Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Journal Article · Fri Jun 20 00:00:00 EDT 2025 · Seismological Research Letters

DOI:https://doi.org/10.1785/0220240331· OSTI ID:3010229

Lin, Jiun-Ting ^[1]; Aguiar, Ana C. ^[1]; Kong, Qingkai ^[1]; Price, Amanda C. ^[1]; Myers, Stephen C. ^[1]

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1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 3010229
• Report Number(s): LLNL--JRNL-865579
• Journal Information: Seismological Research Letters, Journal Name: Seismological Research Letters Journal Issue: 6 Vol. 96; ISSN 0895-0695; ISSN 1938-2057
• Publisher: Seismological Society of America
• Country of Publication: United States
• Language: English