基于多模多频SNR数据逆建模反演海面高度变化

doi:10.11947/j. AGCS.2024.20240249.

测绘学报 ›› 2024, Vol. 53 ›› Issue (11): 2099-2110.doi: 10.11947/j. AGCS.2024.20240249.

• 大地测量学与导航 • 上一篇

基于多模多频SNR数据逆建模反演海面高度变化

陈灵秋¹^,²(), 柴洪洲¹(), 暴景阳³, 王敏¹^,², 郑乃铨⁴

^1.信息工程大学地理空间信息学院，河南　郑州　450001
^2.智慧地球重点实验室，陕西　西安　710000
^3.福建理工大学智慧海洋科学技术学院，福建　福州　350118
^4.山东科技大学测绘与空间信息学院，山东　青岛　266590

收稿日期:2024-06-19 发布日期:2024-12-13
通讯作者: 柴洪洲 E-mail:clqseu@126.com;chaihz1969@163.com
作者简介:陈灵秋（1988—），女，博士生，讲师，研究方向为GNSS遥感。　E-mail：clqseu@126.com
基金资助:
国家自然科学基金(42304043);智慧地球重点实验室基金(KF2023YB01-11)

Sea surface height inversion based on inverse modeling of multi-GNSS and multi-frequency SNR data

Lingqiu CHEN¹^,²(), Hongzhou CHAI¹(), Jingyang BAO³, Min WANG¹^,², Naiquan ZHENG⁴

^1.Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
^2.Key Laboratory of Smart Earth, Xi'an 710000, China
^3.School of Smart Marine Science and Technology, Fujian University of Technology, Fuzhou 350118, China
^4.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

Received:2024-06-19 Published:2024-12-13
Contact: Hongzhou CHAI E-mail:clqseu@126.com;chaihz1969@163.com
About author:CHEN Lingqiu (1988—), female, PhD candidate, lecturer, majors in GNSS remote sensing. E-mail: clqseu@126.com
Supported by:
The National Natural Science Foundation of China(42304043);Key Laboratory of Smart Earth(KF2023YB01-11)

摘要/Abstract

摘要：

利用信噪比（SNR）数据进行逆建模可以反演海面高度及其变化，但反演精度、稳定性依赖初值的精度和SNR数据的时间连续性，基于多模多频SNR数据逆建模反演海平面变化的性能及其在潮汐分析中的应用有待进一步研究。本文为LSP（Lomb-Scargle periodogram）反演结果引入海面动态改正，用于逆建模过程参数初始化，获得稳定、均匀的高精度海面高度反演值，并由反演海面高度开展潮汐调和分析。选取MAYG、BRST和SC02这3个大潮差测站，对其1 a的多模多频SNR数据开展逆建模反演试验，与验潮站实测海面高度对比分析进行算法验证。结果表明，MAYG站逆建模反演海面高度的均方根误差（RMSE）为5.97 cm，BRST站为8.78 cm，SC02站为2.38 cm，海平面变化反演精度达到厘米级；与实测海面高度的潮汐调和分析结果对比，年度、逐月拟合残差中误差高度一致，提取的分潮振幅平均绝对误差（MAE）优于1 cm，提取的迟角MAE在3°以内，潮汐分析提取的潮汐成分和非潮汐水位均具有高度一致性。多模多频SNR数据逆建模反演海面高度能够替代验潮站实测海面高度用于潮汐调和分析。

关键词: GNSS-IR, 海面高度反演, 逆建模, LSP, SNR数据

Abstract:

The inverse modeling utilizing signal-to-noise ratio (SNR) data enables the inversion of sea surface height (SSH) and its variations. However, the accuracy and stability of the inversion process hinge on the precision of the initial values and the temporal continuity of the SNR data. Further investigation is required into the performance of inverse modeling based on multi-mode and multi-frequency SNR data for inverting sea-level changes and its application in tidal analysis. This study introduces a dynamic correction for sea surface variations into the inversion results of the Lomb-Scargle periodogram (LSP), which is utilized for initializing parameters in the inverse modeling process. This approach yields stable and uniform high-precision SSH inversion values, which are then employed to conduct tidal harmonic analysis. Three stations with large tidal ranges, namely MAYG, BRST, and SC02, were selected for inverse modeling and inversion experiments using their one-year multi-mode and multi-frequency SNR data. Algorithm validation was conducted through comparative analysis with in-situ SSH measurements from tide gauges. The results indicate that the root-mean-square error (RMSE) of the SSH inversion via inverse modeling is 5.97 cm for MAYG, 8.78 cm for BRST, and 2.38 cm for SC02, demonstrating centimeter-level accuracy in SSH inversion. When compared with the tidal harmonic analysis results of the observed SSH, the annual and monthly fitting residuals exhibit high consistency in terms of mean square error. The mean absolute error (MAE) of the extracted tidal constituent amplitudes is better than 1 cm, and the MAE of the extracted tidal phase lags is within 3°. Both the tidal components and non-tidal water levels extracted from the tidal analysis demonstrate high consistency. Therefore, the inversion of SSH using multi-mode and multi-frequency SNR data can serve as a viable alternative to in-situ SSH measurements for tidal harmonic analysis.

