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A hydrothermal investigation system for the Qianlong-II autonomous underwater vehicle
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Tao Wu1, *, Chunhui Tao1, 2, Jinhui Zhang1, Ao Wang3, Guoyin Zhang1, Jianping Zhou1, Xianming Deng1
Acta Oceanologica Sinica | 2019, 38(3) : 159 - 165
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Acta Oceanologica Sinica | 2019, 38(3): 159-165
• Marine Technology •
A hydrothermal investigation system for the Qianlong-II autonomous underwater vehicle
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Tao Wu1, *, Chunhui Tao1, 2, Jinhui Zhang1, Ao Wang3, Guoyin Zhang1, Jianping Zhou1, Xianming Deng1
Affiliations
  • 1 Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2 School of Oceanography, Shanghai Jiaotong University, Shanghai 200030, China
  • 3 Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Published: 2019-03-25 doi: 10.1007/s13131-019-1408-4
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Qianlong-II is a fully autonomous underwater vehicle designed for the investigation of submarine resources, particularly polymetallic sulfides. It was used to successfully explore hydrothermal fields on the Southwest Indian Ridge. Here, we summarized the exploration of hydrothermal systems using Qianlong-II, including detailed descriptions of its implementation along with the systems used for data management and fast mapping. We also introduced a method to remove platform magnetic interference using magnetic data while Qianlong-II is spinning. Based on hydrothermal anomalies collected by Qianlong-II, we developed a rapid method for locating hydrothermal vents. Taking one dive as an example, we systemically demonstrated the process for analyzing hydrothermal survey data to locate hydrothermal vents.

Qianlong-II  /  autonomous underwater vehicle  /  data management  /  hydrothermal investigation
Tao Wu, Chunhui Tao, Jinhui Zhang, Ao Wang, Guoyin Zhang, Jianping Zhou, Xianming Deng. A hydrothermal investigation system for the Qianlong-II autonomous underwater vehicle[J]. Acta Oceanologica Sinica, 2019 , 38 (3) : 159 -165 . DOI: 10.1007/s13131-019-1408-4
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1 Introduction
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With the development of seafloor survey platforms, more and more researchers are focusing on hydrothermal field study. The first hydrothermal chimney was found at 21°N in the East Pacific Rise on April 21, 1979 by the human-occupied vehicle, Alvin (Corliss et al., 1979). Since then, plentiful seafloor hydrothermal activities have been found in intensive environments such as mid-ocean ridges, back-arc basins, and intraplate volcanic centers (Rona et al., 1993; Fouquet et al., 1997; Herzig, 1999; Edmonds and German, 2004; Tao et al., 2007).
Hydrothermal investigation has special significance as the unique ecosystems near hydrothermal vents contain abundant genetic resources and may even provide clues to the origin of life (Baross and Hoffman, 1985; Martin et al., 2008). Polymetallic sulfides, products of the interaction between heat, bedrock, and seawater in hydrothermal fields, are considered a submarine resource with broad exploitative prospects found in polymetallic nodules and cobalt-rich crusts (Galley et al., 2007). Hydrothermal sulfides exposed on the seafloor are being mined, allowing researchers to visually study the ore-forming mechanism of polymetallic sulfides. Furthermore, hydrothermal fluids constitute a main source of leakage of materials and heat in the deep earth (Johnson and Pruis, 2003).
However, the spatial distribution and dimensions of mineralized bodies are limited because of the characteristics of hydrothermal sulfide mineralization. Their geophysical signatures are limited to hundreds or even dozens of meters (Tivey and Dyment, 2010). Also, their hydrothermal anomalies are only distinct within hundreds of meters above the seafloor (Thomson et al., 1995), thus they can only be detected using near-bottom surveys. Developments in near-bottom geophysical surveying, including remote-operated vehicles (ROV), human occupied vehicles (HOV), and autonomous underwater vehicles (AUV), have enabled researchers to investigate hydrothermal sulfides.
Surveys of seabed mineral resources conducted using AUVs, such as the Remote Environmental Monitoring Unit System (REMUS) (Allen et al., 1997; Purcell et al., 2002; Shcherbina et al., 2008), the Autonomous Benthic Explorer (ABE) (German et al., 2008), and Sentry (Yoerger et al., 2006; Jakuba et al., 2011), which were carried out by Woods Hole Oceanographic Institute. The R series of AUVs, led by the University of Tokyo, made outstanding contributions to Japanese oceanographic research, particularly through investigations of oceanic ridges and submarine volcanoes (Ura et al., 2001, 2004, 2007).The Autosub AUV in England (Millard et al., 2003) and the Hugin series of AUVs in Norway (Vestgard et al., 1999; Hagen, 2001) have also been successfully used to investigate marine resources.
In China, AUV research began in the 1980s. The AUVs Explorer, CR-01, and CR-02, along with the Qianlong AUV series, were developed in succession (Liu et al., 2002; Wu et al., 2014). The newest AUV in the Qianlong series, Qianlong-II, is a deep-sea investigation system that specializes in exploring deep-sea mineral resources. It successfully explored hydrothermal vent fields on the Southwest Indian Ridge (SWIR). Here we provide a detailed description of the investigation, data management, and fast mapping systems that Qianlong-II uses to perform the hydrothermal investigation.
2 Overview of the investigation system on the Qianlong-II
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2.1 Carrying of equipment
Qianlong-II, which is shown in Fig. 1, is the platform for scientific sensors and has a highly intelligent control system, excellent maneuverability, and high-precision positioning and navigation. The main specifications of Qianlong-II are shown in Table 1. The functions of Qianlong-II include spinning, obstacle avoidance, and navigation with contour altitude or depth, allowing it to accommodate deep-sea hydrothermal surveying, even in complex terrain. Qianlong-II is equipped with a standard suite of control and scientific sensors. The control sensors include a piezometer, a Doppler velocity logger (DVL), an attitude sensor, a PHINS inertial navigation sensor, and forward-looking sonar. Qianlong-II was designed to explore deep-sea resources, particularly hydrothermal sulfides. Therefore, scientific sensors consist of geophysical sensors such as a multi-beam sonar system, a bathymetric sidescan sonar system (BSSS), a three-component magnetometer, and oceanographic sensors made up of a CTD, multi-parameter water probe meters, an oxidation-reduction potential (ORP) sensor and a methane sensor. Some of the above sensors are identified in Fig. 1.
Before conducting hydrothermal investigations using Qianlong-II, sensor reliability should be ensured via calibration or testing, and a magnetic diurnal station should be set up to collect data and rectify daily measurement variations throughout the operation. Before each dive, the methane sensor should be preheated for hours or even several days if the sensor has been unused for a long time. When operating depth is reached, the AUV should be allowed to spin and collect magnetic data to correct for tri-axial magnetometer interference caused by the AUV. It is worth mentioning that the AUV should maintained at a depth of 5–10 m above the seafloor if there are plans to photograph the seafloor to obtain substrate information.
2.2 Navigation method
Acoustic navigation is an important and effective positioning method for underwater platforms and should be aided by long-baseline (LBL), short-baseline (SBL), or ultrashort-baseline (USBL) acoustic systems (Bellingham et al., 1994). Preparation of the LBL system is time-consuming, but its positioning accuracy is high. SBL and USBL systems, which are conventional equipment on ships, are small and comparatively convenient to position. Qianlong-II uses a combination of a DVL (WHN 300 kHz), an inertial navigation system (INS; PHINS 6000: https://www.ixblue.com/products/phins-6000), and an LBL system for positioning (Mu et al., 2013). If the required navigation accuracy is not too high, the LBL system can be replaced with a USBL system.
