利用多元回归分析反演西南印度洋区域海底地形

doi:10.11947/j.AGCS.2020.20180526

测绘学报 ›› 2020, Vol. 49 ›› Issue (2): 147-161.doi: 10.11947/j.AGCS.2020.20180526

利用多元回归分析反演西南印度洋区域海底地形

范雕¹, 李姗姗¹, 杨军军^2,3, 孟书宇⁴, 邢志斌⁵, 张驰¹, 冯进凯¹

1. 信息工程大学, 河南郑州 450001;
2. 华中科技大学精密重力测量国家重大科技基础设施地球物理研究所, 湖北武汉 430074;
3. 华中科技大学物理学院基本物理量测量教育部重点实验室, 湖北武汉 430074;
4. 32022部队, 湖北武汉 430074;
5. 航天工程大学, 北京 102206

收稿日期:2018-11-16 修回日期:2019-08-22 发布日期:2020-03-03
通讯作者: 李姗姗 E-mail:zzy_lily@sina.com
作者简介:范雕(1991-),男,博士生,研究方向为物理大地测量和空间化海洋测绘工程。E-mail:fandiao2311@mails.jlu.edu.cn
基金资助:
国家自然科学基金（41774021；41774018；41504018；41674026；41674082；41574020）；国家重点研发计划（2016YFB0501702）；地理信息工程国家重点实验室开放基金（SKLGIE2016-M-3-2）

Predicting bathymetry by applying multiple regression analysis in the Southwest Indian Ocean Region

FAN Diao¹, LI Shanshan¹, YANG Junjun^2,3, MENG Shuyu⁴, XING Zhibin⁵, ZHANG Chi¹, FENG Jinkai¹

1. Information Engineering University, Zhengzhou 450001, China;
2. Institute of Geophysics and PGMF, Huazhong University of Science and Technology, Wuhan 430074, China;
3. MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics, PGMF and School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China;
4. 32022 Troops, Wuhan 430074, China;
5. Space Engineering University, Beijing 102206, China

Received:2018-11-16 Revised:2019-08-22 Published:2020-03-03
Supported by:
The National Natural Science Foundation of China(Nos. 41774021;41774018;41504018;41674026;41674082;41574020);The National Key Research and Development Program of China(No. 2016YFB0501702);The Fund of State Key Laboratory of Geo-Information Engineering(No. SKLGIE2016-M-3-2)

摘要/Abstract

摘要： 针对海底地形与重力异常和重力异常垂直梯度在相应频段呈现强线性相关的特点，引入多元回归分析技术，提出并详细推导了联合多元重力数据的海底地形建模方法。然后，在西南印度洋SWIR（Southwest India Ridge）所在部分海域开展了海底地形反演试验及地形地貌分析研究。试验结果表明：6种海深模型中，基于多元回归分析技术构建的海深模型（BDVG模型）检核精度最高，相较于S&S V18.1模型和ETOPO1模型精度分别提高了11.51%和57.81%左右；2000 m以上水深海域，各个海深模型的检核精度较高，相对误差波动较小，反映了深海海域具有良好的反演效果；地形起伏剧烈海域或者浅海海域，BDVG海深模型，相较于以重力异常和重力异常垂直梯度作为单一输入源建立的BDG模型和BVGG模型相对误差及相对误差波动变化较小，反映了BDVG模型拥有更好的稳定性，从而体现了联合反演的必要性和优势。Indomed FZ-Gallieni FZ上唯一轴部缺失裂谷洋脊段（27洋脊段）目前属于岩浆供应充足阶段，构造作用的海底扩张对其影响较小；同时由于对称裂离方式影响，27洋脊段沿轴南北对称分布有地形隆起。

关键词: 多元回归分析, 海底地形, 重力异常, 重力异常垂直梯度, 相干性

Abstract: Considering the fact that the sea floor topography and gravity anomaly or vertical gravity gradient anomaly show strong linear correlation in the corresponding frequency bands, the method based on using multivariate regression analysis technique to combine multi-gravity data to construct the seafloor model was proposed. Then, the inversion test and analysis were carried out in the part of SWIR(Southwest India Ridge) in the Southwest Indian Ocean. The results showed that the bathymetry model (BDVG model) based on multiple regression analysis has the highest accuracy compared with other models, which is 11.51% and 57.81% higher than the S&S V18.1 model and ETOPO1 model respectively. The accuracy of each bathymetry model is higher, and the relative error fluctuation is small where the water depth is above 2000 m, reflecting the good inversion effect in the deep sea area. In places where the seafloor is fluctuated drastically or in shallow seaarea, BDVG model has less variation in relative error and relative error fluctuation than BDG model and BVGG model established by gravity anomaly and vertical gravity gradient anomaly as a single input source, reflecting the BDVG model has better stability and the necessity and advantage of joint inversion. The only shaft-deficient rift oceanic ridge section (27 oceanic ridge section) on the Indomed FZ-Gallieni FZ is currently in the stage of sufficient magma supply, and the seafloor expansion has less influence on it. At the same time, due to the influence of the symmetric splitting, several rises are symmetrically distributed along the north and south of the axis.

Key words: multiple regression analysis, seafloor topography, gravity anomaly, vertical gravity gradient, coherence

中图分类号:

P229

范雕, 李姗姗, 杨军军, 孟书宇, 邢志斌, 张驰, 冯进凯. 利用多元回归分析反演西南印度洋区域海底地形[J]. 测绘学报, 2020, 49(2): 147-161.

FAN Diao, LI Shanshan, YANG Junjun, MENG Shuyu, XING Zhibin, ZHANG Chi, FENG Jinkai. Predicting bathymetry by applying multiple regression analysis in the Southwest Indian Ocean Region[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2): 147-161.

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利用多元回归分析反演西南印度洋区域海底地形

Predicting bathymetry by applying multiple regression analysis in the Southwest Indian Ocean Region

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