Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

Li, Ruiheng; Yu, Nian; Zhuang, Qiong; Zhang, Huaiqing

doi:10.1002/nsg.12129

Cited by 11 publications

(9 citation statements)

References 32 publications

(34 reference statements)

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“…As depicted in Figure 3, for the selection of [δ i,min , δ i,max ], it primarily depends on two factors: one is selecting appropriate upper and lower boundary values based on the required layering accuracy in actual exploration, and the other considers the fact that the inversion accuracy of transient electromagnetic data gradually decreases with increasing depth. Hence, the threshold value for the change in layer thickness is set to increase with depth (e.g., during synthetic data simulation, it is set as within 200 M, more than 200 M, and more than 400 M of predicted depth, and the layer thickness thresholds are set to [5,25], [10,50], and [20,100], respectively). Second, since transient electromagnetic inversion exhibits highly nonlinear characteristics, introducing a regularization constraint for model smoothing is crucial to reduce false anomalies and enhance the model fitting effect.…”

Section: Piecewise Regularized Inversion Methodsmentioning

confidence: 99%

“…Additionally, Yang et al [8] utilized the damping least squares method for inverting transient electromagnetic data originating from conical sources; however, this type of inversion method requires a high-quality initial model and is prone to getting stuck in local optima. In contrast, nonlinear inversion approaches involve designing the initial model directly based on a priori information, as seen in works by Wang et al [9] and Li et al [10]. These methods are efficient but heavily dependent on the quality of a priori information, which can be challenging to obtain in large-area exploration.…”

Section: Introductionmentioning

confidence: 99%

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Segmental Regularized Constrained Inversion of Transient Electromagnetism Based on the Improved Sparrow Search Algorithm

Tan,

Ou,

Tan

et al. 2024

Applied Sciences

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The initial inversion model is typically established in a transient electromagnetic nonlinear inversion, assuming the accurate capture of the number of layers in the geoelectric model; however, this assumption leads to significantly poorer inversion results for complex models when obtaining the exact number of layers from available a priori information, which is challenging. This study proposes a segmented regularized inversion method to enhance inversion accuracy and stability under varying conditions. The process involves two key steps: Firstly, a segmented initial model is established based on preliminary information. The layering criteria and layer thickness threshold for each segment are set during inversion to reduce dependence on the accuracy of the preliminary information. Secondly, a segmented regularization constraint is added to the objective function to improve the efficiency and stability of the inversion, as numerous parameters can exacerbate the problem of inversion ambiguity. Subsequently, an improved sparrow search algorithm (ISSA) is utilized to optimize the inversion objective function. This enhances the efficiency of searching for the objective function and the algorithm’s ability to escape local optimal solutions. The proposed method is evaluated using one-dimensional and two-dimensional models with different initial models and inversion algorithms and applied to the inversion of on-site exploration data in a coal mining area in Chongqing. Comparative results demonstrate that the proposed segmented regularization method, based on the improved sparrow search algorithm, exhibits superior practicality and a higher fitting accuracy.

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Section: Piecewise Regularized Inversion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmental Regularized Constrained Inversion of Transient Electromagnetism Based on the Improved Sparrow Search Algorithm

Tan,

Ou,

Tan

et al. 2024

Applied Sciences

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“…If the differences and complementarities among different non‐linear optimization algorithms can be comprehensively utilized, the combination of different global‐searching non‐linear algorithms can not only achieve the complementary advantages between the algorithms, but also open up a new way for the inversion of complex multi‐mode dispersion curves. At present, the combination of various optimization algorithms has been proposed in different fields for specific problems, such as the combination of ANN and flower pollination algorithm to solve the seismic refraction data analysis (Poormirzaee et al., 2022), the combination of GA and PSO to solve the neural network reservoir permeability prediction problem (Ahmadi et al., 2013) and the combination of PSO and differential evolution algorithm to solve transient electromagnetic inversion (Li et al., 2021). Although the combination of global‐searching non‐linear algorithms has achieved some practical application results, there are still some problems that have not been completely solved.…”

Section: Introductionmentioning

confidence: 99%

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

Che,

Shen,

et al. 2024

Near Surface Geophysics

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Dispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.

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“…There are two types of TEM inversion methods: linear inversion and nonlinear inversion methods. Nonlinear inversion methods, such as genetic algorithm [20], simulated annealing algorithm [21], particle swarm optimization [22], and others, start with a random model. The nonlinear inversion method reduces the dependence of the initial model when compared to linear inversion methods such as least square, but the model update is stochastic and requires a lot of forwarding simulation and inversion calculation.…”

Section: Introductionmentioning

confidence: 99%

Tunnel Concealed Karst Cave Joint Detection by Tunnel Seismic and Transient Electromagnetic

et al. 2022

Lithosphere

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The water-rich giant karst cave is the risk source of tunnel construction in the karst area. To reduce the risk of tunnel construction, it is necessary to accurately explore the spatial distribution range of the karst cave in the direction of advance and both sides of the tunnel. Reflection seismic and transient electromagnetic (TEM) are the primary geophysical tools for tunnel advanced prediction; they have strengths and weaknesses. The tunnel seismic can only describe the boundary interface between cave bodies and surrounding rock in the direction of advance. Although the TEM method can detect the spatial distribution range of cave bodies, the inversion results depend on the initial resistivity model. The single method is insufficient to describe the spatial distribution of cave bodies. To overcome the shortcoming of a single method, we developed tunnel seismic and TEM joint detection of karst cavern and established a complete data processing flow. The tunnel seismic migration profiles can describe the interface between the karst cave and surrounding rock in the direction of advance. We put forward a reasonable initial model: (1) the layer’s thicknesses are determined by the impedance interface from the tunnel migration data; (2) the initial resistivity values are determined from the pilot hole and prior geological data. Comparison of the inversion results of different initial models of tunnel face horizontal line data proves that the proposed initial model method can reduce multisolution, save calculation time, and improve inversion accuracy. The multidirectional TEM inversion profiles can describe the spatial distribution of the giant cave. Combined with the results of tunnel seismic and TEM, the interpretation errors can be reduced and the extension of the karst cave can be delineated. Later excavation results also verify the accuracy of the prediction results.

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Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

Cited by 11 publications

References 32 publications

Segmental Regularized Constrained Inversion of Transient Electromagnetism Based on the Improved Sparrow Search Algorithm

Segmental Regularized Constrained Inversion of Transient Electromagnetism Based on the Improved Sparrow Search Algorithm

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

Tunnel Concealed Karst Cave Joint Detection by Tunnel Seismic and Transient Electromagnetic

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