scholarly journals Interpretation of self-potential anomaly over 2-D inclined thick sheet structures and analysis of uncertainty using very fast simulated annealing global optimization

2016 ◽  
Vol 52 (4) ◽  
pp. 439-455 ◽  
Author(s):  
Arkoprovo Biswas ◽  
Shashi Prakash Sharma
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Thick Sheet ◽  
Very Fast Simulated Annealing ◽  
Self Potential

Interpretation of self-potential anomaly over a 2D inclined structure using very fast simulated-annealing global optimization — An insight about ambiguity

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. WB3-WB15 ◽  
Author(s):  
Shashi Prakash Sharma ◽  
Arkoprovo Biswas
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Computation Time ◽  
Optimization Procedure ◽  
Model Space ◽  
Model Parameters ◽  
Local Optimum ◽  
Current Dipole ◽  
Very Fast Simulated Annealing ◽  
Self Potential

A very fast simulated-annealing (VFSA) global optimization procedure is developed for the interpretation of self-potential (SP) anomaly measured over a 2D inclined sheet-type structure. Model parameters such as electric current dipole density ([Formula: see text]), horizontal and vertical locations of the center of the causative body ([Formula: see text] and [Formula: see text]), half-width ([Formula: see text]), and polarization/inclination angle ([Formula: see text]) of the sheet are optimized. VFSA optimization yields a large number of well-fitting solutions in a vast model space. Even though the assumed model space (minimum and maximum limits for each model parameter) is appropriate, it has been observed that models obtained by the VFSA process in the predefined model space could also be geologically erroneous. This offers new insight into the interpretation of self-potential data. Our optimization results indicate that there exist at least two sets of solutions that can fit the observed data equally well. The first set of solutions represents a local optimum and is geologically inappropriate. The second set of solutions represents the actual subsurface structure. The mean model estimated from the latter models represents the global solution. The efficacy of the developed approach has been demonstrated using various synthetic examples. Field data from the Surda area of Rakha Mines, India and the Bavarian woods, Germany are also interpreted. The computation time for finding this versatile solution is very short (52 s on a simple PC) and the proposed approach is found to be more advantageous than other approaches.

Application of very fast simulated annealing and differential evolution in the search for FO-CRS wavefield attributes

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. O81-O92
Author(s):  
German Garabito ◽  
João Carlos R. Cruz
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Seismic Data ◽  
Optimization Algorithms ◽  
Two Dimensions ◽  
Seismic Section ◽  
Data Set ◽  
Computational Costs ◽  
Very Fast Simulated Annealing

The finite-offset common-reflection-surface (FO-CRS) stack method can be used to simulate any common-offset (CO) seismic section by stacking prestack seismic data along the surfaces defined by the paraxial hyperbolic traveltime approximation. In two dimensions, the FO-CRS stacking operator depends on five kinematic wavefield attributes for every time sample of the target CO section. The main problem with this method is identifying a computationally efficient data-driven search strategy for accurately determining the best set of FO-CRS attributes that produce the optimal coherence measure of the seismic signal in the prestack data. Identifying a global optimization algorithm with the best performance is a challenge when solving this optimization problem. This is because the objective function is multimodal and involves a large volume of data, which leads to high computational costs. We introduced a comparative and competitive study through the application of two global optimization algorithms that simultaneously search the FO-CRS attributes from the prestack seismic data, very fast simulated annealing (VFSA) and the differential evolution (DE). By applying this FO-CRS stack to the Marmousi synthetic seismic data set, we have compared the performances of the two optimization algorithms with regard to their efficiency and effectiveness in estimating the five FO-CRS attributes. To analyze the robustness of the two algorithms, we apply them to real land seismic data and show their ability to find the near-optimal attributes and to improve reflection events in noisy data with a very low fold. We reveal that VFSA is efficient in reaching the optimal coherence value with the lowest computational costs, and that DE is effective and reliable in reaching the optimal coherence for determining the best five searched-for attributes. Regardless of the differences, the FO-CRS stack produces enhanced and regularized high-quality CO sections using both global optimization methods.

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.

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