Forward modeling of magnetotelluric transverse electric mode with topography using finite element method

2015 ◽  
Author(s):  
Anggie Susilawati ◽  
Wahyu Srigutomo
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Transverse Electric ◽  
Forward Modeling ◽  
Electric Mode ◽  
Transverse Electric Mode ◽  
Element Method

scholarly journals Computational Finite Element Method (FEM) forward modeling workflow for transcranial Direct Current Stimulation (tDCS) current flow on MRI-derived head: Simpleware and COMSOL Multiphysics tutorial

2019 ◽  
Author(s):  
Ole Seibt ◽  
Dennis Truong ◽  
Niranjan Khadka ◽  
Yu Huang ◽  
Marom Bikson
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Computational Models ◽  
Current Flow ◽  
Forward Modeling ◽  
Fem Model ◽  
Direct Current Stimulation ◽  
Element Method

AbstractTranscranial Direct Current Stimulation (tDCS) dose designs are often based on computational Finite Element Method (FEM) forward modeling studies. These FEM models educate researchers about the resulting current flow (intensity and pattern) and so the resulting neurophysiological and behavioral changes based on tDCS dose (mA), resistivity of head tissues (e.g. skin, skull, CSF, brain), and head anatomy. Moreover, model support optimization of montage to target specific brain regions. Computational models are thus an ancillary tool used to inform the design, set-up and programming of tDCS devices, and investigate the role of parameters such as electrode assembly, current directionality, and polarity of tDCS in optimizing therapeutic interventions. Computational FEM modeling pipeline of tDCS initiates with segmentation of an exemplary magnetic resonance imaging (MRI) scan of a template head into multiple tissue compartments to develop a higher resolution (< 1 mm) MRI derived FEM model using Simpleware ScanIP. Next, electrode assembly (anode and cathode of variant dimension) is positioned over the brain target and meshed at different mesh densities. Finally, a volumetric mesh of the head with electrodes is imported in COMSOL and a quasistatic approximation (stead-state solution method) is implemented with boundary conditions such as inward normal current density (anode), ground (cathode), and electrically insulating remaining boundaries. A successfully solved FEM model is used to visualize the model prediction via different plots (streamlines, volume plot, arrow plot).

3D MT Anisotropic Inversion Based on Unstructured Finite-element Method

2021 ◽  
Vol 26 (1) ◽  
pp. 49-60
Author(s):  
Xiaoyue Cao ◽  
Xin Huang ◽  
Changchun Yin ◽  
Liangjun Yan ◽  
Bo Zhang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Hessian Matrix ◽  
Synthetic Data ◽  
Forward Modeling ◽  
Explicit Calculation ◽  
Computational Time ◽  
Earth Model ◽  
Earth Structures ◽  
Element Method

The conventional 3D magnetotelluric (MT) forward modeling and inversions generally assume an isotropic earth model. However, wrong results can be obtained when using an isotropic model to interpret the data influenced by the anisotropy. To effectively model and recover the earth structures including anisotropy, we develop a 3D MT inversion framework for a triaxial anisotropic model. We use the unstructured finite-element method for our forward modeling. This offers more possibility to simulate more complex underground geology and topography. To solve the inverse modeling problem, we use a limited-memory quasi-Newton algorithm (L-BFGS) with a parallel direct solver for optimization that avoids the explicit calculation of the Hessian matrix and saves the memory and computational time. We validate our algorithm via numerical experiments on both synthetic data and MT survey data from the US Array project.

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