In-situ monitoring device for seabed terrain deformation based on MEMS sensor array

doi:10.3785/j.issn.1008-973X.2022.09.006

Journal of ZheJiang University (Engineering Science)

2022, Vol. 56

Issue (9): 1732-1739 DOI: 10.3785/j.issn.1008-973X.2022.09.006

In-situ monitoring device for seabed terrain deformation based on MEMS sensor array

Yong-qiang GE1(

),Chen CAO1,Jia-wang CHEN1,2,*(

),Chun-ying XU3,Peng ZHOU1,Feng GAO1,Tao LIANG1,Yu-ping FANG1

1. Ocean College, Zhejiang University, Zhoushan 316021, China
2. The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhoushan 316021, China
3. College of Engineering, Shantou University, Shantou 515013, China

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Abstract

In order to meet the urgent need of environmental assessment of the trial production of gas hydrate resources in South China Sea, a seabed terrain deformation monitoring technology and device based on micro-electro-mechanical system (MEMS) sensor array was proposed. The multi-point synchronous acquisition system based on MEMS sensor array was developed, and in-situ terrain monitoring in an area of 30 m×30 m was realized in the laboratory tests. The resolution of terrain deformation monitoring was better than 5 cm level, and the monitoring error was less than 13 mm. The three-dimension seabed terrain deformation vector model was constructed. The bend angle of MEMS sensor and the length of each segment were used to determine the position of the sensor array after deformation, and the subdivision algorithm was used to fit the surface shape of the submarine terrain. The proposed seabed terrain deformation monitoring device has completed in-situ consecutively monitoring for 6 months in gas hydrate trial mining area (water depth of 1203 m). The sea trial results show that MEMS sensor array observed a maximum subsidence of 2 cm and a maximum elevation of 10 cm.

Key words： micro-electro-mechanical system (MEMS) sensor array synchronous acquisition terrain deformation reconstruction in-situ monitoring

Received: 22 September 2021 Published: 28 September 2022

CLC:

P 751

Fund: 国家自然科学基金资助项目（4197060386）；国家科技重大专项资助项目（2017YFC0307703）；海南省重大科技计划资助项目（ZDKJ202019）；浙江省重点研发计划资助项目（2018C03SAA01010，2020C03G2012430）

Corresponding Authors: Jia-wang CHEN E-mail: ge_yongqiang@zju.edu.cn;arwang@zju.edu.cn

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	Yong-qiang GE
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	Chun-ying XU
	Peng ZHOU
	Feng GAO
	Tao LIANG
	Yu-ping FANG

Cite this article:

Yong-qiang GE,Chen CAO,Jia-wang CHEN,Chun-ying XU,Peng ZHOU,Feng GAO,Tao LIANG,Yu-ping FANG. In-situ monitoring device for seabed terrain deformation based on MEMS sensor array. Journal of ZheJiang University (Engineering Science), 2022, 56(9): 1732-1739.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.09.006 OR https://www.zjujournals.com/eng/Y2022/V56/I9/1732

基于MEMS传感阵列的海底地形形变原位监测装置

为了满足南海天然气水合物资源试采环境评价的迫切需要，提出基于微机电系统（MEMS）传感阵列的海底地形形变监测技术及装置. 开发基于MEMS传感阵列的多点同步采集系统，在实验室理想环境测试中，实现 $ 30\;{{\rm{m}}}\times 30\;\mathrm{m} $区域的地形原位监测，地形形变监测分辨率优于5 cm且监测误差小于13 mm. 构建三维海底地形变形矢量模型，利用MEMS传感器的扭转角和各节的长度确定传感阵列变形后的空间位置，采用细分算法拟合获得地形的表面形态. 所提海底地形形变监测装置在水深为1 203 m的天然气水合物试采区完成连续6个月的原位监测. 海试结果表明，MEMS传感阵列观测到的地形最大沉降量为2 cm，最大抬升量为10 cm.

