Reliable automatic processing of seismic events: solving the Swiss cheese problem

W. Törnman, J. Martinsson
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Abstract

BEMIS (Bayesian estimation of mining-induced seismicity) is a fully automatic, near real-time, robust and self-learning seismic processing solution for mining-induced seismic events. A prototype solution is tested in parallel with IMS’s routine manual processing in LKAB’s underground mines in Malmberget and Kiruna, providing four times more accurate earthquake locations based on 290 known blasts, 40 times faster processing time that scales with computer power, and the ability to detect and locate up to six times more events given the same input data. In addition to a fully automatic system, BEMIS provides a variety of unique functions such as quality control of all results, self-learning adaptation and calibrations, tomography, and prediction models of future seismicity. In this paper, we summarise the results from different investigations throughout time and discuss the unique approach considered to obtain reliable auto-processing in a challenging, unknown and changing environment.
可靠的地震事件自动处理:解决瑞士奶酪问题
采矿诱发地震活动性贝叶斯估计(BEMIS)是针对采矿诱发地震事件的一种全自动、近实时、鲁棒和自学习的地震处理方案。在LKAB位于Malmberget和Kiruna的地下矿井中,一个原型解决方案与IMS的常规人工处理并行进行了测试,基于290次已知爆炸,提供了4倍的准确地震位置,处理时间比计算机能力快40倍,并且能够在相同输入数据的情况下检测和定位多达6倍的事件。除了全自动系统外,BEMIS还提供了各种独特的功能,如所有结果的质量控制、自学习适应和校准、层析成像和未来地震活动的预测模型。在本文中,我们总结了不同研究的结果,并讨论了在充满挑战、未知和变化的环境中获得可靠自动处理的独特方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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