Here, we provide an introduction to the aforementioned navigation methods. For the LBL system, at least three transponders should be moored on the seafloor to communicate with the transmitter and receiver of the RAMSES 6000 the LBL system (https://www.ixblue.com/sites/default/files/downloads/ixblue-ps-ramses-02-2014-web.pdf) on the AUV (Baccou and Jouvencel, 2002). The PHINS 6000 periodically sends an acoustic signal and receives responses from the transponders in order to determine and adjust its position while performing the AUV navigation in the water. Further, it is worth mentioning that the positions of the transponders are located by a USBL system on the mothership by letting the mothership run laps. A series of positions are measured for each transponder, and the center of these positions is obtained by the least-squares method, which is regarded as the transponder’s position.
The positioning accuracy of the LBL system is high, with a relative error of <0.1 m; however, absolute error is incurred because the transponders are located using a USBL system. Absolute error (Δd) is a product of the positioning accuracy (p) of the USBL system and the target depth (h) (Eq. (1)). The value of p can assess letting mothership navigate around “8” track line (IXSEA，2004), and it relates to opening angle (α), which is the arctan function of the radius (r) of the “8” track line and h (Eq. (2)). The value of p for the USBL system on Xiangyanghong10, Qianlong-II’s mothership, is 2‰, 2.7‰ and 6.1‰ when α is 30°, 60° and 120°, respectively. For example, its accuracy is approximately 4 m when α=30° and h=2 000 m.
$\Delta d = ph, $
$\alpha = 2{\rm{arctan}}\left({r/h} \right).$
We attempted to use the USBL system along with the LBL system for navigation and positioning while investigating hydrothermal sulfides on the SWIR using Qianlong-II. This system no requires time to moor and measure transponders. Furthermore, INS of PHINS can autonomously navigate as planned, if technology is mature, the mothership could carry out other investigation near area. We conducted a successful near-shore test; however, the technique is far from mature and suffers from drift error and accumulation of error with time.
3 The hydrothermal investigation system of Qianlong-II
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Previously, hydrothermal exploration relied upon deep-tow instruments equipped with sensors that could locate sites of active hydrothermal vent, but the locking length scales are relatively larger range than hydrothermal area with only hundreds (even only dozens) of meters. However, AUV has high-precision positioning and navigation that benefits from an intelligent control system, inertial navigation system, and LBL or USBL system for positioning, and the accuracy of positioning can be determined to few meters. Qianlong-II, which is similar to ABE (German et al., 2008), Sentry (Yoerger et al., 2006; Jakuba et al., 2011), R series AUV (Ura et al., 2001, 2004, 2007), etc., performs a variety of conventional and unconventional surveys including sonar surveys, hydrothermal plume surveys, and near-bottom magnetic and photo surveys. Here, we have depicted a hydrothermal investigation system of Qianlong-II that contains data management, rapid mapping, and anomaly positioning and near-bottom magnetic data correction systems. Further, we observe that this method of magnetic correction is an innovative method.
3.1 Data management system
The control system, navigation system, and survey system of Qianlong-II are relatively independent, and survey information has corresponding physical meaning until matches location, depth, altitude, etc. The data management system, which is shown in Fig. 2a, was developed to improve efficiency. The data management system can record sensor data, including photographs, BSSS documents, and custom files; preprocess and standardize data (e.g., delete erroneous data, transform methane data with hexadecimal to decimal, and convert electric potential information from the magnetometer to magnetic induction); integrate all sensor data together, and match data with corresponding vehicle information (e.g., location, depth, altitude and cruising speed) by time series. Using the data management system, the integrated data can allow to review, statistics and select, and it can be exported document directly according to requirement. Moreover, the system can be used to record, manage and query information related to the voyage, equipment, port and crew.
