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  • 1
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    Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers (2021)
    arXiv
    In:  (Submitted) arXiv e-prints .
    Details
    Publication Date: 2021-11-24
    Description: 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 and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets 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 EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. 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 compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - without review
  • 2
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    SeisBench - A Toolbox for Machine Learning in Seismology (2021)
    arXiv
    In:  (Submitted) arXiv e-prints .
    Details
    Publication Date: 2021-11-23
    Description: Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The abilityof these techniques to efficiently infer the statistical properties of large datasets often provides significantimprovements over traditional techniques when the number of data are large(»millions of examples). Withthe entire spectrum of seismological tasks, e.g., seismic picking and detection, magnitude and source propertyestimation, ground motion prediction, hypocentre determination; among others, now incorporating ML ap-proaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluatethese algorithms, quality controlled benchmark datasets that contain representative class distributions are vital.In addition to this, models require implementation through a common framework to facilitate comparison.Accessing these various benchmark datasets for training and implementing the standardization of models iscurrently a time-consuming process, hindering further advancement of ML techniques within seismology. Thesedevelopment bottlenecks also affect ’practitioners’ seeking to deploy the latest models on seismic data, withouthaving to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a soft-ware package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology.SeisBench standardises access to both models and datasets, whilst also providing a range of common processingand data augmentation operations through the API. Through SeisBench, users can access several seismologicalML models and benchmark datasets available in the literature via a single interface. SeisBench is built to beextensible, with community involvement encouraged to expand the package. Having such frameworks availablefor accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the nextgeneration of ML techniques to seismic data.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - without review
  • 3
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    Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers (2022)
    AGU (American Geophysical Union) | Wiley
    Details
    Publication Date: 2024-02-07
    Description: 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 and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets 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 EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. 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 compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
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    SeisBench - A Toolbox for Machine Learning in Seismology (2022)
    SSA (Seismological Society of America)
    Details
    Publication Date: 2024-02-07
    Description: Machine‐learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground‐motion prediction, hypocenter determination, among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality‐controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time‐consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect “practitioners” seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open‐source framework for deploying ML in seismology—available via GitHub. SeisBench standardizes access to both models and datasets, while also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning (2024)
    AGU (American Geophysical Union) | Wiley
    Details
    Publication Date: 2024-04-11
    Description: Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P-waves and 0.12 s for S-waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Key Points We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning picker Our picker significantly outperforms pickers trained with land-based data with confidence values reflecting the likelihood of outlier picks The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
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    PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning (2023)
    Details
    Publication Date: 2023-05-11
    Description: Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer dataset from 15 deployments in different tectonic settings, comprising ~90,000 P and ~63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation ot the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation (MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments. KEY POINTS • We assembled a database of Ocean Bottom Seismometer waveforms and manual P and S picks, on which we train PickBlue, a deep learning picker. • Our picker significantly outperforms pickers trained with land-based data with confidence values reflecting the likelihood of outlier picks. • The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - without review
  • 7
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    3D Local Earthquake Tomography of the Ecuadorian Margin in the Source Area of the 2016 Mw 7.8 Pedernales Earthquake (2021)
    Details
    Publication Date: 2021-07-21
    Description: Based on manually analyzed waveforms recorded by the permanent Ecuadorian network and our large aftershock deployment installed after the Pedernales earthquake, we derive three‐dimensional Vp and Vp/Vs structures and earthquake locations for central coastal Ecuador using local earthquake tomography. Images highlight the features in the subducting and overriding plates down to 35 km depth. Vp anomalies (∼4.5–7.5 km/s) show the roughness of the incoming oceanic crust (OC). Vp/Vs varies from ∼1.75 to ∼1.94, averaging a value of 1.82 consistent with terranes of oceanic nature. We identify a low Vp (∼5.5 km/s) region extending along strike, in the marine forearc. To the North, we relate this low Vp and Vp/Vs (〈1.80) region to a subducted seamount that might be part of the Carnegie Ridge (CR). To the South, the low Vp region is associated with high Vp/Vs (〉1.85) which we interpret as deeply fractured, probably hydrated OC caused by the CR being subducted. These features play an important role in controlling the seismic behavior of the margin. While subducted seamounts might contribute to the nucleation of intermediate megathrust earthquakes in the northern segment, the CR seems to be the main feature controlling the seismicity in the region by promoting creeping and slow slip events offshore that can be linked to the updip limit of large megathrust earthquakes in the northern segment and the absence of them in the southern region over the instrumental period.
    Description: Plain Language Summary: Using seismic data recorded by the permanent Ecuadorian network and the large emergency installation after the 2016 Pedernales earthquake, we obtained the seismic velocity structure together with precise earthquake locations for the coastal Ecuadorian margin. Our images highlight the heterogeneities of the subduction zone affected by seamounts and ridges comprising the oceanic crust. These features play an important role in controlling the seismic behavior of the margin. While seamounts can contribute to the occurrence of intermediate (M ∼ 7–7.5) megathrust earthquakes in the north, the Carnegie Ridge seems to be the main feature controlling the seismicity in the region by promoting creeping and slow slip events offshore that can be linked to the updip limit of large megathrust earthquakes in the northern segment and the absence of them in the southern region.
    Description: Key Points: 3D Vp and Vp/Vs models were calculated using local earthquake tomography in the region affected by the 2016 Pedernales, Ecuador earthquake Tomographic images highlight the heterogeneities of the margin affected by seamounts and ridges comprising the oceanic crust Carnegie Ridge seems the main feature controlling the seismic activity and the offshore extent of large megathrust earthquakes in the region
    Description: IGEPN
    Description: IRD
    Description: INSU‐CNRS
    Description: ANR
    Description: NERC
    Description: IRIS PASSCAL and NSF RAPID Program Award
    Description: ANID under Programa Formación de Capital Humano Avanzado, Becas Chile
    Description: UCA/JEDI project
    Keywords: 551.22 ; aftershocks ; Ecuador ; megathrust earthquake ; seismic tomography ; subduction zone ; velocity structure
    Type: article
    Location Call Number Limitation Availability
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    CITATION GEO-LEO
  • 8
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    Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers (2022)
    Wiley-AGU
    Details
    Publication Date: 2022-02-11
    Description: 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.
    Description: This work was supported by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT partition. J. Münchmeyer acknowledges the support of the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS). The authors thank the Impuls-und Vernetzungsfonds of the HGF to support the REPORT-DL project under the grant agreement ZT-I-PF-5-53. This work was also partially supported by the project INGV Pianeta Dinamico 2021 Tema 8 SOME (CUP D53J1900017001) funded by Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018.” Open access funding enabled and organized by Projekt DEAL.
    Description: Published
    Description: e2021JB023499
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Keywords: seismic phase recognition ; deep learnig ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Limitation Availability
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    PAPER (OA)
  • 9
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    PICOSS: Python Interface for the Classification of Seismic Signals (2021)
    Elsevier
    Details
    Publication Date: 2023-10-26
    Description: Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine outcome. Here, we present PICOSS (Python Interface for the Classification of Seismic Signals), a modular data-curator platform for volcano-seismic data analysis, including detection, segmentation and classification. PICOSS has exportability and standardization at its core; users can select automatic or manual workflows to select and label seismic data from a comprehensive suite of tools, including deep neural networks. The modular implementation of PICOSS includes a portable and intuitive graphical user interface to facilitate essential data labelling tasks for large-scale volcano seismic studies.
    Description: Published
    Description: 104531
    Description: 8T. Sismologia in tempo reale
    Description: JCR Journal
    Keywords: Volcanoes ; Software ; Classification ; Segmentation ; Detection ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Limitation Availability
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    PAPER (OA)
  • 10
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    SeisBench—A Toolbox for Machine Learning in Seismology (2023)
    Seismological Society of America
    Details
    Publication Date: 2023-02-21
    Description: Machine‐learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground‐motion prediction, hypocenter determination, among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality‐controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time‐consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect “practitioners” seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open‐source framework for deploying ML in seismology—available via GitHub. SeisBench standardizes access to both models and datasets, while also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.
    Description: Published
    Description: 1695–1709
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Limitation Availability
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    PAPER (OA)
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