scholarly journals PEMODELAN INVERSI DATA MAGNETOTELLURIK 1-D MENGGUNAKAN METODA GENETIC ALGORITHM (GA) DENGAN POPULASI MICRO GENETIC ALGORITHM KASUS 3 LAYER DAN 5 LAYER

2019 ◽  
Vol 8 (2) ◽  
pp. 167-180
Author(s):  
Nia Maharani
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Selection Process ◽  
Synthetic Data ◽  
Inversion Method ◽  
Earth Model ◽  
Observation Data ◽  
Linear Inversion ◽  
Synthetic Model ◽  
Micro Genetic Algorithm

This paper discusses non-linear inversion method with Genetic Algorithm (GA) which inspired by natural selection process (survival for the fittest) and genetic using 20 populations (micro genetic algorithm). The method is applied to 1-D magnetotelluric inverted data with model parameter is resistivity as a function of depth. This research only uses synthetic data obtained from synthetic model. The model is homogeneous earth model with 3 and 5 layers. Perturbation of model is performed until minimum misfit between theoritical and observation data achieved. The 3 layers and 5 layers inversion processes are applied to 3 layers and 5 layers earth model respectively, with satisfactory results in other words it can reproduce the synthetic model.

Traveltime inversion of offset vertical seismic profiles—A feasibility study

Geophysics ◽  
1984 ◽  
Vol 49 (3) ◽  
pp. 250-264 ◽  
Author(s):  
L. R. Lines ◽  
A. Bourgeois ◽  
J. D. Covey
Keyword(s):  
Inverse Method ◽  
Synthetic Data ◽  
Real Data ◽  
Seismic Profile ◽  
Inversion Method ◽  
Earth Model ◽  
Seismic Profiles ◽  
Traveltime Inversion ◽  
Vertical Seismic Profiles ◽  
Inversion Techniques

Traveltimes from an offset vertical seismic profile (VSP) are used to estimate subsurface two‐dimensional dip by applying an iterative least‐squares inverse method. Tests on synthetic data demonstrate that inversion techniques are capable of estimating dips in the vicinity of a wellbore by using the traveltimes of the direct arrivals and the primary reflections. The inversion method involves a “layer stripping” approach in which the dips of the shallow layers are estimated before proceeding to estimate deeper dips. Examples demonstrate that the primary reflections become essential whenever the ratio of source offset to layer depth becomes small. Traveltime inversion also requires careful estimation of layer velocities and proper statics corrections. Aside from these difficulties and the ubiquitous nonuniqueness problem, the VSP traveltime inversion was able to produce a valid earth model for tests on a real data case.

Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Biological Evolution ◽  
Synthetic Data ◽  
Posteriori Probability ◽  
Inversion Method ◽  
Model Parameters ◽  
East Texas ◽  
Data Set ◽  
A Posteriori Probability ◽  
Prestack Inversion

In this paper, a prestack inversion method using a genetic algorithm (GA) is presented, and issues relating to the implementation of prestack GA inversion in practice are discussed. GA is a Monte‐Carlo type inversion, using a natural analogy to the biological evolution process. When GA is cast into a Bayesian framework, a priori information of the model parameters and the physics of the forward problem are used to compute synthetic data. These synthetic data can then be matched with observations to obtain approximate estimates of the marginal a posteriori probability density (PPD) functions in the model space. Plots of these PPD functions allow an interpreter to choose models which best describe the specific geologic setting and lead to an accurate prediction of seismic lithology. Poststack inversion and prestack GA inversion were applied to a Woodbine gas sand data set from East Texas. A comparison of prestack inversion with poststack inversion demonstrates that prestack inversion shows detailed stratigraphic features of the subsurface which are not visible on the poststack inversion.

Refinements to the linear velocity inversion theory

Geophysics ◽  
1984 ◽  
Vol 49 (2) ◽  
pp. 112-118 ◽  
Author(s):  
Frank G. Hagin ◽  
Jack K. Cohen
Keyword(s):  
Synthetic Data ◽  
Scattered Wave ◽  
Scattering Data ◽  
Inversion Method ◽  
Impedance Change ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Velocity Inversion ◽  
Linear Algorithm ◽  
Inversion Theory

The linear inversion method presented by Cohen and Bleistein in 1979 gives seriously degraded results when large reflectors are encountered. Obviously there is an irrecoverable loss of information when such a linear algorithm is applied to a nonlinear world. However, in many cases, excellent results can be achieved by suitable postprocessing of the output of the basic linear inversion algorithm. Although a certain degree of helpful postprocessing can be and has been performed by straightforward consideration of the linearization process, we present here a substantially improved postprocessing algorithm. The basis for these improvements is a more accurate scattering model due to Lahlou et al where, among other things, a WKB analysis of the wave equation led to a much more accurate accounting of the geometric spreading of the scattered wave. These notions plus an effective use of traveltime are used in the new algorithm to improve both the estimate of the reflector locations and the estimate of amplitude (velocity or acoustic impedance) change across the reflectors. The basic idea is to insert this idealized scattering data into the original linear algorithm, and then use the result of this computation as a guide in the interpretation of the numerical output of the algorithm. We demonstrate the result of computer implementation of this algorithm on synthetic data, with and without noise, and verify that the postprocessing algorithm produces dramatically improved reflector locations and speed estimates. Moreover, the new algorithm adds only very modest cost to the basic processing, which is, in turn, very competitive in cost to other multidimensional algorithms.

scholarly journals Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1758
Author(s):  
Musatafa Abbas Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad AL-Dhief
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Optimization Problems ◽  
Selection Process ◽  
Optimal Solution ◽  
Selection Theory ◽  
Exploitation And Exploration ◽  
Comparison Of The Results ◽  
Benchmark Test Functions ◽  
Natural Selection Theory

The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

Aerosol size spectra from spectral extinction data: the use of a linear inversion method

1982 ◽  
Vol 21 (9) ◽  
pp. 1578 ◽  
Author(s):  
R. Rizzi ◽  
R. Guzzi ◽  
R. Legnani
Keyword(s):  
Inversion Method ◽  
Extinction Data ◽  
Linear Inversion ◽  
Size Spectra
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