ITS2.1/PS1.2

EDI
Machine Learning in Planetary Sciences and Heliophysics

The increasing amount of data from an increasing number of spacecraft in our solar system shouts out for new data analysis strategies. There is a need for frameworks that can rapidly and intelligently extract information from these data sets in a manner useful for scientific analysis. The community is starting to respond to this need. Machine learning, with all of its different facets, provides a viable playground for tackling a wide range of research questions in planetary and heliospheric physics.

We encourage submissions dealing with machine learning approaches of all levels in planetary sciences and heliophysics. The aim of this session is to provide an overview of the current efforts to integrate machine learning technologies into data driven space research, to highlight state-of-the art developments and to generate a wider discussion on further possible applications of machine learning.

Co-organized by ESSI1/ST1
Convener: Ute Amerstorfer | Co-conveners: Sahib JulkaECSECS, Hannah RuedisserECSECS, Mario D'Amore, Angelo Pio Rossi
Presentations
| Wed, 25 May, 08:30–11:02 (CEST)
 
Room N1

Presentations: Wed, 25 May | Room N1

Chairpersons: Hannah Ruedisser, Ute Amerstorfer
08:30–08:33
08:33–08:43
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EGU22-5739
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ECS
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solicited
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Virtual presentation
Siddhant Agarwal, Nicola Tosi, Pan Kessel, Doris Breuer, and Grégoire Montavon

Mantle convection plays a fundamental role in the long-term thermal evolution of terrestrial planets like Earth, Mars, Mercury and Venus. The buoyancy-driven creeping flow of silicate rocks in the mantle is modeled as a highly viscous fluid over geological time scales and quantified using partial differential equations (PDEs) for conservation of mass, momentum and energy. Yet, key parameters and initial conditions to these PDEs are poorly constrained and often require a large sampling of the parameter space to find constraints from observational data. Since it is not computationally feasible to solve hundreds of thousands of forward models in 2D or 3D, some alternatives have been proposed. 

The traditional alternative to high-fidelity simulations has been to use 1D models based on scaling laws. While computationally efficient, these are limited in the amount of physics they can model (e.g., depth-dependent material properties) and predict only mean quantities such as the mean mantle temperature. Hence, there has been a growing interest in machine learning techniques to come up with more advanced surrogate models. For example, Agarwal et al. (2020) used feedforward neural networks (FNNs) to reliably predict the evolution of entire 1D laterally averaged temperature profile in time from five parameters: reference viscosity, enrichment factor for the crust in heat producing elements, initial mantle temperature, activation energy and activation volume of the diffusion creep. 

We extend that study to predict the full 2D temperature field, which contains more information in the form of convection structures such as hot plumes and cold downwellings. This is achieved by training deep learning algorithms on a data set of 10,525 2D simulations of the thermal evolution of the mantle of a Mars-like planet. First, we use convolutional autoencoders to compress the size of each temperature field by a factor of 142. Second,  we compare the use of two algorithms for predicting the compressed (latent) temperature fields: FNNs and long-short-term memory networks (LSTMs).  On the one hand, the FNN predictions are slightly more accurate with respect to unseen simulations (99.30%  vs. 99.22% for the LSTM). On the other hand, Proper orthogonal decomposition (POD) of the LSTM and FNN predictions shows that despite a lower mean relative accuracy, LSTMs capture the flow dynamics better than FNNs. The POD coefficients from FNN predictions sum up to 96.51% relative to the coefficients of the original simulations, while for LSTMs this metric increases to 97.66%. 

We conclude the talk by stating some strengths and weaknesses of this approach, as well as highlighting some ongoing research in the broader field of fluid dynamics that could help increase the accuracy and efficiency of such parameterized surrogate models.

How to cite: Agarwal, S., Tosi, N., Kessel, P., Breuer, D., and Montavon, G.: Deep learning for surrogate modeling of two-dimensional mantle convection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5739, https://doi.org/10.5194/egusphere-egu22-5739, 2022.

