Monitoring sensors in complex engineering environments often record abnormal data, leading to significant positioning errors. To reduce the influence of abnormal arrival times, we introduce an innovative,outlier-robust localization method that integrates kernel density estimation (KDE) with damping linear correction to enhance the precision of microseismic/acoustic emission (MS/AE) source positioning. Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations, elimination of outliers based on three-dimensional KDE, and refinement using a linear correction with an adaptive damping factor. We validate our method through lead-breaking experiments, demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm (relative error of 15.80%)—outperforming 4 existing methods. Simulations under various system errors, outlier scales, and ratios substantiate our method’s superior performance. Field blasting experiments also confirm the practical applicability, with an average positioning error of 11.71 m (relative error of 7.59%), compared to 23.56, 66.09, 16.95, and 28.52 m for other methods. This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.

thefinancialsupportprovidedbytheNationalKeyResearchandDevelopmentProgramforYoungScientists(No.2021YFC2900400)；PostdoctoralFellowshipProgramofChinaPostdoctoralScienceFoundation(CPSF)(No.GZB20230914);ChinaPostdoctoralScienceFoundation(No.2023M730412)；NationalNaturalScienceFoundationofChina(No.52304123)；ChongqingOutstandingYouthScienceFoundationProgram(No.CSTB2023NSCQ-JQX0027)；

1. Introduction
2. Methodology
2.1. Preliminary location
2.2. Three-dimensional kernel density estimation
2.2.1. Selection of three-dimensional kernel
2.2.2. Construction of the three-dimensional kernel density function
2.2.3. Selection of bandwidth matrix
2.2.4. Calculation of the probability density of the source coordinate
2.3. Filtering the abnormal arrivals
2.4. Relocation using clean arrivals
3. Experimental verification
3.1. Experimental procedures
3.2. Comparison of localization results with other methods
4. Simulation analysis
4.1. Influence of random arrival error
4.2. Influence of outlier scale
4.3. Influence of the proportion of outliers
5. Engineering application
6. Conclusion

[1]Chen J ,Huang H ,Rui Y , et al.Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach[J].International Journal of Mining Science and Technology,2024,34(07):943-956.