AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025420089
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Inversion model combination for microseismic source positioning with Multi-Objective Grasshopper Optimization Algorithm

Cong Pang1,2 ,  Tianwen Zhao3 ,  Guoqing Chen4,5 ,  Sirui Liu1 ,  Xingxing Li1 ,  Ya Xiang1 ,  Piyapatr Busababodhin5*
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1 Institute of Seismology, China Earthquake Administration, Wuhan, Hubei, China
2 Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, Wuhan, Hubei, China
3 Department of Trade and Logistics, Daegu Catholic University, Gyeongsan, Daegu, Republic of Korea
4 Mathematical Modeling Research Center, Chengdu Jincheng College, Chengdu, Sichuan, China
5 Department of Mathematics, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham, Thailand
JSE 2026, 35(1), 025420089 https://doi.org/10.36922/JSE025420089
Submitted: 13 October 2025 | Revised: 26 November 2025 | Accepted: 1 December 2025 | Published: 14 January 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The precise determination of microseismic source locations is one of the core components of theoretical research in microseismic monitoring technology. Multi-objective intelligent optimization is an effective approach for microseismic source positioning, but it faces challenges such as unclear rationality of model combinations, susceptibility to local optima, and significant variability in positioning results. To address these issues, four distinct mathematical models for microseismic source positioning were designed based on the arrival time difference model and the arrival time difference quotient model. These models were then combined in pairs to form six different microseismic source positioning model combinations, which were used as the optimization objective functions for the multi-objective computational algorithm. A set of microseismic source forward modeling experiments based on three-dimensional polyhedral array shapes, two sets of engineering microseismic data validation experiments, and one set of multi-objective computational method comparison experiments were designed. the multi-objective grasshopper optimization algorithm (MOGOA) was introduced to solve the six model combinations and employed in four sets of microseismic source positioning experiments. Multiple statistical metrics were applied to evaluate the performance of each model combination. The experimental results indicate that the microseismic inversion mathematical model combination (TDA, TDA-P1), combined with the MOGOA algorithm’s multi-objective optimization positioning strategy, can achieve high microseismic source positioning accuracy under relatively reliable microseismic event data, and the model calculations are relatively robust. Under microseismic blasting data, the average positioning error over 100 rounds reached 27.6035 m, with standard deviation and interquartile range averages of only 3.2114 m and 5.5896 m, respectively, outperforming other inversion model combinations and similar multi-objective positioning methods. For microseismic event data with significant systematic errors, the microseismic inversion mathematical model combination (TDA-P1, TDQA-P1) demonstrates superior positioning performance, with an average positioning error of 151.1915 m over 100 iterations, significantly outperforming other model combinations. These model combination positioning performance studies hold practical application value in the field of microseismic monitoring.

Keywords
Microseismic source positioning
Multi-objective optimization
Combination of inversion mathematical models
Time difference quotient of arrival
Time difference of arrival
Multi-Objective Grasshopper Optimization Algorithm
Funding
This research was financially supported by Mahasarakham University; the Scientific Research Fund from Institute of Seismology, CEA and National Institute of Natural Hazards, Ministry of Emergency Management of China (Grant No. IS202226322 and IS202436357); the Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station (Grant No. WHYWZ202406); the Spark Program of Earthquake Science and Technology, China Earthquake Administration (Grant No. XH25019C and XH24025YC); the 2025 Hubei Provincial Key Laboratory Targeted Commissioned Project (Grant No. 2025CSA083); the Green Data Integration Intelligence Research and Innovation Project of Chengdu Jincheng College (Grant No. 2025–2027); and the Mathematics and Finance Research Center Project of Dazhou Social Science Federation Key Research Base (Grant No. SCMF202505).
Conflict of interest
The authors declare they have no competing interests.
References
[1]
  1. Feng Q, Han L, Ma L. Microseismic source localization method based on neural network algorithm and dynamic reduction of solution interval. IEEE Geosci Remote Sens Lett. 2024;21:1-5. doi: 10.1109/lgrs.2024.3398043