08:43–08:50
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EGU22-486
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Virtual presentation
Denis Maxheimer, Ioannis Markonis, Masner Jan, Curin Vojtech, Pavlik Jan, and Solomonidou Anezina

The recent developments in computer vision research in the field of Single Image Super Resolution (SISR)

can help improve the satellite imagery data quality and, thus, find application in planetary exploration.

The aim of this study is to enhance planetary surface imagery, in planetary bodies that there are

available data but in a low resolution. Here, we have applied the holistic attention network (HAN)

algorithm to a set of images of Saturn’s moon Titan from the Titan Radar Mapper instrument in its

Synthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can find

correlations among hierarchical layers, channels of each layer, and all positions of each channel, which

can be interpreted as an application and intersection of previously known models. The algorithm used

in our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery dataset

resampled over different resolutions. Our experimental setup was to generate High Resolution (HR)

imagery from eight times lower resolution (x8 scale). We followed the standard workflow for this

purpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, and

finally x8 scale to x4 scale, using subsequently the results of the previous training. The promising results

open a path for further applications of the trained model to improve the imagery data quality, and aid

in the detection and analysis of planetary surface features.

How to cite: Maxheimer, D., Markonis, I., Jan, M., Vojtech, C., Jan, P., and Anezina, S.: Enhancing planetary imagery with the holistic attention network algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-486, https://doi.org/10.5194/egusphere-egu22-486, 2022.

08:50–08:57
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EGU22-692
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ECS
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On-site presentation
Caroline Haslebacher and Nicolas Thomas

Lineaments are prominent features on the surface of Jupiter's moon Europa. Analysing these linear features thoroughly leads to insights on their formation mechanisms and the interactions between the subsurface ocean and the surface. The orientation and position of lineaments is also important for determining the stress field on Europa. The Europa Clipper mission is planned to launch in 2024 and will fly by Europa more than 40 times. In the light of this, an autonomous lineament detection and segmentation tool would prove useful for processing the vast amount of expected images efficiently and would help to identify processes affecting the ice sheet. 

We have trained a convolutional neural network to detect, classify and segment lineaments in images of Europa returned by the Galileo mission. The Galileo images that make up the training set are segmented manually, following a dedicated guideline. For better performance, we make use of synthetically generated data to pre-train the network. The current status of the work will be described.

How to cite: Haslebacher, C. and Thomas, N.: Autonomous lineament detection in Galileo images of Europa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-692, https://doi.org/10.5194/egusphere-egu22-692, 2022.

08:57–09:04
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EGU22-8940
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On-site presentation
Salma Barkaoui, Angel Bueno Rodriguez, Philippe Lognonné, Maarten De Hoop, Grégory Sainton, Mathieu Plasman, and Taichi kawamura

Since deployed on the Martian surface, the seismometer SEIS (Seismic Experiment for Interior Structure) and the APSS (Auxiliary Payload Sensors Suite) of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission have been recorded the daily Martian respectively ground acceleration and pressure. These data are essential to investigate the geophysical and atmospheric features of the red planet. So far, the InSight team were able to detect multiple Martian events. We distinguish two types: the artificial events like the lander modes or the micro-tilts known as glitches or the natural events like the pressure drops which are important to estimate the Martian subsurface and the seismic events used to study the interior structure of Mars. Despite the data complexity, the InSight team was able to catalog these events (Clinton et al 2020 for the seismic event catalog, Banfield et al., 2018, 2020 for the pressure drops catalog and Scholz et al. (2020) for the glitches catalog). However, despite all this effort, we are still in front of multiple challenges. In fact,  the seismic events' detection is limited  due to the SEIS sensitivity, which is the origin of a high noise level that may contaminate the seismic events. Thus, we can miss some of them, especially in the noisy period. Besides, their detection is very challenging and require multiple preprocessing task which is time-consuming. For the pressure drops, the detection method used in Banfield et al.  2020 is limited by a threshold equal to 0.3 Pa. Thus, the rest of pressure drops are not included. Plus, due to lack of energy, the pressure sensor was off for several days. As a result, many pressure drops were missed. As a result, being able to detect them directly on the SEIS data which are, in contrast,  provided continuously, is very important.