3.2 Rapid mapping and anomaly positioning
A three-phase method was employed to locate and map hydrothermal vent sites by ABE (Yoerger et al., 2007). Phase 1 involved a loosely spaced (~250 m) grid survey in the nonbuoyant plume layer (~100–200 m) with the goal of finding the most intense hydrothermal anomalies. In Phase 2, the AUV surveyed 50 m above the seafloor with track lines spaced 30 m apart to intercept rising plume stems while making detailed bathymetric and magnetic field maps of the seafloor. Based on these results, a Phase 3 survey was conducted 5–10 m above the seafloor, during which detailed electronic photographs were produced, which could be combined to make photo-mosaics and determine seafloor characteristics. Referencing to the previous patterns, we developed rapid post-processing software in order to perform hydrothermal investigation. This software can rapidly map and localize anomalies in order to plan the AUV’s next missions.
Figure 2b shows software function and process procedure. With integrated data, the software can remove noise and filter data and classify track-lines as dive, horizontal circle, survey lines, and floating, for processing and analyzing. The software can also automatically and quickly map changing curves with time, planar distribution, and integrated anomaly of each parameter with survey line data. Using the software, we can analyze the depth curve of survey data while AUV moves down and up and compare the consistency of the junction points data, with the same/similar position and different time, which extracted from cross-line and survey lines.
In addition, we developed a rapid system for identifying the locations of hydrothermal vents using comprehensive hydrochemical anomalies and geological interpretation based on photographs. To infer hydrothermal anomalies, we plot survey data on the same time axis, set reasonable thresholds for each parameter (including methane concentration, turbidity and oxidation reduction potential (ORP)), and consider a possible hydrothermal anomaly if one or more parameters exceed the threshold. Meanwhile, we show planar distribution of each survey data and callout near-bottom photos if have in corresponding area. Combining these maps, we can analyze whether a true hydrothermal anomaly exists and delineate it if it does.
3.3 High-precision, near-bottom magnetic data
Qianlong-II contains a three-component magnetometer. Data collected by this instrument can be used to determine the geomagnetic structure of the hydrothermal area and to study inactive hydrothermal sulfide areas (Caratori et al., 2012; Tivey and Dyment, 2010). However, the magnetometer is prone to magnetic disturbances from the surrounding environment (e.g., ferromagnetic materials and strong electric currents from the platform and carried equipment). Effective magnetic anomalies can only be obtained after correcting for magnetic interference. Wu et al. (2018) systematically studied the choice of magnetometer location on the platform and methods of magnetic correction. Using a probe extension pole, they placed the magnetometer far away from batteries, motors, and other sources of interference. The tri-axial magnetometer interference caused by the AUV can be corrected by allowing AUV to rotate horizontally. Here, we suggest a method to allow AUV to hover and rotate instead of circling around a small radius (about 10 m) to obtain AUV spinning data during the Qianlong-II survey of hydrothermal sulfides on the SWIR. This would reduce Qianlong-II circling time and achieve a good correction result (Fig. 3). Furthermore, as our research area on the SWIR is far away from constant geomagnetic observatories, we moored a magnetometer over the seafloor as a temporary geomagnetic station and obtained magnetic changes up to 200 nT in a day, and the diurnal variation was more than the expected variation within 40–60 nT. Thus, it is necessary to consider and reduce daily magnetic variation. The reason for this phenomenon needs further study.
4 Application example
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Following a series of experimental tests on land, in a lake, and at sea, Qianlong-II obtained hydrothermal anomalies and near-bottom magnetic data in a hydrothermal sulfide area on the SWIR from January to March in 2016. The hydrothermal investigation system is useful for the analysis of hydrothermal survey data and the location of hydrothermal vents. Taking the AUV029 dive as an example, we illustrate the analysis procedure using the investigation system.
After the diving mission was completed and Qianlong-II was withdrawn to the deck, survey data, control system data, navigation data, and altitude data were obtained from the system. Further, data were successively entered, and erroneous data (e.g., wrong identifier, broken text field) were automatically checked and deleted by the data management system. This allowed us to obtain an integrated document with a common data format after formatting and merging the data that was entered by the system. Opening the document directly in the software that was used for rapid mapping and anomaly positioning depicted the tracking map and the time and depth curves of the survey data. As depicted in Fig. 4, we intercept moving down/up data from time-depth curve (Fig. 4b), cut survey lines data from track line (Fig. 4a), and obtain horizontal circle data from time-heading curve (Fig. 4d).