 

  1. Yan Z, Zi-Zin W, Lin-Qi C, Hong-Li D. Research on microseismic event localization based on convolutional neural network. J Seismic Explor. 2024;33(6):1-32. doi: 10.36922/jse.corr090325

 

  1. Wang H, Alkhalifah T, Waheed UB, Birnie C. Data-driven microseismic event localization: An application to the Oklahoma Arkoma basin hydraulic fracturing data. IEEE Trans Geosci Remote Sens. 2022;60:1-12. doi: 10.1109/TGRS.2021.3120546

 

  1. Feng Q, Han L, Zhao B. Localizing microseismic events using semi-supervised generative adversarial networks. IEEE Trans Geosci Remote Sens. 2022;60:5923908. doi: 10.1109/tgrs.2022.3225415

 

  1. Xu J, Qiu T, Zeng Z, Zhu F, Han P, Zhang W. Microseismic event location using migration-based stacking with effective parameters’ optimization. IEEE Trans Geosci Remote Sens. 2025;63:5917809. doi: 10.1109/tgrs.2025.3591126

 

  1. Chen Y, Savvaidis A, Fomel S, Saad OM, Chen Y. RFloc3D: A machine-learning method for 3-D microseismic source location using P- and S-wave arrivals. IEEE Trans Geosci Remote Sens. 2023;61:1-10. doi: 10.1109/tgrs.2023.3236572

 

  1. Michel OJ, Tsvankin I. Gradient calculation for waveform inversion of microseismic data in VTI media. J Seismic Explor. 2014;23(3):201-217.

 

  1. Abdullin A, Waheed UB, Suleymanli K, Stanek F. Microseismic source localization using fourier neural operator with application to field data from Utah FORGE. IEEE Trans Geosci Remote Sens. 2025;63:1-10. doi: 10.1109/tgrs.2025.3533635

 

  1. Wang C, He R, Pei C, Sun F, Zhou X, Chen Z. Localization for surface microseismic monitoring based on local equivalent path and virtual field optimization method. IEEE Sens J. 2024;24(23):39270-39284. doi: 10.1109/jsen.2024.3469969

 

  1. Pang C, Xu J, He X, Zuo Y, Li C, Ma W. Research on Location Method of Mine Micro-Seismic Source Based on Inter Quartile Range and Newton-Raphson Method. In: 2023 International Conference on New Trends in Computational Intelligence (NTCI). Qingdao, China; 2023. p. 5-10. doi: 10.1109/ntci60157.2023.10403666

 

  1. Pang C, Chen J, Ma L, et al. A Method for Micro-seismic Source Location Based on Principal Component Analysis and Spatial Discrete Detection. In: 2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC). Chongqing, China; 2023. p. 92-96. doi: 10.1109/itoec57671.2023.10291402

 

  1. Wang Y, Bi W, Fan Q, Xu S, Zhang A. Multi-objective optimization of top-level arrangement for flight test. J Syst Eng Electron. 2025;36(3):714-724. doi: 10.23919/jsee.2025.000019

 

  1. Noori MS, Sahbudin RKZ, Sali A, Hashim F. Multi-objective multi-exemplar particle swarm optimization algorithm with local awareness. IEEE Access. 2024;12:125809-125834. doi: 10.1109/access.2024.3426104

 

  1. Gao L, Liu Z. An integrated external archive local disturbance mechanism for multi-objective snake optimizer. Chin J Electron. 2024;33(4):989-996. doi: 10.23919/cje.2023.00.023

 

  1. Sadeghi AH, Bani EA, Fallahi A, Handfield R. Grey wolf optimizer and whale optimization algorithm for stochastic inventory management of reusable products in a two-level supply chain. IEEE Access. 2023;11:40278-40297. doi: 10.1109/access.2023.3269292

 

  1. Wu X, Zhan J, Ding W, Pedrycz W. GRNN model with feedback mechanism incorporating k-nearest neighbor and modified gray wolf optimization algorithm in intelligent transportation. IEEE Trans Intell Transp Syst. 2025;26(3):3855-3872. doi: 10.1109/tits.2024.3510678

