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| Titel |
Hunting for seamounts using neural networks: learning algorithms for geomorphic studies |
| VerfasserIn |
A. P. Valentine, L. M. Kalnins, J. Trampert |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250062474
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| Zusammenfassung |
| Many geophysical studies rely on finding and analysing particular topographic features: the
various landforms associated with glaciation, for example, or those that characterise regional
tectonics. Typically, these can readily be identified from visual inspection of datasets, but this
is a tedious and time-consuming process. However, the development of techniques
to perform this assessment automatically is often difficult, since a mathematical
description of the feature of interest is required. To identify characteristics of a
feature, such as its spatial extent, each characteristic must also have a mathematical
description. Where features exhibit significant natural variations, or where their
signature in data is marred by noise, performance of conventional algorithms may be
poor.
One potential avenue lies in the use of neural networks, or other learning algorithms, ideal
for complex pattern recognition tasks. Rather than formulating a description of the feature,
the user simply provides the algorithm with a training set of hand-classified examples: the
problem then becomes one of assessing whether some new example shares the
characteristics of this training data. In seismology, this approach is being developed for the
identification of high-quality seismic waveforms amidst noisy datasets (e.g. Valentine &
Woodhouse, 2010; Valentine & Trampert, in review): can it also be applied to topographic
data?
To explore this, we attempt to identify the locations of seamounts from gridded
bathymetric data (e.g. Smith & Sandwell, 1997). Our approach involves assessing small
‘patches’ of ocean floor to determine whether they might plausibly contain a seamount, and if
so, its location. Since seamounts have been extensively studied, this problem provides an
ideal testing ground: in particular, various catalogues exist, compiled using ‘traditional’
approaches (e.g. Kim & Wessel, 2011). This allows us to straightforwardly generate training
datasets, and compare algorithmic performance. In future, we hope to extend the
approach to identifying the seamount’s ‘footprint’ and, by isolating it from the
underlying seafloor, extracting parameters of interest such as height, radius and
volume.
Kim, S.-S. & Wessel, P., 2011. New global seamount census from altimetry-derived gravity data, Geophysical
Journal International, 186, pp.615–631.
Smith, W., and Sandwell, D., 1997. Global seafloor topography from satellite altimetry and ship depth
soundings, Science, 277, pp.1957-1962.
Valentine, A. & Trampert, J., in review. Data-space reduction, quality assessment and searching of seismograms:
Autoencoder networks for waveform data.
Valentine, A. & Woodhouse, J., 2010. Approaches to automated data selection for global seismic tomography,
Geophysical Journal International, 102, pp.1001–1012. |
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