A Little Data Goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning

Lapins, Sacha; Goitom, Berhe; Kendall, J.‐M.; Werner, Maximilian J.; Cashman, Katharine V.; Hammond, James

doi:10.1029/2021jb021910

Cited by 45 publications

(39 citation statements)

References 72 publications

(137 reference statements)

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“…In this special collection, multiple studies further improve the robustness and generalizability of ML-based earthquake detection. The improvements are achieved by utilizing a vision transformer architecture (Saad et al, 2022), by designing cascaded neural networks (Majstorovic et al, 2021), by data augmentation (T. and transfer learning (Lapins et al, 2021), by transforming seismic data into the time-frequency domain before detection (Saad et al, 2021), and by incorporating higher abstraction features and latent space information over the seismic array (Mosher & Audet, 2020;Feng et al, 2022). Baseline neural networks are trained using massive labeled datasets with several tens of thousands of data entries, while transfer learning reduces this requirement to a few thousand.…”

Section: Earthquake Data Applicationsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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Section: Earthquake Data Applicationsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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“…Similarly, machine learning could be applied to induced seismicity, mine blasts, or volcanic signals. While some studies in this direction exist (Chai et al., 2020; Dong et al., 2020; Lapins et al., 2021), there is not yet a comprehensive study. To facilitate such a study, comprehensive benchmark datasets with rich metadata need to be assembled.…”

Section: Open Questionsmentioning

confidence: 99%

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

et al. 2022

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Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human‐like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross‐domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large‐scale benchmark. We compare six previously published deep learning models on eight data sets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer‐Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross‐domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end‐users an easy way to apply these models.

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“…The last few years have seen the release of a rapidly increasing number of seismic catalogs developed by means of enhanced detection methods, including ones based on machine learning (e.g., Lapins et al., 2021 ; Liu et al., 2020 ; Ross et al., 2018 ). These advanced techniques reveal high‐resolution spatiotemporal characteristics of seismicity (e.g., Ross et al., 2019 ; Shelly, 2020 ; Tan, Waldhauser, Ellsworth, et al., 2021 ) that were previously untraceable in catalogs obtained through standard processing procedures (e.g., routine detections and analyst‐reviewed travel time measurements), whose real‐time implementation becomes particularly challenging during aftershock sequences.…”

Section: Introductionmentioning

confidence: 99%

On the Use of High‐Resolution and Deep‐Learning Seismic Catalogs for Short‐Term Earthquake Forecasts: Potential Benefits and Current Limitations

et al. 2022

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Enhanced earthquake catalogs provide detailed images of evolving seismic sequences. Currently, these data sets take some time to be released but will soon become available in real time. Here, we explore whether and how enhanced seismic catalogs feeding into established short‐term earthquake forecasting protocols may result in higher predictive skill. We consider three enhanced catalogs for the 2016–2017 Central Italy sequence, featuring a bulk completeness lower by at least two magnitude units compared to the real‐time catalog and an improved hypocentral resolution. We use them to inform a set of physical Coulomb Rate‐and‐State (CRS) and statistical Epidemic‐Type Aftershock Sequence (ETAS) models to forecast the space‐time occurrence of M3+ events during the first 6 months of the sequence. We track model performance using standard likelihood‐based metrics and compare their skill against the best‐performing CRS and ETAS models among those developed with the real‐time catalog. We find that while the incorporation of the triggering contributions from new small magnitude detections of the enhanced catalogs is beneficial for both types of forecasts, these models do not significantly outperform their respective near real‐time benchmarks. To explore the reasons behind this result, we perform targeted sensitivity tests that show how (a) the typical spatial discretizations of forecast experiments (≥ $\ge $2 km) hamper the ability of models to capture highly localized secondary triggering patterns and (b) differences in earthquake parameters (i.e., magnitude and hypocenters) reported in different catalogs can affect forecast evaluation. These findings will contribute toward improving forecast model design and evaluation strategies for next‐generation seismic catalogs.

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A Little Data Goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning

Cited by 45 publications

References 72 publications

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

On the Use of High‐Resolution and Deep‐Learning Seismic Catalogs for Short‐Term Earthquake Forecasts: Potential Benefits and Current Limitations

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