 

  1. Chen X, Ma M, Liu C, Xie H, Wang S. Research on interference resource optimization based on improved whale optimization algorithm. IEEE Access. 2025;13:83136-83147. doi: 10.1109/access.2025.3569460

 

  1. Zhao ZH, Yin YF, Wang YK, Qin KR, Xue CD. Adaptive ECG signal denoising algorithm based on the improved whale optimization algorithm. IEEE Sens J. 2024;24(21):34788-34797. doi: 10.1109/jsen.2024.3422995

 

  1. Li Y, Yao Y, Hu S, Wen Q, Zhao F. Coverage enhancement strategy for WSNs based on multiobjective ant lion optimizer. IEEE Sens J. 2023;23(12):13762-13773. doi: 10.1109/jsen.2023.3267459

 

  1. Tian F, Zhu L, Shi Q, et al. The three-lead EEG sensor: Introducing an EEG-assisted depression diagnosis system based on ant lion optimization. IEEE Trans Biomed Circ Syst. 2023;17(6):1305-1318. doi: 10.1109/tbcas.2023.3292237

 

  1. Ghaleb SA, Mohamad M, Ghanem WAH, et al. Feature selection by multiobjective optimization: Application to spam detection system by neural networks and grasshopper optimization algorithm. IEEE Access. 2022;10:98475-98489. doi: 10.1109/access.2022.3204593

 

  1. Liu J, Wang A, Qu Y, Wang W. Coordinated operation of multi-integrated energy system based on linear weighted sum and grasshopper optimization algorithm. IEEE Access. 2017;6:42186-42195. doi: 10.1109/access.2018.2859816

 

  1. Lalhmachhuana R, Deb S, Datta S, Singh KR. Multi- Objective based Generation Fuel Cost and Emission Reduction using Grasshopper Optimization Algorithm. In: 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN). Noida, India; 2023. p. 676-680. doi: 10.1109/spin57001.2023.10116665

 

  1. Mokeddem D, Nasri D. A New Levy Flight Trajectory-based Grasshopper Optimization Algorithm for Multi-Objective Optimization Problems. In: 2020 Second International Conference on Embedded and Distributed Systems (EDiS). Oran, Algeria; 2020. p. 76-81. doi: 10.1109/edis49545.2020.9296480

 

  1. Zhaoming LV, Peng R. Improving the efficiency of multi-objective grasshopper optimization algorithm to enhance ontology alignment. Wuhan Univ J Nat Sci. 2022;27(3):240-254. doi: 10.1051/wujns/2022273240

 

  1. Wang C, Li J, Rao H, et al. Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution. Math Biosci Eng MBE. 2021;18(3):2527-2561. doi: 10.3934/mbe.2021129

 

  1. Pang C, Zhao T, Chen G, et al. Earthquake and blast recognition based on CEEMDAN multiscale fuzzy entropy and NSGAIII optimized 1D-CNN neural networks. J Seismic Explor. 2025;34(1):22-42. doi: 10.36922/jse025260029

 

  1. Bahrami B, Khayyambashi MR, Mirjalili S. Multiobjective placement of edge servers in MEC environment using a hybrid algorithm based on NSGA-II and MOPSO. IEEE Int Things J. 2024;11(18):29819-29837. doi: 10.1109/jiot.2024.3409569

 

  1. Li Y, Xie Z, Yang S, Ren Z. A hybrid algorithm based on NSGA-II and MOPSO for multi-objective designs of electromagnetic devices. IEEE Trans Magn. 2023;59(5):7001804. doi: 10.1109/tmag.2023.3250319

 

  1. Poormirzaei R, Hamidzadeh R, Moghadam R, Zarean A. The application of PSO to joint inversion of microtremor Rayleigh waves dispersion curves and refraction traveltimes. J Seismic Explor. 2015;24(4):305-325.
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Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
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