Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Authors: Ali Siahkoohi, Rudy Morel, Maarten V. De Hoop, Erwan Allys, Gregory Sainton, Taichi Kawamura
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a real-data example in which we remove transient, thermally-induced microtilts known as glitches from data recorded by a seismometer during NASA s In Sight mission on Mars.We present two numerical experiments: (1) a synthetic setup in which we can quantify the accuracy of our method; and (2) examples involving seismic data recorded during the NASA In Sight mission. |
| Researcher Affiliation | Academia | 1Department of Computational Applied Mathematics & Operations Research, Rice University 2D epartement d informatique de l ENS, ENS, CNRS, PSL University, Paris, France 3Laboratoire de Physique de l Ecole normale sup erieure, ENS, Universit e PSL, CNRS, Sorbonne Universit e, Universit e Paris Cit e, F-75005 Paris, France 4Institut de Physique du Globe de Paris. |
| Pseudocode | No | The paper describes the method verbally and mathematically, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for partially reproducing the results can be found on Git Hub. Our implementation is based on the original Py Torch code for wavelet scattering covariances (Morel et al., 2022). |
| Open Datasets | Yes | We select these windows of data using an existing catalog and glitches (Scholz et al., 2020) and by further eye examination to ensure no glitch contaminates our dataset.To achieve this, we select about 30 hours of raw data (except for a detrending step) from the U component with a 20Hz sampling rate to fully characterize various aspects of the background noise through the wavelet scattering covariance representation. Next, we window the data and use the windows as training samples from background noise (nk in the context of equation (4)) with the goal of retrieving the marsquake recorded at February 3, 2022 (In Sight Marsquake Service, 2023). |
| Dataset Splits | No | The paper mentions 'training dataset' and 'training samples' but does not specify any explicit validation splits or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running the experiments. |
| Software Dependencies | No | Our implementation is based on the original Py Torch code for wavelet scattering covariances (Morel et al., 2022). |
| Experiment Setup | Yes | We solve the optimization problem in equation (12) using the L-BFGS optimization algorithm (Liu & Nocedal, 1989) using 500 iterations. ...The architecture we use for wavelet scattering covariance computation is two-layer scattering network with J = 8 different octaves with Q = 1 wavelet per octave. |