ESSI1.3 | Machine Learning in Planetary Sciences and Heliophysics
EDI
Machine Learning in Planetary Sciences and Heliophysics
Co-organized by PS1/ST4
Convener: Ute Amerstorfer | Co-conveners: Hannah Theresa RüdisserECSECS, Sahib JulkaECSECS, Mario D'Amore, Günter Kargl
Orals
| Tue, 25 Apr, 08:30–10:15 (CEST)
 
Room 0.51
Posters on site
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
Hall X4
Posters virtual
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
vHall ESSI/GI/NP
Orals |
Tue, 08:30
Tue, 16:15
Tue, 16:15
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.

Orals: Tue, 25 Apr | Room 0.51

Chairpersons: Hannah Theresa Rüdisser, Ute Amerstorfer
08:30–08:35
08:35–08:45
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EGU23-2756
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ECS
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solicited
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On-site presentation
Robert Jarolim, Benoit Tremblay, Andres Munoz-Jaramillo, Kyriaki-Margarita Bintsi, Anna Jungbluth, Miraflor Santos, James Paul Mason, Sairam Sundaresan, Cooper Downs, Ronald Caplan, and Angelos Vourlidas

To understand the solar evolution and effects of solar eruptive events, the Sun is permanently observed by multiple satellite missions. The optically-thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a three-dimensional, evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer observed in extreme ultraviolet (EUV) light. We use a deep learning approach for 3D scene representation that accounts for radiative transfer, to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles, and directly enables height estimates of coronal structures, solar flux ropes, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry even from a limited number of viewpoints. We quantify uncertainties of our model using an ensemble approach that allows us to estimate the model performance in absence of a ground-truth. Our method enables a novel view of our closest star, and is a breakthrough technology for the efficient use of multi-instrument datasets, which paves the way for future cluster missions.

How to cite: Jarolim, R., Tremblay, B., Munoz-Jaramillo, A., Bintsi, K.-M., Jungbluth, A., Santos, M., Mason, J. P., Sundaresan, S., Downs, C., Caplan, R., and Vourlidas, A.: SuNeRF: AI enables 3D reconstruction of the solar EUV corona, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2756, https://doi.org/10.5194/egusphere-egu23-2756, 2023.

08:45–08:55
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EGU23-10705
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On-site presentation
Oleg Stepanyuk and Kamen Kozarev

Solar eruptive events are complex phenomena, which most often include solar flares, filament eruptions, coronal mass ejections (CMEs), and CME-driven shock waves. CME-driven shocks in the corona and interplanetary space are considered to be the main producer of solar energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced. Current understanding points to CME-driven shocks and compressions in the solar corona.

A CME kinematics shows three phases - an initial rising phase (weakly accelerated motion), an impulsive phase and a residual propagation phase with constant or decreasing speed.

Despite significant amount of data available from ground-based (COSMO K-Cor, LOFAR) and remote instruments onboard of heliospheric space missions (SDO AIA, SOHO), processing of the data still requires noticeable effort. Most algorithms currently used in solar feature detection and tracking are known for their limited applicability and complexity of their processing chains, while usage of data-driven approaches for tracking of CME-related phenomena is currently limited due to insufficiency of training sets.

Recently (Stepanyuk et.al, J. Space Weather Space Clim. Vol 12, 20(2022)), we have demonstrated the method and the software(https://gitlab.com/iahelio/mosaiics/wavetrack) for smart characterization and tracking of solar eruptive features based on the a-trous wavelet decomposition technique, intensity rankings and a set of filtering techniques. In this work we use Wavetrack to generate training sets for data-driven feature extraction and characterization. We utilize U-Net, a fully convolutional network which training strategy relies on the strong use of data augmentation to use the available annotated samples more efficiently. U-NET can be trained end-to-end from a very limited set of images, while feature engineering allows to improve this approach even further by expanding available training sets.

Here we present pre-trained models and demonstrate data-driven characterization and tracking of solar eruptive features on a set of CME-events.

How to cite: Stepanyuk, O. and Kozarev, K.: Advanced Multi-Instrument and Multi-Wavelength Image Processing and Feature Tracking for Remote CME Characterization with Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10705, https://doi.org/10.5194/egusphere-egu23-10705, 2023.

08:55–09:05
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EGU23-10654
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ECS
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On-site presentation
Yi Yang, Fang Shen, Yucong Li, and Rongpei Lin

Coronal mass ejections (CMEs) are one of the most violent solar eruptions, which can burst out large amounts of magnetized plasma with speeds up to thousands of kilometers per second. When it reaches the Earth, a CME can cause geomagnetic storm, affecting aviation safety, satellite operations, communications systems and power facilities. Therefore, fast and accurate prediction of CME arrival time is crucial for avoiding severe damaging effects and reducing economic losses. The initial morphology and kinematics of a CME in the corona can be observed by the coronagraphs equipped on the Solar and Heliospheric Observatory (SOHO), so that the coronagraphs should be useful to predict the CME arrival times. In this study, convolutional neural network (CNN) is used to obtain the features of SOHO/LASCO coronagraph pictures related to the CME transit time, and establish a model capable of predicting the CME arrival time. The influence of different hyperparameters of CNN on the prediction results is studied. Further, we add a physical information constraint of the initial velocities of CME to the basic CNN outputs, and found that smaller prediction errors can be obtained. 

