Abstract
Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.
| Original language | English |
|---|---|
| Pages (from-to) | 64-72 |
| Number of pages | 9 |
| Journal | Seismic Record |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2025 |
Funding
The work described in this article was funded by the U.S. National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development, Office of Proliferation Detection. This article has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). This research used resources at the High Flux Isotope Reactor, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract Number DE-AC05-00OR22725. The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this article or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research by the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan, last accessed July 2024). The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. The authors acknowledge helpful suggestions from Erin Cunningham. The authors thank Editor-in-Chief Keith D. Koper, Associate Editor Steven J Gibbons, Yangkang Chen, and Charlotte Bruland for their constructive comments.