A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

Pereira, João Eduardo; Strieder, Adelir José; Amador, Janete Pereira; Silva, José Luiz Silvério da; Filho, Leônidas Luiz Volcato Descovi

doi:10.1016/j.cageo.2009.03.009

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…It starts at a specific point in the solution space and systematically explores patterns or directions from that starting point. The effectiveness of pattern search can be influenced by the choice of the initial point and the patterns used for exploration [35][36][37]. A poor choice of the initial point may lead to suboptimal solutions, and the success of the algorithm is somewhat tied to the quality of the initial model.…”

Section: Hybrid Optimizationsmentioning

confidence: 99%

Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

Verma,

Kant,

Singh

et al. 2023

Int. J. Petrol. Technol.

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This research paper introduces a comparative case study on reservoir characterization through seismic inversion techniques. The study specifically explores sparse layer reflectivity and a hybrid approach involving genetic algorithms and pattern search. The research assesses the effectiveness of these methodologies in delineating subsurface properties, with a particular focus on acoustic impedance. Through meticulous analysis, the paper aims to identify the strengths and limitations of each method, considering factors such as parameter estimation precision, computational efficiency, and adaptability to complex geological structures. The findings contribute valuable insights for selecting optimal seismic inversion techniques in reservoir characterization, advancing our understanding of how the integration of sparse layer reflectivity and hybrid genetic algorithms can enhance subsurface imaging accuracy and reliability. The results obtained from our inversion process significantly enhance the interpretation of seismic data by providing detailed insights into the subsurface. Both the sparse layer reflectivity (SLR) and hybrid genetic algorithm (HGA) algorithms have exhibited outstanding performance when applied to real datasets. The inverted impedance section reveals notable low acoustic impedance ranging from 8000 to 8500 m/s g/cc. This distinct zone, identified as a reservoir (sand channel), is located within the time interval of 1040–1065 ms. Our observations indicate that HGA demonstrates superior correlation results not only in the vicinity of well locations but also over a broader spatial range, suggesting its potential to provide higher-resolution outcomes compared to SLR.

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Section: Hybrid Optimizationsmentioning

confidence: 99%

Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

Verma,

Kant,

Singh

et al. 2023

Int. J. Petrol. Technol.

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show abstract

“…Now, even though there are a lot of heuristic techniques and their use is very common in some areas of science, it is important to note that the application of heuristics in earth sciences is still limited. Only a few case studies have been reported in different sub-areas, such as Pattern Search (PS) applied to aero-geophysical data analysis [37]; Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), Harmony Search (HS), Generalized Simulated Annealing (GSA), and PS applied to hydrology/hydrogeology [38][39][40]; GA applied to meteorology [41]; Spiral Optimization (SO), Gravitational Search (GSA), Bath Algorithm (BA), GA, PSO, and Artificial Neuronal Networks (ANN) and Support Vector Machine (SVM) algorithms applied to seismic/seismology [42][43][44][45]. To our knowledge, there is only one work reported about the use of heuristics in geochemistry [46], in which Ant Colony Optimization (ACO) was used to properly identify geochemical anomalies.…”

Section: Introductionmentioning

confidence: 99%

Geochemical Modeling of the Mantle Partial Melting using Heuristic Exploration: An Optimization Model Applied to Earth Sciences

Soto-Villalobos

López

Walle-García

et al. 2023

Preprint

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An evolution strategy-type heuristic simulation tool was developed to optimize the mantle partial melting inverse modeling. An objective or fitness function was derived from the batch equation to model the source composition and the partial melting degree from the chemistry of near primary liquids. In the search algorithm structure was considered: (a) the geochemical system constraints, (b) an initialization step, and (c) a procedure of mutation and heuristic individual selection. The heuristic simulation was successfully applied in four study cases, as mineralogical and rare earth element (REE) composition of known peridotitic sources. Partial melting conditions were reproduced with a deviation ≤ 10−6 in a reasonably practical time (∼ 2 hours) by using a desktop computer.

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Geochemical modeling of the mantle partial melting using heuristic exploration: an optimization model applied to earth sciences

Soto-Villalobos,

Aguirre-López,

Walle-García

et al. 2023

Earth Sci Inform

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A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

Cited by 6 publications

References 25 publications

Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

Geochemical Modeling of the Mantle Partial Melting using Heuristic Exploration: An Optimization Model Applied to Earth Sciences

Geochemical modeling of the mantle partial melting using heuristic exploration: an optimization model applied to earth sciences

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