scholarly journals High-precision Joint 2D Traveltime Calculation for Seismic Processing

2018 ◽  
Vol 22 (4) ◽  
pp. 327-334 ◽  
Author(s):  
Hui Sun ◽  
Fanchang Meng ◽  
Zhihou Zhang ◽  
Cheng Gao ◽  
Mingchen Liu
Keyword(s):  
High Precision ◽  
Calculation Method ◽  
Numerical Models ◽  
New Method ◽  
Fast Marching Method ◽  
Surrounding Area ◽  
Fast Marching ◽  
Seismic Processing ◽  
Grid Nodes ◽  
Marching Method

Fast Marching Method (FMM) boasts high calculation efficiency and strong adaptability and stability while being applied to seismic traveltime. However, when it is applied to the largescale model, the calculation precision of FMM is insufficient. FMM has poor calculation precision near the source, which is an essential reason for the low accuracy of the whole algorithm. This paper puts forward a joint traveltime calculation method to address the problem. Wavefront Construction (WFC) with a relatively high calculation accuracy rather than FMM is adopted for calculation of the grid nodes’ traveltime near the source. After that, FMM is used to calculate the seismic traveltime in the remaining area. Joint traveltime calculation method greatly improves the calculation accuracy of the source’s surrounding area and the calculation accuracy of FMM. According to the new method, FMM is still adopted for the calculation of most grid nodes in the model, so the high calculation efficiency of FMM is maintained. Multiple numerical models are utilized to verify the above conclusions in the paper. 

scholarly journals Optimized Refraction Travel Time Tomography

2019 ◽  
Vol 9 (24) ◽  
pp. 5439 ◽  
Author(s):  
Sixin Liu ◽  
Zhuo Jia ◽  
Yinuo Zhu ◽  
Xueran Zhao ◽  
Siyuan Cheng
Keyword(s):  
Travel Time ◽  
Calculation Method ◽  
Seismic Refraction ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Forward Algorithm ◽  
Model Composite ◽  
Travel Time Tomography ◽  
Forward Calculation ◽  
Marching Method

In seismic refraction exploration, travel time tomography is the most widely used method in engineering and environmental geophysical exploration. In this paper, we mainly optimize the travel time tomography of refraction. First, with respect to the forward algorithm, we introduce a new travel time calculation method to improve the accuracy and efficiency of forward calculation. Based on the fast marching method (FMM), we introduce an improved forward calculation method called the multi-stencil fast marching method (MSFM). In the process of inversion, we propose a dynamic prior model composite constraint (DPMCC) method based on the T0 difference method from the idea of multi-scale inversion. Meanwhile, we use the prior information to improve the accuracy of inversion. Furthermore, we use the dynamic regularization factor selection method to make the inversion solution more stable and reliable. Finally, we test and analyze the synthetic data and the measured data to verify the effectiveness of the optimized travel time tomography algorithm.

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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