Time dependent monitoring of near surface and ground motion modelling : developing new data processing approaches based on Music Information Retrieval (MIR) strategies Seismology like many scientific fields e.g. music information retrieval and speech signal pro cessing is experiencing exponential growth in the amount of data acquired by modern seismo logical networks. In this thesis I take advantage of the opportunities offered by big data and by the methods developed in the areas of music information retrieval and machine learning to predict better the ground motion generated by earthquakes and to study the properties of the surface layers of the Earth. In order to better predict seismic ground motions I propose two approaches based on unsupervised deep learning methods an autoencoder network and Generative Adversarial Networks. The autoencoder technique explores a massive amount of ground motion data evaluates the required parameters and generates synthetic ground motion data in the Fourier amplitude spectra (FAS) domain. This method is tested on two synthetic datasets and one real dataset. [...]