Displays

Active regions concentrate different geodynamical processes sometimes with complex interactions and retroactions. In order to understand the associated lithospheric deformation and evolution, scientists deduce crustal and mantle structures from sparse, inaccurate and indirect observations. In particular, geophysics aims at retrieving physical properties of crustal or lithospheric media from gravity, electric or seismic measurements. Those indirect tools have been used for decades now to image the Earth Interior at many different scales, from the surface down to the Core.

Besides, density, resistivity or seismic velocity retrieved from geophysical inversions are sensitive to many different factors (temperature, pressure, melt, composition…), each of them impacting the parameters variously. Finally, each of these methods presents its own depth investigation and accuracy, which depends on time lap, network configuration, data wavelength, etc.

In order to distinguish the role of each factor in the lithospheric structure heterogeneity, and to counteract the different method limits, geophysicists have combined their observations in combined schemes for decades now. We will present here how jointly inverting seismic tomography and gravity may help to better understand complex zones implying melt, faults, crustal modification and plate interaction. When mathematical link between the parameters doesn’t exist, we will present a combination of petrophysics and geophysics, that brings new information on past and present dynamical evolution in a magmatic area (East African Rift, Tanzania). Finally, we will address the question of the real benefit of a joint inversion, and whether we can combine all kind of data.

How to cite: Tiberi, C., Clutier, A., Plasman, M., Gautier, S., Parat, F., and Lopez, M.: Joint inversion and collaborative interpretations in complex geodynamical context, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3060, https://doi.org/10.5194/egusphere-egu2020-3060, 2020.

A lens having a diameter of about 40 km and a thickness of 10 km was determined at depths 14-22 km in the junction of the Kyrgyz Range and the Chu Basin Depression Trough of the Northern Tien Shan area by 3D seismic tomography carried out earlier. The following questions are still unanswered: 

- what are its petrophysical characteristics?

- what is the nature of the geophysical anomalies?

- what is the mechanism of its formation?

- how long does it exist within their present boundaries?

In order to address these key issues, it is insufficient to analyze the depth behavior of the P-waves velocities as it was done before. To this end we have built additionally the electrical resistivity, density, lithotypes, temperature, porosity, and fluid saturation models along the N-S collocated seismic and magnetotelluric profile intersecting the study area.

Their integrated analysis enabled to propose a conceptual model of a lens in the Earth’s crust which answers the questions enumerated above.  In particular, it was determined that the lens is characterized by low V_P and V_S velocities and their ratio V_P / V_S;  low resistivity (3–30 Ω.m); low density (at most 2.45 g/cm³); high porosity (above 1.2%) and fluid saturation (above 0.1%); pressure range of 4–6 Kbar; temperature range from T_SCF  = 350-400°C at the lens’ top to T_BDT  = 600–650°C at the bottom, characteristic for the emergence of supercritical fluids and for the solidus of granite, respectively; presence of a cap (a relatively dense, poorly permeable zone) that shields the forming fluid reservoir from above.

Joint analysis of these models made it possible to rule out the molten rocks as a responsible factor for high electrical conductivity and, with a high degree of confidence, assume supercritical fluid nature of the observed petrophysical anomalies. It was supposed that the lens is most likely to be a giant reservoir of supercritical fluids located at the depths between isotherms  T_SCF  and  T_BDT corresponding to the PT-conditions of existence of supercritical fluids, on the one hand, and granite solidus (brittle / ductile transition), on the other hand.

The mechanism of its formation could be explained by dehydration of amphibolites accompanied by dissolution of chlorides which, in turn, leads to the emergence of films with sufficiently high electrical conductivity typical of supercritical highly mineralized solutions. Although this formation scenario fairly well explains the observed anomalies, it does not exclude another mechanism associated with the partially melted material risen from the large depths.

The lens lifetime was determined from properties of the cap. Assuming that for the Cenozoic folding regions, the rock permeability is around 10^-21 m² we could roughly estimate the rate of fluid migration through it. Accordingly the lens lifetime is around 33 million years which is consistent with the age of the Cenozoic activation zones.

How to cite: Spichak, V. and Goidina, A.: Conceptual model of a lens in the upper crust determined from joint analysis of petrophysical models (Northern Tien Shan case study), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-110, https://doi.org/10.5194/egusphere-egu2020-110, 2020.

We investigate the shallow structure of an active normal fault-zone that ruptured the surface during the 30 October 2016 Mw 6.5 Norcia earthquake (central Italy) using a multidisciplinary geophysical approach. The survey site is located in the Castelluccio basin, an intramontane Quaternary depression in the hangingwall of the SW-dipping Vettore-Bove fault system. The Norcia earthquake caused widespread surface faulting affecting also the Castelluccio basin, where the rupture trace follows the 2 km-long Valle delle Fonti fault (VF), displaying a ~3 m-high fault scarp due to cumulative surface slip of Holocene paleo-earthquakes. We explored the subsurface of the VF fault along a 2-D transect orthogonal to the coseismic rupture on recent alluvial fan deposits, combining very high-resolution seismic refraction tomography, multichannel analysis of surface waves (MASW), reflection seismology and electrical resistivity tomography (ERT).

We acquired the ERT profile using an array of 64 steel electrodes, 2 m-spaced. Apparent resistivity data were then modeled via a linearized inversion algorithm with smoothness constraints to recover the subsurface resistivity distribution. The seismic data were recorded by  a190 m-long single array centered on the surface rupture, using 96 vertical geophones 2 m-spaced and a 5 kg hammer source.

Input data for refraction tomography are ~9000 handpicked first arrival travel-times, inverted through a fully non-linear multi-scale algorithm based on a finite-difference Eikonal solver. The data for MASW were extracted from common receiver configurations with 24 geophones; the dispersion curves were inverted to generate several S-wave 1-D profiles, subsequently interpolated to generate a pseudo-2D Vs section. For reflection data, after a pre-processing flow, the picking of the maximum of semblance on CMP super-gathers was used to define a velocity model (VNMO) for CMP ensemble stack; the final stack velocity macro-model (VNMO) from the CMP processing was smoothed and used for post-stack depth conversion. We further processed Vp, Vs and resistivity models through the K-means algorithm, which performs a cluster analysis for the bivariate data set to individuate relationships between the two sets of variables. The result is an integrated model with a finite number of homogeneous clusters.

In the depth converted reflection section, the subsurface of the VF fault displays abrupt reflection truncations in the 5-60 m depth range suggesting a cumulative fault throw of ~30 m. Furthermore, another normal fault appears in the in the footwall. The reflection image points out alternating high-amplitude reflections that we interpret as a stack of alluvial sandy-gravels layers that thickens in the hangingwall of the VF fault. Resistivity, Vp and Vs models provide hints on the physical properties of the active fault zone, appearing as a moderately conductive (< 150 Ωm) elongated body with relatively high-Vp (~1500 m/s) and low-Vs (< 500 m/s). The Vp/Vs ratio > 3 and the Poisson’s coefficient > 0.4 in the fault zone suggest this is a granular nearly-saturated medium, probably related to the increase of permeability due to fracturing and shearing. The results from the K-means cluster analysis also identify a homogeneous cluster in correspondence of the saturated fault zone.

How to cite: Villani, F., Maraio, S., Bruno, P. P., Serri, L., Sapia, V., and Improta, L.: Shallow structural setting of an active normal fault zone in the 30 October 2016 Mw 6.5 central Italy earthquake imaged through a multidisciplinary geophysical approach., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7242, https://doi.org/10.5194/egusphere-egu2020-7242, 2020.

We introduce a methodology for the integration of results from 1D stochastic magnetotelluric (MT) data inversion into deterministic least-square inversions of gravity measurements. The goal of this study is to provide a technique capable of exploiting complementary information between 1D magnetotelluric data and gravity data to reduce the effect of non-uniqueness existing in both methodologies. Complementarity exists in terms of resolution, the 1D MT being mostly sensitive to vertical changes and gravity data sensitive to lateral property variations, but also in terms of the related petrophysics, where the sensitivity to different physical parameters (electrical conductivity and density) allows to distinguish between different contrasts in lithologies.  To this end, we perform a three-step workflow. Stochastic 1D MT inversions are performed first. The results are then fused to create 2D model ensembles. Thirdly, these ensembles are utilised as a source of prior information for gravity inversion. This is achieved by extracting geological information from the ensemble of resistivity model realisations honouring MT data (typically, ensemble comprising several thousands of models) to constrain gravity data inversion.