Based on the data that were obtained when the AUV moved down and up, we analyzed the dependability of some survey data with values closely associated with depth. Figure 5 shows the depth curves for different CTD measurement parameters, including temperature, conductivity, salinity, and velocity, as the AUV changed depth. Each of the measurement results of diving down data is in concordance with the floating up at the same depth. The data of methane concentration, turbidity, and ORP exhibit no direct relation to depth; however, they are effective in order to compare the data on survey lines along with the background.
We plotted these parameters on the same time axis to verify the presence of hydrothermal anomalies. Accoording to the principle, if more than one parameter exhibits an anomaly during the same period, we could infer existence of a hydrothermal anomaly. ORP, turbidity and methane are present and depict an obvious anomaly at approximately 17:30, 21:10 and 00:30, respectively, as depicted in Fig. 6. Therefore, it is almost certain that there are hydrothermal anomalies above the known hydrothermal area. Without any doubt the location and range of the corresponding anomaly could be delineated using their planar distribution, however, we intend to depict their corresponding results as figures and to systematically expand and interpret the hydrothermal and magnetic anomalies in our future publications. Besides, existing survey data also indicated an obvious hydrothermal anomaly, and sulfide samples were obtained from the anomalous area (Tao et al., 2014). We obtained near-bottom photos and presented hydrothermal bioglyphs and sulfides in the anomalous area during the AUV020 dive (Fig. 7).
5 Conclusion
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Qianlong-II is a deep-sea investigation system specially designed for the investigation of deep-sea mineral resources. It has been successfully used to explore hydrothermal fields on the SWIR. Qianlong-II is able to obtain high-resolution micro-geomorphology data, data on hydrothermal anomalies in the water column, and near-bottom magnetic information, and has an effective system for collecting, processing, and analyzing survey data. During the operation of Qianlong-II, we identified a series of problems that need to be addressed. For example, we could supply more room and a standby interface for detection systems and study and select the suitable carrying patterns (integration or combination) according to the investigation task. We should also strengthen the AUVs autonomy, such as, real-time replanning survey lines, or taking photos (AUV should descend 5–10 m up to sea-bottom) over hydrothermal anomaly area. To achieve real autonomy and independent judgment of hydrothermal anomalies, we should collect and accumulate data on the properties of water column anomalies in different types of hydrothermal areas and improve the real-time capabilities to merge, process, and interpret survey data. These improvements will be considered when updating Qianlong-II and when developing the next generation of AUVs.
Acknowledgements
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We thank the experimental team members of the 4 500 m Qianlong-II AUV technology application system.
Funding
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  • The Technology Upgrading and Scientific Applications of the 4 500 m Depth Rated Qianlong II AUV under contract No. 2017YFC0306803; the National Key R&D Program of China under contract No. 2018YFC0309901; the COMRA Major Project under contract Nos DY135-S1-01-06 and DY135-S1-01-01.
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Year 2019 volume 38 Issue 3
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Article Info
doi: 10.1007/s13131-019-1408-4
  • Receive Date：2018-02-03
  • Online Date：2026-03-31
  • Published：2019-03-25
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History
  • Received：2018-02-03
  • Accepted：2018-03-20
Funding
The Technology Upgrading and Scientific Applications of the 4 500 m Depth Rated Qianlong II AUV under contract No. 2017YFC0306803; the National Key R&D Program of China under contract No. 2018YFC0309901; the COMRA Major Project under contract Nos DY135-S1-01-06 and DY135-S1-01-01.
Affiliations
    1 Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2 School of Oceanography, Shanghai Jiaotong University, Shanghai 200030, China
    3 Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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