High-performance Parallel Solver for Integral Equations of Electromagnetics Based on Galerkin Method

A new parallel solver for the volumetric integral equations (IE) of electrodynamics is presented. The solver is based on the Galerkin method which ensures the convergent numerical solution. The main features include: (i) the memory usage is 8 times lower, compared to analogous IE based algorithms, without additional restriction on the background media; (ii) accurate and stable method to compute matrix coefficients corresponding to the IE; (iii) high degree of parallelism. The solver's computational efficiency is shown on a problem of magnetotelluric sounding of the high conductivity contrast media. A good agreement with the results obtained with the second order finite element method is demonstrated. Due to effective approach to parallelization and distributed data storage the program exhibits perfect scalability on different hardware platforms.Comment: The main results of this paper were presented at IAMG 2015 conference Frieberg, Germany. 28 pages, 11 figure

3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms

We present a routine for 3D magnetotelluric (MT) modeling based upon high-order edge finite element method (HEFEM), tailored and unstructured tetrahedral meshes, and high-performance computing (HPC). This implementation extends the PETGEM modeller capabilities, initially developed for active-source electromagnetic methods in frequency-domain. We assess the accuracy, robustness, and performance of the code using a set of reference models developed by the MT community in well-known reported workshops. The scale and geological properties of these 3D MT setups are challenging, making them ideal for addressing a rigorous validation. Our numerical assessment proves that this new algorithm can produce the expected solutions for arbitrarily 3D MT models. Also, our extensive experimental results reveal four main insights: (1) high-order discretizations in conjunction with tailored meshes can offer excellent accuracy; (2) a rigorous mesh design based on the skin-depth principle can be beneficial for the solution of the 3D MT problem in terms of numerical accuracy and run-time; (3) high-order polynomial basis functions achieve better speed-up and parallel efficiency ratios than low-order polynomial basis functions on cutting-edge HPC platforms; (4) a triple helix approach based on HEFEM, tailored meshes, and HPC can be extremely competitive for the solution of realistic and complex 3D MT models and geophysical electromagnetics in general

Large-scale 3D inversion of marine magnetotelluric data: Case study from the Gemini prospect, Gulf of Mexico

Journal ArticleThree-dimensional magnetotelluric (MT) inversion is an emerging technique for offshore hydrocarbon exploration. We have developed a new approach to the 3D inversion of MT data, based on the integral equation method. The Tikhonov regularization and physical constraint have been used to obtain a stable and reasonable solution of the inverse problem. The method is implemented in a fully parallel computer code. We have applied the developed method and software for the inversion of marine MT data collected by the Scripps Institution of Oceanography (SIO) in the Gemini prospect, Gulf of Mexico. The inversion domain was discretized into 1.6 million cells. It took nine hours to complete 51 iterations on the 832-processor cluster with a final misfit between the observed and predicted data of 6.2%. The inversion results reveal a resistive salt structure, which is confirmed by a comparison with the seismic data. These inversion results demonstrate that resistive geoelectrical structures like salt domes can be mapped with reasonable accuracy using the 3D inversion of marine MTdata

3D Magnetotelluric Modeling Using High-Order Tetrahedral Nédélec Elements on Massively Parallel Computing Platforms

We present a routine for 3D magnetotelluric (MT) modeling based upon high-order edge finite element method (HEFEM), tailored and unstructured tetrahedral meshes, and high-performance computing (HPC). This implementation extends the PETGEM modeller capabilities, initially developed for active-source electromagnetic methods in frequency-domain. We assess the accuracy, robustness, and performance of the code using a set of reference models developed by the MT community in well-known reported workshops. The scale and geological properties of these 3D MT setups are challenging, making them ideal for addressing a rigorous validation. Our numerical assessment proves that this new algorithm can produce the expected solutions for arbitrarily 3D MT models. Also, our extensive experimental results reveal four main insights: (1) high-order discretizations in conjunction with tailored meshes can offer excellent accuracy; (2) a rigorous mesh design based on the skin-depth principle can be beneficial for the solution of the 3D MT problem in terms of numerical accuracy and run-time; (3) high-order polynomial basis functions achieve better speed-up and parallel efficiency ratios than low-order polynomial basis functions on cutting-edge HPC platforms; (4) a triple helix approach based on HEFEM, tailored meshes, and HPC can be extremely competitive for the solution of realistic and complex 3D MT models and geophysical electromagnetics in general.This project has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain–France– Andorra program (POCTEFA2014-2020). POCTEFA aims to reinforce the economic and social integration of the French–Spanish–Andorran border. Its support is focused on developing economic, social and environmental cross-border activities through joint strategies favoring sustainable territorial development. BSC authors received funding from the European Union’s Horizon 2020 programme, grant agreement N◦828947 and N◦777778, and from the Mexican Department of Energy, CONACYT-SENER Hidrocarburos grant agreement N◦B-S-69926