Key words: GNSS-IR, sea surface height inversion, inverse modeling, LSP, SNR data

中图分类号:

P229

陈灵秋, 柴洪洲, 暴景阳, 王敏, 郑乃铨. 基于多模多频SNR数据逆建模反演海面高度变化[J]. 测绘学报, 2024, 53(11): 2099-2110.

Lingqiu CHEN, Hongzhou CHAI, Jingyang BAO, Min WANG, Naiquan ZHENG. Sea surface height inversion based on inverse modeling of multi-GNSS and multi-frequency SNR data[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(11): 2099-2110.

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测站名	坐标	潮差/m	距海面高度/m	方位角取值	高度角取值	设备		数据间隔/s
测站名	坐标	潮差/m	距海面高度/m	方位角取值	高度角取值	接收机	天线	数据间隔/s
MAYG	12.78°S，45.26°E	4.0	6	0°~180°，330°~360°	5°~15°	TRIMBLE ALLOY	TRM59800.00	30
BRST	48.38°N，4.50°W	7.0	17	130°~330°	8°~20°	TRIMBLE ALLOY	TRM57971.00	30
SC02	48.55°N，123.01°W	3.5	5	50°~240°	5°~13°	TRIMBLE NETR9	TRM59800.80	15

GNSS	信号频率	SNR类型
GNSS	信号频率	BRST	MAYG	SC02
	L1：1 575.42 MHZ	S1C	S1C	S1
GPS	L2：1 227.60 MHZ	S2X	S2X	S2
	L5：1 176.45 MHZ	S5X	S5X	S5
GLON-ASS	G1：1602+K×9/16 MHZ	S1C，S1P	S1C，S1P	S1
GLON-ASS	G2：1246+K×7/16 MHZ	S2C，S2P	S2C，S2P	S2
	E1：1 575.42 MHZ	S1X	S1X	S1
	E5a：1 176.45 MHZ	S5X	S5X	S5
Galileo	E5b：1 207.140 MHZ	S7X	S7X	S7
Galileo	E5（E5a+E5b）：1 191.795 MHZ	S8X	S8X	S8
	E6：1 278.75 MHZ	S6X	—	—
	B1：1 561.098 MHZ	S2I	S2I	—
	B3：1 268.52 MHZ	S6I	S6I	—
BeiDou	B2：1 207.140 MHZ	S7I	S7I	—
	B1C：1 575.42 MHZ（BDS-3）	S1X	S1X	—
	B2a：1 176.45 MHZ（BDS-3）	S5X	S5X	—

GNSS	频率	SNR类型	滑动窗口SNR弧段数	RMSE/cm	相关系数
GPS	L1	GPS-S1C	2	48.30	0.843 75
	L1，L2	GPS-S1C；GPS-S2X	10	7.90	0.995 26
	L1，L2，L5	GPS-S1C；GPS-S2X；GPS-S5X	14	6.87	0.996 74
GLO GPS GLO GAL GPS	L1	GPS-S1C；GLO-S1C；GLO-S1P	7	20.18	0.968 56
GLO GPS GLO GAL GPS	L1	GPS-S1C；GLO-S1C；GLO-S1P；GAL-S1X	9	14.63	0.982 97
GPS GLO GAL BDS	L1	GPS-S1C；GLO-S1C；GLO-S1P；GA L-S1X；BDS-S2I	14	9.41	0.992 95
		GPS-S1C；GPS-S2X；GLO-S1C；GLO-S1P；GLO-S2C	；
	L1，L2	GLO-S2P；GA L-S1X；GAL-S7X；BDS-S1X；BDS-S2I；	45	3.53	0.999 14
		BDS-S6I；BDS-S7I；
	L1，L2，L5	全部	63	3.59	0.999 19

