EMRP1.16

The presence of magnetite in granitoids is usually rare in the Iberia Peninsula, but can occur depending on the crystal fractionation processes and redox conditions. Here we studied the Manteigas granodiorite in order to characterize and quantify the nature and abundance of ferromagnetic minerals by using petrographic, Isothermal Remanent Magnetization (IRM) curves, and Frequency-dependent Susceptibility (KfD%) experiments. Igneous rocks of the Variscan age are particularly abundant in Central Portugal, including the Manteigas granodiorite that crops out in the Serra da Estrela region (Central Iberian Zone), Portugal. The Manteigas granodiorite is classified as a medium- to coarse-grained slightly porphyritic biotite rock and was dated by U-Th-Pb methods on zircon at 481.1 ± 5.9 Ma. Petrographic studies show that Manteigas is mainly composed of quartz, Ca-plagioclase, K-feldspar, and biotite. As accessory minerals, apatite, chlorite, magnetite ± hematite, and zircon are identified. Muscovite is rare and most of secondary origin. Monazite, sphene-leucoxene, and brookite-anatase are also present but in minor amounts. Microstructures indicate a slight deformation that is reflected in the undulatory extinction in quartz, microfractures in K- feldspar, and curved biotites. Sometimes microfractures in feldspar are filled by later Fe-oxide/hydroxide. Values of magnetic susceptibility (K_m) indicate that the Manteigas granodiorite belongs to the magnetite-type rocks, with K_m values higher than 1.9 x 10^-3 SI (1.9 x 10^-3 SI < K_m < 188.37 x 10^-3 SI). This is consistent with oxidizing conditions in the magma genesis [1]. Furthermore, the oxygen isotope composition (δ¹⁸O) measured on whole-rock samples ranges between 8.8 ‰ and 8.9 ‰ [1,2], which suggests a mantle contribution. The analysis of IRM data through the Cumulative Log-Gaussian (CLG) function with the software developed by Kruiver et al. [3] indicates the presence of a single ferromagnetic s.l. component, with values of mean coercivity (Log B1/2) of 1.61 mT and dispersion parameter (DP) of 0.36, typical of magnetite [4]. The value of the IRM at saturation (SIRM) of 18 A/m indicates a significant contribution of magnetite. We also fitted the IRM curve by using a Skewed Generalized Gaussian function with the MaxUnmix software [5] and obtained similar results. Kfd% analyses were conducted in four samples. Values of the Kfd% are inferior to 6%, suggesting a very weak contribution of superparamagnetic particles. Acknowledgments: The first author is financially supported by UIDP/04683/2020 project (FCT-Portugal). This work is also supported by national funding awarded by FCT under UIDB/04683/2020 project. References: [1] Sant’Ovaia, H., Olivier, P., Ferreira, N., Noronha, F., Leblanc, D. 2010. J. Struct. Geol. 32, 1450-1465. [2] Neiva, A., Williams, I.S., Ramos, J.M.F., Gomes, M.E.P. Silva, M.M.V.G., Antunes, I.M.H.R. 2019. Lithos 111, 186-202. [3] Kruiver, P.P., Dekkers, M.J., Heslop, D. 2001. Earth Planet. Sci. Lett. 189, 269–276. [4] Egli, R., 2003. J. Geophys. Res. 108, 2081. [5] Maxbauer, D.P., Feinberg, J. M., Fox, D.L. 2016.  Comput. Geosci. 95, 140–145.

How to cite: Cruz, C., Dias, J., Font, E., Noronha, F., and Sant'Ovaia, H.: Disclosing the redox conditions in Central Portugal magmatism: the Manteigas granodiorite case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6004, https://doi.org/10.5194/egusphere-egu22-6004, 2022.

Several hypabyssal dykes and masses, generated during the Permo-Carboniferous, outcrop throughout southwest Europe. In Portugal, this magmatic event is related to the transition from a post-collisional to extensional setting that followed the Variscan orogeny and represented by porphyries, lamprophyres, and dolerites which intruded into older metasediments and granites. The magnetic mineralogy, susceptibility, and fabrics of selected dykes from northern Portugal were studied using methodologies such as IRM curve acquisition and treatment, frequency-dependence of the magnetic susceptibility (Kfd), and low-field AMS.

Based on the bulk magnetic susceptibility, the felsic lithotypes are paramagnetic (K_m = 0.9-148.2 µSI) but mostly composed of diamagnetic minerals (i.e., quartz and feldspars). AMS in these rocks is mainly carried by iron-bearing silicates, such as biotite and cordierite, as well as ilmenite. However, other iron oxides, namely hematite, goethite, or fine-grained magnetite, also play an important role in the magnetic anisotropy. By contrast, in most of the mafic lithologies (K_m = 238.2-15,640.7 µSI), magnetite is an essential component. Other iron-rich minerals such as biotite, amphibole, and pyroxene also influence the anisotropy of these rocks.

Following the IRM data treatment (Kruiver et al., 2001; Maxbauer et al., 2016), all samples reveal at least one magnetite grain population whose mean coercivities (B_1/2) and dispersion parameter (DP) range from 18.2 to 70.8 mT and 0.26 to 0.40, respectively. Petrographic observations suggest that most magnetite composing the mafic rocks is multidomain-type. However, Kfd measurements (4.66-18.18%) indicate that superparamagnetic particles are likely to exist in some lithologies, inducing low bulk susceptibilities and anomalous AMS fabrics. On the other hand, inverse fabrics are probably associated with hydrothermal and/or post-magmatic alterations.

Many felsic specimens display a normal fabric, where the magnetic minerals were oriented along the magma flow direction within the dyke and undisturbed by tectonic strains. Such observation is compatible with the average low anisotropy degree (P_para% = 0.92-4.28%), implying passive emplacement of the melts, and possibly reflects a weak contribution from single-domain-magnetite. Rare cases where the magnetic fabric is intermediate presumably point to more intense deformations.

Magnetite on the mafic rocks is mainly primary. By contrast, since the felsic dykes derived from anatexis of pelitic sources, their generation occurred under reducing conditions, being similar to Ilmenite-type granites. As such, most magnetite in the felsic samples is probably secondary, having resulted from exsolution from Fe-bearing minerals. However, the presence of primary magnetite cannot be ruled out due to possible magma mixing, as suggested by the coexistence of mantled and non-mantled feldspars. The less evolved member involved in the mixing process is likely to carry a more oxidized composition, bearing the potential to crystallize primary magnetite.

This work was supported by the Portuguese Foundation for Science and Technology (FCT), through the project reference UIDB/UIDP/04683/2020 and ICT (Institute of Earth Sciences). The main author is also financially supported by FCT through an individual Ph.D. grant (reference SFRH/BD/138818/2018). References: Kruiver, P.P., Dekkers, M.J., Heslop, D., 2001. Earth Planet. Sci. Lett. 189, 269–276. Maxbauer, D.P., Feinberg, J. M., Fox, D.L., 2016. Computers & Geosciences 95, 140–145.

How to cite: Oliveira, A., Sant'Ovaia, H., and Brites, H.: Unveiling the magnetic mineralogy and magma flow dynamics within subvolcanic dykes from northern Portugal, Central Iberian Zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6331, https://doi.org/10.5194/egusphere-egu22-6331, 2022.

Seismic amplitude versus offset inversion has gained increased attention over the years and is a pragmatic tool applied to retrieve the seismic and petrophysical properties of the geological layers. The prediction of these petro-elastic properties plays an important role in litho-fluids identification and quantitative seismic reservoir characterization. However, imaging of these subsurface variables from the pre-stack seismic data requires minimizing the objective function and is generally solved by using a gradient-descent based optimization method. This method requires computing the gradients of the cost function with reference to the rock’s variables. We have introduced a model-based non-linear AVO inversion strategy that is based on the computation of the adjoint-state gradients. The forward seismic modelling is carried out by convolving the seismic wavelet with the reflectivity series modelled by using the linearized AVO approximation. The optimization method known as L-BFGS is implemented to attain the best optimal model. The novelty of this work is the adjoint-state solution of the linearized AVO equation. This inversion method has been successfully applied on single and multi-traced seismic data simulated with different seismic noise levels. The modelled examples show that the presented non-linear inversion method accurately extract the seismic properties including seismic (P and S) wave velocities and bulk density. Even at some realistic seismic noise levels, the true and extracted model show good agreement which demonstrates the wide application to solve the AVO inverse modelling problems.

How to cite: Ahmed, N., Weibull, W. W., and Grana, D.: Adjoint-state Method Based Strategy for Non-linear Seismic AVO Inversion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-209, https://doi.org/10.5194/egusphere-egu22-209, 2022.

Tectonic fault zones are subject to normal stress variations with a wide range of spatiotemporal scales, resulting in stress field alteration. These perturbations can spread over a wide range of frequencies and amplitudes from the high frequency passage of seismic waves generated by earthquakes, to the low frequency of solid earth tides and underground fluid injection cycles. As a result of these normal stress perturbations, critically stressed faults can be reactivated. The resulting slip mode is then controlled by fault friction and elastic properties of the surrounding rock. Existing works show that complex behaviors may arise from the interplay between friction changes with slip and slip rate and stress perturbations.

To shed light on the mechanics of fault dynamic triggering we performed experiments in a Biaxial Apparatus in a Double Direct Shear configuration under critically stable stiffness conditions (K/Kc~1). We used powdered quartz gouge (Min-U-Sil 40) as starting material, and conducted experiments at reference normal stress of σ_n = 10-13.5 MPa. After shearing the material and reaching a steady state sliding, normal stress oscillations were applied with various amplitudes, varying from A = 0.5-2 MPa, and periods, T = 0.5-50 s. In addition, we used the laboratory derived friction parameters as input for forward modeling using Rate-and-State friction laws in order to assess if these laws can explain our data. Our results show that creeping faults, under critical stiffness conditions, are sensitive to normal stress perturbations showing a variety of slip behaviors depending on amplitude and frequency of the oscillations:

• Oscillation frequency has a major effect on fault stability. Low and high frequencies cause a Coulomb-like response of the shear stress, that is accompanied by a complex frictional response with slow events and period doubling. At the critical frequency predicted by the Rate-and-State friction, we observe dynamic weakening resulting in regular stick-slip events.
• Oscillation amplitude also plays a role with the main effect depending on the magnitude of the perturbation.
• Using a modified Rate-and-State equation (Linker and Dieterich, 1992), we are able to accurately model the laboratory data.

Our results show that normal stress perturbation on a laboratory creeping fault, at critical stiffness condition, can reproduce the entire spectrum of fault slip behavior depending on the oscillation properties.

How to cite: Pignalberi, F., Scuderi, M. M., Noël, C., Marone, C., and Collettini, C.: Stress triggering and the spectrum of fault slip behaviors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3748, https://doi.org/10.5194/egusphere-egu22-3748, 2022.

Parametric analysis of laboratory Acoustic Emission (AE) during rock deformation laboratory experiments has revealed periodic trends and precursory behaviour of the rupture source, as crack damage nucleates, grows and coalesces into a fault zone. Due to the heterogeneity of rocks and the different effective pressures, finding a full prediction of rupture mechanisms is still an open goal. We consider the AE rates and the derived source mechanisms to constrain the stress-strain regime, while scattering and seismic velocity structure define the evolving medium state as the most important attributes for the neural network model to learn. 4x10cm samples of Alzo granite were deformed at confining pressures of 5-40 MPa, whilst AE are recorded. Source mechanisms, as well as AE rates with relation to incremental strain, highlight distinct pre-failure phases. Scattering and seismic velocity measurements indicate the evolving mechanical conditions. A 10MPa simulation test on a model trained with data from 5, 20 and 40 MPa highlights good accuracy when predicting sample failure.

It remains a challenge to generate a ‘generic’ model that can be applied over all experimental conditions. Nonetheless, estimation of parameter importance has highlighted that some physical parameters are better for predicting strain, whilst others are better at stress. This importance can vary in time, suggesting a strong sensitivity of AE properties to the dynamic conditions of the fault zone. Small input changes can strongly affect output, therefore multiple models need to be trained in order to confirm the stability of the forecast. We aim to improve the understanding of the analysis through the search of repeating trends and the identification of consistent variations in key time-varying trends. Seismic scattering shows an early relevance, interpreted as due to the breakup of low frequency surface waves as microcracks begin to coalesce. However the reduction of importance at the later phases of deformation is less obvious. Further investigations are needed to identify at which deformation stage individual parameters are more important and segment time series accordingly.

How to cite: Vinciguerra, S., King, T., and Benson, P.: Using AE based Machine Learning Approaches to Forecast Rupture during Rock Deformation Laboratory Experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3183, https://doi.org/10.5194/egusphere-egu22-3183, 2022.