How to cite: Yang, Y., Shen, F., Li, Y., and Lin, R.: Predicting the 1 AU Arrival Time of Coronal Mass Ejections Based on Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10654, https://doi.org/10.5194/egusphere-egu23-10654, 2023.

09:05–09:15
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EGU23-7941
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ECS
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On-site presentation
Federico Sabbatini and Catia Grimani

Machine learning models trained to reproduce space mission observations are precious resources to fill gaps of missing data in measurement time series or to perform data forecasting within a reasonable uncertainty degree. The latter option is of particular importance for future space missions that will not host instrumentation dedicated to interplanetary medium parameter monitoring. The future LISA mission for low-frequency gravitational wave detection, for instance, will benefit of particle detectors to measure the galactic cosmic-ray integral flux variations and magnetometers that will allow to monitor the passage of large scale magnetic structures through the three LISA spacecraft as part of a diagnostics subsystem. Unfortunately, no instruments dedicated to solar wind speed measurements will be present on board the spacecraft constellation. Moreover, LISA, scheduled to launch in 2035, will trail Earth on the ecliptic at 50 million km distance, far from the orbits of other space missions dedicated to the interplanetary medium monitoring.

Based on precious lessons learned with LISA Pathfinder, the ESA LISA precursor mission, about the correlation between galactic cosmic-ray flux short-term variations and solar wind speed increases, we built a machine learning ensemble model able to reconstruct the solar wind trend only on the basis of contemporaneous and preceding observations of galactic cosmic-ray flux variations. Details about the model creation and performance will be presented, together with a description of the underlying data set, weak predictors and training phase. Advantages and limitations will be discussed, showing that the model performance may be enhanced by providing interplanetary magnetic field intensity observations as additional input data, with the goal of providing the LISA mission with an effective solar wind speed predictive tool.

How to cite: Sabbatini, F. and Grimani, C.: Machine learning ensemble models for solar wind speed prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7941, https://doi.org/10.5194/egusphere-egu23-7941, 2023.

09:15–09:25
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EGU23-2897
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On-site presentation
Jeremiah Johnson, Dogacan Ozturk, Hyunju Connor, Donald Hampton, Matthew Blandin, and Amy Keesee

Dynamic interactions between the solar wind and the magnetosphere give rise to dramatic auroral forms that have been instrumental in the ground-based study of magnetospheric dynamics. The general mechanism of aurora types and their large-scale patterns are well-known, but the morphology of small- to meso-scale auroral forms observed in all-sky imagers and their relation to magnetospheric dynamics  and the coupling of the magnetosphere to the upper atmosphere remain in question. Machine learning has the potential to provide answers to these questions, but most existing auroral image data lack the ground-truth labels required for supervised learning and conventional statistical analyses. To mitigate this issue, we propose a novel self-supervised semi-supervised algorithm to automatically label the THEMIS all-sky image database. Specifically, we adapt the self-supervised Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all-sky images. These representations are finetuned using a small set of manually labeled data from the Oslo Aurora THEMIS (OATH) dataset, after which semi-supervised classification is used to train a classifier, beginning by training on the manually labeled OATH dataset and gradually incorporating the classifier’s most confident predictions on unlabeled data into the training dataset as ground-truth. We demonstrate that (a) classifiers fit to the learned representations of the manually labeled images achieve state–of–the–art performance, improving the classification accuracy by almost 10% over the current benchmark on labeled data; and (b) our model’s learned representations naturally cluster into more clusters than manually assigned categories, suggesting that existing categorizations are coarse and may obscure important connections between auroral types and their drivers. Finally, we introduce AuroraClick, a citizen science project with the goal of manually annotating a large representative sample of THEMIS all-sky images for the validation of our current models and the training of future models.  

How to cite: Johnson, J., Ozturk, D., Connor, H., Hampton, D., Blandin, M., and Keesee, A.: Automatic Classification of THEMIS All-Sky Images via Self-Supervised Semi-Supervised Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2897, https://doi.org/10.5194/egusphere-egu23-2897, 2023.

09:25–09:35
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EGU23-8430
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On-site presentation
Longwei Chen, Phil Livermore, Leyuan Wu, Sjoerd de Ridder, and Chong Zhang

As is known, neural networks can universally approximate any complex functions. This ground truth naturally makes it a suitable candidate for solution representation of complex partial differential equation (PDE) governed. For planetary magnetic field modelling problem, spherical harmonic functions are most used as standard modelling method. Spherical harmonic method requires globally nearly uniformly distributed observations. Meanwhile this method has quite limited ability for conducting regional field modelling. Instead, neural networks have great potential to deal with global or regional modelling problems. In this work, we thoroughly investigate the representative ability of neural networks for magnetic field modelling problem at global and regional scale, and concentrate on a specific neural network, that is physics-informed neural networks (PINNs) for implementation. PINNs makes it easier to incorporate different kinds of informed physics within a uniform optimization framework. Through synthetic model tests and partial mathematical proof, we showcase the importance of employing natural boundary condition, Laplace equation constraint and Poisson equation constraint at suitable collocation points for a reasonable and accurate magnetic field representation and introduce the detailed scheme for implementation. Finally, we use newly released Juno mission measurements, and present a global PINNs model for Jupiter's magnetic field, and a regional PINNs model for Great Blue Spot (GBS) region. Comparison with spherical harmonic model has been conducted to evaluate the correctness and flexibility of PINNs models.

