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  • Articles  (3)
  • Physics - Geophysics; Physics - Geophysics  (2)
  • Cephalopod  (1)
  • Oxford University Press  (3)
  • 2020-2023  (3)
  • 1940-1944
  • 1
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    Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data (2021)
    Oxford University Press
    Details
    Publication Date: 2022-03-16
    Description: This article has been accepted for publication in Geophysical Journal International ©: The Authors 2021. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved. Uploaded in accordance with the publisher's self-archiving policy.
    Description: In a recent study (Jozinovi\'c et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around the VIRGO gravitational waves observatory sited near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinovi\'c et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.
    Description: RISE (Union's Horizon 2020 research and innovation programme, grant agreement No.821115)
    Description: Published
    Description: 704–718
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Physics - Geophysics; Physics - Geophysics ; machine learning ; ground motion prediction ; seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Relative relocation of earthquakes without a predefined velocity model: an example from a peculiar seismic cluster on Katla volcano's south-flank (Iceland) (2022)
    Oxford University Press
    Details
    Publication Date: 2022-10-28
    Description: Relative relocation methods are commonly used to precisely relocate earthquake clusters consisting of similar waveforms. Repeating waveforms are often recorded at volcanoes, where, however, the crust structure is expected to contain strong heterogeneities and therefore the 1D velocity model assumption that is made in most location strategies is not likely to describe reality. A peculiar cluster of repeating low-frequency seismic events was recorded on the south flank of Katla volcano (Iceland) from 2011. As the hypocentres are located at the rim of the glacier, the seismicity may be due to volcanic or glacial processes. Information on the size and shape of the cluster may help constraining the source process. The extreme similarity of waveforms points to a very small spatial distribution of hypocentres. In order to extract meaningful information about size and shape of the cluster, we minimize uncertainty by optimizing the cross-correlation measurements and relative-relocation process. With a synthetic test we determine the best parameters for differential-time measurements and estimate their uncertainties, specifically for each waveform. We design a relocation strategy to work without a predefined velocity model, by formulating and inverting the problem to seek changes in both location and slowness, thus accounting for azimuth, take-off angles and velocity deviations from a 1D model. We solve the inversion explicitly in order to propagate data errors through the calculation. With this approach we are able to resolve a source volume few tens of meters wide on horizontal directions and around 100 meters in depth. There is no suggestion that the hypocentres lie on a single fault plane and the depth distribution indicates that their source is unlikely to be related to glacial processes as the ice thickness is not expected to exceed few tens of meters in the source area.
    Description: Published
    Description: 1244–1257
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Physics - Geophysics; Physics - Geophysics
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
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    A draft genome sequence of the elusive giant squid, Architeuthis dux (2020)
    Oxford University Press
    Details
    Publication Date: 2022-10-27
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in da Fonseca, R. R., Couto, A., Machado, A. M., Brejova, B., Albertin, C. B., Silva, F., Gardner, P., Baril, T., Hayward, A., Campos, A., Ribeiro, A. M., Barrio-Hernandez, I., Hoving, H. J., Tafur-Jimenez, R., Chu, C., Frazao, B., Petersen, B., Penaloza, F., Musacchia, F., Alexander, G. C., Osorio, H., Winkelmann, I., Simakov, O., Rasmussen, S., Rahman, M. Z., Pisani, D., Vinther, J., Jarvis, E., Zhang, G., Strugnell, J. M., Castro, L. F. C., Fedrigo, O., Patricio, M., Li, Q., Rocha, S., Antunes, A., Wu, Y., Ma, B., Sanges, R., Vinar, T., Blagoev, B., Sicheritz-Ponten, T., Nielsen, R., & Gilbert, M. T. P. A draft genome sequence of the elusive giant squid, Architeuthis dux. Gigascience, 9(1), (2020): giz152. doi: 10.1093/gigascience/giz152.
    Description: Background: The giant squid (Architeuthis dux; Steenstrup, 1857) is an enigmatic giant mollusc with a circumglobal distribution in the deep ocean, except in the high Arctic and Antarctic waters. The elusiveness of the species makes it difficult to study. Thus, having a genome assembled for this deep-sea–dwelling species will allow several pending evolutionary questions to be unlocked. Findings: We present a draft genome assembly that includes 200 Gb of Illumina reads, 4 Gb of Moleculo synthetic long reads, and 108 Gb of Chicago libraries, with a final size matching the estimated genome size of 2.7 Gb, and a scaffold N50 of 4.8 Mb. We also present an alternative assembly including 27 Gb raw reads generated using the Pacific Biosciences platform. In addition, we sequenced the proteome of the same individual and RNA from 3 different tissue types from 3 other species of squid (Onychoteuthis banksii, Dosidicus gigas, and Sthenoteuthis oualaniensis) to assist genome annotation. We annotated 33,406 protein-coding genes supported by evidence, and the genome completeness estimated by BUSCO reached 92%. Repetitive regions cover 49.17% of the genome. Conclusions: This annotated draft genome of A. dux provides a critical resource to investigate the unique traits of this species, including its gigantism and key adaptations to deep-sea environments.
    Description: R.R.F. thanks the Villum Fonden for grant VKR023446 (Villum Fonden Young Investigator Grant), the Portuguese Science Foundation (FCT) for grant PTDC/MAR/115347/2009; COMPETE-FCOMP-01-012; FEDER-015453, Marie Curie Actions (FP7-PEOPLE-2010-IEF, Proposal 272927), and the Danish National Research Foundation (DNRF96) for its funding of the Center for Macroecology, Evolution, and Climate. H.O. thanks the Rede Nacional de Espectrometria de Massa, ROTEIRO/0028/2013, ref. LISBOA-01-0145-FEDER-022125, supported by COMPETE and North Portugal Regional Operational Programme (Norte2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). A.C. thanks FCT for project UID/Multi/04423/2019. M.P. acknowledges the support from the Wellcome Trust (grant number WT108749/Z/15/Z) and the European Molecular Biology Laboratory. M.P.T.G. thanks the Danish National Research Foundation for its funding of the Center for GeoGenetics (grant DNRF94) and Lundbeck Foundation for grant R52–5062 on Pathogen Palaeogenomics. S.R. was supported by the Novo Nordisk Foundation grant NNF14CC0001. A.H. is supported by a Biotechnology and Biological Sciences Research Council David Phillips Fellowship (fellowship reference: BB/N020146/1). T.B. is supported by the Biotechnology and Biological Sciences Research Council-funded South West Biosciences Doctoral Training Partnership (training grant reference BB/M009122/1). This work was partially funded by the Lundbeck Foundation (R52-A4895 to B.B.). H.J.T.H. was supported by the David and Lucile Packard Foundation, the Netherlands Organization for Scientific Research (#825.09.016), and currently by the Deutsche Forschungsgemeinschaft (DFG) under grant HO 5569/2-1 (Emmy Noether Junior Research Group). T.V. and B. Brejova were supported by grants from the Slovak grant agency VEGA (1/0684/16, 1/0458/18). F.S. was supported by a PhD grant (SFRH/BD/126560/2016) from FCT. A.A. was partly supported by the FCT project PTDC/CTA-AMB/31774/2017. C.C. and Y.W. are partly supported by grant IIS-1526415 from the US National Science Foundation. Computation for the work described in this article was partially supported by the DeiC National Life Science Supercomputer at DTU.
    Keywords: Cephalopod ; Invertebrate ; Genome assembly
    Repository Name: Woods Hole Open Access Server
    Type: Article
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