SUMMARY

Testing with synthetic data sets is a vital stage in an algorithm’s development for benchmarking the algorithm’s performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic noise that would be present in recorded data sets. The first section of this paper focuses on comparing the effects of WGN and realistic modelled noise on standard microseismic event detection and imaging algorithms using synthetic data sets with recorded noise as a benchmark. The data sets with WGN underperform on the trace-by-trace algorithm while overperforming on algorithms utilizing the full array. Throughout, the data sets with realistic modelled noise perform near identically to the recorded noise data sets. The study concludes by testing an algorithm that simultaneously solves for the source location and moment tensor of a microseismic event. Not only does the algorithm fail to perform at the signal-to-noise ratios indicated by the WGN results but the results with realistic modelled noise highlight pitfalls of the algorithm not previously identified. The misleading results from the WGN data sets highlight the need to test algorithms under realistic noise conditions to gain an understanding of the conditions under which an algorithm can perform and to minimize the risk of misinterpretation of the results.

中文翻译：

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论基准测试算法在实际噪声条件下的重要性

概要

使用综合数据集进行测试是算法开发中至关重要的阶段，它是基准测试算法性能的基准。合成数据集的常见添加是高斯白噪声（WGN），用于模拟在记录的数据集中会出现的噪声。本文的第一部分着重于比较WGN和实际建模的噪声对标准微地震事件检测和成像算法的影响，这些噪声使用合成数据集以记录的噪声为基准进行检测。使用WGN的数据集在逐迹跟踪算法上表现不佳，而在利用完整阵列的算法上却表现不佳。整个过程中，具有逼真的建模噪声的数据集的性能几乎与记录的噪声数据集相同。该研究通过测试一种算法来结束，该算法可同时求解微震事件的震源位置和矩张量。该算法不仅无法在WGN结果指示的信噪比下执行，而且具有逼真的建模噪声的结果突出了该算法先前未发现的陷阱。WGN数据集产生的误导性结果突出表明，需要在现实的噪声条件下测试算法，以了解算法可以执行的条件，并最大程度地减少错误解释结果的风险。