In this regard, the aim of this study is to overcome these challenges and thus improve the Martian events detection and provide an updated catalog automatically. For that, we were inspired of one of the main technics used today in data processing and analysis in a complete automatic way: it is the Machine Learning and particularly in our case is the Deep Learning. The architecture used for that is the “Hybrid Recurrent Scattering Neural Network” (Bueno et al 2021)  adapted for Mars

How to cite: Barkaoui, S., Bueno Rodriguez, A., Lognonné, P., De Hoop, M., Sainton, G., Plasman, M., and kawamura, T.: Mars events polyphonic detection, segmentation and classification with a hybrid recurrent scattering neural network using InSight mission data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8940, https://doi.org/10.5194/egusphere-egu22-8940, 2022.

09:04–09:11
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EGU22-2765
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Presentation form not yet defined
Michael Fernandes, Nicolas Thomas, Benedikt Elser, Angelo Pio Rossi, Alexander Pletl, and Gabriele Cremonese

Spectroscopy provides important information on the surface composition of Mars. Spectral data can support studies such as the evaluation of potential (manned) landing sites as well as supporting determination of past surface processes. The CRISM instrument on NASA’s Mars Reconnaissance Orbiter is a high spectral resolution visible infrared mapping spectrometer currently in orbit around Mars. It records 2D spatially resolved spectra over a wavelength range of 362 nm to 3920 nm. At present data collected covers less than 2% of the planet. Lifetime issues with the cryo-coolers prevents limits further data acquisition in the infrared band. In order to extend areal coverage for spectroscopic analysis in regions of major importance to the history of liquid water on Mars (e.g. Valles Marineris, Noachis Terra), we investigate whether data from other instruments can be fused to extrapolate spectral features in CRISMto these non-spectral imaged areas. The present work will use data from the CaSSIS instrument which is a high spatial resolution colour and stereo imager onboard the European Space Agency’s ExoMars Trace Gas Orbiter (TGO). CaSSIS returns images at 4.5 m/px from the nominal 400 km altitude orbit in four colours. Its filters were selected to provide mineral diagnostics in the visible wavelength range (400 – 1100 nm). It has so far imaged around 2% of the planet with an estimated overlap of ≲0.01% of CRISM data. This study introduces a two-step pixel based reconstruction approach using CaSSIS four band images. In the first step advanced unsupervised techniques are applied on CRISM hyperspectral datacubes to reduce dimensionality and establish clusters of spectral features. Given that these clusters contain reasonable information about the surface composition, in a second step, it is feasible to map CaSSIS four band images to the spectral clusters by training a machine learning classifier (for the cluster labels) using only CaSSIS datasets. In this way the system can extrapolate spectral features to areas unmapped by CRISM. To assess the performance of this proposed methodology we analyzed actual and artificially generated CaSSIS images and benchmarked results against traditional correlation based methods. Qualitative and quantitative analyses indicate that by this novel procedure spectral features of in non-spectral imaged areas can be predicted to an extent that can be evaluated quantitatively, especially in highly feature-rich landscapes.

How to cite: Fernandes, M., Thomas, N., Elser, B., Rossi, A. P., Pletl, A., and Cremonese, G.: Extrapolation of CRISM based spectral feature maps using CaSSIS four-band images with machine learning techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2765, https://doi.org/10.5194/egusphere-egu22-2765, 2022.

09:11–09:18
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EGU22-2994
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ECS
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Virtual presentation
Robert Jarolim, Astrid Veronig, Tatiana Podladchikova, Julia Thalmann, Dominik Narnhofer, Markus Hofinger, and Thomas Pock

Solar flares and coronal mass ejections (CMEs) are the main drivers for severe space weather disturbances on Earth and other planets. While the geo-effects of CMEs give us a lead time of about 1 to 4 days, the effects of flares and flare-accelerated solar energetic particles (SEPs) are very immediate, 8 minutes for the enhanced radiation and as short as about 20 minutes for the highest energy SEPs arriving at Earth. Thus, predictions of solar flare occurrence at least several hours ahead are of high importance for the mitigation of severe space weather effects.

