Supplemental Material: Bedrock gorge incision via anthropogenic meander cutoff

Expanded methods for discharge and grain size estimation; access information for digital imagery and elevation data; precipitation and discharge data; and field data collected during this study.

Supplemental Material: Bedrock gorge incision via anthropogenic meander cutoff

Expanded methods for discharge and grain size estimation; access information for digital imagery and elevation data; precipitation and discharge data; and field data collected during this study.

Formation timescale model for a small sized Martian fluvial valley

<p>Most of the Martian fluvial valleys formed in the early period (around the Noachian-Hesperian boundary or before), but the formation durations are not well determined, however it would be important to understand as it is related to the climatic history and the reason for specific morphology of the Red Planet’s valleys. We estimated the formation duration for a hundred different sections of a small (~81 km long) Martian valley (called Tinto B, which is East of Palos crater and Tinro Vallis), using ArcMap and Excel software. We used the HRSC DTM (Digital Terrain Model), which was resampled from the resolution of 50 m/px to 100 m/px, because the used THEMIS TI dataset (used for grain size estimation) has a 100 m/px resolution. The visual morphological analysis we used the CTX images.  For the calculation we considered the cross-sectional profile of the valley as a trapezoid shape and calculated the hydraulic radius for it and used several hydraulic variables, like average flow velocity, bedload transport rate, sediment and flow discharge [1], slope of energy grade line. For the formation timescale calculation the Meyer-Peter and Muller bedload transport equation [2] was used. For the sediment grain size estimation we used the THEMIS TI dataset and calculated the shear stress and the Shield parameter from it [3]. The bed of the valley is covered with aeolic sand, which does not represent the grain size of the eroded bedrock, which probably took part in the original formation process. For this reason we sampled the grain size from exposures on both sides of the valley walls, where the original bedrock represented to the best approximation and continuously. The main aim of the work is to compare the different sections of the analysed valley by the final assumed age and different variables and morphology. The detailed morphology analísation comes from the previously made erosion-accumulation results [4] and the visual analysis of the valley. From the given section results the median formation time scale of the valley can be calculated also. With this method the Martian valleys can be comparable with the terrestrial mars-analog valleys after the same calculations. The estimated formation timescale of the whole valley will be compared with the result of the crater size frequency distribution based statistically estimated age of the valley bed. </p> <p> </p> <p>References:</p> <p>[1] - M. R.T. Hoke, B. M. Hynek, G. E. Tucker, Formation timescales of large Martian valley networks, Earth and Planetary Science Letters, 2011, Volume 312, Issues 1–2</p> <p>[2] - M. Wong, G. Parker, Reanalysis and correction of bed-load relation of Meyer-Peter and Muller using their own database, J. Hyrdaul. Eng, 2006, pp. 1159-1168</p> <p>[3] - L. K. Fenton, J. L. Bandfield, A. W. Ward, Aeolian process on Mars: atmospheric modeling and GIS analysis, Journal of Geophysical Research, 2003</p> <p>[4] - V. Steinmann, Á. Kereszturi, L. Mari, Geomorphological analysis of Tinto-B Vallis on Mars, Hungarian Geographical Bulletin, 2020, pp 333-348</p>

A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties

This paper examines the performance of the commonly used neural-network-based classifiers for investigating a structural noise in metals as grain size estimation. The biggest problem which aims to identify the object structure grain size based on metal features or the object structure itself. When the structure data is obtained, a proposed feature extraction method is used to extract the feature of the object. Afterwards, the extracted features are used as the inputs for the classifiers. This research studies is focused to use basic ultrasonic sensors to obtain objects structural grain size which are used in neural network. The performance for used neural-network-based classifier is evaluated based on recognition accuracy for individual object. Also, traditional neural networks, namely convolutions and fully connected dense networks are shown as a result of grain size estimation model. To evaluate robustness property of neural networks, the original samples data is mixed for three types of grain sizes. Experimental results show that combined convolutions and fully connected dense neural networks with classifiers outperform the others single neural networks with original samples with high SN data. The Dense neural network as itself demonstrates the best robustness property when the object samples not differ from trained datasets.