Intelligent Melting Probes - How to Make the Most out of our Data
Abstract
Direct exploration of glaciers, ice sheets, or subglacial environments poses a big challenge. Different technological solutions have been proposed and deployed in the last decades, examples being hot-water drills or different melting probe designs. Most of the recent engineering concepts integrate a variety of different on-board sensors, e.g. temperature sensors, pressure sensors, or an inertial measurement unit. Not only do individual sensors provide valuable insight into the current state of the probe, yet often they also contain a wealth of additional information when analyzed collectively. This quite naturally raises the question: How can we make most out of our data? We find that it is necessary to implement intelligent data integration and sensor fusion strategies to retrieve a maximum amount of information from the observations. In this contribution, we are inspired by the engineering design of the IceMole, a minimally invasive, steerable melting probe. We will talk about two sensor integration strategies relevant to IceMole melting scenarios. At first, we will present a multi-sensor fusion approach to accurately retrieve subsurface position and attitude information. It uses an extended Kalman filter to integrate data from an on-board IMU, a differential magnetometer system, the screw feed, as well as the travel time of acoustic signals originating from emitters at the ice surface. Furthermore, an evidential mapping algorithm estimates a map of the environment from data of ultrasound phased arrays in the probe's head. Various results from tests in a swimming pool and in glacier ice will be shown during the presentation. A second block considers the fluid-dynamical state in the melting channel, as well as the ambient cryo-environment. It is devoted to retrieving information from on-board temperature and pressure sensors. Here, we will report on preliminary results from re-analysing past field test data. Knowledge from integrated sensor data likewise provides valuable input for the parameter identification and verification of data based models. Due to the concept of not focusing on the physical laws, this approach can still be used, if modifications are done. It is highly transferable and hasn't been exploited rigorously so far. This could be a potential future direction.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.C53B0713K
- Keywords:
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- 0720 Glaciers;
- CRYOSPHEREDE: 0758 Remote sensing;
- CRYOSPHEREDE: 0774 Dynamics;
- CRYOSPHEREDE: 0794 Instruments and techniques;
- CRYOSPHERE