Unsupervised representation learning for clustering SEIS data in continuous records with deep scattering network

Abstract

AGU Fall Meeting 2019 in San Francisco, 9-13 December 2019Exploring the internal structure and the dynamics of our solar system is mandatory to understand the behavior of our universe and its origin. One of the tools chosen by NASA is seismology particularly in order to constrain the parameters of the deep interior structure of the red planet via the Insight (Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport) mission. InSight was successfully landed on November 26th, 2018 in Elysium Planitia with geophysical instruments a short-period seismometer and a broadband seismometer (SEIS, Seismic Experiment for Interior Structure). Both seismometers are now installed directly on Mars surface and enable to analyze the continuous seismic signal.But, before making the structure inversion, we need to extract the features from SEIS data. However, those features may nevertheless be hidden into noise, or may escape from analysis due to the limitations imposed by the current methodologies.Therefore, the aim of this study is to overcome this problem by well extracting, recognizing and classifying the instrument signals using Machine Learning and Deep Learning new strategies inspired from the Deep scattering network.This is very promising for the SIES data as, we¿re going to be able not only to detect the familiar signals, but the exciting part is the unseen or the unknown ones. This technique is used to clean the data from the glitches. In fact, this tool has recently proved to be powerful in signal processing, data automatic feature extraction and may even be helpful to detect new types of signals. Those new signals can reveal unknown processes and lead to new discoveries about Mars physical processes.The method used in this study is divided into three fundamental steps. The first one, to make an automatic feature extraction using the Deep scattering transform which is a convolution neural network that computes a cascade of wavelets calculations and filtering operations to get a stable waveform representation stable to local deformations and overlapping at multiple times and frequencies.. The second step is to use those features for signal classification using Machine Learning classifier Gaussian Mixture Network. Finally, we update the wavelet mother bank depending on the results of the classification error minimization using Adam stochastic gradient descent