Observations and simulations of solar flares suggest that the structure and evolution of the active region’s magnetic field is a key component for energetic eruptions. The recent advances in deep learning provide tools to directly learn complex relations from multi-dimensional data. Here, we present a novel deep learning method for short-term solar flare prediction. The algorithm is based on the HMI photospheric line-of-sight magnetic field and its temporal evolution together with the coronal evolution as observed by multi-wavelengths EUV filtergrams from the AIA instrument onboard the Solar Dynamics Observatory. We train a neural network to independently identify features in the imaging data based on the dynamic evolution of the coronal structure and the photospheric magnetic field evolution, which may hint at flare occurrence in the near future.

We show that our method  can reliably predict flares six hours ahead, with 73% correct flaring predictions (89% when considering only M- and X-class flares), and 83% correct quiet active region predictions.

In order to overcome the “black box problem” of machine-learning algorithms, and thus to allow for physical interpretation of the network findings, we employ a spatio-temporal attention mechanism. This allows us to extract the emphasized regions, which reveal the neural network interpretation of the flare onset conditions. Our comparison shows that predicted precursors are associated with the position of flare occurrence, respond to dynamic changes, and align with characteristics within the active region.

How to cite: Jarolim, R., Veronig, A., Podladchikova, T., Thalmann, J., Narnhofer, D., Hofinger, M., and Pock, T.: Interpretable Solar Flare Prediction with Deep Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2994, https://doi.org/10.5194/egusphere-egu22-2994, 2022.

09:18–09:25
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EGU22-6371
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Virtual presentation
Hualin Xiao, Säm Krucker, Daniel Ryan, Andrea Battaglia, Erica Lastufka, Etesi László, Ewan Dickson, and Wen Wang

The Spectrometer Telescope for Imaging X-rays (STIX) is an instrument onboard Solar Orbiter. It measures X-rays emitted during solar flares in the energy range from 4 to 150 keV and takes X-ray images by using an indirect imaging technique, based on the Moiré effect. STIX instrument
consists of 32 pairs of tungsten grids and 32 pixelated CdTe detector units. Flare Images can be reconstructed on the ground using algorithms such as back-projection, forward-fit, and maximum-entropy after full pixel data are downloaded. Here we report a new image reconstruction and
classification model based on machine learning. Results will be discussed and compared with those from the traditional algorithms.

How to cite: Xiao, H., Krucker, S., Ryan, D., Battaglia, A., Lastufka, E., László, E., Dickson, E., and Wang, W.: STIX solar flare image reconstruction and classification using machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6371, https://doi.org/10.5194/egusphere-egu22-6371, 2022.

09:25–09:32
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EGU22-11501
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ECS
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Virtual presentation
Dmitrii Vorobev, Mark Blumenau, Mikhail Fridman, Olga Khabarova, and Vladimir Obridko

We propose a new method for automatic detection of solar magnetic tornadoes based on computer vision methods. Magnetic tornadoes are magneto-plasma structures with a swirling magnetic field in the solar corona, and there is also evidence for the rotation of plasma in them. A theoretical description and numerical modeling of these objects are very difficult due to the three-dimensionality of the structures and peculiarities of their spatial and temporal dynamics [Wedemeyer-Böhm et al, 2012, Nature]. Typical sizes of magnetic tornadoes vary from 102 km up to 106 km, and their lifetime is from several minutes to many hours. So far, quite a few works are devoted to their study, and there are no accepted algorithms for detecting solar magnetic tornadoes by machine methods. An insufficient number of identified structures is one of many problems that do not allow studying physics of magnetic tornadoes and the processes associated with them. In particular, the filamentous rotating structures are well delectable only at the limb, while one can only make suppositions about their presence at the solar disk.
Our method is based on analyzing SDO/AIA images at wavelengths 171 Å, 193 Å, 211 Å and 304 Å, to which several different algorithms are applied, namely, the convolution with filters, convolutional neural network, and gradient boosting. The new technique is a combination of several approaches (transfer learning & stacking) that are widely used in various fields of data analysis. Such an approach allows detecting the structures in a short time with sufficient accuracy. As test objects, we used magnetic tornadoes previously described in the literature [e.g., Wedemeyer et al 2013, ApJ; Mghebrishvili et al. 2015 ApJ]. Our method made it possible to detect those structures, as well as to reveal previously unknown magnetic tornadoes.