分潮名称	振幅/cm			迟角/（°）
分潮名称	实测	反演	差值	实测	反演	差值
M₂	104.140	101.321	2.819	26.340	27.712	-1.372
S₂	53.158	51.651	1.507	65.869	67.282	-1.413
N₂	18.720	18.548	0.172	6.941	9.068	-2.127
K₁	14.137	12.903	1.234	4.217	6.181	-1.964
K₂	14.130	14.036	0.094	61.530	63.575	-2.045
O₁	8.953	8.177	0.776	6.960	5.983	0.977
NU₂	4.172	3.900	0.272	5.612	6.449	-0.837
P₁	4.103	3.844	0.259	5.442	359.752	5.690

分潮名称	振幅/cm			迟角/（°）
分潮名称	实测	反演	差值	实测	反演	差值
M₂	204.800	202.198	2.602	105.745	108.121	-2.376
S₂	75.207	74.373	0.834	145.625	148.074	-2.449
N₂	41.406	40.674	0.732	87.886	89.718	-1.832
K₂	21.641	21.180	0.461	142.788	145.508	-2.720
MU₂	8.700	7.881	0.819	101.937	105.252	-3.315
2N₂	8.092	7.220	0.872	77.744	79.905	-2.161
NU₂	7.735	7.136	0.599	83.953	86.322	-2.369
SSA	7.434	6.841	0.593	83.262	84.007	-0.745
L₂	6.755	6.428	0.327	112.853	119.561	-6.708
K₁	6.563	6.826	-0.263	73.204	74.202	-0.998
O₁	6.466	6.558	-0.092	327.962	328.277	-0.315
M₄	5.842	4.509	1.333	99.788	106.965	-7.177

基于多模多频SNR数据逆建模反演海面高度变化

Sea surface height inversion based on inverse modeling of multi-GNSS and multi-frequency SNR data

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测站名	海面高度序列	分潮数	解释原始信号方差比例/（%）	中误差/cm
MAYG	验潮站实测	59	98.9	8.87
MAYG	逆建模反演	59	98.9	8.80
BRST	验潮站实测	59	98.9	16.57
BRST	逆建模反演	59	98.7	17.63
SC02	验潮站实测	59	98.0	11.55
SC02	逆建模反演	59	98.0	11.37

[1]	王笑蕾, 南阳, 何秀凤, 宋敏峰. 考虑潮波特性的GNSS-IR潮位反演方法[J]. 测绘学报, 2024, 53(3): 482-492.
[2]	何佳星, 郑南山, 丁锐, 张克非, 陈天悦. 粒子群优化卷积神经网络GNSS-IR土壤湿度反演方法[J]. 测绘学报, 2023, 52(8): 1286-1297.
[3]	王笑蕾, 牛紫瑾, 何秀凤, 李润川. 沿海沉降变化GNSS定位及GNSS-IR组合监测[J]. 测绘学报, 2023, 52(1): 32-40.
[4]	王洁, 王娜子, 徐天河, 高凡, 贺匀峤. 组合GNSS观测值反演海面高度[J]. 测绘学报, 2022, 51(2): 201-211.
[5]	王笑蕾, 何秀凤, 宋敏峰, 陈殊, 牛紫瑾. 多模多频GNSS-IR水位反演中的频间偏差分析及改正[J]. 测绘学报, 2022, 51(11): 2328-2338.
[6]	何秀凤, 王杰, 王笑蕾, 宋敏峰. 利用多模多频GNSS-IR信号反演沿海台风风暴潮[J]. 测绘学报, 2020, 49(9): 1168-1178.

分潮名称	振幅/cm			迟角/（°）
分潮名称	实测	反演	差值	实测	反演	差值
K₁	75.622	74.272	1.350	279.796	279.176	0.620
M₂	55.797	55.408	0.389	10.178	10.260	-0.082
O₁	42.672	42.275	0.398	258.082	258.272	-0.190
P₁	24.121	23.789	0.332	279.097	279.126	-0.029
S₂	13.365	13.133	0.232	34.911	34.183	0.729
N₂	11.968	12.019	-0.051	343.233	343.324	-0.091
Q₁	7.265	7.199	0.066	251.248	251.345	-0.098