Detection of Martian dust storms using mask regional convolutional neural networks

Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with $$50\%$$ 50 % overlap ($$mAP_{50}$$ m A P 50 ), which is around $$62.1\%$$ 62.1 % .

Development, Implementation and Testing of Algorithm to Detect Ionopause-Like Structure Using Parallel Processing in the Martian Atmosphere

This study assesses the Martian ionopause using MAVEN datasets between periapsis and 150-600 km. Ionopause is an abrupt reduction of the electron density with increasing altitude. It is also required to verify the simultaneous increase of the electron temperature and variability below 400 km. To address this issue, we have adopted a computational approach in determining the ionopause-like density structure of the ionospheric profile. From computing thermal & magnetic pressures, radial magnetic field components, ionopause-like density gradient are detected and stored. The ionopause (theoretically) is formed where the total ionospheric pressure equals solar wind dynamic pressure. The present algorithm consists of a comprehensive set of conditions to be performed on the dataset sequentially. These include datasets from various instruments simultaneously observed. The primary objective of the present study is to describe the implementation and testing of this algorithm for big datasets of the Martian ionosphere and extract ionopause-like density gradient using automation. Keywords: Ionopause, Mars, Remote sensing, MAVEN dataset, Parallel-processing

MAVEN Observations of Periodic Low-altitude Plasma Clouds at Mars

Ion escape to space through the interaction of solar wind and Mars is an important factor influencing the evolution of the Martian atmosphere. The plasma clouds (explosive bulk plasma escape), considered an important ion escaping channel, have been recently identified by spacecraft observations. However, our knowledge about Martian plasma clouds is lacking. Based on the observations of the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft, we study a sequence of periodic plasma clouds that occurred at low altitudes (∼600 km) on Mars. We find that the heavy ions in these clouds are energy-dispersed and have the same velocity, regardless of species. By tracing such energy-dispersed ions, we find the source of these clouds is located in a low-altitude ionosphere (∼120 km). The average tailward moving flux of ionospheric plasma carried by clouds is on the order of 107 cm−2 s−1, which is one order higher than the average escaping flux for the magnetotail, suggesting explosive ion escape via clouds. Based on the characteristics of clouds, we suggest, similar to the outflow of Earth’s cusp, these clouds might be the product of heating due to solar wind precipitation along the open field lines, which were generated by magnetic reconnection between the interplanetary magnetic field and crustal fields that occurred above the source.