A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation

Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth's interior.Fil: Manassero, María Constanza. Macquarie University; AustraliaFil: Afonso, Juan Carlos. Macquarie University; AustraliaFil: Zyserman, Fabio Ivan. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Zlotnik, Sergio. Universidad Politécnica de Catalunya; EspañaFil: Fomin, I.. Macquarie University; Australi

Doctor of Philosophy

dissertationThe motivation for this work is the forward and inverse problem for magnetotellurics, a frequency domain electromagnetic remote-sensing geophysical method used in mineral, geothermal, and groundwater exploration. The dissertation consists of four papers. In the first paper, we prove the existence and uniqueness of a representation of any vector field in H(curl) by a vector lying in H(curl) and H(div). It allows us to represent electric or magnetic fields by another vector field, for which nodal finite element approximation may be used in the case of non-constant electromagnetic properties. With this approach, the system matrix does not become ill-posed for lowfrequency In the second paper, we consider hexahedral finite element approximation of an electric field for the magnetotelluric forward problem. The near-null space of the system matrix for low frequencies makes the numerical solution unstable in the air. We show that the proper solution may obtained by applying a correction on the null space of the curl. It is done by solving a Poisson equation using discrete Helmholtz decomposition. We parallelize the forward code on multicore workstation with large RAM. In the next paper, we use the forward code in the inversion. Regularization of the inversion is done by using the second norm of the logarithm of conductivity. The data space Gauss-Newton approach allows for significant savings in memory and computational time. We show the efficiency of the method by considering a number of synthetic inversions and we apply it to real data collected in Cascade Mountains. The last paper considers a cross-frequency interpolation of the forward response as well as the Jacobian. We consider Pade approximation through model order reduction and rational Krylov subspace. The interpolating frequencies are chosen adaptively in order to minimize the maximum error of interpolation. Two error indicator functions are compared. We prove a theorem of almost always lucky failure in the case of the right hand analytically dependent on frequency. The operator's null space is treated by decomposing the solution into the part in the null space and orthogonal to it

A reduced order approach for probabilistic inversions of 3D magnetotelluric data II: joint inversion of MT and surface-wave data

Joint probabilistic inversions of magnetotelluric (MT) and seismic data have great potential for imaging the thermochemical structure of the lithosphere as well as mapping fluid/melt pathways and regions of mantle metasomatism. In this contribution, we present a novel probabilistic (Bayesian) joint inversion scheme for 3D MT and surface-wave dispersion data particularly designed for large-scale lithospheric studies. The approach makes use of a recently developed strategy for fast solutions of the 3D MT forward problem (Manassero et al., 2020, https://doi.org/10.1093/gji/ggaa415) and combines it with adaptive Markov chain Monte Carlo (MCMC) algorithms and parallel-in-parallel strategies to achieve extremely efficient simulations. To demonstrate the feasibility, benefits and performance of our joint inversion method for imaging the temperature and conductivity structures of the lithosphere, we apply it to two numerical examples of increasing complexity. The inversion approach presented here is timely and will be useful in the joint analysis of MT and surface wave data that are being collected in many parts of the world. This approach also opens up new avenues for the study of trans-lithospheric and trans-crustal magmatic systems, the detection of metasomatized mantle, and the incorporation of MT into multi-observable inversions for the physical state of the Earth's interior.We thank Farshad Salajegheh for providing part of his Matlab codes for plotting results 850 Special thanks to Kate Selway and Anandaroop Ray for their suggestions at different 851 stages of this work. The 3D rendering views were created using ParaView (Ahrens et al., 852 2005). 853 MCM thanks support from an International Macquarie Research Excellence Schol854 arship (iMQRES). MCM and JCA acknowledge support from ARC Grant DP160103502, 855 ARC Linkage Grant LP170100233, the ARC Centre of Excellence Core to Crust Fluids 856 Systems (http://www.ccfs.mq.edu.au) and the Centre for Earth Evolution and Dynam857 ics, Geoscience Australia and the European Space Agency via the “3D Earth - A Dy858 namic Living Planet”. FZ acknowledges support from CONICET through grant PIP 112- 859 201501-00192. SZ has been funded by the Spanish Ministry through grant DPI2017-85139- 860 C2-2-R, by Catalan government through grant 2017-SGR-1278 and by the EU’s Hori861 zon 2020 research and innovation programme under the Marie Sklodowska-Curie grant 862 agreement No 777778.Peer ReviewedPostprint (author's final draft