@article{PenaHeidbachMorenoetal.2019, author = {Pena, Carlos and Heidbach, Oliver and Moreno, Marcos and Bedford, Jonathan and Ziegler, Moritz 0. and Tassara, Andres Ollero and Oncken, Onno}, title = {Role of Lower Crust in the Postseismic Deformation of the 2010 Maule Earthquake: Insights from a Model with Power-Law Rheology}, series = {Pure and applied geophysics}, volume = {176}, journal = {Pure and applied geophysics}, number = {9}, publisher = {Springer}, address = {Basel}, issn = {0033-4553}, doi = {10.1007/s00024-018-02090-3}, pages = {3913 -- 3928}, year = {2019}, abstract = {The surface deformation associated with the 2010 M-w 8.8 Maule earthquake in Chile was recorded in great detail before, during and after the event. The high data quality of the continuous GPS (cGPS) observations has facilitated a number of studies that model the postseismic deformation signal with a combination of relocking, afterslip and viscoelastic relaxation using linear rheology for the upper mantle. Here, we investigate the impact of using linear Maxwell or power-law rheology with a 2D geomechanical-numerical model to better understand the relative importance of the different processes that control the postseismic deformation signal. Our model results reveal that, in particular, the modeled cumulative vertical postseismic deformation pattern in the near field (< 300 km from the trench) is very sensitive to the location of maximum afterslip and choice of rheology. In the model with power-law rheology, the afterslip maximum is located at 20-35 km rather than > 50 km depth as suggested in previous studies. The explanation for this difference is that in the model with power-law rheology the relaxation of coseismically imposed differential stresses occurs mainly in the lower crust. However, even though the model with power-law rheology probably has more potential to explain the vertical postseismic signal in the near field, the uncertainty of the applied temperature field is substantial, and this needs further investigations and improvements.}, language = {en} } @article{FournierSteinerBrochetetal.2022, author = {Fournier, Bertrand and Steiner, Magdalena and Brochet, Xavier and Degrune, Florine and Mammeri, Jibril and Carvalho, Diogo Leite and Siliceo, Sara Leal and Bacher, Sven and Pe{\~n}a-Reyes, Carlos Andr{\´e}s and Heger, Thierry Jean}, title = {Toward the use of protists as bioindicators of multiple stresses in agricultural soils}, series = {Ecological indicators : integrating monitoring, assessment and management}, volume = {139}, journal = {Ecological indicators : integrating monitoring, assessment and management}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1470-160X}, doi = {10.1016/j.ecolind.2022.108955}, pages = {8}, year = {2022}, abstract = {Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.}, language = {en} } @article{PenaMetzgerHeidbachetal.2022, author = {Pe{\~n}a, Carlos and Metzger, Sabrina and Heidbach, Oliver and Bedford, Jonathan and Bookhagen, Bodo and Moreno, Marcos and Oncken, Onno and Cotton, Fabrice}, title = {Role of poroelasticity during the early postseismic deformation of the 2010 Maule megathrust earthquake}, series = {Geophysical research letters}, volume = {49}, journal = {Geophysical research letters}, number = {9}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {0094-8276}, doi = {10.1029/2022GL098144}, pages = {11}, year = {2022}, abstract = {Megathrust earthquakes impose changes of differential stress and pore pressure in the lithosphere-asthenosphere system that are transiently relaxed during the postseismic period primarily due to afterslip, viscoelastic and poroelastic processes. Especially during the early postseismic phase, however, the relative contribution of these processes to the observed surface deformation is unclear. To investigate this, we use geodetic data collected in the first 48 days following the 2010 Maule earthquake and a poro-viscoelastic forward model combined with an afterslip inversion. This model approach fits the geodetic data 14\% better than a pure elastic model. Particularly near the region of maximum coseismic slip, the predicted surface poroelastic uplift pattern explains well the observations. If poroelasticity is neglected, the spatial afterslip distribution is locally altered by up to +/- 40\%. Moreover, we find that shallow crustal aftershocks mostly occur in regions of increased postseismic pore-pressure changes, indicating that both processes might be mechanically coupled.}, language = {en} }