PhD position (keywords: machine learning, icequakes) at Bremen University, Germany

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PhD position (keywords: machine learning, icequakes) at Bremen University, Germany

Tanja Fromm
Hello,

We are looking for a PhD candidate to develop machine learning
algorithms for icequake detection.

Location: Bremen/Bremerhaven, Germany

Disciplines: seismology, applied mathematics, computer science

Keywords: ice shelf dynamics, icequakes, machine learning, numerical
algorithms

Motivation: The young and rapidly evolving discipline of cryoseismology
exploits icequakes to monitor the impact of the globally warming climate
on ice sheets. The geophysical observatory at Neumayer Station,
Antarctica has recorded a time series of over 20 years of seismological
data, which includes not only earthquakes, but also icequakes caused by
the surrounding ice dynamics. Routine observatory processing has not
analysed cryogenic events, because they are far too numerous for manual
analysis. To tap this valuable seismological data reservoir and uncover
changes of the coastal ice shelf environment over the past two decades,
we need efficient algorithms that can automatically detect and classify
icequakes. Modern machine learning techniques are in principle able to
discriminate between earthquakes and icequakes (Hammer et al. 2015), but
they were developed for tasks in computer vision with millions of data
sets for training. In almost all other applications - including this
project - well annotated training is rare. Hence, we need to tackle a
'small data' problem in a big data environment.
Directional detections of the Neumayer network. Detections from short
distances (blue band) are not routinely analysed due to missing
discrimination tools between icequakes and earthquakes.

Aim: To circumvent this, this PhD project will apply techniques from
transfer learning and so called active learning strategies, where the
algorithm determines during training those data sets, where detailed
annotation is required. For the task of discriminating between different
seismic events, the project will build on recent developments for deep
learning applied to inverse problems (Arridge et al. 2019). With this
approach, the PhD candidate will develop a fast and efficient algorithm
to reanalyse the lifetime dataset of the Neumayer geophysical
observatory and to operate on the real-time data stream. The resulting
consistent catalogue of cryogenic events then allows monitoring any
changes in the stress state of the coastal shelf ice over time.

Objectives: (1) Develop an algorithm for automatic detection and
discrimination of icequakes, (2) Apply to Neumayer seismological
archive, (3) Statistically analyse temporal variations in icequake
occurrence.

Competences: The candidate should have a solid background in mathematics
with programming skills or in computer science with emphasis on applied
mathematics. Knowledge in signal processing is of advantage.

https://www.mardata.de/topics/knowledge-discovery-in-marine-data-software-and-literature/monitoring-a-stressed-ice-shelf-machine-learning-algorithms-to-detect-icequakes-in-20-years-of-seismological-records-at-neumayer-station-antarctica/



--
Dr. Tanja Fromm
Geophysical Observatory Neumayer III

D-3180
Phone: +49 (0)471-4831-2009

Alfred Wegener Institute for Polar and Marine Research
Helmholtz Association of German Research Centers
Geophysics

P.O. Box 12 01 61
Am Alten Hafen 26
27568 Bremerhaven
Germany




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