How to cite: Vorobev, D., Blumenau, M., Fridman, M., Khabarova, O., and Obridko, V.: Automatic detection of solar magnetic tornadoes based on computer vision methods., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11501, https://doi.org/10.5194/egusphere-egu22-11501, 2022.

09:32–09:39
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EGU22-10105
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ECS
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On-site presentation
Sofija Durward

The solar wind and its variability is well understood at Earth. However, at distances larger than 1AU the is less clear, mostly due to the lack of in-situ measurements. In this study we use transfer learning principles to infer solar wind conditions at Mars in periods where no measurements are available, with the aim of better illuminating the interaction between the partially magnetised Martian plasma environment and the upstream solar wind. Initially, a convolutional neural network (CNN) model for forecasting measurements of the interplanetary magnetic field, solar wind velocity, density and dynamic pressure is trained on terrestrial solar wind data. Afterwards, knowledge from this model is incorporated into a secondary CNN model which is used for predicting solar wind conditions upstream of Mars up to 5 hours in the future. We present the results of this study as well as the opportunities to expand this method for use at other planets.

How to cite: Durward, S.: Forecasting solar wind conditions at Mars using transfer learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10105, https://doi.org/10.5194/egusphere-egu22-10105, 2022.

09:39–09:46
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EGU22-12762
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Presentation form not yet defined
Determination of magnetopause and bow shock crossings at Mercury using neural network modelling of MESSENGER data
(withdrawn)
Alexander Lavrukhin, David Parunakian, Dmitry Nevskiy, Sahib Julka, and Ute Amerstorfer
09:46–09:53
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EGU22-5721
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ECS
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On-site presentation
Ambre Ghisalberti, Nicolas Aunai, and Bayane Michotte de Welle

The magnetopause (MP) and the bow shock (BS) are the two boundaries bounding the magnetosheath, the region between the magnetosphere and the solar wind. Their position and shape depend on the upstream solar wind and interplanetary magnetic field conditions.

Predicting their shape and position is the starting point of many subsequent studies of processes controlling the coupling between the Earth’s magnetosphere and its interplanetary environment. We now have at our disposal an important amount of data from a multitude of spacecraft missions allowing for good spatial coverage, as well as algorithms based on statistical learning to automatically detect the two boundaries. From the data of 9 satellites over 20 years, we identified around 19000 crossings of the BS and 36000 crossings of the MP. They were used, together with their associated upstream conditions, to train a regression model to predict the shape and position of the boundaries. 

Preliminary results indicate that the obtained models outperform analytical models without making simplifying assumptions on the geometry and the dependency over control parameters.

How to cite: Ghisalberti, A., Aunai, N., and Michotte de Welle, B.: Magnetopause and bow shock models with machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5721, https://doi.org/10.5194/egusphere-egu22-5721, 2022.

