Gravity Inversion for Heterogeneous Sedimentary Basin with b-Spline Polynomial Approximation using Differential Evolution Algorithm

Geophysics ◽  
2021 ◽  
pp. 1-63
Author(s):  
Arka Roy ◽  
Chandra Prakash Dubey ◽  
M. Prasad

A MATLAB-based inversion program, b-Spline Polynomial Approximation using Differential Evolution Algorithm (SPODEA), is introduced to recover the concealed basement geometry under heterogeneous sedimentary basins. Earlier inversion techniques used the discretized subsurface interface topography into a grid of juxtaposed elementary prisms to estimate the basement depth of a basin. Such discretization leads to the failure of depth profile continuity and requires a higher number of inversion parameters for achieving the desired accuracy. The novel approach of SPODEA overcomes such limitations of earlier inversion techniques. SPODEA is based on the segment-wise b-spline optimization technique to estimate the basement depth by using high-order polynomials.Moreover, it can achieve an optimal misfit with a minimal parametric information, which reduces the computational expense. The proposed inversion approach uses the differential evolution (DE) algorithm that provides real parametric optimization and uses b-splines for an accurate estimation of continuous depth profiles. The efficiency of the proposed algorithm is illustrated with two complex synthetic sedimentary basin models comprised of constant and depth varying density distributions. Furthermore, the uncertainty analysis of the proposed inversion technique is evaluated by incorporating white Gaussian noise into the synthetic models. Finally, the utility of SPODEA is illustrated by inverting gravity anomalies for two different real sedimentary basins. It produces geologically reasonable outcomes that are in close agreement with basement structures from previously reported results.

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO

2013 ◽  
Vol 8 (999) ◽  
pp. 1-6
Author(s):  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Mohd Shahir Shamsir ◽  
Lian En Chai ◽  
...  

Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.


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