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.

中文翻译：

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通过异常鲁棒核密度估计方法提高微震/声发射源定位精度


复杂工程环境中的监控传感器经常会记录异常数据，导致明显的定位误差。为了减少异常到达时间的影响，我们引入了一种创新的异常鲁棒定位方法，该方法将核密度估计（KDE）与阻尼线性校正相结合，以提高微震/声发射（MS/AE）源定位的精度。我们的方法通过三步过程系统地解决异常到达时间：通过 4 到达组合进行初始定位，基于三维 KDE 消除异常值，以及使用具有自适应阻尼因子的线性校正进行细化。我们通过突破性实验验证了我们的方法，证明定位精度提高了 23% 以上，最大误差为 9.12 毫米（相对误差为 15.80%），优于 4 种现有方法。各种系统误差、异常值尺度和比率下的模拟证实了我们的方法的卓越性能。现场爆破实验也证实了该方法的实际适用性，平均定位误差为11.71 m（相对误差为7.59%），而其他方法的平均定位误差为23.56、66.09、16.95和28.52 m。这项研究具有重要意义，因为它增强了 MS/AE 源定位在面对数据异常时的鲁棒性。它还为现实工程和安全监控应用提供了实用的解决方案。