How to cite: Chen, L., Livermore, P., Wu, L., de Ridder, S., and Zhang, C.: Modelling Jupiter's global and regional magnetic fields using physics-informed neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8430, https://doi.org/10.5194/egusphere-egu23-8430, 2023.

09:35–09:45
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EGU23-15160
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ECS
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On-site presentation
Lukas Maes, Markus Fraenz, and Daniel Heyner

The BepiColombo mission will arrive at Mercury in 2025. It consists of two spacecraft, which both have a magnetometer on board. One of the science objectives of these instruments is to study the structure of Mercury’s magnetosphere and its dynamical interaction with the solar wind. To study this statistically, a large dataset of observations of the magnetopause (the magnetosphere’s outer boundary) is needed. However, identifying such magnetopause crossings in magnetic field data requires visual inspection by humans with expert knowledge and as such is a very time consuming process. We therefore design an algorithm to automatically detect the Hermean magnetopause in magnetometer time series data, making use of a convolutional neural network.

Since no BepiColombo data (in orbit) is available yet, we train the network on MESSENGER magnetometer data. However, we formulate the problem and design the architecture of the network in such a way that the algorithm should be easily transferable to BepiColombo magnetometer data, avoiding the possible impact of any instrumental particularities or orbital biases.

The goal is to have a neural network which is directly applicable to BepiColombo magnetometer data, as soon as the observations start and without any further training, thereby eliminating the necessity of manually creating a new dataset of BepiColombo magnetopause crossings.

How to cite: Maes, L., Fraenz, M., and Heyner, D.: Detecting the magnetopause of Mercury by neural network — using MESSENGER data to train for BepiColombo., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15160, https://doi.org/10.5194/egusphere-egu23-15160, 2023.

09:45–09:55
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EGU23-14927
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ECS
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On-site presentation
|
Daniel Le Corre, Nigel Mason, Jeronimo Bernard-Salas, Nick Cox, and David Mary

Pits, or pit craters, are roughly circular depressions found in planetary surfaces which are generally formed through gravitational collapse. Pits will be primary targets for future space exploration and habitability for their presence on most rocky Solar System surfaces and their potential to be entrances to sub-surface cavities. This is particularly true on the Moon and Mars where future astronauts will also be exposed to high radiation dosages whilst on the surface. However, since pits are rarely found to have corresponding high-resolution elevation data, tools are required for approximating their depths in order to find those which are the ideal candidates for exploration and habitation.

We develop a tool that automatically calculates a pit’s apparent depth – the depth at the edge of its shadow - by measuring the shadow’s width as it appears in satellite imagery. The tool can produce a profile of the apparent depth along the entire length of the shadow, using just one cropped single- or multi-band image of a pit. Thus, allowing for the search for possible cave entrances to continue where altimetry or stereo image data is not available. Shadows are automatically extracted using k-means clustering with silhouette analysis for automatic cluster validation. We will present the results of testing the shadow extraction upon shadow-labelled Mars Reconnaissance Orbiter HiRISE imagery of Martian pits, as well as the findings of applying the tool to HiRISE images of Atypical Pit Craters (APCs) from the Mars Global Cave Candidate Catalog (MGC3) [1]. We will also present preliminary results of applying our tool to Lunar Reconnaissance Orbiter Narrow Angle Camera data taken of Lunar pits catalogued in the Lunar Pit Atlas [2].

[1] – Cushing et al. (2015). Atypical pit craters on Mars: New insights from THEMIS, CTX, and HiRISE observations, Journal of Geophysical Research: Planets, 120, 1023–1043

[2] – Wagner & Robinson (2021). Occurrence and Origin of Lunar Pits: Observations from a New Catalog, in 52nd Lunar and Planetary Science Conference, Lunar and Planetary Science Conference, p. 2530

How to cite: Le Corre, D., Mason, N., Bernard-Salas, J., Cox, N., and Mary, D.: Automatically Calculating Depths of Martian and Lunar Pits with Satellite Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14927, https://doi.org/10.5194/egusphere-egu23-14927, 2023.

09:55–10:05
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EGU23-16941
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ECS
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On-site presentation
Okta Bramantio Swida, Bernard Foing, and Constantijn Vleugels

Aiming to unravel the astrobiology of Mars, the Perseverance mission came with a lot of unknowns. With the surface level knowledge that we have already known, The High Resolution Imaging Science Experiment (HiRISE) can already determine the observation or experimental sites through the images generated from the orbiter. Although the resolution is high, with the power of a 1-meter-size object determinator, we can always expect so much more from the ground-level observation.

