<b>Inversion of potential field data by genetic algorithms</b>

Boschetti, Fabio; Dentith, Mike; List, Ron

doi:10.1046/j.1365-2478.1997.3430267.x

Cited by 53 publications

(18 citation statements)

References 17 publications

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“…They complement the study by coupling heuristic and deterministic methods in their inversion scheme. Boschetti et al (1997) also present a genetic algorithm that simultaneously generates a large number of different estimates to various potential field inverse problems. Smith and Ferguson (2000) develop an interesting tomographic inversion of refracted seismic waves using a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

et al. 2015

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We developed a new strategy, based on graph theory concepts, to invert gravity data using an ensemble of simple point masses. Our method consisted of a genetic algorithm with elitism to generate a set of possible solutions. Each estimate was associated to a graph to solve the minimum spanning tree (MST) problem. To produce unique and stable estimates, we restricted the position of the point masses by minimizing the statistical variance of the distances of an MST jointly with the data-misfit function during the iterations of the genetic algorithm. Hence, the 3D spatial distribution of the point masses identified the skeleton of homogeneous gravity sources. In addition, our method also gave an estimation of the anomalous mass of the source. So, together with the anomalous mass, the skeleton could aid other 3D methods with promising geometric a priori parameters. Several tests with different values of regularizing parameter were made to bespeak this new regularizing strategy. The inversion results applied to noise-corrupted synthetic gravity data revealed that, regardless of promising starting models, the estimated distribution of point masses and the anomalous mass offered valuable information about the homogeneous sources in the subsurface. Tests on real data from a portion of Quadrilátero Ferrífero, Minas Gerais state, Brazil, were performed for complementary analysis of the proposed inversion method.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

et al. 2015

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“…In this sense, optimization techniques based on Genetic Algorithms have opened up a new possibility for solving the inverse problem in a non-linear context in several areas of geophysics such as seismic applications (Boschetti et al 1996;Billings et al 1994;Sen and Stoffa 1995, etc.). In the study of the gravity inversion problem, Boschetti et al (1997) used this optimization method, but only to detect the border between two bodies with different density contrasts.…”

Section: Introductionmentioning

confidence: 99%

Using a genetic algorithm for 3-D inversion of gravity data in Fuerteventura (Canary Islands)

Montesinos¹,

Arnoso²,

Vieira³

2005

Int J Earth Sci (Geol Rundsch)

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The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the non-linearity inherent in such applications. We have implemented a genetic algorithm to efficiently invert a set of gravity data. Employing several fixed density contrasts, this algorithm determines the geometry of the sources of the anomaly gravity field in a 3-D context. The genetic algorithms, based on Darwin's theory of evolution, seek the optimum solution from an initial population of models, working with a set of parameters by means of modifications in successive iterations or generations. This searching method traditionally consists of three operators (selection, crossover and mutation) acting on each generation, but we have added a further one, which smoothes the obtained models. In this way, we have designed an efficient inversion gravity method, confirmed by both a synthetic example and a real data set from the island of Fuerteventura. In the latter case, we identify crustal structures related to the origin and evolution of the island. The results show a clear correlation between the sources of gravity field in the model and the three volcanic complexes recognized in Fuerteventura by other geological studies.

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“…The problem results in 60 parameters to recover (60-D). This is a high-dimensional, but fairly simple, optimization problem (Boschetti et al 1997). The second problem is to recover the seismic velocity of the subsurface (which also relates to rock density) from measurements of the travel time of seismic waves, from a number of sources, to several receivers located along a survey line on the Earth's surface.…”

Section: Real-world Problemsmentioning

confidence: 99%

A local linear embedding module for evolutionary computation optimization

Boschetti

2007

J Heuristics

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A Local Linear Embedding (LLE) module enhances the performance of two Evolutionary Computation (EC) algorithms employed as search tools in global optimization problems. The LLE employs the stochastic sampling of the data space inherent in Evolutionary Computation in order to reconstruct an approximate mapping from the data space back into the parameter space. This allows to map the target data vector directly into the parameter space in order to obtain a rough estimate of the global optimum, which is then added to the EC generation. This process is iterated and considerably improves the EC convergence. Thirteen standard test functions and two real-world optimization problems serve to benchmark the performance of the method. In most of our tests, optimization aided by the LLE mapping outperforms standard implementations of a genetic algorithm and a particle swarm optimization. The number and ranges of functions we tested suggest that the proposed algorithm can be considered as a valid alternative to traditional EC tools in more general applications. The performance improvement in the early stage of the convergence also suggests that this hybrid implementation could be successful as an initial global search to select candidates for subsequent local optimization.Evolutionary Computation (EC) is often used as a global search method in real world optimization problems. This is due to the fact that many real world optimization problems are nonlinear and result in multimodal objective functions. In such applications, local optimization methods, (e.g., matrix inversion, steepest descent, conjugate gradients) which are prone to trapping in local minima, have limited success.Given some (usually measured) target data, an optimization problem seeks to reconstruct the unknown parameters that, via the use of an appropriate function, can F. Boschetti ( ) CSIRO-EM,

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Inversion of potential field data by genetic algorithms

Cited by 53 publications

References 17 publications

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

Using a genetic algorithm for 3-D inversion of gravity data in Fuerteventura (Canary Islands)

A local linear embedding module for evolutionary computation optimization

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