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4 results for “rock classification”
First rock glacier inventory of the Peruvian Andes: distribution, classification and climatic characterization
<p>The contours of the Rock glaciers (RGs) were manually digitized, identifying a total of 2271 (967 active, 507 inactive, 311 intact and 486 relict) covering 109.1±1.5 km2 and distributed in 15 of the 20 cordilleras of Peru. The Minimum Altitude Front (MAF) of RGs is 4339 m a.s.l., while most of them are between 4800 to 5000 m a.s.l. For the Southwest zone (Z-IV) where 94% of the RGs are located, the Binary Logistic Regression Model (BLRM) shows that MAAT and slope are the variables that have the greatest influence on the presence of 76.5% RGs evaluated. While in the North (Z-I), Central (Z-II) and Southeastern (Z-III) zones, the topoclimatic variables analyzed did not show statistical significance. This first inventory represents an important input for modeling the spatial distribution of permafrost in the Peruvian Tropical Andes.</p>
Rock physics models of gas hydrate bearing sediments – the classification, simulation workflow, and challenges
<p>This study reviews the rock physics models for simulating the elastic properties of gas hydrate bearing sediments. Considering that it is confusing to select the appropriate model for a specific study from the various models, we classify the models into five categories according to different principles. We also summarize a general workflow of the modeling process, elaborate the possible models in each step and bring up the potential sources of uncertainties. Besides, we explicate the general problems of the current models and raise several potential research directions. This study provides us a clear view of the rock physics models, the associated uncertainties, as well as the general modeling workflow of gas hydrate bearing sediments, and also provides some implications for future studies.</p>
Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance <Dataset>
<p>This is the dataset of manuscript entitled "Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance". The manuscript is currently under review. Full access of this dataset will be released once the manuscript is accepted.</p>
Data from: Integration of homogeneous structural region identification and rock mass quality classification
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OpenNeuro
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