关键词： 微机电系统(MEMS)传感阵列, 同步采集, 地形变形重构, 原位监测

Fig.1 Schematic diagram of deployment of micro-electro-mechanical system sensor array in seabed

Fig.2 Terrain monitoring device

Fig.3 Micro-electro-mechanical system sensor array, sensor node and sensor chamber

Fig.4 Cascading of sensor nodes on micro-electro-mechanical system senor array

Tab.1 Relationship between inquiry interval and data loss rate of acquisition board

Fig.5 Schematic diagram of data acquisition and control system of micro-electro-mechanical system sensor array

Fig.6 3D reconstruction process of terrain deformation

Fig.7 Schematic diagram of micro-electro-mechanical system sensor bending and torsion

Fig.8 Arc model of micro-electro-mechanical system sensor array

Fig.9 Monitoring area of micro-electro-mechanical system sensor array

Fig.10 Schematic diagram of interpolation algorithm

Fig.11 35 MPa pressure test of terrain monitoring system

Tab.2 Monitoring performance of micro-electro-mechanical system sensor array

Fig.12 Deployment and recovery of terrain deformation monitoring device

Fig.13 Terrain deformation monitoring device in seabed

Fig.14 3D mapping of seabed terrain deformation


[1]	RUPPEL C D, WAITE W F Grand challenge: timescales and processes of methane hydrate formation and breakdown, with application to geologic systems[J]. Journal of Geophysical Research: Solid Earth, 2020, 125 (8): e2018JB016459

[2]	YOU K, FLEMINGS P B, MALINVERNO A, et al Mechanisms of methane hydrate formation in geological systems[J]. Reviews of Geophysics, 2019, 57 (4): 1146- 1196 doi: 10.1029/2018RG000638

[3]	CHONG Z, YANG S, BABU P, et al Review of natural gas hydrates as an energy resource: prospects and challenges[J]. Applied Energy, 2016, 162: 1633- 1652 doi: 10.1016/j.apenergy.2014.12.061

[4]	XU C, CHEN J, ZHU H, et al Experimental research on seafloor mapping and vertical deformation monitoring for gas hydrate zone using nine-axis MEMS sensor tapes[J]. IEEE Journal of Oceanic Engineering, 2018, 44 (4): 1090- 1101

[5]	朱超祁, 张民生, 刘晓磊, 等海底天然气水合物开采导致的地质灾害及其监测技术[J]. 灾害学, 2017, 32 (3): 51- 56 ZHU Chao-qi, ZHANG Min-sheng, LIU Xiao-lei, et al Gas hydrates: production, geohazards and monitoring[J]. Journal of Catastrophology, 2017, 32 (3): 51- 56 doi: 10.3969/j.issn.1000-811X.2017.03.010

[6]	XU C, CHEN J, YAN D, et al Review of underwater cable shape detection[J]. Journal of Atmospheric and Oceanic Technology, 2016, 33 (3): 597- 606 doi: 10.1175/JTECH-D-15-0112.1

[7]	李岳明, 李晔, 盛明伟, 等 AUV 搭载多波束声纳进行地形测量的现状及展望[J]. 海洋测绘, 2016, 36 (4): 7- 11 LI Yue-ming, LI Ye, SHENG Ming-wei, et al Application and development of topographic survey using multibeam echosounder equipped on AUV[J]. Hydrographic Surveying and Charting, 2016, 36 (4): 7- 11 doi: 10.3969/j.issn.1671-3044.2016.04.002

[8]	YOKOYAMA T, SHIMOYAMA M, MATSUDA S, et al. Monitoring system of seafloor subsidence for methane hydrate production test [C]// SPWLA 18th Formation Evaluation Symposium of Japan. Chiba: [s. n.], 2012, 23(3): 425–434.

[9]	SAITO H, YOKOYAMA T. Development of seafloor displacement monitoring system using a 3-component servo-accelerometer [C]// OCEANS 2008 – MTS/IEEE Kobe Techno-Ocean. Kobe: IEEE, 2008: 1-4.

