An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results.

[1]  Xiujuan Lei,et al.  Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm , 2016, Science China Information Sciences.

[2]  Norman S. Neidell,et al.  Amplitude variation with offset , 1986 .

[3]  Irfan Akca,et al.  Applications of hybrid genetic algorithms in seismic tomography , 2011 .

[4]  Haibin Duan,et al.  Pigeon inspired optimization approach to model prediction control for unmanned air vehicles , 2016 .

[5]  Jinghuai Gao,et al.  Multimutation Differential Evolution Algorithm and Its Application to Seismic Inversion , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Xuesong Yan,et al.  Spark-based intelligent parameter inversion method for prestack seismic data , 2019, Neural Computing and Applications.

[7]  Stefan Krause,et al.  Swarm intelligence in animals and humans. , 2010, Trends in ecology & evolution.

[8]  Haibin Duan,et al.  Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning , 2014, Int. J. Intell. Comput. Cybern..

[9]  Paul G. Richards,et al.  Quantitative Seismology: Theory and Methods , 1980 .

[10]  Xuesong Yan,et al.  An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem , 2019, Concurr. Comput. Pract. Exp..

[11]  Zhou Yu,et al.  An improved differential evolution algorithm for nonlinear inversion of earthquake dislocation , 2014 .

[12]  M. J. F. I. Alfonso,et al.  Some Improvements on Relativistic Positioning Systems , 2018, Applied Mathematics and Nonlinear Sciences.

[13]  Subhashis Mallick,et al.  Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm , 1995 .

[14]  Uğur Özcan,et al.  A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing , 2017, J. Intell. Manuf..

[15]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[16]  Xuesong Yan,et al.  Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm , 2017, Cluster Computing.

[17]  Kalachand Sain,et al.  Traveltime and constrained AVO inversion using FDR PSO , 2016 .

[18]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[19]  Wei Gao,et al.  Ontology learning algorithm for similarity measuring and ontology mapping using linear programming , 2017, J. Intell. Fuzzy Syst..

[20]  Sam Zandong Sun,et al.  PSO non-linear pre-stack inversion method and the application in reservoir prediction , 2012 .

[21]  Ivan Priezzhev,et al.  Nonlinear Multitrace Seismic Inversion Using Neural Network and Genetic Algorithm - "Genetic Inversion , 2008 .

[22]  Haibin Duan,et al.  Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration , 2015 .

[23]  Antonio Hernández Zavala,et al.  CSIMFS: An algorithm to tune fuzzy logic controllers , 2017, J. Intell. Fuzzy Syst..

[24]  Xiaohua Wang,et al.  Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Weijian Mi,et al.  An AIMMS-based decision-making model for optimizing the intelligent stowage of export containers in a single bay , 2019, Discrete & Continuous Dynamical Systems - S.

[26]  Bo Zhang,et al.  Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Xueqiang Zhang,et al.  Differential evolution algorithm for nonlinear inversion of high-frequency Rayleigh wave dispersion curves , 2014 .

[28]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[29]  Xuesong Yan,et al.  Elastic parameter inversion problem based on brain storm optimization algorithm , 2018, Memetic Computing.

[30]  S. Sankareswaran.  A Novel Pigeon Inspired Optimization in Ovarian Cyst Detection , 2016 .

[31]  Xuesong Yan,et al.  Intelligent inversion method for pre-stack seismic big data based on MapReduce , 2018, Comput. Geosci..

[32]  Nilanjan Dey,et al.  Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings , 2016, Neural Computing and Applications.