Abstract
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.
Highlights
First proposed in the early 1990s, the swarm intelligence algorithm is a stochastic optimisation algorithm that is largely inspired by the phenomena of biological swarm intelligence in nature and mimics the behaviour of social animals [1]
As the optimisation algorithm judges the performance of each individual based on the fitness function converted from the objective function, the performance of the objective function constructed for the inversion problem is the main factor affecting the effectiveness of the pre-stack amplitude variation with offset (AVO) elastic parameter inversion
This study proposed an improved pigeon-inspired optimisation (PIO) algorithm that is more suitable for solving the inversion
Summary
First proposed in the early 1990s, the swarm intelligence algorithm is a stochastic optimisation algorithm that is largely inspired by the phenomena of biological swarm intelligence in nature and mimics the behaviour of social animals [1]. Inspired by the homing behaviour of pigeons in nature, in 2014, Duan et al [7] proposed the pigeon-inspired optimisation (PIO) algorithm, a pigeon homing behaviour-based swarm intelligence optimisation algorithm that has made remarkable achievements in recent years in various fields, such as unmanned aerial vehicle (UAV) formation, parameter change control, and image processing. The three-dimensional route planning for unmanned combat aerial vehicles (UCAVs) is a complex optimisation problem that focuses on the routing optimisation of aircraft with different constraints in complex, dynamic environments [9] To address this problem, Zhang and Duan proposed a new predator-prey PIO (PPPIO) algorithm, which improves the global optimisation performance and convergence rate by adopting the concept of predator and prey. The experimental comparison results with other optimisation algorithms indicate that the proposed IPIO algorithm can achieve better inversion results
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