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  • 1
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    Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization (2017)
    BioMed Central
    Details
    Publication Date: 2017-03-27
    Electronic ISSN: 1752-0509
    Topics: Biology
    Published by BioMed Central
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  • 2
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    HUNER: improving biomedical NER with pretraining (2019)
    Oxford University Press
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    Publication Date: 2019-06-27
    Description: Motivation Several recent studies showed that the application of deep neural networks advanced the state-of-the-art in named entity recognition (NER), including biomedical NER. However, the impact on performance and the robustness of improvements crucially depends on the availability of sufficiently large training corpora, which is a problem in the biomedical domain with its often rather small gold standard corpora. Results We evaluate different methods for alleviating the data sparsity problem by pretraining a deep neural network (LSTM-CRF), followed by a rather short fine-tuning phase focusing on a particular corpus. Experiments were performed using 34 different corpora covering five different biomedical entity types, yielding an average increase in F1-score of ∼2 pp compared to learning without pretraining. We experimented both with supervised and semi-supervised pretraining, leading to interesting insights into the precision/recall trade-off. Based on our results, we created the stand-alone NER tool HUNER incorporating fully trained models for five entity types. On the independent CRAFT corpus, which was not used for creating HUNER, it outperforms the state-of-the-art tools GNormPlus and tmChem by 5–13 pp on the entity types chemicals, species and genes. Availability and implementation HUNER is freely available at https://hu-ner.github.io. HUNER comes in containers, making it easy to install and use, and it can be applied off-the-shelf to arbitrary texts. We also provide an integrated tool for obtaining and converting all 34 corpora used in our evaluation, including fixed training, development and test splits to enable fair comparisons in the future. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile (2019)
    Oxford University Press
    Details
    Publication Date: 2019-01-01
    Description: Magnitude estimation is a central task in seismology needed for a wide spectrum of applications ranging from seismicity analysis to rapid assessment of earthquakes. However, magnitude estimates at individual stations show significant variability, mostly due to propagation effects, radiation pattern and ambient noise. To obtain reliable and precise magnitude estimates, measurements from multiple stations are therefore usually averaged. This strategy requires good data availability, which is not always given, for example for near real time applications or for small events. We developed a method to achieve precise magnitude estimations even in the presence of only few stations. We achieve this by reducing the variability between single station estimates through a combination of optimization and machine learning techniques on a large catalogue. We evaluate our method on the large scale IPOC catalogue with 〉100 000 events, covering seismicity in the northern Chile subduction zone between 2007 and 2014. Our aim is to create a method that provides low uncertainty magnitude estimates based on physically meaningful features. Therefore we combine physics based correction functions with boosting tree regression. In a first step, we extract 110 features from each waveform, including displacement, velocity, acceleration and cumulative energy features. We correct those features for source, station and path effects by imposing a linear relation between magnitude and the logarithm of the features. For the correction terms, we define a non-parametric correction function dependent on epicentral distance and event depth and a station specific, adaptive 3-D source and path correction function. In a final step, we use boosting tree regression to further reduce interstation variance by combining multiple features. Compared to a standard, non-parametric, 1-D correction function, our method reduces the standard deviation of single station estimates by up to 57percent⁠, of which 17percent can be attributed to the improved correction functions, while boosting tree regression gives a further reduction of 40percent⁠. We analyse the resulting magnitude estimates regarding their residuals and relation to each other. The definition of a physics-based correction function enables us to inspect the path corrections and compare them to structural features. By analysing feature importance, we show that envelope and P wave derived features are key parameters for reducing uncertainties. Nonetheless the variety of features is essential for the effectiveness of the boosting tree regression. To further elucidate the information extractable from a single station trace, we train another boosting tree on the uncorrected features. This regression yields magnitude estimates with uncertainties similar to the single features after correction, but without using the earthquake location as required for applying the correction terms. Finally, we use our results to provide high precision magnitudes and their uncertainties for the IPOC catalogue.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 4
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    Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network (2021)
    Oxford University Press
    Details
    Publication Date: 2021-04-12
