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The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473, Wireless sensor and actor networks (WSANs) compose of ordinary sensor networks and actor devices equipped with various capabilities. These networks are crucial technologies which are located at the communication layer of Internet of things. Actors (agents) are employed to provide decentralized control of the sensor networks. We propose an actor controlled framework for localization and clustering in sensor networks. To the best of our knowledge, our framework is the first which offers both localization and clustering within constant message complexity per node. In this structure, a mobile actor device traverses the sensor network to localize and cluster the sensor nodes. Total energy consumption of the sensor nodes using this method are reduced compared to the distributed approaches. Moreover, cluster quality of our algorithms is better than their counterparts. We show that our framework provides a suitable infrastructure for cluster-based applications in an energy efficient manner. © 2021 IEEE.

Outlier mining is an important task for finding anomalous objects. In practice, however, there is not always a clear distinction between outliers and regular objects as objects have different roles w.r.t. different attribute sets. An object may deviate in one subspace, i.e. a subset of attributes. And the same object might appear perfectly regular in other subspaces. One can think of subspaces as multiple views on one database. Traditional methods consider only one view (the full attribute space). Thus, they miss complex outliers that are hidden in multiple subspaces. In this work, we propose Outrank, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures.