Information Entropy Initialized Concrete Autoencoder for Optimal Sensor
Placement and Reconstruction of Geophysical Fields
- URL: http://arxiv.org/abs/2206.13968v1
- Date: Tue, 28 Jun 2022 12:43:38 GMT
- Title: Information Entropy Initialized Concrete Autoencoder for Optimal Sensor
Placement and Reconstruction of Geophysical Fields
- Authors: Nikita Turko, Alexander Lobashev, Konstantin Ushakov, Maxim Kaurkin,
Rashit Ibrayev
- Abstract summary: We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements.
We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands.
We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
- Score: 58.720142291102135
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a new approach to the optimal placement of sensors for the problem
of reconstructing geophysical fields from sparse measurements. Our method
consists of two stages. In the first stage, we estimate the variability of the
physical field as a function of spatial coordinates by approximating its
information entropy through the Conditional PixelCNN network. To calculate the
entropy, a new ordering of a two-dimensional data array (spiral ordering) is
proposed, which makes it possible to obtain the entropy of a physical field
simultaneously for several spatial scales. In the second stage, the entropy of
the physical field is used to initialize the distribution of optimal sensor
locations. This distribution is further optimized with the Concrete Autoencoder
architecture with the straight-through gradient estimator and adversarial loss
to simultaneously minimize the number of sensors and maximize reconstruction
accuracy. Our method scales linearly with data size, unlike commonly used
Principal Component Analysis. We demonstrate our method on the two examples:
(a) temperature and (b) salinity fields around the Barents Sea and the Svalbard
group of islands. For these examples, we compute the reconstruction error of
our method and a few baselines. We test our approach against two baselines (1)
PCA with QR factorization and (2) climatology. We find out that the obtained
optimal sensor locations have clear physical interpretation and correspond to
the boundaries between sea currents.
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