Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

Yi-jian Luo , Yi-an Cui , Jing Xie , He-shun-zi Lu , Jian-xin Liu

Journal of Central South University ›› 2021, Vol. 28 ›› Issue (6) : 1797 -1812.

PDF
Journal of Central South University ›› 2021, Vol. 28 ›› Issue (6) : 1797 -1812. DOI: 10.1007/s11771-021-4732-8
Article

Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

Author information +
History +
PDF

Abstract

Prticle swarm optimization (PSO) is adopted to invert the self-potential anomalies of simple geometry. Taking the vertical semi-infinite cylinder model as an example, the model parameters are first inverted using standard particle swarm optimization (SPSO), and then the searching behavior of the particle swarm is discussed and the change of the particles’ distribution during the iteration process is studied. The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively, thus PSO achieves remarkable results in the inversion of SP anomalies. Finally, six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions, inertia weights and learning factors are introduced for the inversion of the cylinder model, and the effectiveness of these algorithms is verified by numerical experiments. The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value, and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.

Keywords

self-potential / inversion / particle swarm optimization

Cite this article

Download citation ▾
Yi-jian Luo, Yi-an Cui, Jing Xie, He-shun-zi Lu, Jian-xin Liu. Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization. Journal of Central South University, 2021, 28(6): 1797-1812 DOI:10.1007/s11771-021-4732-8

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

JardaniA, DupontJ P, RevilA. Self-potential signals associated with preferential groundwater flow pathways in sinkholes. Journal of Geophysical Research: Solid Earth, 2006, 111(B9): B09204

[2]

XieJ, CuiY-a, ZhangL-j, MaC-y, YangB, ChenX-l, LiuJ-X. 3D forward modeling of seepage self-potential using finite-infinite element coupling method. Journal of Environmental and Engineering Geophysics, 2020, 253381-390

[3]

KawadaY, KasayaT. Marine self-potential survey for exploring seafloor hydrothermal ore deposits. Scientific Reports, 2017, 7(1): 13552

[4]

HeritianaA R, RivaR, RalayR, BoniR. Evaluation of flake graphite ore using self-potential (SP), electrical resistivity tomography (ERT) and induced polarization (IP) methods in east Coast of Madagascar. Journal of Applied Geophysics, 2019, 169: 134-141

[5]

MehaneeS A. Tracing of paleo-shear zones by self-potential data inversion: Case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes. Earth, Planets and Space, 2015, 67(1): 1-33

[6]

EppelbaumL V. Quantitative analysis of self-potential anomalies in archaeological sites of Israel: An overview. Environmental Earth Sciences, 2020, 79(15): 1-15

[7]

AbbasM, JardaniA, Soueid AhmedA, RevilA, BrigaudL, BégassatP, DupontJ P. Redox potential distribution of an organic-rich contaminated site obtained by the inversion of self-potential data. Journal of Hydrology, 2017, 554111-127

[8]

CuiY-a, ZhuX-x, WeiW-s, LiuJ-x, TongT-G. Dynamic imaging of metallic contamination plume based on self-potential data. Transactions of Nonferrous Metals Society of China, 2017, 27(8): 1822-1830

[9]

XieJ, CuiY-a, ZhangL-j, GuoY-j, WangJ-x, FanidiM, LiuJ-X. Numerical modeling of biogeobattery system from microbial degradation of underground organic contaminant. SN Applied Sciences, 2020, 2(2): 1-11

[10]

MehaneeS A. An efficient regularized inversion approach for self-potential data interpretation of ore exploration using a mix of logarithmic and non-logarithmic model parameters. Ore Geology Reviews, 2014, 57: 87-115

[11]

SindirgiP, ÖzyalinŞ. Estimating the location of a causative body from a self-potential anomaly using 2D and 3D normalized full gradient and Euler deconvolution. Turkish Journal of Earth Sciences, 2019, 28(4): 640-659

[12]

OlivetiI, CardarelliE. Self-potential data inversion for environmental and hydrogeological investigations. Pure and Applied Geophysics, 2019, 176(8): 3607-3628

[13]

LiuS, HuX-y, LiuT-Y. A stochastic inversion method for potential field data: Ant colony optimization. Pure and Applied Geophysics, 2014, 171(7): 1531-1555

[14]

AgarwalA, ChandraA, ShalivahanS, SinghR K. Grey wolf optimizer: A new strategy to invert geophysical data sets. Geophysical Prospecting, 2018, 66(6): 1215-1226

[15]

LiuS, LiangM, HuX-Y. Particle swarm optimization inversion of magnetic data: Field examples from iron ore deposits in China. Geophysics, 2018, 83(4): J43-J59

[16]

ZhangJ, ShenP, ZhaoW-n, GuoX-b, WangX, ChenS, XuX-G. AVA simultaneous inversion of prestack seismic data using particle swarm optimization. Journal of Earth Science, 2018, 29(6): 1390-1397

[17]

LiS-y, WangS-m, WangP-f, SuX-l, ZhangX-s, DongZ-H. An improved grey wolf optimizer algorithm for the inversion of geoelectrical data. Acta Geophysica, 2018, 66(4): 607-621

[18]

AbdelazeemM, GobashyM, KhalilM H, AbdrabouM. A complete model parameter optimization from self-potential data using Whale algorithm. Journal of Applied Geophysics, 2019, 170: 103825