[10]	辛明真. 基于GNSS-A浮标的水下非差定位关键技术研究[D]. 青岛: 山东科技大学, 2016. Xin Ming-zhen. The research on key technologies of non-differential method based on GNSS-A buoys underwater positioning system [D]. Qingdao: Shandong University of Science and Technology, 2016.

[11]	杨元喜, 刘焱雄, 孙大军, 等. 2020. 海底大地基准网建设及其关键技术[J]. 中国科学: 地球科学, 50(7): 936-945. YANG Yuan-xi, LIU Yan-xiong, SUN Da-jun, et al. Seafloor geodetic network establishment and key technologies [J]. SCIENTIA SINICA Terrae, 2020, 50(7): 936-945.

[12]	刘经南, 陈冠旭, 赵建虎, 等海洋时空基准网的进展与趋势[J]. 武汉大学学报:信息科学版, 2019, 44 (1): 20- 40 LIU Jing-nan, CHEN Guan-xu, ZHAO Jian-hu, et al Development and trends of marine space-time frame network[J]. Geomatics and Information Science of Wuhan University, 2019, 44 (1): 20- 40

[13]	YOKOTA Y, ISHIKAWA T Shallow slow slip events along the nankai trough detected by GNSS-A[J]. Science Advances, 2020, 6 (3): eaay5786 doi: 10.1126/sciadv.aay5786

[14]	YOKOTA Y, ISHIKAWA T, WATANABE S I Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations[J]. Scientific Data, 2018, 5 (4): 2303- 2322

[15]	MISHORY J DARPA Eyes proposers’s day for undersea positioning system[J]. Inside the Pentagon, 2015, 31 (15): 11

[16]	ZHANG K, LI Y, ZHAO J, et al Underwater navigation based on real-time simultaneous sound speed profile correction[J]. Marine Geodesy, 2016, 39 (1): 98- 111 doi: 10.1080/01490419.2015.1082521

[17]	SCHERWATH M, THOMSEN L, RIEDEL M, et al Ocean observatories as a tool to advance gas hydrate research[J]. Earth and Space Science, 2019, 6 (12): 2644- 2652 doi: 10.1029/2019EA000762

[18]	XU C, CHEN J, GE Y, et al Monitoring the vertical changes of a tidal flat using a MEMS accelerometer array[J]. Applied Ocean Research, 2020, 101 (1): 102186

[19]	GE Y, CHEN J, CAO C, et al. Development and sea trial of the terrain monitoring device based on mems sensing array [C]// IOP Conference Series Earth and Environmental Science. Beijing: [s.n.], 2021, 861: 072008.

[20]	BENNETTP V, ABDOUN T, BARENDSE M Evaluation of soft clay field consolidation using mems-based in-place inclinometer-accelerometer array[J]. Geotechnical Testing Journal, 2015, 38 (3): 290- 302

[21]	ZHU C, JIA Y, WANG Z, et al Dynamics of bottom boundary layers in the yellow river subaqueous delta based on long-term in-situ observations[J]. Acta Geologica Sinica, 2017, 91 (1): 369- 370 doi: 10.1111/1755-6724.13095

[22]	PRIOR D B, SUHAYDA J N, LU N, et al Storm wave reactivation of a submarine landslide[J]. Nature, 1989, 341 (6237): 47 doi: 10.1038/341047a0

[23]	UCHIMURA T, TOWHATA I, WANG L, et al Precaution and early warning of surface failure of slopes using tilt sensors[J]. Soils and Foundations, 2015, 55 (5): 1086- 1099 doi: 10.1016/j.sandf.2015.09.010

[24]	CHEN J, CAO C, GE Y, et al Experimental research on data synchronous acquisition method of subsidence monitoring in submarine gas hydrate mining area[J]. Sensors, 2019, 19 (19): 4319 doi: 10.3390/s19194319

[25]	GE Y, CHEN J, CAO C, et al Design and test of a MEMS accelerometer array for submarine landslide displacement monitoring[J]. Marine Technology Society Journal, 2021, 55 (1): 5- 16 doi: 10.4031/MTSJ.55.1.7

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