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This dataset has been generated by propagating seismic waves in heteroegenous geologies. Geologies are 3D domains of size 9.6 x 9.6 x 9.6 km. The source is placed at (4.8km, 4.8km, -8.4km). It is parametrized as a moment tensor with M0 = 2.47 · 1016 N.m, strike = 48°, dip = 45°, and rake = 88°. The propagation of seismic waves is simulated with SEM3D, a High-Performance Code based on the Spectral Element Method. The simulation lasts for 20s. Ground motion is recorded at the surface by a grid of 16 x 16 sensors (600m spacing between two consecutive sensors). This dataset contains velocity timeseries for the sensor located at coordinates (1390m, 1390m). There are 3 timeseries associated with the three components (EW = East-West, NS = North-South, Z = upward). Timeseries are given at a 100Hz frequency. Due to the definition of the mesh elements in the numerical simulation, results are accurate only up to a 5Hz frequency. Timeseries still contain frequencies above 5Hz but they have no physical meaning. Each file is given as a numpy array. Each row corresponds to one numerical simulation (i.e. one geology). Columns correspond to the time steps (0, 0.01, 0.02, ..., 19.98, 19.99).
seismogram, deep learning, seismology, artificial intelligence
seismogram, deep learning, seismology, artificial intelligence
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