Comparative Evaluation of the Fast Marching Method and the Fast Evacuation Method for Heterogeneous Media

2021 ◽  
Vol 35 (13) ◽  
pp. 1056-1080
Author(s):  
Severino F. Galán
Keyword(s):  
Comparative Evaluation ◽  
Heterogeneous Media ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Marching Method

PyKonal: A Python Package for Solving the Eikonal Equation in Spherical and Cartesian Coordinates Using the Fast Marching Method

2020 ◽  
Vol 91 (4) ◽  
pp. 2378-2389
Author(s):  
Malcolm C. A. White ◽  
Hongjian Fang ◽  
Nori Nakata ◽  
Yehuda Ben-Zion
Keyword(s):  
Open Source ◽  
Eikonal Equation ◽  
Heterogeneous Media ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Cartesian Coordinates ◽  
Source Codes ◽  
High Level ◽  
Marching Method ◽  
Python Package

Abstract This article introduces PyKonal: a new open-source Python package for computing travel times and tracing ray paths in 2D or 3D heterogeneous media using the fast marching method for solving the eikonal equation in spherical and Cartesian coordinates. Compiled with the Cython compiler framework, PyKonal offers a Python application program interface (API) with execution speeds comparable to C or Fortran codes. Designed to be accurate, stable, fast, general, extensible, and easy to use, PyKonal offers low- and high-level API functions for full control and convenience, respectively. A scale-independent implementation allows problems to be solved at micro, local, regional, and global scales, and precision can be improved over existing open-source codes by combining different coordinate systems. The resulting code makes state-of-the-art computational capabilities accessible to novice programmers and is efficient enough for modern research problems in seismology.

scholarly journals Fast Marching Method Aplication for Forward Modelling of Seismic Wave Propagation

2018 ◽  
Vol 16 (3) ◽  
pp. 1
Author(s):  
Wahyu Srigutomo ◽  
Ghany Hanifan Muslim
Keyword(s):  
Wave Propagation ◽  
Viscosity Solution ◽  
Seismic Wave ◽  
Eikonal Equation ◽  
Heterogeneous Media ◽  
Seismic Wave Propagation ◽  
Time Travel ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Marching Method

One of the classical problem in seismology is to determine time travel and ray path of seismic wave betweentwo points at a given heterogeneous media. This problem is expressed by eikonal equation and can be seen as a propagation of a wavefront and interface evolution. One of methods to solve this problem is Fast Marching Method abbreviated as FMM. This method is used to produce entropy-satisfying viscosity solution of eikonal equation. FMM combines viscosity solution of Hamilton-Jacobi equation and Huygen's Principle that centered on min-heap data structure to determine the minimum value at every loop. In this study, FMM is applied to determine time travel and raypath of seismic wave. FMM also is used to determine the location of wavesource using simple algorithm. From our forward modeling schemes, it is found that FMM is an accurate, robust, and effcient method to simulate seismic wave propagation. For further study, FMM also can be used to be a part of passive seismic inverse scheme to locate hypocenter location.

A Generalized Fast Marching Method on Unstructured Triangular Meshes

2013 ◽  
Vol 51 (6) ◽  
pp. 2999-3035 ◽  
Author(s):  
E. Carlini ◽  
M. Falcone ◽  
Ph. Hoch
Keyword(s):  
Fast Marching Method ◽  
Triangular Meshes ◽  
Fast Marching ◽  
Marching Method

scholarly journals Tsunami wave propagation by voronoi diagram

2018 ◽  
Vol 7 (3) ◽  
pp. 1233
Author(s):  
V Yuvaraj ◽  
S Rajasekaran ◽  
D Nagarajan
Keyword(s):  
Wave Propagation ◽  
Voronoi Diagram ◽  
Tsunami Wave ◽  
Fast Marching Method ◽  
Coastal Regions ◽  
Fast Marching ◽  
Tsunami Waves ◽  
The Finite Difference Method ◽  
Run Up ◽  
Marching Method

Cellular automata is the model applied in very complicated situations and complex problems. It involves the Introduction of voronoi diagram in tsunami wave propagation with the help of a fast-marching method to find the spread of the tsunami waves in the coastal regions. In this study we have modelled and predicted the tsunami wave propagation using the finite difference method. This analytical method gives the horizontal and vertical layers of the wave run up and enables the calculation of reaching time.  

Generalized fast marching method: applications to image segmentation

2008 ◽  
Vol 48 (1-3) ◽  
pp. 189-211 ◽  
Author(s):  
Nicolas Forcadel ◽  
Carole Le Guyader ◽  
Christian Gout
Keyword(s):  
Image Segmentation ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Marching Method

scholarly journals A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method

2019 ◽  
Vol 28 (4) ◽  
pp. 517-532 ◽  
Author(s):  
Sangeeta K. Siri ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Median Filter ◽  
Neutrosophic Set ◽  
Intensity Level ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Abdominal Ct ◽  
Novel Approach ◽  
Abdominal Ct Scan ◽  
Marching Method

Abstract Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.

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