In our investigations, we generate synthetic data using the 3D geological structural framework of the Mansfield area  (Victoria, Australia) and subsequently perform stochastic MT inversions using a 1D trans-dimensional Markov chain Monte Carlo sampler. These inversions are designed to account for the uncertainty introduced by the presence of non-1D structures.  Following this, the 1D probabilistic ensembles for each site are fused into an ensemble of 2D models which can then be used for further modelling. The fusion method incorporates prior knowledge in terms of spatial lateral continuity and lithological sequencing, to create an image that reflects different scenarios from the ensemble of models from 1D MT inversion. It identifies several domains across the considered area where it is plausible for the different lithologies to occur. This information is then used to constrain gravity inversion using a clustering algorithm by varying the weights assigned to the different lithologies spatially accordingly with the domains defined from MT inversions.

Our results reveal that gravity inversion constrained by MT modelling results in this fashion provide models that present a lower model misfit and are geologically closer to the causative model than without MT-derived prior information. This is particularly true in areas poorly constrained by gravity data such as the basement. Importantly, in this example, the basement is better imaged by the combination of both gravity and MT data than by the separate techniques. The same applies, to a lesser extent, to dipping geological structures closer to surface. In the case of the Mansfield area, the synthetic modelling investigation we performed shows the potential of the workflow introduced here and that it can be confidently applied to real world data.

How to cite: Giraud, J., Seillé, H., Visser, G., Lindsay, M., and Jessell, M.: Utilisation of stochastic MT inversion results to constrain potential field inversion , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15067, https://doi.org/10.5194/egusphere-egu2020-15067, 2020.

The EarthScope USArray project provides high quality magnetotelluric and seismic observations, which have been used to identify tectonic boundaries of the USA. Combining these data sets together with satellite gravity observations, we investigate how the different data sets can complement each other in order to find a consistent model of the subsurface. Using a cross-gradient constraint, we first invert the magnetotelluric and gravity data sets in order to demonstrate the feasibility of our approach and to identify any difficulties. Once a joint conductivity and density model is found, we perform a full joint inversion of all three data sets. By comparison with models derived from separate inversions of the individual observables we can show how the different data sets interact. Examining the magnitude of the cross-gradient lets us distinguish parts of the model where a good agreement of the recovered structures has been achieved from those where differing patterns are necessary in order to achieve an acceptable data fit. In this presentation we will give an overview of our approach, highlight our strategy and show results from individual and joint inversions.

How to cite: Weise, B., Moorkamp, M., and Fishwick, S.: Coupling USArray and satellite gravity data – an integrated conductivity, density and seismic velocity model of the western USA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7436, https://doi.org/10.5194/egusphere-egu2020-7436, 2020.

Geophysical exploration of natural resources is challenging because of complex and/or narrow geological structures to image. Geophysical models should provide an image at a scale large enough to understand the complex geology but with the adequate resolution to resolve features like faults. One solution to overcome this difficulty is to integrate large multiphysics datasets to provide complementary insight of the geology. New approaches involve joint inversion of all datasets in a common process where models are coupled together. Geometrical or quantitative interpretation of the joint models image several physical properties shaping the same pattern of the target resources. In reality, models resulting from joint inversion are still challenging to interprete. Most of the joint inversion techniques are based on parameters relationship or geometrical constraint which imply common interfaces between models. This assumption may be wrong since geophysical methods have different sensitivity to the same geological object.

Geophysical integration cover a wide range of approach from the visual interpretation of model presented side by side to sophistical statistical analyses such as automatic clustering. We present here a geophysical models integration based on principal component analysis (PCA). PCA allow to gain insight on a multi-variable system with high level of interaction. PCA aims to reorganize the system by finding a new set of variables distributed along new orthogonal axis and keeping most of the variance from the data. Thus geophysical interaction are highlighted along components that can be interpreted in terms of patterns. We applied this integration method to gravity, ambient noise tomography and resistivity models obtained from joint inversion in the framework of unconventional geothermal exploration in Massif Central, France. PCA of the log-resistivity, the density contrast and the Vs velocity model has 3 independent components. The first one (PC1) representing 69% of the total variance of the system is highly influenced by the parameter coupling enforced in the joint inversion process. PC1 allows to point to geophysical structures that may be related to the geothermal system. The second component (PC2) represents 22% of the total variance and is strongly correlated to the resistivity distribution The correlation with the surface geology suggests that it may be a fault marker. The third component (PC1: 9% of the total variance) is still above the nul hypothesis and seems to describe the 3D geometry of the geological units. This statistical approach may help the geophysical interpretation into a possible geothermal conceptual model

How to cite: ars, J., Tarits, P., Hautot, S., Bellanger, M., and Coutant, O.: Integration of multi parameters geophysical models using PCA : application to geothermal exploration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-497, https://doi.org/10.5194/egusphere-egu2020-497, 2020.

Reflections of seismic waves are strongly distorted by the presence of complex geological structures (e.g. salt bodies) and their vertical resolution is usually of the order of a few tens of meters, imposing limitations in the construction of subsurface models. One way to improve the reliability of such models is to integrate reflection seismic data with other types of geophysical data, such as gravimetric data, since the latter provide an additional link to map geological structures that exhibit density contrasts with respect to their surroundings. In a previous study, we developed a cooperative inversion method of 2D post-stack and migrated reflection seismic data, and gravimetric data. Using that inversion method, we minimize two problems: (1) the problem of the distortion of reflection seismic data due to the presence of complex geological bodies and (2) the problem of the greater ambiguity and the commonly lower resolution of the models obtained only from gravimetric anomalies. The method incorporates a technique to decrease the number of variables and is solved by optimization of the gravity inverse problem, thus reducing computing time. The objective function of cooperative inversion was minimized using three different methods of optimization: (1) simplex, (2) simulated annealing, and (3) genetic algorithm. However, these optimization methods have internal parameters which affect the convergence rate and objective function values. These parameters are usually chosen accordingly to previous references. Although the usage of these standard values is widely accepted, the best values to assure effectiveness and stability of convergence are case-dependent. In the present study, we propose a sensitivity analysis on the internal parameters of the optimization methods for the previously presented cooperative inversion. First, we developed the standard case, which is an inversion performed using all parameters at their standard values. Then, the sensitivity analysis is performed by running multiple inversions, each one with a set of parameters. Each set is obtained by modifying the value of a single parameter either for a lower or for a higher value, keeping all other values at their standard values. The results obtained by each setting are compared to the results of the standard case. The compared results are both the number of evaluations and the final value of the objective function. We then classify parameters accordingly to their relative influence on the optimization processes. The sensitivity analysis provides insight into the best practices to deal with object-based cooperative inversion schemes. The technique was tested using a synthetic model calculated from the Benchmark BP 2004, representing an offshore sedimentary basin containing salt bodies and small hydrocarbons reservoirs.

How to cite: Gonçalves, M. and Leite, E.: Sensitivity analysis of optimization parameters on cooperative inversion of seismic reflection and gravity data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-881, https://doi.org/10.5194/egusphere-egu2020-881, 2020.

Seismic Tomography is a method to image the interior of solid media, and is often used to map properties in the subsurface of the Earth. In order to better interpret the resulting images it is important to assess imaging uncertainties. Since tomography is significantly nonlinear, Monte Carlo sampling methods are often used for this purpose but the ‘curse of dimensionality’ generally makes them computationally intractable for large data sets and high-dimensional parameter spaces. To extend uncertainty analysis to larger systems we introduce variational inference methods to conduct seismic tomography. In contrast to Monte Carlo stochastic sampling, variational methods solve the Bayesian inference problem as an optimization problem, yet still provide probabilistic results.

We apply variational inference to solve two types of tomographic problems using synthetic and real data: travel time tomography and full waveform inversion. We test two different variational methods: automatic differential variational inference (ADVI) and Stein variational gradient descent (SVGD). In each case we compare the results to solutions given by a variety of Monte Carlo methods.

In the travel time tomography example we show that ADVI provides a robust mean velocity model but biased uncertainties due to an implicit Gaussian approximation, and that it cannot be used to find multi-modal Bayesian posterior probability distributions. SVGD produces an accurate match to the fully probabilistic results of Markov chain Monte Carlo analysis, but at significantly reduced computational cost – provided that gradients of model parameters with respect to data can be calculated efficiently.

In our waveform inversion example, the SVGD method produces results of similar quality to published results from an efficient Hamiltonian Monte Carlo analysis, at around the same cost. However, that particular Monte Carlo method has significant ‘hidden’ costs: these are necessarily incurred by running a substantial number of pre-run tests to determine suitable settings of run-time parameters, and are not generally included in quoted cost estimates. By contrast, SVGD has relatively low pre-run costs. In addition, SVGD is significantly easier to parallelize, and for very large problems can be run in minibatch mode; this is impossible for Monte Carlo methods without incurring probabilistic errors as so-called ‘detailed balance’ can not be maintained in minibatch Hamiltonian Monte Carlo. We therefore contend that the variational method may have greater potential to extend probabilistic analysis to higher dimensional tomographic systems than current Monte Carlo methods.

How to cite: Curtis, A. and Zhang, X.: Variational Probabilistic Tomography, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6016, https://doi.org/10.5194/egusphere-egu2020-6016, 2020.

A Bayesian model for prediction and uncertainty quantification of subsurface lithology/fluid classes, petrophysical properties and elastic material properties conditional on seismic amplitude-versus-offset measurements is defined. We demonstrate the proposed methodology  on a real Norwegian Sea gas discovery in 3D in a seismic inversion framework.

The likelihood model is assumed to be Gaussian, and it is constructed in two steps. First, the reflectivity coefficients of the elastic material properties are computed based on the linear Aki Richards approximation valid for weak contrasts. The reflectivity coefficients are then convolved in depth with a wavelet.  We assume a Markov random field prior model for the lithology/fluid classes with a first order neighborhood system to ensure spatial coupling. Conditional on the lithology/fluid classes we define a Gauss-linear petrophysical and rock physics model. The marginal prior spatial model for the petrophysical properties and elastic attributes is then a multivariate Gaussian mixture random field.

The convolution kernel in the likelihood model restricts analytic assessment of the posterior model since the neighborhood system of the lithology/fluid classes is no longer a simple first order neighborhood. We propose a recursive algorithm that translates the Gibbs formulation into a set of vertical Markov chains. The vertical posterior model is approximated by a higher order Markov chain, which is computationally tractable. Finally, the approximate posterior model is used as a proposal model in a Markov chain Monte Carlo algorithm. It can be verified that the Gaussian mixture model is a conjugate prior with respect to the Gauss-linear likelihood model; thus, the posterior density for petrophysical properties and elastic attributes is also a Gaussian mixture random field.

We compare the proposed spatially coupled 3D model to a set of independent vertical 1D inversions. We obtain an increase of the average acceptance rate of 13.6 percentage points in the Markov chain Monte Carlo algorithm compared to a simpler model without lateral spatial coupling. At a blind well location we obtain a reduction of at most 60 % in mean absolute error and root mean square error for the proposed spatially coupled 3D model.

How to cite: Fjeldstad, T. and Omre, H.: Joint Bayesian spatial inversion of lithology/fluid classes, petrophysical properties and elastic attributes – A Norwegian Sea gas discovery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6859, https://doi.org/10.5194/egusphere-egu2020-6859, 2020.

We present a modular open-source software framework - Kite (http://pyrocko.org) - for rapid post-processing of spaceborne InSAR-derived surface displacement maps. The software enables swift parametrisation, post-processing and sub-sampling of the displacement measurements that are compatible with common InSAR processors (e.g. SNAP, GAMMA, ISCE, etc.) and online processing centers delivering unrwapped InSAR data products, such as NASA ARIA or LiCSAR. The post-processing capabilities include removal of first-order atmospheric phase delays through elevation correlation estimations and regional atmospheric phase screen (APS) estimations based on atmospheric models (GACOS), masking of displacement data, adaptive data sub-sampling using quadtree decomposition and data error covariance estimation.

Kite datasets integrate into forward modelling and optimisation frameworks Grond (Heiman et al., 2019) and BEAT (Vasyura-Bathke et al., 2019), both software packages aim to ease and streamline the joint optimisation of earthquake parameters from InSAR and GPS data together with seismological waveforms. These data combinations will improve the estimation of earthquake rupture parameters. Establishing this data processing software framework we want to bridge the gap between InSAR processing software and seismological modelling frameworks, to contribute to a timely and better understanding of earthquake kinematics. This approach paves the way to automated inversion of earthquake models incorporating space-borne InSAR data.

Under development is the processing of InSAR displacement time series data to link simultaneous modelling of co- and post-seismic transient deformation processes from InSAR observations to physical earthquake cycle models.

We demonstrate the framework’s capabilities with an analysis of the 2019 Ridgecrest earthquakes from InSAR surface displacements (provided by NASA ARIA) combined with GNSS displacements using the Bayesian bootstrapping strategy from the Grond inverse modelling tool.

How to cite: Isken, M. P., Sudhaus, H., Heimann, S., Vasyura-Bathke, H., Steinberg, A., and Dahm, T.: Kite - bridging InSAR displacement analysis and earthquake modelling: the 2019 Ridgecrest earthquakes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8164, https://doi.org/10.5194/egusphere-egu2020-8164, 2020.

Seismic waves propagating in attenuative materials are generally inhomogenous waves which, unlike homogeneous waves, have different directions of propagation and attenuation. The degree of wave inhomogeneity can be represented by the inhomogeneity parameter D which varies from 0 to infinity (Cerveny & Psencik, 2005). The dissipation (1/Q)  factors of   inhomogeneous waves vary according to the different definitions. Based on the complex energy balance equations (Carcione, 2001) and the mixed specification of the slowness vector (Cerveny & Psencik, 2005), explicit formulas for the dissipation factors of P- and SV-waves are developed under the two different definitions, (1) 1/Q_V, the ratio of the time-averaged dissipated energy density to the time-averaged strain-energy density, and (2) 1/Q_T, the time-averaged dissipated energy density to the time-averaged energy density. By setting the degree of wave inhomogeneity D as zero, these dissipation factor expressions are reduced to their special case versions as homogeneous waves, i.e., 1/Q_VH = -Im(M)/Re(M) and 1/Q_TH = 2αv/ω , where, M is the wave modulus, α the attenuation coefficient, v the phase velocity and ω the frequency. An example viscoelastic material is chosen to represent the dissipative features of a reservoir for which P-waves are normally more dissipative than S-waves. The calculated dissipation factors of P-waves under the two definitions (i.e. 1/Q_PV and 1/Q_PT) decrease with increasing degree of wave inhomogeneity. For the counterpart S waves, 1/Q_SV is independent of the degree of wave inhomogeneity and 1/Q_ST shows the trend of increasing with increasing degree of wave inhomogeneity. These findings can be explained by the limiting dissipation factors (defined at the infinite degree of inhomogeneity) which all depend only on the shear modulus.  To ensure the correctness of our results, we repeated each step of the investigation  in a parallel way based on Buchen’s (1971) classic real value energy balance equation, including derivation of explicit formulas for 1/Q_PV and 1/Q_PT , with inhomogeneity angle γ  ( -π/2 < γ < π/2) representing the degree of inhomogeneity of the plane wave. We also obtain the inhomogeneity-independent formula for 1/Q_SV, and  exactly the same phase velocity and dissipation factor dispersion results for the example material.

Acknowledgements

We are grateful to the College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals, Kingdom of Saudi Arabia for supporting this research.

References

Buchen, P.W., 1971. Plane waves in linear viscoelastic media, Geophysical Journal of the Royal Astronomical Society, 23, 531-542.

Carcione, J. M., 2001. Wave fields in real media:Wave propagation in anisotropic, anelastic and porous media: Pergamon Press, Inc.

Cerveny, V. & Psencik, I., 2005. Plane waves in viscoelastic anisotropic media—I. Theory, Geophysical Journal International, 161, 197–212.

How to cite: Liu, X., Greenhalgh, S., Zhou, B., and Li, H.: Inhomogeneous waves in isotropic anelastic media: explicit expressions for Q, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1365, https://doi.org/10.5194/egusphere-egu2020-1365, 2020.

We present a novel numerical method called the Distributional Finite Difference method (DFD) for modeling elastodynamic wave propagation in complex heterogeneous media. Efficient wave propagation modeling is crucial for solving the inverse problem where thousands of simulations are needed to infer the Earth's internal structure. The proposed method elegantly combines advantages of the finite difference method (FD) and of the spectral element method (SEM). In the past decades, the Spectral Element Method has become a popular alternative to the Finite Difference method for modeling wave propagation through the Earth. Though this can be debated, SEM is often considered to be more accurate and flexible than FD. This is because SEM has exponential convergence, it allows to accurately model material discontinuities, and complex structures can be meshed using multiple elements. In the mean time, FD is often thought to be simpler and more computationally efficient, in particular because it relies on structured meshed that are well adapted to computational architectures.  The DFD method divides the computational domain in multiple blocks or elements that can be arbitrary large. Within each block, the computational operations needed to model wave propagation are very similar that of FD which leads to high efficiency. When using smaller elements, the DFD approach allows to mesh certain regions of space having complex geometry, thus ensuring high flexibility. The DFD method permits simple specification of boundary conditions and accurately account for free surfaces and solid/fluid interfaces. Further, depending on the chosen basis functions, the DFD method can achieve "spectral like accuracy",  only a few (say 3) points per wavelength are need to model wave propagation accurately, i.e., with acceptable numerical dispersion. This results in significant reduction in memory usage. We present numerical examples demonstrating the advantages of the DFD method in various situations. We show that the DFD method si well adapted for modeling 3D wave propagation through the Earth. 

How to cite: masson, Y. and Faucher, F.: The distributional finite difference method: an efficient method for modeling seismic wave propagation through 3D heterogeneous geological media., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20071, https://doi.org/10.5194/egusphere-egu2020-20071, 2020.

Noise correlation functions are shaped by both noise sources and Earth structure. The extraction of information is thus inevitably affected by source-structure trade-offs. Resorting to the principle of Green’s function retrieval deceptively renders the distribution of ambient noise sources unimportant and existing trade-offs are typically ignored. In our approach, we consider correlation functions as self-consistent observables. We account for arbitrary noise source distributions in both space and frequency, and for the complete seismic wave propagation physics in 3-D heterogeneous and attenuating media. We are therefore not only able to minimize the detrimental effect of a wrong (homogeneous) source distribution on 3D Earth structure by including it as an inversion parameter, but also to quantify underlying trade-offs.

The forward problem of modeling correlation functions and the computation of sensitivity kernels for noise sources and Earth structure are implemented based on the spectral-element solver Salvus. We extend the framework with the evaluation of second derivatives in terms of Hessian-vector products. In the context of probabilistic inverse problems, the inverse Hessian matrix in the vicinity of an optimal model with vanishing first derivatives and under the assumption of Gaussian statistics can be interpreted as an approximation of the posterior covariance matrix. The Hessian matrix therefore contains all the information on resolution and trade-offs that we are trying to retrieve. We investigate the geometry of trade-offs and the effect of the measurement type. In addition, since we only invert for sources at the surface of the Earth, we study how potential scatterers at depth are mapped into the inferred source distribution.

A profound understanding of the physics behind correlation functions and the quantification of trade-offs is essential for full waveform ambient noise inversion that aims to exploit waveform details for the benefit of improved resolution compared to traditional ambient noise tomography.

How to cite: Sager, K., Boehm, C., and Tsai, V.: Source-structure trade-offs in noise correlation functions in 3-D, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9650, https://doi.org/10.5194/egusphere-egu2020-9650, 2020.

In the context of seismic imaging, the full waveform inversion (FWI) is more and more popular. Because of its lower numerical cost, the acoustic approximation is often used, especially at the exploration geophysics scale, both for tests and for real data. Moreover, some research domains such as helioseismology face true acoustic medium for which FWI can be useful. In this work, we show that the general acoustic inverse problem based on limited frequency band data is intrinsically non-unique, making any general acoustic FWI impossible. Our work is based on two tools: particle relabelling and homogenization. On the one hand, the particle relabelling method shows it is possible to deform a true medium based on a smooth mapping into a new one without changing the signal recorded at seismic stations. This is a potentially strong source of non-uniqueness for an inverse problem based a seismic data. Nevertheless, in the elastic case, the deformed medium loses the elastic tensor minor symmetries and, in the acoustic case, it implies density anisotropy. It is therefore not a source of non-uniqueness for elastic or isotropic acoustic inverse problems, but it is for the anisotropic acoustic case. On the other hand, the homogenization method shows that any fine-scale medium can be up-scaled into an effective medium without changing the waveforms in a limited frequency band. The effective media are in general anisotropic, both in the elastic and acoustic cases, even if the true media are isotropic at a fine scale. It implies that anisotropy is in general present and needs to be inverted. Therefore, acoustic anisotropy can not be avoided in general. We conclude, based on a particle relabelling and homogenization arguments, that the acoustic FWI solution is in general non-unique. We show, in 2-D numerical FWI examples based on the Gauss-Newton iterative scheme, the effects of this non-uniqueness in the local optimization context. We numerically confirm that the acoustic FWI is in general non-unique and that finding a physical solution is not possible.

How to cite: Capdeville, Y., Lyu, C., Al-Attar, D., and Zhao, L.: Intrinsic non-uniqueness of the acoustic full waveform inverse problem, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22320, https://doi.org/10.5194/egusphere-egu2020-22320, 2020.

A key requirement by the European CCS directive for the safe operation of geological CO₂ storage is the operator's responsibility to demonstrate containment of the injected CO₂ and conformance between its actual and modelled behavior. Understanding the subsurface behavior and long-term fate of the injected CO₂ requires the quantification of key reservoir parameters (e.g. pore pressure, CO₂ saturation and strain in the overburden). Reliable quantification of these parameters and distinction between them pose a challenge for conventional monitoring techniques, which could be overcome by combining advanced multi-disciplinary and multi-method monitoring techniques in a joint inversion.

Within the aCQurate project, we aim to develop a new technology for accurate CO₂ monitoring using Quantitative joint inversion for large-scale on-shore and off-shore storage applications. In previous applications of joint inversion to CO₂ monitoring, we successfully combined the strengths and advantages of different geophysical monitoring techniques (i.e. seismics with its high spatial resolution and geoelectrics with its high sensitivity to changes in CO₂ saturation), using a cross-gradient approach to achieve structural similarity between the different models. While this structural joint inversion provides a robust link between models of different geophysical monitoring techniques, it lacks a quantitative calibration of the model parameters based on valid rock-physics models. This limitation is addressed by extending the previously developed structural joint inversion method into a hybrid structural-petrophysical joint inversion, which allows integration of cross-property relations, e.g. derived from well logs.

The hybrid structural-petrophysical joint inversion integrates relevant geophysical monitoring techniques in a modular way, including seismic, electric and potential field methods (FWI, CSEM, ERT, MMR and gravity). It is implemented using a Bayes formulation, which allows proper weighting of the different models and data sets, as well as the relevant structural and petrophysical joint inversion constraints during the joint inversion.

The hybrid joint inversion is designed for on-shore and off-shore CO₂ storage applications and will be demonstrated using synthetic data from the CaMI Field Research Station (CaMI.FRS) in Canada. CaMI.FRS is operated by the Containment and Monitoring Institute (CaMI) of CMC Research Institutes, Inc., and provides an ideal platform for the development and deployment of advanced CO₂ monitoring technologies. CO₂ injection occurs at 300 m depth into the Basal Belly River sandstone formation, which is monitored using a large variety of geophysical and geochemical monitoring techniques. In preparation for the application to real monitoring data, we present the application of the joint inversion to synthetic full waveform inversion (FWI) and electrical resistivity tomography (ERT) data, derived for a geostatic model with dynamic fluid flow simulations.

In addition to obtaining a better understanding of the subsurface behavior of the injected CO₂ at CaMI.FRS, our goal is to mature the joint inversion technology further towards large-scale CO₂ storage applications, e.g. on the Norwegian Continental Shelf.

Acknowledgements

Funding is provided by the Norwegian CLIMIT program (project number 616067), Equinor ASA, CMC Research Institutes, Inc., University of Calgary, Lawrence Berkeley National Laboratory (LBNL), Institut national de la recherche scientifique (INRS), Quad Geometrics Norway AS and GFZ German Research Centre For Geosciences (GFZ).

How to cite: Rippe, D., Jordan, M., Macquet, M., Lawton, D., Romdhane, A., and Eliasson, P.: Quantitative CO2 monitoring at the CaMI Field Research Station (CaMI.FRS), Canada, using a hybrid structural-petrophysical joint inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8163, https://doi.org/10.5194/egusphere-egu2020-8163, 2020.

