Simulated annealing velocity analysis: automating the picking process

Geophysics ◽  
2020 ◽  
pp. 1-48
Author(s):  
Danilo Velis
Keyword(s):  
Simulated Annealing ◽  
Prior Information ◽  
Simulated Annealing Algorithm ◽  
Piecewise Linear ◽  
Velocity Analysis ◽  
3D Space ◽  
Common Depth Point ◽  
Automated Method ◽  
Very Fast Simulated Annealing ◽  
Nmo Correction

We propose an automated method for velocity picking that allows to estimate appropriate velocity functions for the normal moveout (NMO) correction of common depth point (CDP) gathers, valid for either hyperbolic or nonhyperbolic trajectories. In the hyperbolic velocity analysis case the process involves the simultaneous search (picking) of a certain number of time-velocity pairs where the semblance, or any other coherence measure, is high. In the nonhyperbolic velocity analysis case, a third parameter, usually associated with the layering and/or the anisotropy, is added to the searching process. The proposed technique relies on a simple but effective search of a piecewise linear curve defined by a certain number of nodes in a 2D or 3D space that follows the semblance maxima. The search is carried out efficiently using a constrained very fast simulated annealing algorithm. The constraints consist of static and dynamic bounding restrictions, which are viewed as a means to incorporate prior information about the picking process. This allows to avoid those maxima that correspond to multiples, spurious, and other meaningless events. Results using synthetic and field data show that the proposed technique permits to automatically obtain accurate and consistent velocity picks that lead to flattened events, in agreement with the manual picks. As an algorithm, the method is very flexible to accommodate additional constraints (e.g. preselected events) and depends on a limited number of parameters. These parameters are easily tuned according to data requirements, available prior information, and the user's needs. The computational costs are relatively low, ranging from a fraction of a second to, at most, 1-2 seconds per CDP gather, using a standard PC with a single processor.

Global optimization strategies for implementing 3D common-reflection-surface stack using the very fast simulated annealing algorithm: Application to real land data

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. V253-V261 ◽  
Author(s):  
German Garabito
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computational Cost ◽  
Velocity Model ◽  
Global Search ◽  
Optimization Strategy ◽  
Very Fast Simulated Annealing ◽  
Common Reflection Surface ◽  
Annealing Algorithm

The 3D common-reflection-surface (CRS) stack operator depends on eight kinematic wavefield attributes that must be extracted from the prestack data. These attributes are obtained by an efficient optimization strategy based on the maximization of the coherence measure of the seismic reflection events included by the CRS stacking operator. The main application of these kinematic attributes is to simulate zero-offset stacked data; however, they can also be used for regularization of the prestack data, prestack migration, and velocity model determination. The initial implementations of the 3D CRS stack used grid-search techniques to determine the attributes in several steps with the drawback that accumulated errors can deteriorate the final result. In this work, the global optimization very fast simulated annealing algorithm is used to search for the kinematic attributes by applying three optimization strategies for implementing CRS stacking: (1) simultaneous global search of five kinematic attributes of the 3D common-diffraction-surface stacking operator, (2) two-step global optimization strategy to first search for three attributes and then five attributes of the CRS stacking operator, and (3) simultaneous global search of eight kinematic attributes of the CRS operator. The proposed CRS stacking algorithms are applied to land data of the Potiguar Basin, Brazil. It is demonstrated that the one-step optimization strategy of the eight parameters produces the best results, however, with a higher computational cost.

scholarly journals Using Very Fast Simulated Annealing in the Weighted AB Semblance method

2019 ◽  
Vol 37 (4) ◽  
Author(s):  
Marcelo Souza ◽  
Milton Porsani
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
Real Data ◽  
Velocity Spectrum ◽  
Velocity Analysis ◽  
Inversion Algorithm ◽  
Coherence Measure ◽  
Reflection Seismic ◽  
Very Fast Simulated Annealing ◽  
Velocity Spectra