Coffee break
Chairpersons: Ute Amerstorfer, Hannah Ruedisser
10:20–10:27
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EGU22-9077
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ECS
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On-site presentation
Hannah Ruedisser, Andreas Windisch, Ute V. Amerstorfer, Tanja Amerstorfer, Christian Möstl, Martin A. Reiss, and Rachel L. Bailey

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past,
different machine learning approaches have been used to automatically detect events in existing time series resulting from
solar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facing
the large amount of data from different instruments. We propose a pipeline using a Network similar to the ResUNet++ (Jha et al. (2019)), for the automatic detection of ICMEs. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other datasets.
The method has been tested on in situ data from WIND. Additionally, it produced reasonable results on STEREO A and STEREO B datasets
with less input parameters. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

How to cite: Ruedisser, H., Windisch, A., Amerstorfer, U. V., Amerstorfer, T., Möstl, C., Reiss, M. A., and Bailey, R. L.: Automatic Detection of Interplanetary Coronal Mass Ejections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9077, https://doi.org/10.5194/egusphere-egu22-9077, 2022.

10:27–10:34
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EGU22-9589
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Presentation form not yet defined
Prediction of Plasma Pressure in the Outer Part of the Inner Magnetosphere using Machine Learning
(withdrawn)
Songyan Li, Elena Kronberg, and Christoforos Mouikis
10:34–10:41
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EGU22-1014
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ECS
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On-site presentation
Emmanuel De Leon, Nicolas Gilet, Xavier Vallières, Luca Bucciantini, Pierre Henri, and Jean-Louis Rauch

The Waves of HIgh frequency and Sounder for Probing Electron density by Relaxation
(WHISPER) instrument, is part of the Wave Experiment Consortium (WEC) of the CLUSTER II
mission. The instrument consists of a receiver, a transmitter, and a wave spectrum
analyzer. It delivers active (when in sounding mode) and natural electric field spectra. The
characteristic signature of waves indicates the nature of the ambient plasma regime and, combined
with the spacecraft position, reveals the different magnetosphere boundaries and regions. The
thermal electron density can be deduced from the characteristics of natural waves in natural mode
and from the resonances triggered in sounding mode, giving access to a key parameter of scientific
interest and major driver for the calibration of particles instrument.
Until recently, the electron density derivation required a manual time/frequency domain
initialization of the search algorithms, based upon visual inspection of WHISPER active and natural
spectrograms and other datasets from different instruments onboard CLUSTER.
To automate this process, knowledge of the region (plasma regime) is highly desirable. A Multi-
Layer Perceptron model has been implemented for this purpose. For each detected region, a GRU,
recurrent network model combined with an ad-hoc algorithm is then used to determine the electron
density from WHISPER active spectra. These models have been trained using the electron density
previously derived from various semi-automatic algorithms and manually validated, resulting in an
accuracy up to 98% in some plasma regions. A production pipeline based on these models has been
implemented to routinely derive electron density, reducing human intervention up to 10 times. Work
is currently ongoing to create some models to process natural measurements where the data volume
is much higher and the validation process more complex. These models of electron density
automated determination will be useful for future other space missions.

How to cite: De Leon, E., Gilet, N., Vallières, X., Bucciantini, L., Henri, P., and Rauch, J.-L.: Automatic detection of the electron density from the WHISPER instrument onboard CLUSTER II, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1014, https://doi.org/10.5194/egusphere-egu22-1014, 2022.

10:41–10:48
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EGU22-9621
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On-site presentation
Georgios Balasis, Alexandra Antonopoulou, Constantinos Papadimitriou, Adamantia Zoe Boutsi, Omiros Giannakis, and Ioannis A. Daglis

Machine learning (ML) techniques have been successfully introduced in the fields of Space Physics and Space Weather, yielding highly promising results in modeling and predicting many disparate aspects of the geospace. Magnetospheric ultra-low frequency (ULF) waves play a key role in the dynamics of the near-Earth electromagnetic environment and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence, we are now able to use more robust approaches for automated ULF wave identification and classification. Here, we present results employing various neural networks (NNs) methods (e.g. Fuzzy Artificial Neural Networks, Convolutional Neural Networks) in order to detect ULF waves in the time series of low-Earth orbit (LEO) satellites. The outputs of the methods are compared against other ML classifiers (e.g. k-Nearest Neighbors (kNN), Support Vector Machines (SVM)), showing a clear dominance of the NNs in successfully classifying wave events.

