Better Together: Ensemble Learning for Earthquake Detection and Phase Picking

Yuan, Congcong; Ni, Yiyu; Lin, Youzuo; Denolle, Marine

doi:10.1109/tgrs.2023.3320148

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…To date, only a few studies have evaluated the performance of different automated detection approaches with respect to one another or have attempted to combine detection techniques to achieve the best possible outcome Woollam et al, 2022;Yuan et al, 2023). Further, most of these previous studies have typically only examined select model pairs based on one or a few training datasets (e.g., Vaezi & Van der Baan, 2015), and they largely focus on small magnitude, tectonic-related seismic events.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Ho,

Walter,

Hansen

et al. 2024

Preprint

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As seismic data collection continues to grow, advanced automated processing techniques for robust phase identification and event detection are becoming increasingly important. However, the performance, benefits, and limitations of different automated detection approaches have not been fully evaluated. Our study examines how the performance of conventional techniques, including the Short-Term Average/Long-Term Average (STA/LTA) method and cross-correlation approaches, compares to that of various deep learning models. We also evaluate the added benefits that transfer learning may provide to machine learning applications. Each detection approach has been applied to three years of seismic data recorded by stations in East Antarctica. Our results emphasize that the most appropriate detection approach depends on the data attributes and the study objectives. STA/LTA is well-suited for applications that require rapid results even if there is a greater likelihood for false positive detections, and correlation-based techniques work well for identifying events with a high degree of waveform similarity. Deep learning models offer the most adaptability if dealing with a range of seismic sources and noise, and their performance can be enhanced with transfer learning, if the detection parameters are fine-tuned to ensure the accuracy and reliability of the generated catalog. Our results in East Antarctic provide new insight into polar seismicity, highlighting both cryospheric and tectonic events, and demonstrate how automated event detection approaches can be optimized to investigate seismic activity in challenging environments.

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Section: Introductionmentioning

confidence: 99%

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Ho,

Walter,

Hansen

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Ho,

Walter,

Hansen

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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As seismic data collection continues to grow, advanced automated processing techniques for robust phase identification and event detection are becoming increasingly important. However, the performance, benefits, and limitations of different automated detection approaches have not been fully evaluated. Our study examines how the performance of conventional techniques, including the Short‐Term Average/Long‐Term Average method and cross‐correlation approaches, compares to that of various deep learning models. We also evaluate the added benefits that transfer learning may provide to machine learning‐based seismic applications. Each detection approach has been applied to 3 years of data recorded by stations in East Antarctica, which contain both cryospheric and tectonic‐related seismic events. Our results demonstrate that by integrating different event detection approaches with transfer learning, the strengths of each approach can maximize seismic detection accuracy and reliability while also enhancing the completeness of associated event catalogs. That said, model performance can vary, depending on the quality of the data set as well as the data augmentation schemes and training strategies employed. Our results in East Antarctica provide new insight into polar seismicity and demonstrate how automated event detection approaches can be optimized to investigate seismic activity in challenging environments.

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From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude

Shi,

Meier,

Villiger

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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The application of machine learning techniques in seismology has greatly advanced seismological analysis, especially for earthquake detection and seismic phase picking. However, machine learning approaches still face challenges in generalizing to data sets that differ from their original training setting. Previous studies focused on retraining or transfer‐learning models for these scenarios, but require high‐quality labeled data sets. This paper demonstrates a new approach for augmenting already trained models without the need for additional training data. We propose four strategies—rescaling, model aggregation, shifting, and filtering—to enhance the performance of pre‐trained models on out‐of‐distribution data sets. We further devise various methodologies to ensemble the individual predictions from these strategies to obtain a final unified prediction result featuring prediction robustness and detection sensitivity. We develop an open‐source Python module quakephase that implements these methods and can flexibly process input continuous seismic data of any sampling rate. With quakephase and pre‐trained ML models from SeisBench, we perform systematic benchmark tests on data recorded by different types of instruments, ranging from acoustic emission sensors to distributed acoustic sensing, and collected at different scales, spanning from laboratory acoustic emission events to major tectonic earthquakes. Our tests highlight that rescaling is essential for dealing with small‐magnitude seismic events recorded at high sampling rates as well as larger magnitude events having long coda and remote events with long wave trains. Our results demonstrate that the proposed methods are effective in augmenting pre‐trained models for out‐of‐distribution data sets, especially in scenarios with limited labeled data for transfer learning.

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Better Together: Ensemble Learning for Earthquake Detection and Phase Picking

Cited by 6 publications

References 44 publications

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude

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