BP neural network and improved differential evolution for transient electromagnetic inversion

Zhang, Huaiqing; Zhuang, Qiong; Li, Ruiheng; Chen, Yue

doi:10.1016/j.cageo.2020.104434

Cited by 24 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various parameters of sediment layers affect the reflection, transmission, and the paths and directions of sound wave propagation at the seabed boundary. Therefore, investigating the properties of seafloor sediments is crucial for understanding the propagation patterns of marine acoustic fields (Li et al, 2020). In this context, seawater is considered as a homogeneous isotropic fluid medium, and the seafloor sediments as an elastomeric medium.…”

Section: Methodsmentioning

confidence: 99%

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

Wang,

Cui,

Zhu

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

IntroductionExisting methods primarily focus on earth acoustic parameters inversion under specific layered structures. However, they face challenges with experimental data from unknown seabed stratification, hindering accurate parameter inversion.MethodsTo address this, a novel algorithm combines Back Propagation Neural Network (BPNN) for distinguishing seabed stratification and inverting acoustic parameters. Simulated sound pressure data disturb seabed parameters as input, enabling feature recognition for training the neural network inversion model. Acoustic parameters are then estimated under identified stratification using the sound field model.ResultsThe inversion model is validated using simulation and pool shrinkage data. Results show the neural network model effectively stratifies simulation and experimental data, providing accurate inversion results for acoustic parameters corresponding to distinct layers.DiscussionThe neural network model's accuracy and practicality are confirmed through hierarchical judgment of scale test data and acoustic parameter inversion. This approach introduces a new perspective for shallow sea acoustic parameter inversion, offering a promising application scenario.

show abstract

Section: Methodsmentioning

confidence: 99%

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

Wang,

Cui,

Zhu

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…By referring to the solutions of other heuristic optimization algorithms to solve engineering problems, and improving the way of evolution of heuristic algorithms, an applicable multi-threading technology can be developed based on the characteristics of the optimizer [31], and the search efficiency can be improved by optimizing the population of candidate solutions [32][33][34]. The binary scheme selector [35], the optimization strategy selector [36][37][38], and the dynamic parameter selector [36,37,39] can be used.…”

Section: Machine Learning Inversionmentioning

confidence: 99%

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

Li,

Di,

Zuo

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion—a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

show abstract

“…The coefficient m controls the reactive power performance. The time-domain-induced vertical magnetic field H z (t) and induced electromotive force (EMF) ε can be transferred from frequency-domain induced magnetic field signals [3], and be obtained using the piecewise linear approximation method as follows [27,50]:…”

Section: Tem Forward and Inversionmentioning

confidence: 99%

TEM Strata Inversion Imaging with IP Effect Based on Enhanced GCN by Extracting Long-Dependency Features

Li,

Di,

Tian

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

Utilizing neural network models to inverse time-domain electromagnetic signals enables rapid acquisition of electrical structures, a non-intrusive method widely applied in geological and environmental surveys. However, traditional multi-layer perceptron (MLP) feature extraction is limited, struggling with cases involving complex electrical media with induced polarization effects, thereby limiting the inversion model’s predictive capacity. A graph-topology-based neural network model for strata electrical structure imaging with long-dependency feature extraction was proposed. We employ graph convolutional networks (GCN) for capturing non-Euclidean features like resistivity-thickness coupling and Long Short-Term Memory (LSTM) to capture long-dependency features. The LSTM compensates for GCN’s constraints in capturing distant node relationships. Using case studies with 5-strata and 9-strata resistivity models containing induced polarization effects, compared to traditional MLP networks, the proposed model utilizing time-domain features and graph-topology-based electrical structure extraction significantly improves performance. The mean absolute error in inversion misfit is reduced from 10–20% to around 2–3%.

show abstract

BP neural network and improved differential evolution for transient electromagnetic inversion

Cited by 24 publications

References 16 publications

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

TEM Strata Inversion Imaging with IP Effect Based on Enhanced GCN by Extracting Long-Dependency Features

Contact Info

Product

Resources

About