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首页> 外文期刊>Journal of environmental & engineering geophysics >Assessing the Quality of Electromagnetic Data for the Discrimination of UXO Using Figures of Merit
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Assessing the Quality of Electromagnetic Data for the Discrimination of UXO Using Figures of Merit

机译:利用品质因数评估用于区分UXO的电磁数据质量

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摘要

The need for assessing data quality in Unexploded ordnance (UXO) remediation problems arises from two Sources. In the planning stage it is essential that the data are acquired in sufficient numbers and with Sufficient accuracy to answer the detection or discrimination problem of relevance. At the interpretation stage it is critical to objectively assess whether the data are Of Sufficient quality to warrant subsequent processing, inversion, and classification. Faced with this practical challenge of defining data quality we propose a Figure of Merit (FOM). FOM is a reliability indicator derived from quantities that affect the quality of data. Such as anomaly coverage, line spacing. station spacing, instrument noise, survey location errors, etc. The FOM can also include informative features Of the inversion. Such Lis the variance of key model parameters, and thus it depends on the inverse model to be applied. Anomalies associated with higher values of FOM should have increased reliability in classification. Anomalies below a critical threshold will not be suitable for advanced analysis. In this paper, we apply the FOM framework to guide the practical Interpretation of field data collected at Camp Sibert Lis part of the Environmental Security Technology Certification Program (ESTCP) Discrimination Study Pilot Project. Using simulations of electromagnetic (EM) data for different quality of survey designs, we examine the Success rate of inversions to identify key FOM parameters that can explain unreliable inversion results. In this manner, the relationship between FOM and reliability is calibrated on synthetic data before application to the interpretation of field data. A trust index for each inversion Can Subsequently be included into a discrimination algorithm to help establish a priority dig list. We find that incorporating the FOM in the classification procedure Significantly reduces the number of non-UXO Items that need to be excavated to recover all UXO.
机译:有两种来源需要评估未爆弹药(UXO)修复问题中的数据质量。在计划阶段,必须以足够的数量和足够的准确性获取数据,以回答相关性的检测或区分问题。在解释阶段,客观地评估数据是否具有足够的质量以保证后续的处理,反演和分类至关重要。面对定义数据质量的实际挑战,我们提出了一个品质因数(FOM)。 FOM是从影响数据质量的数量得出的可靠性指标。如异常覆盖,行距。测站间距,仪器噪声,测量位置误差等。FOM还可以包括反演的信息功能。这种Lis是关键模型参数的方差,因此它取决于要应用的逆模型。与较高的FOM值相关的异常应增加分类的可靠性。低于临界阈值的异常不适用于高级分析。在本文中,我们将使用FOM框架来指导对在环境安全技术认证计划(ESTCP)歧视研究试点项目中的Sibert Lis营地收集的现场数据进行实用的解释。使用针对不同质量的调查设计的电磁(EM)数据模拟,我们检查了反演的成功率,以确定可以解释不可靠反演结果的关键FOM参数。以这种方式,在应用于现场数据的解释之前,对合成数据校准了FOM和可靠性之间的关系。随后可以将每个反演的信任索引包含到判别算法中,以帮助建立优先级摘要列表。我们发现,将FOM纳入分类过程可显着减少需要挖掘以恢复所有UXO的非UXO项目的数量。

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