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互联网运营深海爬行器在长达 7 年的部署期间(2009-2016 年)所采集的环境数据集的质量控制和预分析处理。

Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009-2016).

机构信息

Department of Physics and Earth Sciences, Jacobs University, 28759 Bremen, Germany.

Instituto de Ciencias del Mar (ICM-CSIC), 08003 Barcelona, Spain.

出版信息

Sensors (Basel). 2020 May 25;20(10):2991. doi: 10.3390/s20102991.

Abstract

Deep-sea environmental datasets are ever-increasing in size and diversity, as technological advances lead monitoring studies towards long-term, high-frequency data acquisition protocols. This study presents examples of pre-analysis data treatment steps applied to the environmental time series collected by the Internet Operated Deep-sea Crawler "Wally" during a 7-year deployment (2009-2016) in the Barkley Canyon methane hydrates site, off Vancouver Island (BC, Canada). Pressure, temperature, electrical conductivity, flow, turbidity, and chlorophyll data were subjected to different standardizing, normalizing, and de-trending methods on a case-by-case basis, depending on the nature of the treated variable and the range and scale of the values provided by each of the different sensors. The final pressure, temperature, and electrical conductivity (transformed to practical salinity) datasets are ready for use. On the other hand, in the cases of flow, turbidity, and chlorophyll, further in-depth processing, in tandem with data describing the movement and position of the crawler, will be needed in order to filter out all possible effects of the latter. Our work evidences challenges and solutions in multiparametric data acquisition and quality control and ensures that a big step is taken so that the available environmental data meet high quality standards and facilitate the production of reliable scientific results.

摘要

深海环境数据集的规模和多样性不断增加,随着技术的进步,监测研究朝着长期、高频数据采集协议发展。本研究以加拿大温哥华岛(BC)外 Barkley 峡谷甲烷水合物区的“Wally”号深海网络遥控爬行器在 7 年(2009-2016 年)部署期间收集的环境时间序列为例,展示了应用于预处理数据分析步骤的实例。对压力、温度、电导率、流量、浊度和叶绿素数据分别采用不同的标准化、归一化和去趋势方法,具体取决于处理变量的性质以及每个不同传感器提供的数据的范围和尺度。最终的压力、温度和电导率(转换为实用盐度)数据集可直接使用。另一方面,在流量、浊度和叶绿素的情况下,需要与描述爬行器运动和位置的数据一起进行更深入的处理,以滤除后者可能产生的所有影响。我们的工作证明了多参数数据采集和质量控制方面的挑战和解决方案,并确保了一个重要的步骤,即现有的环境数据达到高质量标准,并有助于产生可靠的科学结果。

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