ABSTRACTThe conventional velocity analysis does not consider AVO effects in reflection seismic data. These conditions lead to obtaining of inadequate velocity fields, making it difficult to execute other steps in seismic processing. To overcome this problem, researchers developed the Weighted AB semblance method, a coherence measure which deals with AVO effects in velocity spectra. It is based on the application of two sigmoid weighting functions to AB semblance, which depend on four coefficients. The values of these coefficients directly influence the resolution of the resulting velocity spectrum. In this work, we apply the inversion algorithm Very Fast Simulated Annealing (VFSA) to obtain these values. Numerical experiments show that VFSA is a quite effective method, obtaining correct coefficient values and allowing the generation of the velocity spectrum with an excellent resolution for both synthetic and real data. Results also proved that Weighted AB semblance is an optimal coherence measure to be used in velocity spectrum, because it is insensitive to AVO effects and reversal polarity and presents considerably a better resolution than conventional semblance.Keywords: velocity analysis, AVO, high-resolution velocity spectra RESUMOA análise de velocidades convencional não considera efeitos de AVO em dados sísmicos de reflexão. Essas condições levam à obtenção de campos de velocidades inadequados, dificultando a execução de outras etapas do processamento sísmico. Para superar esse problema, pesquisadores desenvolveram o método AB semblance Ponderado, uma medida de coerência que lida com efeitos de AVO em espectros de velocidades. Ela ´e baseada na aplicação de duas funções sigmoides à AB semblance, que depende de quatro coeficientes. Os valores desses coeficientes influenciam diretamente a resolução do espectro de velocidade resultante. Nesse trabalho, n´os aplicamos o algoritmo de inversão Very Fast Simulated Annealing (VFSA) para obter esses valores. Experimentos numéricos mostram que VFSA é um método bastante eficaz, obtendo valores corretos dos coeficientes e permitindo a geração do espectro de velocidade com uma excelente resolução tanto para dados sintéticos quanto para dados reais. Resultados também provam que o AB semblance Ponderado ´e uma medida de coerência ótima para ser usada no espectro de velocidade, porque ela é insensível aos efeitos de AVO e apresenta resolução consideravelmente melhor do que a semblance convencional.Palavras-chave: análise de velocidades, AVO, espectro de velocidades de alta resolução.

2.5D controlled-source electromagnetic inversion using very fast simulated annealing algorithm

2019 ◽  
Author(s):  
Piyoosh Jaysaval ◽  
Debanjan Datta ◽  
Mrinal K. Sen ◽  
Adrien F. Arnulf ◽  
Bertrand Denel ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Controlled Source ◽  
Very Fast Simulated Annealing ◽  
Annealing Algorithm

Automatic NMO correction and velocity estimation by a feedforward neural network

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1696-1707 ◽  
Author(s):  
Carlos Calderón‐Macas ◽  
Mrinal K. Sen ◽  
Paul L. Stoffa
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Spatial Data ◽  
Velocity Estimation ◽  
Feedforward Neural Network ◽  
Velocity Analysis ◽  
Seismic Velocities ◽  
Lateral Velocity ◽  
Data Set ◽  
Nmo Correction

We describe a new method of automatic normal moveout (NMO) correction and velocity analysis that combines a feedforward neural network (FNN) with a simulated annealing technique known as very fast simulated annealing (VFSA). The task of the FNN is to map common midpoint (CMP) gathers at control locations along a 2-D seismic line into seismic velocities within predefined velocity search limits. The network is trained while the velocity analysis is performed at the selected control locations. The method minimizes a cost function defined in terms of the NMO-corrected data. Network weights are updated at each iteration of the optimization process using VFSA. Once the control CMP gathers have been properly NMO corrected, the derived weights are used to interpolate results at the intermediate CMP locations. In practical situations in which lateral velocity variations are expected, the method is applied in spatial data windows, each window being defined by a separate FNN. The method is illustrated with synthetic data and a real marine data set from the Carolina Trough area.

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