Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning
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 algor...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Seismological Society of America
2025-01-01
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| Series: | The Seismic Record |
| Online Access: | https://doi.org/10.1785/0320240046 |
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| _version_ | 1850235728246603776 |
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| author | Chengping Chai Omar Marcillo Monica Maceira Junghyun Park Stephen Arrowsmith James O. Thomas Joshua Cunningham |
| author_facet | Chengping Chai Omar Marcillo Monica Maceira Junghyun Park Stephen Arrowsmith James O. Thomas Joshua Cunningham |
| author_sort | Chengping Chai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5db5d2258a88482ba1d9372bb46284fc |
| institution | OA Journals |
| issn | 2694-4006 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Seismological Society of America |
| record_format | Article |
| series | The Seismic Record |
| spelling | doaj-art-5db5d2258a88482ba1d9372bb46284fc2025-08-20T02:02:09ZengSeismological Society of AmericaThe Seismic Record2694-40062025-01-0151647210.1785/0320240046tsr2024046Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine LearningChengping Chai0https://orcid.org/0000-0002-6792-6014Omar Marcillo1Monica Maceira2https://orcid.org/0000-0003-1248-2185Junghyun Park3https://orcid.org/0000-0001-6370-6877Stephen Arrowsmith4https://orcid.org/0000-0002-9150-0363James O. Thomas5https://orcid.org/0009-0004-5802-0810Joshua Cunningham6Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.Southern Methodist University, Dallas, Texas, U.S.A.Southern Methodist University, Dallas, Texas, U.S.A.Consolidated Nuclear Security, LLC, Amarillo, Texas, U.S.A.Consolidated Nuclear Security, LLC, Amarillo, Texas, U.S.A.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.https://doi.org/10.1785/0320240046 |
| spellingShingle | Chengping Chai Omar Marcillo Monica Maceira Junghyun Park Stephen Arrowsmith James O. Thomas Joshua Cunningham Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning The Seismic Record |
| title | Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning |
| title_full | Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning |
| title_fullStr | Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning |
| title_full_unstemmed | Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning |
| title_short | Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning |
| title_sort | exploring continuous seismic data at an industry facility using unsupervised machine learning |
| url | https://doi.org/10.1785/0320240046 |
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