    Description: Summary Precise real time estimates of earthquake magnitude and location are essential for early warning and rapid response. While recently multiple deep learning approaches for fast assessment of earthquakes have been proposed, they usually rely on either seismic records from a single station or from a fixed set of seismic stations. Here we introduce a new model for real-time magnitude and location estimation using the attention based transformer networks. Our approach incorporates waveforms from a dynamically varying set of stations and outperforms deep learning baselines in both magnitude and location estimation performance. Furthermore, it outperforms a classical magnitude estimation algorithm considerably and shows promising performance in comparison to a classical localization algorithm. Our model is applicable to real-time prediction and provides realistic uncertainty estimates based on probabilistic inference. In this work, we furthermore conduct a comprehensive study of the requirements on training data, the training procedures and the typical failure modes. Using three diverse and large scale data sets, we conduct targeted experiments and a qualitative error analysis. Our analysis gives several key insights. First, we can precisely pinpoint the effect of large training data; for example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Second, the basic model systematically underestimates large magnitude events. This issue can be mitigated, and in some cases completely resolved, by incorporating events from other regions into the training through transfer learning. Third, location estimation is highly precise in areas with sufficient training data, but is strongly degraded for events outside the training distribution, sometimes producing massive outliers. Our analysis suggests that these characteristics are not only present for our model, but for most deep learning models for fast assessment published so far. They result from the black box modeling and their mitigation will likely require imposing physics derived constraints on the neural network. These characteristics need to be taken into consideration for practical applications.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 5
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    The transformer earthquake alerting model: A new versatile approach to earthquake early warning (2020)
    Oxford University Press
    Details
    Publication Date: 2020-12-24
    Description: Summary Earthquakes are major hazards to humans, buildings and infrastructure. Early warning methods aim to provide advance notice of incoming strong shaking to enable preventive action and mitigate seismic risk. Their usefulness depends on accuracy, the relation between true, missed and false alerts, and timeliness, the time between a warning and the arrival of strong shaking. Current approaches suffer from apparent aleatoric uncertainties due to simplified modelling or short warning times. Here we propose a novel early warning method, the deep-learning based transformer earthquake alerting model (TEAM), to mitigate these limitations. TEAM analyzes raw, strong motion waveforms of an arbitrary number of stations at arbitrary locations in real-time, making it easily adaptable to changing seismic networks and warning targets. We evaluate TEAM on two regions with high seismic hazard, Japan and Italy, that are complementary in their seismicity. On both datasets TEAM outperforms existing early warning methods considerably, offering accurate and timely warnings. Using domain adaptation, TEAM even provides reliable alerts for events larger than any in the training data, a property of highest importance as records from very large events are rare in many regions.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 6
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    Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations (2023)
    GFZ Data Services
    Details
    Publication Date: 2023-04-11
    Description: Abstract
    Description: The present dataset is a comprehensive earthquake catalogue for the Northern Chile subduction zone forearc covering the period 2007-2021, determined from IPOC seismic station data (GFZ and CNRS-INSU 2006; https://doi.org/10.14470/pk615318) plus some auxiliary stations (IPOC = Integrated Plate Boundary Observatory Chile; http://www.ipoc-network.org). The method of automatized earthquake catalogue retrieval, the different relocation steps as well as the different earthquake class labels, and the structures outlined by the seismicity are described in detail in Sippl et al. (2023). The catalogue builds on the one from Sippl et al. (2018; https://doi.org/10.5880/GFZ.4.1.2018.001), but uses a slightly deviating parameter set and a new event category. The columns of the data files are: year, month, day, hour, minute, second, latitude [dec. degrees], longitude [dec. degrees], depth [km], magnitude [ML], identifier The identifier term provides a first-order spatial classification of the seismicity, an explanation is given in Sippl et al. (2023).
    Keywords: IPOC ; Chile ; Integrated Plate Observatory Chile ; EARTH SCIENCE ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE OCCURRENCES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 PLATE BOUNDARIES
    Type: Dataset , Dataset
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    DATA GFZ
  • 7
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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
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  • 8
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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
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  • 9
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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
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  • 10
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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
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