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Layer conceptEarth2014Global forward modelling of the Earth's gravitational potential, a classical problem in geophysics and geodesy, is relevant for a range of applications such as gravity interpretation, isostatic hypothdoi:10.1007/s10712-016-9382-2...
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Soil organic carbon Machine learning Carbon cycle Satellite data Google Earth Engine Climate change Peatland 1. Introduction Storing an approximately 1,500 petagrams of carbon (Pg C) in the first meter alone (Scharlemann et al., 2014), soil is the largest organic carbon pool in the world (Ti...
To evaluate the model’s generalization ability more objectively, we keep a large proportion of the data out of training. Therefore, we divide the dataset into a training set, validation set, and test set according to the ratio of 4:1:5. That is, 640 tiles are used for training the ...
We present results of numerical investigation of regimes of steady thermal convective dynamo in a plane layer of electrically conducting fluid rotating about the vertical axis and subjected to large-scale perturbations. Keywords: convection in rotating fluid; convective dynamo; nonlinear magnetohydrodynamic...
(within ±40° magnetic latitude, MLAT) are first high-pass filtered with a cutoff period of 40 s, corresponding to an along-track wavelength of about 300 km. The magnetic latitude we used is calculated by the Apex or Quasi-Dipole magnetic field model, which has been defined by Richmond ...
Making Earth movePictures are falling off the walls.Lights are turning on and off.Your bed is shaking.What's going on?It's an earthquake!Earthquakes are measured on a scale of 0---9 called the Richter Seale(里氏震级).A size 9 earthquake is the strongest and most dangerous.What causes ...
of 1000 days is adopted to suppress vertical computational noise. Newtonian damping and Rayleigh friction represented as linear drag have a time scale of 0.2 days in the lower boundary layer and 30 days at mid-levels. With the above dissipation settings, the model takes about 25 days to ...
If the annual power production for a very large region with a high spatio-temporal resolution is needed, the number of NLPs to be solved will still be extremely high. In order to circumvent this, we propose machine-learning techniques for training a regression model on the solutions of the ...