 

The Mars Perseverance rover is equipped with a pair of Mastcam-Z set cameras that are equipped in a manner to simulate the human eye for depth determination in image processing. The instruments can process stereo colour images of the ground level. These images can be used to make detailed maps of the Mars surface scenery at ground level with high precision.

 

Building and analyzing these images can take days to process on Earth manually. But if we utilise machine learning tools and onsite computation, it might save a lot of time for the mission. The current model used in the Mars Perseverance is the AutoNav Mark 4 with a lot of tasks, including spacecraft positioning, in-flight orbit determination, target tracking, and ephemeris calculations. All those might be computationally expensive to process. Therefore, the aim of this research is to develop a simple algorithm to do object and slope determinations to feed into an autonomous path determination process. The data fed into the algorithm are panoramic images captured by the MastCam-Z mounted on Mars Perseverance.

How to cite: Swida, O. B., Foing, B., and Vleugels, C.: Mars Perseverance Panoramic Image for Self-Determination Mission Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16941, https://doi.org/10.5194/egusphere-egu23-16941, 2023.

10:05–10:15
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EGU23-11898
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ECS
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On-site presentation
Salome Gruchola, Marek Tulej, Peter Keresztes Schmidt, Rustam Lukmanov, Andreas Riedo, and Peter Wurz

We present the analysis of a 2.06 Ga apatite crystal obtained from an ultramafic phoscorite rock from the Phalaborwa Complex (Limpopo Province, South Africa) [1]. A space-prototype laser ablation ionisation mass spectrometer (LIMS) [2,3] was used to study the chemical composition of the sample. Mass spectra were recorded from a sample area of 0.6x0.6 mm2, with a spatial resolution of 30 μm and sub-micrometre depth resolution.

Apatite is a calcium phosphate mineral expressed by the chemical stoichiometric formula [Ca5(PO4)3(F, Cl, OH)]. The halogen site, occupied by F, Cl, and OH, corresponds to an isomorpous series with fluor-, chlor- and hydroxyl-apatite end members, respectively. Apatite, being an accessory mineral in igneous and other rocks, commonly contains a range of other elements that do not fit well into the major rock forming minerals, such as rare earth elements (REE). These are suitable targets for investigating physical and chemical conditions in igneous rocks and the volatile evolution of magmas.

The analysis of the spectra recorded with our LIMS system for the abundances of the elements of interest at each location were performed in two steps. First, the abundances of each element across the sampled area were compiled in element maps. And second, an unsupervised machine learning algorithm based on clustering and network analysis was applied to the data set of analysed mass spectra to separate it into groups of distinct chemical composition. Subsequently, a more detailed analysis was conducted on each of the recovered groups to assign the corresponding mineral. In addition to the group of spectra belonging to apatite, which was assigned to fluorapatite, other minerals were identified, amongst others olivine. This method yields an unsupervised approach to identify different mineralogical entities present within a sample. This network analysis method was previously applied to a 1.88 Ga Gunflint sample (Ontario, Canada) to separate spectra recorded from the host (chert) from spectra containing signatures of organic matter from fossilized microbes [4].

Given that the data were recorded using a miniature mass spectrometer designed for space flight, this analysis demonstrates the analytical capabilities of our LIMS system that could be achieved in-situ on other planetary bodies in our Solar System, for example on the Moon or on Mars. The current performance of this miniature LIMS instrument to study the chemical composition of apatite is sufficiently high to measure volatiles (H, F, Cl) and nearly all relevant mineral and partially trace elements (Na, C, Mg, Si, S, K, Mn, Fe, Sr, Ba), including REE (La, Ce, Pr, Sm) which allows for a systematic quantitative analysis of their distribution.

[1] Tulej, M. et al., 2022, https://doi.org/10.3390/universe8080410.

[2] Riedo, A. et al., 2012, https://doi.org/10.1002/jms.3104.

[3] Tulej, M. et al., 2021, https://doi.org/10.3390/app11062562.

[4] Lukmanov, R.A. et al., 2022, https://doi.org/10.3389/frspt.2022.718943

How to cite: Gruchola, S., Tulej, M., Keresztes Schmidt, P., Lukmanov, R., Riedo, A., and Wurz, P.: Composition Analysis of an Apatite Crystal using a Space-Prototype Mass Spectrometric Instrument and Machine Learning for Unsupervised Mineralogical Phase Detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11898, https://doi.org/10.5194/egusphere-egu23-11898, 2023.

Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall X4

Chairpersons: Hannah Theresa Rüdisser, Ute Amerstorfer
X4.200
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EGU23-7529
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ECS
Florian Auer-Welsbach, Andreas Windisch, and Giacomo Nodjoumi

The detection and classification of landforms on planetary surfaces is a time-consuming task which deeply relies on expert knowledge. Such a process can be partially automated and optimized in a resource-efficient way using image processing algorithms. By classifying the surface into different landforms, such as volcanic craters, asteroid impacts, dunes, and more, several analyses can be performed, for instance the widely used crater counting age estimation method. In addition, by conducting these analyses, information about the characteristics and properties of a planet can be revealed. One of the major challenges for the implementation of these algorithms is to provide a generalized model. In many cases the generalization error tends to be very large and therefore a satisfactory accuracy on the test data set cannot be accomplished. This prevents reliable evaluation of new unseen data. In this work, a multi-class image segmentation algorithm is presented, which is based on a U-net convolutional neural network architecture. U-nets classify each pixel of a given input image and can thus produce segmentation masks for various landforms. Given that enough labeled data is available, such a classifier can replace manual detection and classification, thereby saving resources by providing a fast method for landform detection.

How to cite: Auer-Welsbach, F., Windisch, A., and Nodjoumi, G.: Landform detection on Mars using image segmentation with a u-net convolutional neural network architecture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7529, https://doi.org/10.5194/egusphere-egu23-7529, 2023.

X4.201
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EGU23-11228
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ECS
Semantic segmentation of dust storms from Mars images using convolutional neural network architectures
(withdrawn)
Peyman Nazifi, Andreas Windisch, and Giacomo Nodjoumi
X4.202
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EGU23-17564
An Investigation of Meteor Properties using Machine Learning and Deep Learning
(withdrawn)
Andreas Windisch, Lucas Mandl, and Apostolos Christou
X4.203
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EGU23-542
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ECS
Florine Enengl, Sigvald Marholm, Sayan Adhikari, Richard Marchand, and Wojciech J. Miloch

In this work, we show the first achievement of inferring the electron temperature in ionospheric conditions from synthetic data using fixed-bias Langmuir probes operating in the electron saturation region. This was done using machine learning, as well as by altering the probe geometry. The electron temperature is inferred at the same rate as the currents are sampled by the probes. For inferring the electron temperature along with the electron density and the floating potential, a minimum number of three probes is required. Furthermore does one probe geometry need to be distinct from the other two, since otherwise the probe setup may be insensitive to temperature. This can be achieved by having either one shorter probe or a probe of a different geometry, e.g. two longer and a shorter cylindrical probe or two cylindrical probes and a spherical probe. We use synthetic plasma parameter data and calculate the synthetic collected probe currents to train a neural network (using TensorFlow) and verify the results with a test set as well as with data from the International Reference Ionosphere (IRI) model. A table with computed currents collected by a spherical probe by Laframboise was extended to calculate currents of the synthetic plasma parameters for high eta values (eta >25) to cover a large altitude range (100-500 km, within Earth's ionosphere). The extrapolated values were benchmarked with Particle-in-Cell simulations. Finally, we evaluate the robustness and errors of different probe setups that can be used to infer the electron temperature. As the inferred temperatures are compared to results from the International Reference Ionosphere model, we verify the validity of the inferred temperature in altitudes ranging from about 100-500 km. We show that electron temperature inference from different combinations of spherical and cylindrical probes - three cylindrical probes, three spherical probes, four cylindrical and a spherical probe - can be achieved. Even minor changes in the probe sizing enable the temperature inference and result in root mean square relative errors (RMSRE) between inferred and ground truth data of under 3%. With further optimizations, the RMSRE can even be decreased to under 1%. When limiting the temperature inference to 120-450 km altitude an RMSRE of under 0.7% is achieved for all probe setups. In future, the multi-needle Langmuir Probe (m-NLP) instrument dimensions can be adapted for higher temperature inference accuracy.

How to cite: Enengl, F., Marholm, S., Adhikari, S., Marchand, R., and Miloch, W. J.: Electron Temperature Inference from Fixed Bias Langmuir Probes Set-Ups in Ionospheric Conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-542, https://doi.org/10.5194/egusphere-egu23-542, 2023.

X4.204
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EGU23-5254
Oliver Stenzel, Lukas Maes, and Martin Hilchenbach

Laser altimeters create large amounts of data that often have to be preprocessed and checked before further use. The BepiColombo mission to Mercury is set to arrive in December 2025 and observations with the BepiColombo Laser Altimeter (BELA, (Benkhoff et al., 2010; Thomas et al., 2021)) will start during the following spring. These measurements are planned to be used to derive information about the tides of Mercury (Thor et al., 2020). Careful assessment, selection, and filtering on the raw data is needed to extract the small tidal signal. Until the BELA data becomes available artificial data and records from other missions have to be used to study the data selection strategy. We present our work on MESSENGER Laser Altimeter (MLA, (Cavanaugh et al., 2007)) using a convolutional neural network to sort observations on an orbit by orbit basis into different classes. The already existing neural network (Stenzel and Hilchenbach, 2021; Stenzel, Thor and Hilchenbach, 2021) is tuned and a new test data set is created.

 

Benkhoff, J. et al. (2010) ‘BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals’, Planetary and Space Science, 58(1), pp. 2–20. Available at: https://doi.org/10.1016/j.pss.2009.09.020.