Muon Tomography is an imaging technique that uses muons, a natural background radiation, as a means of observing the the earth’s subsurface. Muons are elementary particles like electrons but with a much greater mass that gives them a high penetrative power across matter. With suitable detectors it is possible to create muographs (muon radiographs) to obtain the column density distribution of the surveyed region. This project is a collaboration between University of Évora and the Laboratory of Instrumentation and Experimental Particle Physics (LIP). Both are Portuguese institutions that intend to apply the muon tomography in the geophysics field. The chosen location was the Lousal Mine, an abandoned and well mapped mine in Portugal with all the support infrastructures necessary that make it an ideal location to test the muon telescope developed by us. The detection will take place inside a mine gallery about 18 m below the surface. The telescope will do a geological reconnaissance of the ground above the gallery with the intention of mapping structures and ore masses already known and of improving the existing information with new data. This will serve to test the performance and sensitivity of the muon telescope, made of particle detectors called RPCs. A working prototype was put in place to gather preliminary information and establish the requirements of the equipment. After that, a muon telescope equipped with four RPC detectors, with an area of 1 m² each, was assembled and has been collecting muons inside the Lousal Mine for the last few months. The tomographic aspect of the work is born from placing the telescope in different locations inside the mine and by orienting it to observe in different directions. Simulations of the muons detection have been made using GEANT4 software. The simulations allow to study the expected result of muographs produced by the muon flux passing through a simulated ground with different characteristics. The aim of this work is to combine the muography information with gravimetry data, from a gravimetric survey that will be carried on site, through a joint inversion of both data sets in order to obtain 3D density profiles of the observed region. Other geophysical methods are being applied above the mine to survey the surface, using photogrammetry, and the ground, using GPR and seismic refraction. These methods give knowledge about the arrangement of the ground, can be compared with previous acquired information and will help to perfect the 3D density profiles.

How to cite: Teixeira, P., Cazon, L., Caldeira, B., Blanco, A., Borges, J., Adringa, S., Assis, P., Tomé, B., Luz, R., Nogueira, J., Lopes, L., Bezzeghoud, M., Ferreira, M., Nogueira, P., Espírito Santo, C., Galaviz, D., Barão, F., and Pimenta, M.: Muon Tomography applied in the Lousal Mine (Portugal), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11834, https://doi.org/10.5194/egusphere-egu2020-11834, 2020.

There are many inversion algorithms that have been developed in the literature to obtain the resistivity distribution of the subsurface. Recovered resistivity values are usually lower/higher than the actual resistivity as a consequence of the inversion algorithms. As a consequence, Identification of geologic units based on resistivity distribution can be done on a relative scale. In general, identification of different geologic units is a post step inversion process based on resistivity distribution in the study region.  I have presented a technique to enhance the resistivity image using cooperative inversion (named as fuzzy cooperative resistivity tomography) where two additional input parameters are added as the number of geologic units in the model (i.e. number of cluster) and the cluster center values of the geologic units (mean resistivity value of each geologic unit). Fuzzy cooperative resistivity tomography fulfills three needs: (1) to obtain a resistivity model which will satisfy the fitting between measured and modeled data, (2) the recovered resistivity model will be guided by additional a priori parametric information, and (3) resistivity distribution and geologic separation will be accomplished simultaneously (i.e. no post inversion step will be needed). Fuzzy cooperative resistivity tomography is based on fuzzy c-means clustering technique which is an unsupervised machine learning algorithm. The highest membership value which is a direct outcome from the FCRT corresponds to a geology separation result. To obtain a geology separation result, I adopted the defuzzification method to assign a single geologic unit for each model cell based on the membership values. Various synthetic and field example data show that FCRT is an effective approach to differentiate between various geologic units. However, I have also noted that this approach is only effective when measured data sets are sensitive to particular geologic units. This is the limitation of the presented FCRT.

How to cite: Singh, A.: Unstructured Grid based Fuzzy Cooperative Resistivity Tomography for Electrical and Electromagnetic data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-379, https://doi.org/10.5194/egusphere-egu2020-379, 2020.

This work consists on the interpretation of multichannel seismic profiles complemented and supported by gravimetric and magnetic forward modeling, on the region surrounding the underwater volcano Fontanelas (Estremadura Spur, west of Lisbon).

The Fontanelas seamount (FSM) is a volcanic cone about 3000 m high from its top to its submerged base that coincides with a strong magnetic anomaly (~350 nT). From dredged samples it is known that it is consists of altered pillow-lavas of ultrabasic and basic alkaline composition (foidites and alkaline basalts) (Miranda et al., 2010). It has been associated with onshore Upper Cretaceous alkaline magmatic events due to its enrichment in incompatible elements and similar isotopic elementary signatures (Miranda et al., 2009 and 2010). The FSM is located halfway between the onshore Sintra intrusive complex and the Tore seamount, between which a 300 km long tectono-magmatic lineament of intrusive/extrusive alkaline bodies of Upper Cretaceous age has been proposed, based on the existence of several other magnetic anomalies (Neres et al., 2014).

Magnetic and gravimetric modeling allowed to constrain the location, depth, extension and geometry of the magmatic bodies in the seismic reflection profiles that were used to map and dating the magmatic bodies and tectonic events.

The joint modeling of these three geophysical methods (seismic, magnetic and gravimetric) allowed for the production of an integrated tectono-magmatic-sedimentary model of the Estremadura Spur. The existence of a complex volcanic and subvolcanic system in the Estremadura Spur was confirmed, including several intrusive bodies, besides the Fontanelas volcano: sills, secondary volcanic cones, large laccolith-type intrusions in the Upper Jurassic. Some extensional rift faults were used as magma conduits for sills plugs and volcanoes.  Magmatic bodies localized compressive strain during the tectonic inversion of the Lusitanian basin during the Alpine compression.

The age of the magmatic bodies is constrained by seismic stratigraphy as prior to the Campanian (83.9 Ma), which allows to associate them with the onshore Upper Cretaceous alkaline magmatic event (Sintra, Sines, Monchique, Lisbon Volcanic Complex, minor intrusive bodies), also correlative of the alkaline magmatism existing offshore along the Madeira-Tore Rise (Merle et al., 2018).

This work will be the basis of future studies regarding the heat dissipation from the intrusion of the magmatic bodies over time in order to estimate the temperatures that surrounding rocks have reached.

Support by Landmark Graphics Corporation, Oasis Montaj (Geosoft), FCT (project UID/GEO/50019/2019- Instituto Dom Luiz) and DGEG is acknowledged.      

Merle, R., et al. (2018). Australian Journal of Earth Sciences, 65(5), 591-605. https://doi.org/10.1080/08120099.2018.1471005

Miranda R., et al. (2009). Cretaceous Research, 30, Elsevier, 575-586. https://doi.org/10.1016/j.cretres.2008.11.002.

Miranda, R., et al. (2010). In X Congresso de Geoquímica dos Países de Língua Portuguesa e XVI Semana de Geoquímica, 28 de Março a 1 de Abril de 2010. http://hdl.handle.net/10400.9/1246

Neres, M., et al. (2014). Geophysical Journal International, 199(1), 78-101. https://doi.org/10.1093/gji/ggu250

Pereira, R., et al. (2016). Journal of the Geological Society, 174(3), 522-540. https://doi.org/10.1144/jgs2016-050

How to cite: Simões, P., Neres, M., and Terrinha, P.: Joint modeling of seismic, magnetic and gravimetric data unravels the extent of the Late Cretaceous Magmatic Province on the Estremadura Spur offshore West Iberia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-571, https://doi.org/10.5194/egusphere-egu2020-571, 2020.

Understanding the driving mechanisms of continental breakup is still a key question in global geodynamics. The Namibian continental margin and Walvis Ridge offer an ideal area for related studies, because it accumulates on- and offshore magmatic features, associated with crustal stretching, a potential plume impact, and continental breakup.

While previous studies in the area all agree on the general occurrence of these features, they have shown some contradictory results for their extent and depth. Therefore, we jointly invert different geophysical data sets to gain a deeper, three-dimensional insight into the continent-ocean-transition zone. In this study, we test three different cross-gradient coupling approaches for Magnetotelluric, Gravity and Seismic data sets or models. First, a fixed 3D density model is used as a structural constraint to MT data inversion. It’s impact is limited, due to large model areas with constant density values, and thus zero density gradients. Second, satellite gravity and MT data are jointly inverted. Both data sets reach a satisfactory misfit and the gravity data constraint slightly modifies the interpreted earth model. Third, a fixed 2D velocity model is used as a structural constraint for a 3D MT data inversion. Some assumptions had to be made to account for the dimensionality difference, but a sufficiently good data fit was achieved, and inversion benefits from a gradient structural model for the cross-gradient coupling. Earth model modifications through this velocity model constraint resemble the results from the joint Gravity-MT data inversion. The analysis of the three approaches, yields new insights into the cross-gradient coupling concept for joint inversion.

Interpreting these three earth models, we believe, that continental break-up in the South Atlantic is neither driven solely by a large plume, nor by pure tectonic forces. High resistivites, velocities and densities in the lower crust point to an accumulation of plume material. However, the size of these features is not big enough to explain the Gondawana break-up as a result of a mega-plume arrival. Indications for a tectonically driven break-up initiation include evidence for extensive crustal stretching, and often an abrupt change to oceanic regime, with the upwelling asthenosphere in juxtaposition to the stretched continental lithosphere. As our models indicate a broader transitional zone, we exclude a pure tectonically driven continental break-up. Our favoured explanation incorporates aspects of both hypothesis, where an accumulation of so-called secondary plumes initiate rifting and break-up. These would be smaller plumes, rising from mid-mantle depths, which might have a common source in the deep mantle.

How to cite: Franz, G., Moorkamp, M., Jegen, M., Berndt, C., and Rabbel, W.: Comparison of coupling methods in joint inversion along the Namibian continental margin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20720, https://doi.org/10.5194/egusphere-egu2020-20720, 2020.

We present a new inverse approach to invert satellite gravity gradients for the Moho depth under consideration of a laterally varying density contrast between crust and mantle. The inverse problem is linearized and solved with the classical Gauss-Newton algorithm in a spherical geometry. To ensure stable solutions, the Jacobian is smoothed with second-order Tikhonov regularization. During the inversion, the Moho depth is discretized into tesseroids by reference Moho depth and density contrast, from which the gravitational effect can be calculated. As a computational benefit, the Jacobian is calculated only once and afterwards weighted with the laterally varying density contrast. We look for a Moho depth model that simultaneously explains the gravity gradient field and a least misfit to existing seismic Moho depth determinations. We perform the inversion both on regional and global scale.

The laterally varying density contrast is based on different tectonic units, which are defined by independent global geological and geophysical data, such as regionalization of dispersion curves. This is beneficial in remote areas, where seismic investigations are very sparse and the crustal structure is to a large extent unknown. Applying the inversion to the Amazonian Craton and its surroundings shows a lower density contrast at the Moho depth for the continental interior compared to oceanic domains. This is in accordance with the tectono-thermal architecture of the lithosphere. The inverted values of the density vary between 300-450 kg/m³. The inverted Moho depth shows a clear separation between the Sao Francisco Craton and shallower Amazonian Craton.

Gravity inversion with a laterally varying density contrast requires a uniform reference Moho depth. On a global scale, we utilize our inversion to estimate a reference Moho depth that is in accordance with crustal buoyancy. The inverted density contrasts show a similar trend like the regional study area. The inverted Moho depth shows expected tectonic features. Our method of computing the Jacobian once and weighting with lateral variable density contrasts is a valuable optimization of standard gravity inversion.

How to cite: Haas, P., Ebbing, J., Szwillus, W., and Tabelow, P.: The role of a laterally varying density contrast for gravity inversion of the Moho depth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7145, https://doi.org/10.5194/egusphere-egu2020-7145, 2020.

Combining two or more geophysical datasets with different resolutions and characteristics is now a common practice to recover one or more physical properties. Building 3D geological models for mineral exploration targeting is often an expensive task even for inversion of a single dataset, because of extremely complicated structures with small scale targets. In this context, seismic methods, among all other traditional techniques in mineral exploration, are receiving increasing attention due to their higher resolution in depth. With more limited spatial coverage and higher resolution, they are usually used to refine the interpretation of potential field data.

As each seismic survey is designed for a particular intention with specific targets and may not be available in all regions of interests, we develop an iterative cooperative inversion algorithm for inverting gravity and seismic travel-time data. This enables the utilization of localized high-resolution seismic data in a larger full 3D volume which is covered by gravity data. Geological information in the form of probabilistic geological modelling is used to extend information away from the high-resolution data and constrain the inversion result. We use these data as the prior model and to derive constraints incorporated into the objective function of gravity inversion. This allows us to obtain information about the probability of the presence of lithologies associated with the formation of mineral systems. To ensure structural consistency between density and velocity we develop a geologically constrained structure-based coupling technique following the same principle as the cross-gradient technique but with a higher degree of freedom in spatial directions. We apply local structure-based constraints conditioned by a geological probability distribution, which is considering direction and magnitude and provide a higher degree of freedom for model variations. An investigation of the proposed methodology and a proof-of-concept using realistic synthetic data are presented. Our results reveal that the methodology has the potential to constrain the gravity inversion results using the limited seismic data.

How to cite: Rashidifard, M., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.: Cooperative inversion of gravity and seismic data with different spatial coverage, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12461, https://doi.org/10.5194/egusphere-egu2020-12461, 2020.

We present a generalized 3-D multiphysics joint inversion scheme with a focus on large-scale regional problems. One of the key features of this scheme is the formulation of the structure coupling as a sparsity-promoting joint regularization. This approach makes it possible to simplify the structure of the objective function and to keep the number of hyperparameters relatively low, so that the inversion framework complexity scales well with respect to the number of geophysical methods and possible reference models used. To further simplify adding geophysical solvers to the framework and to optimize the discretization, we propose an alternating minimization scheme that decouples the inversion and the joint regularization steps. Decoupling is achieved by introducing an auxiliary multi-parameter model. This allows the individual subproblems to make use of problem-tailored grids and specialized optimization algorithms. As we will see, this is in particular important for the regularization subproblem. In contrast to straightforward 'cooperative inversion' formulation, decoupled inversion steps appear to be regularized by a standard quadratic model-norm penalty, and as a result existing separate inversion codes can be used with minimal, if any, modifications. The developed scheme is applied to magnetotelluric, seismic and gravity data and tested on synthetic model examples.

How to cite: Molodtsov, D., Kiyan, D., and Bean, C.: Decoupling strategy for large-scale multiphysics joint inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11958, https://doi.org/10.5194/egusphere-egu2020-11958, 2020.

Vallès Basin (NE-Spain) is a neogene basin with mainly granitic bedrock and delimited at NW by one normal fault (Vallès fault). This basin presents significant geothermal anomalies reflected with surficial hot thermal waters. Previous studies carried out in the 70s, to define its energy resource potential using one single geophysical technique, were not enough to clearly interpret the subsoil structure and many uncertainties remain still unsolved.

The aim of this work is to combine two different geophysical techniques for collaborative interpretation of the Vallès Basin structure in order to reduce the uncertainties: 2D gravity profiles and seismic noise H/V spectral ratio measurements distributed in the whole basin area. 2D gravity profiles provide subsurface structural information and basement depth from density models obtained after modelling and inversion processes; whereas the seismic noise H/V spectral ratio technique determines the soil fundamental frequency, which helps to locate the boundary between soft sediments and hard rock materials using empirical equations. Therefore, bedrock geometry and infill sediments structure can be estimated, which is crucial to understand ongoing processes related to the surface geothermal evidences.

The work methodology consists of combining both geophysical methods comparing the density models obtained from Bouguer anomaly in the 2D gravity profiles with the soft soil-hard rock contact surface obtained from the seismic noise H/V spectral measurement. The co-validation between them is carried out overlapping these two individual geophysical results and complementing models between them to obtain the best fit. Despite using different geophysical techniques to reduce ambiguities, a final discussion about lithology of sediments, geometry of basement and location of main faults is always needed. In this case, two equally probable models are proposed to interpret the Vallès Basin structure. One of them presents a shallow basin with granitic basement below. The other one presents a deeper basin, the granitic bedrock is located at 2000 m depth, with conglomerate deposits near the main fault. In both cases, the obtained models detect the Vallès fault as a sub-vertical fault which slightly diminishes its slope from 1200-1400 m in depth.

These new results in the Vallès Basin provide valuable information for geothermal purposes, but should be completed with more geophysical data to assure the geological model. As a future work, the gravity data will be extended at the whole basin in order to create a 3D geological model. To accomplish this objective, it will be fundamental to construct a very dense mesh of gravity points (good resolution) which affords a plausible hypothesis about the basin geological structure.

How to cite: Gabàs, A., Macau, A., Bellmunt, F., Benjumea, B., Sedano, J., and Figueras, S.: Geophysical basin characterization using seismic noise H/V spectral ratio and gravity data (Vallès Basin NE-Spain)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19542, https://doi.org/10.5194/egusphere-egu2020-19542, 2020.

Local and teleseismic body wave inversions are two approaches commonly used to obtain 3D Earth velocity models for shallow and mantle scale, respectively. However, each method used separately is poorly resolved at the mantle/crust boundary while imaging that interface is important to understand the geodynamic processes (e.g. magmatic underplating, mantle delamination, crustal thinning or thickening) occurring at this depth. In order to develop a high-resolved final velocity model, the two approaches were combined. First, an irregular grid was settled, with a higher density of nodes at crustal scale (from 0 to 40 km) and an increasing node step when approaching the limits of the model. Then, an a priori 3D crustal velocity model (from an independent local tomography) was inserted within the 1D IASP91 lithospheric one. Finally, the teleseismic tomographic inversion was carried out at crust-to-upper mantle scale using this new mixed initial model and teleseismic data. We applied the method on a real case that includes both tectonic and magmatic processes, the North Tanzanian Divergence (NTD). Synthetic tests showed that we had no resolution between 0 and 35 km. However, a fine crustal grid with the 3D local model helps to better constrain ray paths, limiting the artefacts and smearing from the mantle to the crust, enhancing details, sharpening the velocity anomalies and modifying the geometry of anomalies at depth (> 150 km). Following these tests, we propose then a final scheme in which we include the a priori crustal 3D velocity model in the finer crustal grid, and we prevent the inversion from modifying it. This insertion of strong crustal constraints in teleseismic inversion provides sharper spatial resolution at both crustal and mantle scales, including areas with poor ray coverage, beneath the NTD region. Our strategy allows to counteract the degradation of the results in areas with low velocity zones (such as rift and hotspot), where the seismic rays go around these anomalies.

How to cite: Clutier, A., Gautier, S., and Tiberi, C.: High-resolution teleseismic body-wave tomography with a 3D initial crustal model for crust-to-upper mantle images in highly heterogeneous media., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3564, https://doi.org/10.5194/egusphere-egu2020-3564, 2020.

With the steadily increasing availability and density of seismic data, full-waveform inversion (FWI) can reveal the Earth's subsurface with unprecedented resolution. FWI, however, carries a significant computational burden. Even with the ever-increasing power of high-performance computing resources, these massive compute requirements inhibit substantial progress, and require algorithmic and technological innovations for global and continental scale inversions.
In this contribution, we present an approach to FWI where we achieve significant computational savings through wavefield adapted meshing [1] combined with a stochastic optimization scheme [2]. This twofold strategy allows us (a) to solve the wave equation at lower costs, and (b) to reduce the number of required simulations. In laterally smooth media, we can construct meshes which are adapted to the expected complexity of the wavefield. By optimally designing a unique mesh for each source, we can reduce the computational cost of the forward and adjoint simulations by an order of magnitude. The stochastic optimization scheme is based on a dynamic mini-batch L-BFGS approach, which adaptively subsamples the event catalogue and requires significantly fewer wavefield simulations to converge to a model than conventional FWI. An additional benefit of the dynamic mini-batches is that they seamlessly allow for the inclusion of more sources in an inversion without a considerable additional computational cost.
We demonstrate a prototype FWI for this approach towards a global scale inversion with real data.

[1] Thrastarson, S., van Driel, M., Krischer, L., Afanasiev, M., Boehm, C., van Herwaarden, DP., Fichtner, A., 2019. Accelerating numerical wave propagation by wavefield adapted meshes, Part II: Full-waveform inversion. Submitted to Geophysical Journal International
[2] van Herwaarden, DP., Boehm, C., Afanasiev, M., Krischer, L., van Driel, M., Thrastarson, S., Trampert, J., Fichtner, A. 2019. Accelerated full-waveform inversion using dynamic mini-batches. Submitted to Geophysical Journal International

How to cite: Thrastarson, S., van Herwaarden, D.-P., Krischer, L., Boehm, C., van Driel, M., Afanasiev, M., and Fichtner, A.: Whole Earth Full-Waveform Inversion With Wavefield Adapted Meshes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18459, https://doi.org/10.5194/egusphere-egu2020-18459, 2020.

We present an evolutionary full-waveform inversion based on dynamic mini-batch optimization, which naturally exploits redundancies in observed data from different sources and allows the model to evolve along with the amount of available information in the data.

Quasi-random subsets (mini-batches) of sources are used to approximate the misfit and the gradient of the complete dataset. The size of the mini-batch is dynamically controlled by the desired quality of the approximation of the full gradient. Within each mini-batch, redundancy is minimized by selecting sources with the largest angular differences between their respective gradients, and spatial coverage is maximized by selecting candidate events with Mitchell’s best-candidate algorithm. Information from sources included in a previous mini-batch is incorporated into each gradient calculation through a quasi-Newton approximation of the Hessian, and a consistent misfit measure is achieved through the inclusion of a control group of sources.

By design, the dynamic mini-batch approach has several main advantages: (1) The use of mini-batches with adaptive sizes minimizes the number of redundant simulations per iteration, thus potentially leading to significant computational savings. (2) Curvature information is accumulated and used during the inversion, using a stochastic quasi-Newton method. (3) Data from new events or different time windows can seamlessly be incorporated during the iterations, thereby enabling an evolutionary mode of full-waveform inversion.

To illustrate our method, we start an inversion for upper mantle structure beneath the African plate. Starting from a smooth 1-D background model for a dataset recorded in the years 1990 to 1995, we then sequentially add more and more recent data into the inversion and show how the model can evolve as a function of data coverage. The mini-batch sampling approach allows us to incorporate data from several hundred earthquakes without increasing the computational burden, thereby going significantly beyond previous regional-scale full-waveform inversions.

How to cite: van Herwaarden, D.-P., Boehm, C., Afanasiev, M., Thrastarson, S., Krischer, L., Trampert, J., and Fichtner, A.: Evolutionary full-waveform inversion with dynamic mini-batches, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17921, https://doi.org/10.5194/egusphere-egu2020-17921, 2020.

In challenging environments with natural seismicity and where active source acquisition is expensive and dangerous, the question arises whether naturally occurring earthquakes offer useful information for hydrocarbon exploration. Here, we report on an experiment that installed 252/247 receivers to acquire data for two periods of 7 months, in the presence of significant rugged topography (elevations from 500 m to 3500 m). The station density is about 1 per 25 km^2 (compared to, e.g., USArray, where the average station spacing was 70 km). Data were recorded in a frequency band from 0.2 Hz to 50 Hz. Several thousand seismic events originating within the array bounds were identified in these data. A compressional-speed tomographic velocity model was derived using first-arriving phases. Centroid moment tensor (CMT) solutions have been obtained for about 4% of the identified events using Green’s function-based multicomponent waveform inversion, assuming a layered velocity model. We are now working to improve that model by performing elastic full-waveform inversion for three-dimensional compressional and shear-wave speed perturbations, honoring the topography, after a prior full-wavefield-based reassessment of the earthquake source mechanisms. We are also aiming to increase the number of events considered in the inversion while weighting the data based on estimates of data quality. This is assessed with a flexible automated procedure that considers a variety of data attributes over a range of frequencies. We run simulations using the spectral-element package SPECFEM3D on a cluster that employs 4 GPU cards per simulation. We identify the promising areas of good initial fit from the highest-quality seismic traces and gradually bring the predictions in line with the observations via LBFGS model optimization. We review the results of our work so far, discuss how to continue to bring best practices from global seismology down to the regional scale, and consider the implications for using such passive experiments to complement or replace active exploration in such challenging zones.

How to cite: Liu, Q., Simons, F., Gao, F., Williamson, P., and Tromp, J.: Earthquake-based Full-Waveform Inversion at the Exploration Scale from Dense Broadband Array Data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12246, https://doi.org/10.5194/egusphere-egu2020-12246, 2020.

Numerical simulation of seismic wavefield is helpful to understand the propagation law of seismic wave in complex media. In addition, accurate simulation of seismic wave propagation is of great importance for seismic inversion. The discontinuous Galerkin finite element method(DG-FEM) combines the advantages of finite element method(FEM) and finite volume method(FVM) to effectively simulate the propagation characteristics of seismic waves in complex medium.

In this study, we use the hp-adaptive DG -FEM to perform accurate simulation of seismic wave propagation in complex topography and medium, and compare the results with the analytical solution of the Generalized Reflection/Transmission(GRT) coefficient method. Furthermore, ADE CFS-PML is modified and applied to DG-FEM, which greatly reduces the impact of artificial boundaries.

How to cite: Xu, Y., Chen, X., Han, D., and Zhang, W.: Complex topography and media implementation using hp-adaptive DG-FEM for seismic wave modeling , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21047, https://doi.org/10.5194/egusphere-egu2020-21047, 2020.

In-seam seismic methods have been widely used in underground coal mine exploitation since early 80’s. They are helpful for identification of stress concentration zones or to locate geological disturbances within the coal seam. Usually, such surveys are optimized to perform seismic tomography. Therefore, sources and receivers are located on the opposite sides of the longwall. Results are produced in form of velocity maps of body-waves for rock-coal-rock medium or maps of group velocity and frequency of Airy-phase of dispersive waves trapped inside the coal seam, so-called channel waves. However, with the above geometry, the high-resolution imaging of the rock mass close to the roadway, including excavation-damaged zone (EDZ), is hampered by the available ray coverage.  In order to overcome this limitation, sources and receivers should be mounted in the same roadway. There is also a fundamental problem contributing to the lack of a robust method to image such area, which is the complexity of the seismic wavefield in the vicinity of the EDZ in a coal seam, where both surface tunnel waves and Rayleigh and Love-type channel waves overlap. We address this problem using numerical simulations. We use finite-difference method and viscoelastic model with petrophysical parameters for coal and host rock layers representative for the Upper Silesia mining district. First, we analyze seismic waves propagation within simple rock-coal-rock model, particularly channel waves dispersion properties. Then, we add a roadway with 3-meter thick EDZ to the model. Velocity and density within the EDZ linearly decrease up to 70% close to the free surface of excavation. By analyzing particle motion close to the free-surface, we observe that for very short wavelengths, the main energy is traveling as a fundamental mode of Rayleigh surface tunnel wave (for horizontal components). However, for longer wavelengths, the main energy is focused around frequency of Airy-phase of fundamental mode of Love-type channel wave. Eventually, we insert 10% Gaussian-shape velocity anomaly with 20 m width in the middle of the roadway to the model and investigate changes in frequency and group velocity of Airy-phase of Love-type channel waves for different offsets. We notice that the group velocity and frequency of maximum energy correspond to the velocity anomaly. For longer offsets, these parameters are approaching theoretical values for undisturbed medium. We conclude that because the group velocity of the Airy-phase is close to the coal S-wave velocity, it can be possible to image the velocity of such wave in the vicinity of the roadway, especially when the thickness of the coal seam is known.  

This research is supported by Polish National Science Centre grant no UMO-2018/30/Q/ST10/00680.

How to cite: Czarny, R. and Malinowski, M.: Modeling of seismic wave propagation around coal mine roadway with presence of excavation-damaged zone , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4797, https://doi.org/10.5194/egusphere-egu2020-4797, 2020.

Full waveform inversion (FWI) is one of the most attractive geophysical inversion methods that reconstruct models with higher quality by exploiting the information of full wave-field. Despite its high resolution and successful practical applications, there still exist several obstacles to the successful application of FWI for passive earthquake sources, such as the high non-linearity for model convergence and demand for accurate source information, such as the moment tensor, the source time function, etc. To alleviate the requirement for a priori source information in waveform inversion, we propose a new method called Waveform Energy Focusing Tomography (WEFT), which backpropagates the observed wavefield from the receivers, not the data residuals like in conventional FWI, and tries to maximize the back-propagated wavefield energy around the source location over a short period around the origin time. Therefore, there is no need to provide the focal mechanism and source time function in advance. To better reconstruct the passive sources, the least-squares moment tensor migration approach is used, and the Hessian matrix is approximated using either analytic expression or raytracing. Since waveform fitting is superseded by simpler energy maximization, the nonlinearity of WEFT is weaker than that of FWI, and even less-accurate initial velocity model can be used. These advantages of WEFT make it more practical  for challenging earthquake data, especially for local small magnitude earthquakes where both velocity model and earthquake source information are unknown.

How to cite: Hu, Y., Li, J., and Zhang, H.: Waveform Energy Focusing Tomography for Passive Sources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3504, https://doi.org/10.5194/egusphere-egu2020-3504, 2020.

How to cite: Gebraad, L., Zunino, A., Fichtner, A., and Mosegaard, K.: HMCtomo: A framework for Hamiltonian Monte Carlo sampling of Bayesian geophysical inverse problems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9394, https://doi.org/10.5194/egusphere-egu2020-9394, 2020.

In this study, we develop a new adjoint- and full-waveform inversion approach for low-amplitude seismic phases that are typically below noise in individual recordings. The methodology aims at enhancing weak signals from body wave phases, which can be used in full-waveform inversion for inferring structural and boundary parameters in the earth. The new approach is based on the formulation of misfit functionals and corresponding adjoint sources for stacks of suitably time-shifted recordings. 

To tackle this problem, we compute synthetic waveforms using spectral-elements for models with and without topographic variations along mantle discontinuities. We focus on global underside reflections which are reportedly almost always undetectable in real seismograms due to their low amplitudes and are considerably affected by topography. We enforce phase alignment on a chosen reference seismogram recorded at an average distance among the selected stations. A time shift towards the reference is applied to all seismograms according to their epicentral distance calculated by 1-D ray tracing. A set of time shifts is calculated by cross-correlation in time windows around predicted traveltimes of the desired phase. Using this set of time shifts, we sum the waveforms creating the main stack for each model.

We use the two linear stacks as observed and synthetic (with and without topography, respectively) and develop a least-squares misfit measurement which gives rise to an adjoint source determined by the time shift between stacks. The expectation is that computing the traveltime Fréchet kernel with respect to volumetric and boundary model parameters will show the exact sensitivity of the enhanced signal and save time from computing each station kernel separately. Upon achieving signal enhancement of the desired phases, we can ensure that these can be used for better informing updates of the initial model given the higher quality measurement of the observable.

This method once fully developed will allow us to leverage information of many recordings by reducing incoherent signal and enhancing weak seismic phases. The computation of sensitivity kernels in our study has a twofold importance. Firstly, it helps us realise whether the stacking technique indeed enhances the desired signal and whether it is ideal for precursor waves. Secondly, the exact sensitivity kernels show us the way of incorporating finite-frequency effects of weak but informative phases and introducing non-linear inversion for improving imaging while reducing some computational cost. 

How to cite: Koroni, M. and Fichtner, A.: Full-waveform inversion for signal enhancement of weak amplitude phases using beamforming and adjoint methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9096, https://doi.org/10.5194/egusphere-egu2020-9096, 2020.

How to cite: Perez Alemany, H., Sladen, A., Monteiller, V., and Delouis, B.: Influence of subduction zone complexities on teleseismic records, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4739, https://doi.org/10.5194/egusphere-egu2020-4739, 2020.

Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of waveforms, InSAR or GPS displacement data, or for seismic source characterization. At its core, Pyrocko is a  library  and  framework  providing  building  blocks  for researchers  and  students  wishing  to  develop  their  own applications. Pyrocko contains a few standalone applications for everyday seismological practice. These include the Snuffler program, an extensible seismogram browser and workbench, the Cake tool, providing travel-time and ray-path computations for 1D layered earthmodels, Fomosto, a tool to manage pre-calculated Green’s function stores, Jackseis, a command-line tool for common waveform archive data manipulations, Colosseo, a tool to create synthetic earthquake scenarios, serving waveforms and static displacements, and new, Sparrow, a 3D geophysical data visualization tool. This poster gives a glimpse of Pyrocko’s features, for more examples and tutorials visit https://pyrocko.org/.

How to cite: Heimann, S., Kriegerowski, M., Isken, M., Vasyura-Bathke, H., Cesca, S., Nooshiri, N., Petersen, G., Metz, M., Steinberg, A., Sudhaus, H., and Dahm, T.: Pyrocko - A Versatile Software Framework for Seismology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18734, https://doi.org/10.5194/egusphere-egu2020-18734, 2020.