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<p>Vertical Electrical Sounding (VES) data inversion is a complex non-linear problem requiring robust global optimization methods. While techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are widely used, the Flower Pollination Algorithm (FPA) offers distinct advantages: (1) faster convergence due to its Lévy flight mechanism, (2) handling the overfitting problem, and (3) superior balance between exploration and exploitation. In this study, we enhance FPA with adaptive switch probability to further optimize its performance for VES inversion. The adaptive switch probability in FPA dynamically adjusts the balance between global exploration (early iterations) and local exploitation (late iterations), improving convergence speed and accuracy compared to fixed-probability FPA. This is achieved by gradually reducing the switch probability&#xa0;p&#xa0;from = 0.8&#xa0;to = 0.2&#xa0;over iterations, optimizing the search process for VES inversion. Tests were conducted using synthetic VES data with 3-layer (Q-type and H-type) and 4-layer. The adaptive FPA demonstrated superior performance to conventional FPA, reducing misfit errors by 30–50% across synthetic models (e.g., 0.0011% vs. 0.0044% for H-type) while maintaining 40% faster convergence rates. Improvement was quantified using three key criteria: (1) final misfit error, (2) iteration count to convergence, and (3) parameter uncertainty, with the adaptive version consistently outperforming in all metrics. Adaptive switch FPA was also applied to VES field data for groundwater exploration in Leihitu village, Central Maluku, Indonesia. The adaptive FPA's subsurface reconstructions were validated through direct comparison with IP2WIN commercial software, showing superior resolution in identifying layer boundaries (≤ 5% deviation in thickness estimates) and resistivity values (≤ 8% relative error). In Leihitu village, the freshwater aquifer (18–22 Ωm) at 22–33.8&#xa0;m depth sandwiched between impermeable limestone layers (&gt; 300 Ωm) and the aquifer is sandstone which has good porosity. In the future, the FPA adaptive switch can be tested to solve complex non-linear geophysical inversion problems such as Controlled Source Audio-frequency Magnetotellurics (CSAMT), Magnetotelluric (MT), Transient Electromagnetic (TEM), and others.</p>

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Adaptive Flower Pollination Algorithm (FPA) for Vertical Electrical Sounding (VES) Inversion Modelling

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Samsul Bahri,
  • Aditya Ramadhan,
  • Philipus Josepus Patty

摘要

Vertical Electrical Sounding (VES) data inversion is a complex non-linear problem requiring robust global optimization methods. While techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are widely used, the Flower Pollination Algorithm (FPA) offers distinct advantages: (1) faster convergence due to its Lévy flight mechanism, (2) handling the overfitting problem, and (3) superior balance between exploration and exploitation. In this study, we enhance FPA with adaptive switch probability to further optimize its performance for VES inversion. The adaptive switch probability in FPA dynamically adjusts the balance between global exploration (early iterations) and local exploitation (late iterations), improving convergence speed and accuracy compared to fixed-probability FPA. This is achieved by gradually reducing the switch probability p from = 0.8 to = 0.2 over iterations, optimizing the search process for VES inversion. Tests were conducted using synthetic VES data with 3-layer (Q-type and H-type) and 4-layer. The adaptive FPA demonstrated superior performance to conventional FPA, reducing misfit errors by 30–50% across synthetic models (e.g., 0.0011% vs. 0.0044% for H-type) while maintaining 40% faster convergence rates. Improvement was quantified using three key criteria: (1) final misfit error, (2) iteration count to convergence, and (3) parameter uncertainty, with the adaptive version consistently outperforming in all metrics. Adaptive switch FPA was also applied to VES field data for groundwater exploration in Leihitu village, Central Maluku, Indonesia. The adaptive FPA's subsurface reconstructions were validated through direct comparison with IP2WIN commercial software, showing superior resolution in identifying layer boundaries (≤ 5% deviation in thickness estimates) and resistivity values (≤ 8% relative error). In Leihitu village, the freshwater aquifer (18–22 Ωm) at 22–33.8 m depth sandwiched between impermeable limestone layers (> 300 Ωm) and the aquifer is sandstone which has good porosity. In the future, the FPA adaptive switch can be tested to solve complex non-linear geophysical inversion problems such as Controlled Source Audio-frequency Magnetotellurics (CSAMT), Magnetotelluric (MT), Transient Electromagnetic (TEM), and others.