Cavanaugh, J.F. et al. (2007) ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’, Space Science Reviews, 131(1), pp. 451–479. Available at: https://doi.org/10.1007/s11214-007-9273-4.

Stenzel, O. and Hilchenbach, M. (2021) ‘Towards machine learning assisted error identification in orbital laser altimetry for tides derivation’, pp. EPSC2021-688. Available at: https://doi.org/10.5194/espc2021-688.

Stenzel, O., Thor, R. and Hilchenbach, M. (2021) ‘Error identification in orbital laser altimeter data by machine learning’, pp. EGU21-14749. Available at: https://doi.org/10.5194/egusphere-egu21-14749.

Thomas, N. et al. (2021) ‘The BepiColombo Laser Altimeter’, Space Science Reviews, 217(1), p. 25. Available at: https://doi.org/10.1007/s11214-021-00794-y.

Thor, R.N. et al. (2020) ‘Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter’, Astronomy & Astrophysics, 633, p. A85. Available at: https://doi.org/10.1051/0004-6361/201936517.

 

How to cite: Stenzel, O., Maes, L., and Hilchenbach, M.: AI Assisted Data Selection of Laser Altimeter Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5254, https://doi.org/10.5194/egusphere-egu23-5254, 2023.

X4.205
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EGU23-6968
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ECS
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Daniel Collin, Stefano Bianco, Guillermo Gallego, and Yuri Shprits

One of the main sources of solar wind disturbances are coronal holes which can be identified in extreme ultra-violet (EUV) images of the Sun. Previous research has shown the connection between coronal holes and an increase of the solar wind speed at Earth. The time lag between the appearance of coronal holes on the visible side of the Sun and its effects on Earth is 2-5 days. In this study, a machine learning model predicting the solar wind speed originating from coronal holes is proposed. It is based on the analysis of solar EUV images. A segmentation algorithm is applied to the images in order to identify coronal holes and derive their characteristics (e.g. area, location). We also present a new method to calculate the geoeffective coronal hole area: Instead of specifying in advance a sector of the solar surface in which the area is measured and a lag time between area measurement and the arrival of the solar wind, the specification of this sector and the corresponding delay are formulated as a mathematical optimization problem and included in the machine learning model. This approach facilitates an improvement of the prediction accuracy and also prolongs the prediction horizon, as the solar wind speed can be predicted up to approximately 5 days in advance of the disturbance. Several machine learning model architectures are explored. We also study how the time evolution can be included in the model.

How to cite: Collin, D., Bianco, S., Gallego, G., and Shprits, Y.: Forecasting solar wind speed by machine learning based on coronal hole characteristics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6968, https://doi.org/10.5194/egusphere-egu23-6968, 2023.

X4.206
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EGU23-8946
Joey Mukherjee

To monitor the results of our instrument on a daily basis, we create a series of daily plots that are generated in an automated fashion.  In our case, we are creating two plot types for four spacecraft for five different species.  Unfortunately, due to circumstances beyond our control (primarily network and system issues), plots were failing and if not monitored daily, they were unavailable when finally needed.  

To solve this problem, we investigated using Computer Vision (OpenCV) to validate our generation of daily plots.  It was surprisingly easy and more advantageous than trying to either monitor it daily or more simplistic methods.  By using the cloud, we were able to improve throughput as well.  Future work would be to use Computer Vision to analyze the data within the plots for actual scientific study.

How to cite: Mukherjee, J.: Using Artificial Intelligence/Computer Vision for Automated Plot Validation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8946, https://doi.org/10.5194/egusphere-egu23-8946, 2023.

X4.207
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EGU23-850
Emre Isik, Nurdan Karapinar, and Selim Göktug Cankurtaran

Active-region emergence on the Sun shows a degree of clumpiness in both space and time. At a given time, multiple active regions can be seen in what is called active-region- or sunspot-group nests. This tendency also increases the potential to produce large flares and associated CMEs. In the literature, the nesting tendency of active regions is reported in the range of 30-50 per cent, but no statistically robust and ML-based approaches exist so far. Quantifying the nesting degree along an activity cycle and determining its spatial and temporal scales are important to investigate the processes that cause this phenomenon. 

In this study, we estimate the latitudinal and longitudinal extents of active region nesting using both continuum and magnetogram data, using SDO/HMI synoptic magnetograms and Kislovodsk Mountain Astronomical Station (KMAS) sunspot group data. We carry out kernel density estimation (Fig. 1) and unsupervised ML techniques (e.g., DBSCAN and Gaussian mixtures) in spatial and spatio-temporal domains. Our study reveals trends in the emergence characteristics of sunspot groups on the Sun.


Figure 1: Kernel density estimation with a Gaussian kernel on the time-longitude plane. The dot size indicates sunspot group areas in MSH. 

How to cite: Isik, E., Karapinar, N., and Cankurtaran, S. G.: Unsupervised learning of active-region nesting on the Sun, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-850, https://doi.org/10.5194/egusphere-egu23-850, 2023.