How to cite: Balasis, G., Antonopoulou, A., Papadimitriou, C., Boutsi, A. Z., Giannakis, O., and Daglis, I. A.: Machine Learning Techniques for Automated ULF Wave Recognition in Swarm Time Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9621, https://doi.org/10.5194/egusphere-egu22-9621, 2022.

10:48–10:55
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EGU22-12830
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ECS
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On-site presentation
Matyas Szabo-Roberts, Karolina Kume, Artem Smirnov, Irina Zhelavskaya, and Yuri Shprits

Generating reliable databases of electron density measurements over a wide range of geomagnetic conditions is essential for improving empirical models of electron density. The Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm has been developed for automated extraction of electron density from Van Allen Probes electric field measurements, and has been shown to be in good agreement with existing semi-automated methods and empirical models. The extracted electron density data has since then been used to develop the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model, an empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices.
In this study we re-implement the NURD algorithm in both Python and Matlab, and compare the performance of these implementations to each other and previous NURD results. We take advantage of a labeled training data set now being available for the full duration of the Van Allen Probes mission to train the network and generate an electron density data set for a significantly longer time period. We perform detailed comparisons between this output, electron density produced from Van Allen Probes electric field measurements using the AURA semi-automated algorithm, and electron density obtained from existing empirical models. We also present preliminary results from the PINE plasmasphere model trained on this extended NURD electron density data set.

How to cite: Szabo-Roberts, M., Kume, K., Smirnov, A., Zhelavskaya, I., and Shprits, Y.: Re-implementing and Extending the NURD Algorithm to the Full Duration of the Van Allen Probes Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12830, https://doi.org/10.5194/egusphere-egu22-12830, 2022.

10:55–11:02
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EGU22-12480
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Virtual presentation
Maria Elena Innocenti, Sophia Köhne, Simon Hornisch, Rainer Grauer, Jorge Amaya, Jimmy Raeder, Banafsheh Ferdousi, James "Andy" Edmond, and Giovanni Lapenta

The large amount of data produced by measurements and simulations of space plasmas has made it fertile ground for the application of classification methods, that can support the scientist in preliminary data analysis. Among the different classification methods available, Self Organizing Maps, SOMs [Kohonen, 1982] offer the distinct advantage of producing an ordered, lower-dimensional representation of the input data that preserves their topographical relations. The 2D map obtained after training can then be explored to gather knowledge on the data it represents. The distance between nodes reflects the distance between the input data: one can then further cluster the map nodes to identify large scale regions in the data where plasma properties are expected to be similar.

In this work, we train SOMs using data from different simulations of different aspects of the heliospheric environment: a global magnetospheric simulation done with the OpenGGCM-CTIM-RCM code, a Particle In Cell simulation of plasmoid instability done with the semi-implicit code ECSIM, a fully kinetic simulation of single X point reconnection done with the Vlasov code implemented in MuPhy2.

We examine the SOM feature maps, unified distance matrix and SOM node weights to unlock information on the input data. We then classify the nodes of the different SOMs into a lower and automatically selected number of clusters, and we obtain, in all three cases, clusters that map well to our a priori knowledge on the three systems. Results for the magnetospheric simulations are described in Innocenti et al, 2021. 

This classification strategy then emerges as a useful, relatively cheap and versatile technique for the analysis of simulation, and possibly observational, plasma physics data.

Innocenti, M. E., Amaya, J., Raeder, J., Dupuis, R., Ferdousi, B., & Lapenta, G. (2021). Unsupervised classification of simulated magnetospheric regions. Annales Geophysicae Discussions, 1-28. 

https://angeo.copernicus.org/articles/39/861/2021/angeo-39-861-2021.pdf

How to cite: Innocenti, M. E., Köhne, S., Hornisch, S., Grauer, R., Amaya, J., Raeder, J., Ferdousi, B., Edmond, J. "., and Lapenta, G.: A versatile exploration method for simulated data based on Self Organizing Maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12480, https://doi.org/10.5194/egusphere-egu22-12480, 2022.