Heterogeneous Locking and Earthquake Potential on the South Peru Megathrust From Dense GNSS Network
Abstract
The Central Andes subduction has been the theater of numerous large earthquakes since the beginning of the 21st Century, notably the 2001 Mw = 8.4 Arequipa, 2007 Mw = 8.0 Pisco and 2014 Mw = 8.1 Iquique earthquakes. We present an analysis of 47 permanent and 26 survey global navigation satellite system (GNSS) measurements acquired in Central-South Peru between 2007 and 2022 to better understand the frictional properties of the megathrust interface. Using a trajectory model that mimics the different phases of the cycle, we extract a coherent interseismic GNSS field at the scale of the Central Andes from Lima to Arica (12–18.5°S). Interseismic models on a 3D slab geometry indicate that the locking level is relatively high and concentrated between 20 and 40-km depth. Locking distributions indicate a high spatial variability of the coupling along the trench, with the presence of many locked patches that spatially correlate with the seismotectonic segmentation. Our study confirms the presence of a creeping segment where the Nazca Ridge is subducting; we also observe a lighter apparent decrease of coupling related to the Nazca Fracture Zone (NFZ). However, since the Nazca Ridge appears to behave as a strong barrier, the NFZ is less efficient to arrest seismic rupture propagation. Considering various uncertainty factors, we discuss the implication of our coupling estimates with size and timing of large megathrust earthquakes considering both deterministic and probabilistic approaches. We estimate that the South Peru segment could have a Mw = 8.4–9.0 earthquake potential depending principally on the considered seismic catalog and the seismic/aseismic slip ratio.
Copyright and License
© 2024. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Acknowledgement
This work was supported by funding from European Research Council (ERC), CNES, CNRS-INSU, and LabEx Osug@2020 (Investissements d'avenir—ANR10LABX56). A. Socquet received funding from the ERC CoG 865963 DEEP-trigger. B. Lovery salary was covered by Grants from CNES and ERC DEEPtrigger. We thank all organizations and persons that are supporting and operating continuous GNSS stations and campaign measurements used in this study. The continuous GNSS stations in Chile used in this work are maintained and distributed by the Centro Simologico Nacional, Universidad de Chile. The IGP/ISTerre/Caltech GNSS stations in Peru were installed by ISTerre and Caltech in collaboration with IGP, and are now maintained by the Instituto Geofisico del Peru. The IGN network in Peru is maintained by the IGN. We thank the French national pool of portable geodetic instruments GPSMOB-RESIF (INSU-CNRS) for providing the geodetic instruments for the GNSS campaigns. Finally, we thank former engineers who worked on the operational implementation of the GipsyX GNSS processing at ISTerre, including Gaëlle Deschamps-Huygen and Rémi Molaro-Maqua.
Data Availability
All GNSS time series used in this manuscript are available at (Socquet, Tsapong-Tsague, et al., 2023): https://doi.org/10.17178/GNSS.products.SouthAmerica_GIPSYX.daily. RINEX data from CSN network are available at http://gps.csn.uchile.cl. RINEX data from RBMC are available at https://www.ibge.gov.br/en/geosciences/geodetic-positioning/geodetic-networks/19213-brazilian-network-for-continuous-monitoring-of-the-gnss-systems.html?=&t=downloads. RINEX data from SNAPP campaigns are available from UNAVCO (Simons et al., 2010): 1994 campaign at https://www.unavco.org/data/gps-gnss/data-access-methods/dai1/data_request.php?gid=1325&ds=1&parent_link=Campaign&pview=original, 1996 campaign at https://www.unavco.org/data/gps-gnss/data-access-methods/dai1/data_request.php?gid=1493&ds=2&parent_link=Campaign&pview=original, and 2001 campaign at https://www.unavco.org/data/gps-gnss/data-access-methods/dai1/data_request.php?gid=2128&ds=1&parent_link=Campaign&pview=original. RINEX data from campaigns in South Peru between 2012 and 2022 are available through the following links: 2012 campaign (Socquet, Cotte, et al., 2023) at https://doi.org/10.15148/12160e27-0951-41b7-be97-efc4fec7ff96, 2013 campaign (Socquet, Norabuena, & Cotte, 2023) at https://doi.org/10.15148/14ab8f86-3453-4a44-b921-ec3b0a133ef6, 2015 campaign (Socquet & Norabuena, 2023) at https://doi.org/10.15148/376f13e4-e333-4da1-aa10-76f4eae08ffc, 2016 campaign (Nocquet & Socquet, 2023) at https://doi.org/10.15148/f410d57f-2e67-4174-9e7e-17ae3fd55d99, 2018 campaign (Socquet, Nocquet, et al., 2023) at https://doi.org/10.15148/94cc6056-84d5-4c92-a043-6db8b769babf, and 2022 campaign (Socquet, Villegas, et al., 2023) at https://gpscope.dt.insu.cnrs.fr/campagnes/data/2022-041/rinex/. RINEX data from IGP/ISTerre/Caltech continuous stations in South Peru are available at https://doi.osug.fr/data/public/GNSS_products/Peru/RINEX/peru_igp/. RINEX data from IGN network are available at https://doi.osug.fr/data/public/GNSS_products/Peru/RINEX/peru_ign/. The trajectory model analysis has been performed using the ITSA software, hosted on GriCAD GitLab repository (https://gricad-gitlab.univ-grenoble-alpes.fr/isterre-cycle/itsa), and described in Marill et al. (2021). Geographical illustrations were made using the Generic Mapping Tools version 6 package (Wessel et al., 2019) licensed under LGPL version 3 or later, available at https://www.generic-mapping-tools.org/. Other illustrations were made with Matplotlib v3.7.1 (Hunter, 2007), available under the Matplotlib license at https://matplotlib.org.
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Additional details
- ISSN
- 2169-9356
- European Research Council
- 865963
- Centre National d'Etudes Spatiales
- Institut National des Sciences de l'Univers
- Agence Nationale de la Recherche
- ANR10LABX56
- Caltech groups
- Division of Geological and Planetary Sciences, Seismological Laboratory