[19]

GobashyM, AbdelazeemM, AbdrabouM, KhalilM H. Estimating model parameters from self-potential anomaly of 2D inclined sheet using whale optimization algorithm: Applications to mineral exploration and tracing shear zones. Natural Resources Research, 2020, 29(1): 499-519

[20]

SrigutomoW, HeriyantoM, AufaM H. Gravity inversion of talwani model using very fast simulated annealing. Journal of Mathematical and Fundamental Sciences, 2019, 51(2): 177-190

[21]

YuP, WangJ-l, WuJ-s, WangD-W. Constrained joint inversion of gravity and seismic data using the simulated annealing algorithm. Chinese Journal of Geophysics, 2007, 50(2): 465-475

[22]

Sungkono, WarnanaD D. Black hole algorithm for determining model parameter in self-potential data. Journal of Applied Geophysics, 2018, 148189-200

[23]

El-KalioubyH M, Al-GarniM A. Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks. Journal of Geophysics and Engineering, 2009, 6(1): 29-34

[24]

DasM B, SundararajanN. Analysis of self-potential anomalies due to 2D horizontal cylindrical structures—An artificial neural network approach. Arabian Journal of Geosciences, 2016, 9(7): 1-10

[25]

Di MaioR, PiegariE, RaniP, CarbonariR, VitaglianoE, MilanoL. Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach. Journal of Applied Geophysics, 2019, 162: 152-163

[26]

SchwarzbachC, BörnerR U, SpitzerK. Two-dimensional inversion of direct current resistivity data using a parallel, multi-objective genetic algorithm. Geophysical Journal International, 2005, 162(3): 685-695

[27]

WangH, LiuM-l, XiZ-z, PengX-l, HeH. Magnetotelluric inversion based on BP neural network optimized by genetic algorithm. Chinese Journal of Geophysics, 2018, 61(4): 1563-1575

[28]

WuX-m, LiangL-m, ShiY-z, FomelS. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 2019, 84(3): IM35-IM45

[29]

WuX-m, LiangL-m, ShiY-z, GengZ, FomelS. Multitask learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophysical Journal International, 2019, 219(3): 2097-2109

[30]

Sungkono. An efficient global optimization method for self-potential data inversion using micro-differential evolution. Journal of Earth System Science, 2020, 129(1): 1-22

[31]

KennedyJ, EberhartRParticle swarm optimization, 1995, New York, IEEE, 19421948

[32]

Fernández-MartínezJ L, García-GonzaloE, NaudetV. Particle swarm optimization applied to solving and appraising the streaming-potential inverse problem. Geophysics, 2010, 75(4): WA3-WA15

[33]

CuiY-a, ZhuX-x, ChenZ-x, LiuJ-w, LiuJ-X. Performance evaluation for intelligent optimization algorithms in self-potential data inversion. Journal of Central South University, 2016, 23(10): 2659-2668

[34]

Fernández-MartínezJ L, García-GonzaloE, Fernández-AlvarezJ P. Theoretical analysis of particle swarm trajectories through a mechanical analogy. International Journal of Computational Intelligence Research, 2008, 4(2): 93-104

[35]

PekşenE, YasT, KaymanA Y, ÖzkanC. Application of particle swarm optimization on self-potential data. Journal of Applied Geophysics, 2011, 75(2): 305-318

[36]

ZhuX-x, CuiY, LiX-y, TongT-g, JiT-X. Inversion of self-potential anomalies based on particle swarm optimization. Journal of Central South University (Science and Technology), 2015, 46(2): 579-585

[37]

GöktürklerG, BalkayaÇ. Inversion of self-potential anomalies caused by simple-geometry bodies using global optimization algorithms. Journal of Geophysics and Engineering, 2012, 9(5): 498-507

[38]

ShiY, EberhartRA modified particle swarm optimizer, 1998, Anchorage, AK, USA, IEEE, 6973

[39]

AngelineP JUsing selection to improve particle swarm optimization, 1998, Anchorage, AK, USA, IEEE, 8489

[40]

Fernández MartínezJ L, García GonzaloE, Fernández MuñizZ, MukerjiT. How to design a powerful family of particle swarm optimizers for inverse modelling. Transactions of the Institute of Measurement and Control, 2012, 34(6): 705-719

[41]

RatnaweeraA, HalgamugeS K, WatsonH CSelf-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, 2004, New Jersey, IEEE, 240255

[42]

TreleaI C. The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters, 2003, 85(6): 317-325

[43]

Fernández MartínezJ L, García GonzaloE. The PSO family: Deduction, stochastic analysis and comparison. Swarm Intelligence, 2009, 3(4): 245-273

[44]

ClercM, KennedyJThe particle swarm-explosion, stability, and convergence in a multidimensional complex space, 2004, New Jersey, IEEE, 5873

[45]

Monteiro SantosF A. Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization. Computers & Geosciences, 2010, 36(9): 1185-1190

[46]

WolpertD H, MacreadyW G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82

[47]

AleardiM, PieriniS, SajevaA. Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems. Geophysics, 2019, 84(5): R767-R781

[48]

AleardiM, MazzottiA. 1D elastic full-waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm-Gibbs sampler approach. Geophysical Prospecting, 2017, 65(1): 64-85

AI Summary AI Mindmap
PDF

112

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/