Modified symbiotic organisms search (SOS) algorithm for controlled-source audio-frequency magnetotellurics (CSAMT) one-dimensional (1D) modelling

Grandis, Hendra; Sungkono, Sungkono

doi:10.1007/s12040-021-01808-7

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…In this case, the hybrid PSO-GWO algorithm converged rather slowly but achieved lower final misfits compared to the original PSO and GWO. Based on the previous similar tests with CSAMT synthetic data, misfit for synthetic data with 5% Gaussian noise relative to data without noise is in the order of 0.05 (Grandis and Sungkono, 2022). In these tests, a misfit up to 0.06 is considered acceptable.…”

Section: Application To Synthetic Csamt Datamentioning

confidence: 99%

“…Therefore, global optimization methods are often favoured to overcome fundamental problems often encountered in local search or gradient-based inversions, i.e. possible convergence to local minima and sensitivity to the initial model selection (Wen et al, 2019;Grandis and Sungkono, 2022). In recent years, population-based global optimization algorithms are gaining interest to resolve complex problems in engineering and extended to geophysical inversions.…”

Section: Introductionmentioning

confidence: 99%

“…(Grandis and Sungkono, 2022),Copyright held(s) by author(s), publishing rights belongs to publisher, pp. 65-80, DOI: 10.17794/rgn.2023.3.6(14) …”

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confidence: 99%

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Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Junian,

Grandis

2023

MGPB

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The Controlled Source Audio-frequency Magnetotellurics (CSAMT) is a geophysical method utilizing artificial electromagnetic signal source to estimate subsurface resistivity structures. One-dimensional (1D) inversion modelling of CSAMT data is non-linear and the solution can be estimated by using global optimization algorithms. Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) are well-known population-based algorithms having relatively simple mathematical formulation and implementation. Hybridization of PSO and GWO algorithms (called hybrid PSO-GWO) can improve the convergence capability to the global solution. This study applied the hybrid PSO-GWO algorithm for 1D CSAMT inversion modelling. Tests were conducted with synthetic CSAMT data associated with 3-layer, 4-layer and 5-layer earth models to determine the performance of the algorithm. The results show that the hybrid PSO-GWO algorithm has a good performance in obtaining the minimum misfit compared to the original PSO and GWO algorithms. The hybrid PSO-GWO algorithm was also applied to invert CSAMT field data for gold mineralization exploration in the Cibaliung area, Banten Province, Indonesia. The algorithm was able to reconstruct the resistivity model very well which is confirmed by the results from inversion of the data using standard 2D MT inversion software. The model also agrees well with the geological information of the study area.

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Section: Application To Synthetic Csamt Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Junian,

Grandis

2023

MGPB

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“…Currently, the most commonly data processing is 1D inversion, but unfortunately, it cannot reflect the underground 3D structures precisely. Therefore, in the future, developing multi-information joint inversions under geological structure constraints, electromagnetic big data technology [85], and 3D exploration technology are likely to become the trends. Taking the seismic inversion as an example, OVT processing technology and full waveform inversion are potential technologies of wide azimuth seismic data processing [86][87][88].…”

Section: Data Processingmentioning

confidence: 99%

Review and Challenges in the Geophysical Mapping of Coal Mine Water Structure

Chen

Zhang

et al. 2022

Geofluids

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Generally, the water-bearing structures of coal mines mainly include coal seam, roof, coal seam, floor, and goaf, while the main water-conducting structures include faults, collapsed columns, and collapsed goaf areas. The most commonly used methods for the detection of the above structures include the seismic method, high-density electrical method, controlled source audio-frequency magnetotelluric method, and transient electromagnetic method. Theoretically, the seismic methods have a higher resolution, which can be used to determine the targets’ geometry, but unable to determine whether the target is filled with water, while the electromagnetic methods are capable of this, although with lower resolution. Therefore, it is necessary to adopt the comprehensive geophysical prospecting in the actual field measurement of the coal mine water to guarantee the detection accuracy. Based on this, in the passage, first, we introduced the characteristics of the water-bearing and water-conducting structures and then analyzed the detection results of different methods by examples. Finally, we pointed out that, first, it is essential to develop the mine or airborne methods for the sake of convenience; and second, it is time that we adopt three-dimensional detection technology, multisource and multiresolution detection technology, and electromagnetic big data technology for high accuracy.

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“…Te symbiotic organisms search (SOS) algorithm [12], inspired from relationships among the ecosystem species, is a new promising nature-inspired algorithm. It has been widely used in various practical problems, such as the load frequency control problem [13], the distributed generators allocation problem [14], the resource leveling problem [15], the structural optimization [16], and the geophysical inversion problem [17,18].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems

Zhao

Liu

2023

Complexity

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The symbiotic organisms search (SOS) algorithm is a current effective meta-heuristic algorithm, which is been applied to solve various types of optimization problems. However, the SOS can easily lead to overexploration in the parasitism phase, and it is difficult to balance between exploration and exploitation capabilities. In the present work, two extended versions of the SOS are proposed. Two different weight strategies (i.e., random-weight and adaptive-weight) are utilized to generate the weighted mutual vector, respectively. Meanwhile, the best organism is employed to produce the modified artificial parasite vector. The performance of the two improved algorithms is evaluated on 35 test functions. The results demonstrate that the proposed algorithms are able to provide very promising results. Furthermore, five real-world problems are solved by the two newly proposed methods. Experimental results demonstrate that the presented algorithms are more efficient than the compared algorithms. All the obtained results further indicate that the two proposed algorithms are competitive and provide better results when compared to a wide range of algorithms, including SOS and its five modified versions, as well as ten other meta-heuristic algorithms.

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Modified symbiotic organisms search (SOS) algorithm for controlled-source audio-frequency magnetotellurics (CSAMT) one-dimensional (1D) modelling

Cited by 4 publications

References 35 publications

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Review and Challenges in the Geophysical Mapping of Coal Mine Water Structure

Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems

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