Search results for "soft"
showing 10 items of 9809 documents
FAME: Software for analysing rock microstructures
2016
Determination of rock microstructures leads to a better understanding of the formation and deformation of polycrystalline solids. Here, we present FAME (Fabric Analyser based Microstructure Evaluation), an easy-to-use MATLAB®-based software for processing datasets recorded by an automated fabric analyser microscope. FAME is provided as a MATLAB®-independent Windows® executable with an intuitive graphical user interface. Raw data from the fabric analyser microscope can be automatically loaded, filtered and cropped before analysis. Accurate and efficient rock microstructure analysis is based on an advanced user-controlled grain labelling algorithm. The preview and testing environments simplif…
First Results of Hyperspectral Scene Generation in Preparation of the Chime Imaging Spectrometer Mission
2021
End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes w…
The effect of rheological approximations in 3-D numerical simulations of subduction and collision
2018
Abstract Subduction and collision zones evolve differently from one another due to different rheological properties, different amounts of regional isostatic compensation, and the different mechanisms by which forces are applied to the convergent plates. The rheology of mantle and lithosphere is known to have the largest influence on the dynamics of subduction and continental collision. However, previous 3-D geodynamic models of subduction/collision processes have used various rheological approximations, making their results difficult to compare, since there is no clear understanding on the extent of these approximations on the dynamics. Here, we test the effect of rheological approximations…
Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
2021
Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…
An autonomous petrological database for geodynamic simulations of magmatic systems
2022
SUMMARY Self-consistent modelling of magmatic systems is challenging as the melt continuously changes its chemical composition upon crystallization, which may affect the mechanical behaviour of the system. Melt extraction and subsequent crystallization create new rocks while depleting the source region. As the chemistry of the source rocks changes locally due to melt extraction, new calculations of the stable phase assemblages are required to track the rock evolution and the accompanied change in density. As a consequence, a large number of isochemical sections of stable phase assemblages are required to study the evolution of magmatic systems in detail. As the state-of-the-art melting diag…
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Development of branching brittle and ductile shear zones: A numerical study
2017
Continental collision zones are usually associated with large-scale strike-slip shear zones. In most cases these shear zones are complex and consist of multiple strands, varying in width, length, and total displacement. Here we present 2-D numerical models to simulate the formation of such shear zones at different depth levels within the crust, under either brittle (frictional/plastic) or ductile conditions. Localization of shear zones is initiated by a material contrast (heterogeneity) of the material parameters. We systematically test the rate of strain-weakening in brittle and in ductile regimes to understand its influence on the development of shear zone networks. Our simulations sugges…
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval
2017
Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
2020
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…
The 2009 Edition of the GEISA Spectroscopic Database
2011
The updated 2009 edition of the spectroscopic database GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques; Management and Study of Atmospheric Spectroscopic Information) is described in this paper. GEISA is a computer-accessible system comprising three independent sub-databases devoted, respectively, to: line parameters, infrared and ultraviolet/visible absorption cross-sections, microphysical and optical properties of atmospheric aerosols. In this edition, 50 molecules are involved in the line parameters sub-database, including 111 isotopologues, for a total of 3,807,997 entries, in the spectral range from 10-6 to 35,877.031cm-1.The successful performances of the new …