Particle swarm optimization for seismic velocity analysis

Fortini, C.; Maggi, Davide; Lipari, Vincenzo; Milano, Politecnico di; Ferla, Maurizio

doi:10.1190/segam2013-0850.1

Cited by 7 publications

(17 citation statements)

References 5 publications

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“…The PSO algorithm was applied to NMO, NHMO and AD–NMO analysis by Fortini et al . () by treating moveout analysis as a global optimization problem. We describe quantitatively the steps involved in implementing the PSO algorithm in the context of the optimization problem with cost function ) in Appendix .…”

Section: Methodsmentioning

confidence: 99%

“…Fortini et al . () suggested 20,000 function evaluations as a suitable rule‐of‐thumb stopping criteria for their PSO moveout inversion. The problem with such a criterion is that it is heavily problem dependent (Zielinski et al .…”

Section: Methodsmentioning

confidence: 99%

“…Fortini et al . () treat the semblance domain as the solution space in which the maximum semblance for each reflector is treated as the goal of the optimization procedure. Fortini et al .…”

Section: Introductionmentioning

confidence: 99%

“…Fortini et al . () make no mention of how they localized the optimization problem for each reflector and applied no convergence criteria other than a rule‐of‐thumb number of evaluations until termination suggestion of 20,000 function evaluations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

Wilson

Gross

2019

Geophysical Prospecting

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The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Fortini et al . () treat the semblance domain as the solution space in which the maximum semblance for each reflector is treated as the goal of the optimization procedure. Fortini et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

Wilson

Gross

2019

Geophysical Prospecting

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“…The abstractness of this process is useful while locating a suitable receiver location as more information of other receiver positioning can be obtained. In passive network monitoring, research has been conducted in developing an automatic survey design technique based on PSO algorithm 12,13,14 . This work serves as the main motivation for this hydrocarbon exploration survey optimization 15,16,17 .…”

Section: Particle Swarm Optimization (Pso): a Heuristic Search Methodsmentioning

confidence: 99%

Optimizing Acquisition Geometry in Shallow Gas Cloud Using Particle Swarm Optimization Approach

Latiff¹,

Ghosh²,

Latiff³

2017

IJCIS

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Many hydrocarbon explorations in mature fields have been severely affected by complex and overburdening issues, such as shallow gas accumulation, gas pockets, and gas seepage. In this work, a new forward modelling technique is proposed in evaluating the potential survey design for fields affected by shallow gas cloud. In recent years, the implementation of innovative acquisition layouts has been producing significantly better seismic images, especially in the low illumination subsurface area. However, the uncertainty of the effectiveness in new acquisition design subsurface coverage always become a major stumbling block. To overcome this constraint, an optimization approach is suggested through the smart source and receiver location arrangement on the surface, with significant alignment to the conventional source and receiver arrangement approach. The particle swarm optimization (PSO) method is used to find the source-receiver configuration with maximum subsurface illumination coverage for the gas affected field situated in Malaysia Basin. Implementation of the PSO algorithm requires both a velocity model building process and wave field extrapolation from a target reflector to the surface level. The wave field data then was used to simulate receiver optimization outputs which eventually determined the subsurface illumination coverage. The results from the new optimization method for both synthetic model and Malaysia Basin data, offer a greater understanding of the consequences of obstacles caused by shallow anomalies with respect to seismic acquisition, data processing, and interpretation.

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Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

Zhang

2023

Geophysical Prospecting

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An accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.

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Particle swarm optimization for seismic velocity analysis

Cited by 7 publications

References 5 publications

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

Optimizing Acquisition Geometry in Shallow Gas Cloud Using Particle Swarm Optimization Approach

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

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