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Codes to reproduce Figure 7 (Figure 8 can also be plotted by changing two lines of the code) of the following paper:
- Viens L. and Van Houtte C., Denoising ambient seismic field correlation functions with convolutional autoencoders (submitted to GJI). A preprint (not peer-reviewed) of the paper is available at https://eartharxiv.org/q4m2t/.
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The autoencoder is composed of an encoder part and a decoder part:
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The Codes folder contains 5 files:
- The Reproduce_Fig_7.py file is the python code to reproduce Figure 7.
- The functions_for_autoencoders.py file contains functions to bandpass filter the data with a Butterworth filter, denoise the SC functions with the SVDWF method (Moreau et al., 2017), and compute the stretching to retrieve dv/v measurements
- The ConvDeNoise_NS7M_station.h5 contains the weights of ConvDeNoise trained for the NS7M station (Requires Keras 2.2.4)
- The Test_data.mat contains 16 days of raw SC functions at the NS7M station, reference waveforms to compute the dv/v,... (e.g., all the data required to reproduce Figure 7).
- The ConvDeNoise_core.py file is the convolutional denoising autoencoder main code that was used to compute the ConvDeNoise_NS7M_station.h5 file (requires the raw SC functions, please email me for the training set, file is too big for Github)