived gravity data two layers of wavelet decomposition result of data；
SLL represents the low frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHL1 represents the first layer horizontal high-frequent coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SLH1 represents the first layer vertical high frequency coefficient component of Gravity Satellite two layers of wavelet decomposition result of data；
SHH1 represents the diagonal high frequency coefficient component of first layer of Gravity Satellite two layers of wavelet decomposition result of data；
FLL represents the low frequency coefficient component of fused data；
FHL1 represents the first layer horizontal high-frequent coefficient component of fused data；
FLH1 represents the first layer vertical high frequency coefficient component of fused data；
FHH1 represents the diagonal high frequency coefficient component of first layer of fused data；
FHL2 represents the second layer horizontal high-frequent coefficient component of fused data；
FLH2 represents the second layer vertical high frequency coefficient component of fused data；
FHH2 represents the diagonal high frequency coefficient component of the second layer of fused data.
2. a kind of satellite-derived gravity data data according to claim 1 and Gravity Satellite data fusion method, its feature exist
In：The method of interpolation processing is the mode of cubic algebraic curves in the step (1).
3. a kind of satellite-derived gravity data data according to claim 1 and Gravity Satellite data fusion method, its feature exist
In：The wavelet decomposition number of plies is equal to 2 in the step (2) and (3), and wavelet basis function is bior2.2 small echos.
4. a kind of satellite-derived gravity data data according to claim 1 and Gravity Satellite data fusion method, its feature exist
In：The weight factor of Weighted Fusion in the step (5), satellite-derived gravity data data and Gravity Satellite data are respectively x1=
0.5, y1=0.5.
5. a kind of satellite-derived gravity data data according to claim 1 and Gravity Satellite data fusion method, its feature exist
In：The weight factor of Weighted Fusion in the step (6), satellite-derived gravity data data and Gravity Satellite data are respectively x2=
0.5 and y2=0.5.
6. a kind of satellite-derived gravity data data according to claim 1 and Gravity Satellite data fusion method, its feature exist
In：The basic function of wavelet reconstruction is bior2.2 small echos in the step (7).
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109239792A (en) * | 2018-10-25 | 2019-01-18 | 中国石油大学(华东) | The satellite-derived gravity data data and shipborne gravimetric data data fusion method that fractal interpolation and net―function combine |
| CN109283590A (en) * | 2018-08-29 | 2019-01-29 | 国家海洋局第海洋研究所 | Multi-source gravimetric data fusion method based on wavelet transformation |
| CN117034196A (en) * | 2023-08-15 | 2023-11-10 | 自然资源部第一海洋研究所 | Data fusion method based on multi-source gravity high-low frequency information sliding window weighting |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3888122A (en) * | 1974-08-09 | 1975-06-10 | Us Navy | Method and apparatus for obtaining the fine scale structure of the earth{3 s gravity field |
| CN103344954A (en) * | 2013-07-08 | 2013-10-09 | 国家海洋局第二海洋研究所 | Submarine topography construction method based on multi-source water depth data fusion |
| CN104035138A (en) * | 2014-04-25 | 2014-09-10 | 西安测绘研究所 | Accurate and rapid calculation method for global and local ocean disturbing gravity |
| CN104933291A (en) * | 2015-05-06 | 2015-09-23 | 中国石油大学(华东) | Method for the production of mean sea surface height products based on satellite altimeter data network function interpolation |
| CN106405589A (en) * | 2016-06-24 | 2017-02-15 | 西安科技大学 | Method and device for determining global ionized layer grid model |
-
2017
- 2017-09-18 CN CN201710838007.4A patent/CN107578068B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3888122A (en) * | 1974-08-09 | 1975-06-10 | Us Navy | Method and apparatus for obtaining the fine scale structure of the earth{3 s gravity field |
| CN103344954A (en) * | 2013-07-08 | 2013-10-09 | 国家海洋局第二海洋研究所 | Submarine topography construction method based on multi-source water depth data fusion |
| CN104035138A (en) * | 2014-04-25 | 2014-09-10 | 西安测绘研究所 | Accurate and rapid calculation method for global and local ocean disturbing gravity |
| CN104933291A (en) * | 2015-05-06 | 2015-09-23 | 中国石油大学(华东) | Method for the production of mean sea surface height products based on satellite altimeter data network function interpolation |
| CN106405589A (en) * | 2016-06-24 | 2017-02-15 | 西安科技大学 | Method and device for determining global ionized layer grid model |
Non-Patent Citations (1)
| Title |
|---|
| 王海瑛等: "卫星测高数据的沿轨迹重力异常反演法及其应用", 《测绘学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109283590A (en) * | 2018-08-29 | 2019-01-29 | 国家海洋局第海洋研究所 | Multi-source gravimetric data fusion method based on wavelet transformation |
| CN109239792A (en) * | 2018-10-25 | 2019-01-18 | 中国石油大学(华东) | The satellite-derived gravity data data and shipborne gravimetric data data fusion method that fractal interpolation and net―function combine |
| CN109239792B (en) * | 2018-10-25 | 2019-07-16 | 中国石油大学(华东) | The satellite-derived gravity data data and shipborne gravimetric data data fusion method that fractal interpolation and net―function combine |
| CN117034196A (en) * | 2023-08-15 | 2023-11-10 | 自然资源部第一海洋研究所 | Data fusion method based on multi-source gravity high-low frequency information sliding window weighting |
| CN117034196B (en) * | 2023-08-15 | 2024-05-07 | 自然资源部第一海洋研究所 | Data fusion method based on multi-source gravity high-low frequency information sliding window weighting |
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