Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

Frontiers in Marine Science

Marine Science

Anders Tengberg¹  Camilla Saetre²  Astrid Marie Skålvik³  Ranveig N. Bjørk⁴  Kjell-Eivind Frøysa⁵ 

[1] Aanderaa Data Instruments AS, Bergen, Norway;Department of Physics and Technology, University of Bergen, Bergen, Norway;Department of Physics and Technology, University of Bergen, Bergen, Norway;NORCE Norwegian Research Center, Bergen, Norway;NORCE Norwegian Research Center, Bergen, Norway;NORCE Norwegian Research Center, Bergen, Norway;Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway;

关键词: data quality;    sensor;    self-validation;    in-situ;    calibration;   

DOI  :  10.3389/fmars.2023.1152236

 received in 2023-01-27, accepted in 2023-03-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

In this paper we give an overview of factors and limitations impairing deep-sea sensor data, and we show how automatic tests can give sensors self-validation and self-diagnostic capabilities. This work is intended to lay a basis for sophisticated use of smart sensors in long-term autonomous operation in remote deep-sea locations. Deep-sea observation relies on data from sensors operating in remote, harsh environments which may affect sensor output if uncorrected. In addition to the environmental impact, sensors are subject to limitations regarding power, communication, and limitations on recalibration. To obtain long-term measurements of larger deep-sea areas, fixed platform sensors on the ocean floor may be deployed for several years. As for any observation systems, data collected by deep-sea observation equipment are of limited use if the quality or accuracy (closeness of agreement between the measurement and the true value) is not known. If data from a faulty sensor are used directly, this may result in an erroneous understanding of deep water conditions, or important changes or conditions may not be detected. Faulty sensor data may significantly weaken the overall quality of the combined data from several sensors or any derived model. This is particularly an issue for wireless sensor networks covering large areas, where the overall measurement performance of the network is highly dependent on the data quality from individual sensors. Existing quality control manuals and initiatives for best practice typically recommend a selection of (near) real-time automated checks. These are mostly limited to basic and straight forward verification of metadata and data format, and data value or transition checks against pre-defined thresholds. Delayed-mode inspection is often recommended before a final data quality stamp is assigned.

【 授权许可】

Unknown   
Copyright © 2023 Skålvik, Saetre, Frøysa, Bjørk and Tengberg