Electromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three-fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black-box internal (factory) calibration impeded direct access to raw data, which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and that ML can form a viable approach in tackling the drift problem instrument specific in the near future.

中文翻译：

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使用机器学习的频域电磁学中基于环境温度和相对湿度的漂移校正

电磁仪器响应会受到信号漂移的影响，这会导致给定位置随时间变化的响应。如果不加以纠正，时空混叠可能会出现，并且可能会在数据中观察到全局趋势或突然变化，这与地下电磁变化无关。通过执行静态地面测量，我们表征了不同电磁仪器的漂移模式。接下来，我们在高出地面约 4 米的高处进行静态测量，以收集构成新绝对校准方法基础的数据集。通过额外记录环境温度变化、电池电压和相对湿度，使用机器学习 (ML) 方法对信号漂移与这些参数之间的关系进行建模。结果表明，可以减轻信号漂移的影响；然而，完全消除它们是不可能的。原因有三个：（1）ML 算法还没有充分适应准确预测；(2) 环境温度、相对湿度和电池电压不能充分解释信号不稳定；(3) 黑盒内部（工厂）校准阻碍了对原始数据的直接访问，从而妨碍了对所提出方法的准确评估。然而，结果表明这些挑战并非不可克服，ML 可以形成一种可行的方法来解决不久的将来仪器特定的漂移问题。(1) ML算法还没有充分适应准确预测；(2) 环境温度、相对湿度和电池电压不能充分解释信号不稳定；(3) 黑盒内部（工厂）校准阻碍了对原始数据的直接访问，从而妨碍了对所提出方法的准确评估。然而，结果表明这些挑战并非不可克服，ML 可以形成一种可行的方法来解决不久的将来仪器特定的漂移问题。(1) ML算法还没有充分适应准确预测；(2) 环境温度、相对湿度和电池电压不能充分解释信号不稳定；(3) 黑盒内部（工厂）校准阻碍了对原始数据的直接访问，从而妨碍了对所提出方法的准确评估。然而，结果表明这些挑战并非不可克服，ML 可以形成一种可行的方法来解决不久的将来仪器特定的漂移问题。