The Tharsis mantle source of depleted shergottites revealed by 90 million impact craters

The only martian rock samples on Earth are meteorites ejected from the surface of Mars by asteroid impacts. The locations and geological contexts of the launch sites are currently unknown. Determining the impact locations is essential to unravel the relations between the evolution of the martian interior and its surface. Here we adapt a Crater Detection Algorithm that compile a database of 90 million impact craters, allowing to determine the potential launch position of these meteorites through the observation of secondary crater fields. We show that Tooting and 09-000015 craters, both located in the Tharsis volcanic province, are the most likely source of the depleted shergottites ejected 1.1 million year ago. This implies that a major thermal anomaly deeply rooted in the mantle under Tharsis was active over most of the geological history of the planet, and has sampled a depleted mantle, that has retained until recently geochemical signatures of Mars’ early history.

The surface of (4) Vesta in visible light as seen by Dawn/VIR

Aims. We analyzed the surface of Vesta at visible wavelengths, using the data of the Visible and InfraRed mapping spectrometer (VIR) on board the Dawn spacecraft. We mapped the variations of various spectral parameters on the entire surface of the asteroid, and also derived a map of the lithology. Methods. We took advantage of the recent corrected VIR visible data to map the radiance factor at 550 nm, three color composites, two spectral slopes, and a band area parameter relative to the 930 nm crystal field signature in pyroxene. Using the howardite-eucrite-diogenite meteorites data as a reference, we derived the lithology of Vesta using the variations of the 930 and 506 nm (spin-forbidden) band centers observed in the VIR dataset. Results. Our spectral parameters highlight a significant spectral diversity at the surface of Vesta. This diversity is mainly evidenced by impact craters and illustrates the heterogeneous subsurface and upper crust of Vesta. Impact craters also participate directly in this spectral diversity by bringing dark exogenous material to an almost entire hemisphere. Our derived lithology agrees with previous results obtained using a combination of infrared and visible data. We therefore demonstrate that it is possible to obtain crucial mineralogical information from visible wavelengths alone. In addition to the 506 nm band, we identified the 550 nm spin-forbidden one. As reported by a laboratory study for synthetic pyroxenes, we also do not observe any shift of the band center of this feature across the surface of Vesta, and thus across different mineralogies, preventing use of the 550 nm spin-forbidden band for the lithology derivation. Finally, the largest previously identified olivine rich-spot shows a peculiar behavior in two color composites but not in the other spectral parameters.

Split-Attention Networks with Self-Calibrated Convolution for Moon Impact Crater Detection from Multi-Source Data

Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability.