Joint inversion of long-period magnetotelluric data and surface-wave dispersion curves for anisotropic structure: Application to data from Central Germany

Geophysical datasets sensitive to different physical parameters can be used to improve resolution of Earth's internal structure. Herein, we jointly invert long-period magnetotelluric (MT) data and surface-wave dispersion curves. Our approach is based on a joint inversion using a genetic algorithm for a one-dimensional (1-D) isotropic structure, which we extend to 1-D anisotropic media. We apply our new anisotropic joint inversion to datasets from Central Germany demonstrating the capacity of our joint inversion algorithm to establish a 1-D anisotropic model that fits MT and seismic datasets simultaneously and providing new information regarding the deep structure in Central Germany. The lithosphere/asthenosphere boundary is found at approx. 84 km depth and two main anisotropic layers with coincident most conductive/seismic fast-axis direction are resolved at lower crustal and asthenospheric depths. We also quantify the amount of seismic and electrical anisotropy in the asthenosphere showing an emerging agreement between the two anisotropic coefficients

Crustal constraint through complete model space screening for diverse geophysical datasets facilitated by emulation

Deep crustal constraint is often carried out using deterministic inverse methods, sometimes using seismic refraction, gravity and electromagnetic datasets in a complementary or “joint” scheme. With increasingly powerful parallel computer systems it is now possible to apply joint inversion schemes to derive an optimum model from diverse input data. These methods are highly effective where the uncertainty in the system is small. However, given the complex nature of these schemes it is often difficult to discern the uniqueness of the output model given the noise in the data, and the application of necessary regularization and weighting in the inversion process means that the extent of user prejudice pertaining to the final result may be unclear. We can rigorously address the subject of uncertainty using standard statistical tools but these methods also become less feasible if the prior model space is large or the forward simulations are computationally expensive. We present a simple Monte Carlo scheme to screen model space in a fully joint fashion, in which we replace the forward simulation with a fast and uncertainty-calibrated mathematical function, or emulator. This emulator is used as a proxy to run the very large number of models necessary to fully explore the plausible model space. We develop the method using a simple synthetic dataset then demonstrate its use on a joint data set comprising first-arrival seismic refraction, MT and scalar gravity data over a diapiric salt body. This study demonstrates both the value of a forward Monte Carlo approach (as distinct from a search-based or conventional inverse approach) in incorporating all kinds of uncertainty in the modelling process, exploring the entire model space, and shows the potential value of applying emulator technology throughout geophysics. Though the target here is relatively shallow, the methodology can be readily extended to address the whole crust

Reactivation of Fault Systems by Compartmentalized Hydrothermal Fluids in the Southern Andes Revealed by Magnetotelluric and Seismic Data

In active volcanic arcs such as the Andean volcanic mountain belt, magmatically‐sourced fluids are channelled through the brittle crust by faults and fracture networks. In the Andes, volcanoes, geothermal springs and major mineral deposits have a spatial and genetic relationship with NNE‐trending, margin‐parallel faults and margin‐oblique, NW‐trending Andean Transverse Faults (ATF). The Tinguiririca and Planchón‐Peteroa volcanoes in the Andean Southern Volcanic Zone (SVZ) demonstrate this relationship, as their spatially associated thermal springs show strike alignment to the NNE‐oriented El Fierro Thrust Fault System. We constrain the fault system architecture and its interaction with volcanically sourced hydrothermal fluids using a combined magnetotelluric (MT) and seismic survey that was deployed for 20 months. High conductivity zones are located along the axis of the active volcanic chain, delineating fluids and/or melt. A distinct WNW‐trending cluster of seismicity correlates with resistivity contrasts, considered to be a reactivated ATF. Seismicity occurs below 4 km, suggesting activity is limited to basement rocks, and the cessation of seismicity at 9 km delineates the local brittle‐ductile transition. As seismicity is not seen west of the El Fierro fault, we hypothesize that this structure plays a key role in compartmentalizing magmatically‐derived hydrothermal fluids to the east, where the fault zone acts as a barrier to cross‐fault fluid migration and channels fault‐parallel fluid flow to the surface from depth. Increases in fluid pressure above hydrostatic may facilitate reactivation. This site‐specific case study provides the first three‐dimensional seismic and magnetotelluric observations of the mechanics behind the reactivation of an ATF

Transdimensional inversion of receiver functions and surface wave dispersion

International audienceWe present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station

Stochastic Inversion of P-to-S Converted Waves for Mantle Composition and Thermal Structure: Methodology and Application

We present a new methodology for inverting P‐to‐S receiver function (RF) waveforms directly for mantle temperature and composition. This is achieved by interfacing the geophysical inversion with self‐consistent mineral phase equilibria calculations from which rock mineralogy and its elastic properties are predicted as a function of pressure, temperature, and bulk composition. This approach anchors temperatures, composition, seismic properties, and discontinuities that are in mineral physics data, while permitting the simultaneous use of geophysical inverse methods to optimize models of seismic properties to match RF waveforms. Resultant estimates of transition zone (TZ) topography and volumetric seismic velocities are independent of tomographic models usually required for correcting for upper mantle structure. We considered two end‐member compositional models: the equilibrated equilibrium assemblage (EA) and the disequilibrated mechanical mixture (MM) models. Thermal variations were found to influence arrival times of computed RF waveforms, whereas compositional variations affected amplitudes of waves converted at the TZ discontinuities. The robustness of the inversion strategy was tested by performing a set of synthetic inversions in which crustal structure was assumed both fixed and variable. These tests indicate that unaccounted‐for crustal structure strongly affects the retrieval of mantle properties, calling for a two‐step strategy presented herein to simultaneously recover both crustal and mantle parameters. As a proof of concept, the methodology is applied to data from two stations located in the Siberian and East European continental platforms.This work was supported by a grant from the Swiss National Science Foundation (SNF project 200021_159907). B. T. was funded by a Délégation CNRS and Congé pour Recherches et Conversion Thématique from the Université de Lyon to visit the Research School of Earth Sciences (RSES), The Australian National University (ANU). B. T. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 79382

Operating under high-risk conditions in temporary organizations (monograph)

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Self-designing networks and structural influences on safety: Developing a theory on the relation between organizational design and safety in temporary organizations that operate in a dynamic environment

Safety and Security Scienc