X4.208
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EGU23-2719
Anna Morozova, Ricardo Gafeira, Teresa Barata, and Tatiana Barlyaeva

A PCA-NN model for the total electron content (TEC) for the midlatitudinal region (Iberian Peninsula) presented here uses the principal component analysis (PCA) to decompose TEC variations into different modes and to reconstruct/forecast amplitudes of these modes using neural networks (NN) with different sets of space weather parameters as predictors.

Feedforward, convolutional and recurrent NN algorithms are tested with different sets of predictors. The performance of the models is tested on 3.5 years of observational data obtained at the declined phase of the 24th solar cycle, which allows us to estimate the models’ performance in relation to the solar activity level. The advantages and disadvantages of different NN algorithms are discussed.

How to cite: Morozova, A., Gafeira, R., Barata, T., and Barlyaeva, T.: Different types of PCA-NN model for TEC with space weather parameters as predictors: advantages and disadvantages of different NN algorithms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2719, https://doi.org/10.5194/egusphere-egu23-2719, 2023.

X4.209
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EGU23-3379
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ECS
Samira Tasnim, Ying Zou, Claudia Borries, Carsten Baumann, Brian Walsh, Krishna Khanal, Connor O'Brien, and Huaming Zhang

Having precise knowledge of the near-Earth solar wind (SW) and the embedded interplanetary magnetic field (IMF) is of critical importance to space weather operation due to the usage of SW and IMF in almost all magnetospheric and ionospheric models. The most widely used data source, OMNI, propagates SW properties from Lagrangian point L1 to the Earth’s bow shock by estimating the propagation time of the SW. However, the time difference between OMNI timeshifted IMF and the best match-up of IMF can reach ˜15 min. Firstly, we aim to develop an improved statistical algorithm to contribute to the SW propagation delay problem of space weather prediction. The algorithm focuses on matching SW features around the L1 point and upstream of the bow shock by computing the variance, cross-correlation coefficient, the plateau-shaped magnitude index, and the non-dimensional measure of average error index between the measurements at the two locations. The obtained propagation times are then compared to OMNI. Factors that limit the OMNI accuracy are also examined. Secondly, the automatic algorithm allows us to generate large sets of input and target variables using multiple spacecraft pairs at L1 and near-Earth locations to train, validate, and test machine learning models to specify and forecast near-Earth SW conditions. Finally, we offer a machine learning (ML) approach to specify and predict the propagation time from L1 monitors to a given location upstream or at the bow shock and forecast near-Earth SW conditions with the gradient boosting and random forest prediction models in the form of an ensemble of decision trees.

How to cite: Tasnim, S., Zou, Y., Borries, C., Baumann, C., Walsh, B., Khanal, K., O'Brien, C., and Zhang, H.: Estimation and Prediction of Solar Wind Propagation from L1 Point to Earth’s Bow Shock, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3379, https://doi.org/10.5194/egusphere-egu23-3379, 2023.

X4.210
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EGU23-4069
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ECS
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Feng Xuedong and Yang Jian

Abstract: Plasma-sheet bubbles play a major role in the process of magnetotail particle injections. They are defined as fast flows with reduced plasma density or pressure accompanied by magnetic field dipolarization. Typically, we can detect these bubbles from in-situ observations, but subjective uncertainty needs human verification. In this study, we combine three different methods including MINImally RandOm Convolutional KErnel Transform (MINIROCKET), 1D and 2D convolution neural network (CNN) to identify bubbles. The imbalanced training dataset consists of bubble and non-bubble events with a ratio of 1:40 from year 2007 to 2020. The results indicate that the accuracy of the all three models is around 99%, and the precision and recall rates of all three models are above 80% in both the validation and test datasets. The three methods are combined with the intersection set as the minimum set of predictions and the union set as the maximum set. The methods greatly reduce the number of false positives. To identify bubbles in the observations of year 2021, our neural network model is found to be comparably good to the traditional criterial and manual inspections. Using joint machine learning forecasting methods, we can easily and automatically identify bubbles without a priori knowledge like a domain expert.

Keywords: plasma-sheet bubble, multivariate time series classification, sample imbalanced, image identification

How to cite: Xuedong, F. and Jian, Y.: Plasma-Sheet Bubble Identification Using Muitivariate Time Series Classification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4069, https://doi.org/10.5194/egusphere-egu23-4069, 2023.

X4.211
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EGU23-8676
Victor Bacu

To accurately predict potential future impacts with the Earth, it is crucial to continuously examine the area around it for Near Earth Objects (NEOs) and particularly Near Earth Asteroids (NEAs). Large data sets of astronomical images must be analyzed in order to accomplish this task. NEARBY [1] offers such a processing and analysis platform based on Cloud computing. Despite the fact that this method is automated, the results are validated by human observers after potential asteroids have been identified from the raw data. It is crucial that the amount of candidate objects does not outweigh the available human resources. We believe we can maximize the advantages of having access to enormous amounts of data in the field of astronomy by combining artificial intelligence with the use of high-performance distributed processing infrastructures like Cloud-based solutions. This research is carried out as part of the CERES project which aims to design and put into practice a software solution that can classify objects found in astronomical images. The objective is to identify and recognize asteroids. We use machine learning techniques to develop an asteroid classification model in order to achieve this goal. It is essential to reduce the number of false negative findings. The major objective of the current paper is to assess how well deep CNNs perform when it comes to categorizing astronomical objects, particularly asteroids. We will compare the outcomes of several of the most well-known deep convolutional neural networks (CNNs), including InceptionV3, Xception, InceptionResNetV2, and ResNet152V2. These cutting-edge classification CNNs are used to investigate the best approach to this specific classification challenge, either through full-training or through fine-tuning.

Acknowledgment: This work was partially supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-0796, within PNCDI III. This research was partially supported by the project 38 PFE in the frame of the programme PDI-PFE-CDI 2021.

References:

1. Bacu, V., Sabou, A., Stefanut, T., Gorgan, D., Vaduvescu, O., NEARBY platform for detecting asteroids in astronomical images using cloud-based containerized applications, 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 371-376

How to cite: Bacu, V.: Software solution for detecting asteroids using machine learning techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8676, https://doi.org/10.5194/egusphere-egu23-8676, 2023.

X4.212
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EGU23-12354
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ECS
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Shuyi Meng, Shuo Yao, and Zexin Cheng

The origin of cold materials identified by different criteria is unclear. They are highly suspected to be the erupted prominence. However, some cold materials defined by charge depletion exist in both solar wind and ICMEs. Recently, solar observations show failed prominence eruption in CMEs that it did not propagate into the interplanetary space. Besides, the related prominence eruptions of the earth-directed ICMEs at 1 au are difficult to identify before the launch of STEREO mission. This work uses Random Forest (RF) that is an interpretable classifier of supervised machine learning to study the distinct signatures of prominence cold materials (PCs) compared to quiet solar wind (SW) and ICMEs. 12 parameters measured by ACE at 1 au are used in this study, which are proton moments, magnetic field component Bz, He/H, He/O, Fe/O, mean charge of oxygen and carbon, C6+/C5, C6+/C4+, and O7+/O6+. According to the returned weights from RF classifier and the training accuracy from one black box classifier, the most important in situ signatures of PCs are obtained. Next, the trained RF classifier is used to check the category of the origin-unknown cold materials in ICMEs. The results show that most of the cold materials are from prominence, but 2 of them are possibly from quiet solar wind. The most distinct signatures of PCs are lower charges of C and O, proton temperature, and He/O. This work provides quantitative evidence for the charges of C and O being most effective solid criteria. Considering the obvious overlaps on key parameters between SW, ICMEs, and PCs, multi-parameter classifier of machine learning show an advantage in separating them than solid criteria.

How to cite: Meng, S., Yao, S., and Cheng, Z.: Key Signatures of Prominence Materials and Category of Unknown-origin Cold Materials identified by Machine Learning Classifier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12354, https://doi.org/10.5194/egusphere-egu23-12354, 2023.

X4.213
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EGU23-13085
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ECS
Different ways of modeling relativistic electron flux in the outer radiation belt using neural networks
(withdrawn)
Maximilian Pfitzer, Yuri Shprits, and Artem Smirnow

Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall ESSI/GI/NP

Chairpersons: Ute Amerstorfer, Hannah Theresa Rüdisser
vEGN.9
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EGU23-7761
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ECS
Hind AlRiyami, Claus Gebhardt, and Christopher Lee

Deep-learning methods are of interest for the analysis of imagery and digital elevation models from Mars orbiting satellites. They detect various atmosphere and surface characteristics. For instance, these include dust storms and craters [1,2]. We approach this topic by using the deep-learning-based crater detection algorithm DeepMars2 [3,4]. The algorithm is applied to two digital elevation models (DEMs) of the Mars surface. The DEMs are based on the satellite instruments MOLA/MGS (Mars Orbiter Laser Altimeter/Mars Global Surveyor) and HRSC/MEX (High Resolution Stereo Camera/Mars Express) and have different resolution. Crater detection statistics are compared between both DEMs.

[1] Alshehhi, R., Gebhardt, C. Detection of Martian dust storms using mask regional convolutional neural networks. Prog Earth Planet Sci 9, 4 (2022). https://doi.org/10.1186/s40645-021-00464-1

[2] R. Alshehhi and C. Gebhardt, "Automated Geological Landmarks Detection on Mars Using Deep Domain Adaptation From Lunar High-Resolution Satellite Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2274-2283, 2022, doi: 10.1109/JSTARS.2022.3156371.

[3] Lee, C. (2019). Automated crater detection on Mars using deep learning. Planetary and Space Science, 170, 16-28. https://doi.org/10.1016/j.pss.2019.03.008

[4] Lee, C. & Hogan, J. (2021). Automated crater detection with human level performance. Computers & Geosciences, 147, 104645. https://doi.org/10.1016/j.cageo.2020.104645

How to cite: AlRiyami, H., Gebhardt, C., and Lee, C.: Comparison study on the deep-learning-based detection of Mars craters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7761, https://doi.org/10.5194/egusphere-egu23-7761, 2023.