SUMMARY

Processing of geophysical data is a time consuming task involving many different steps. One approach for accelerating and automating processing of geophysical data is to look towards machine learning (ML). ML encompasses a wide range of tools, which can be used to automate complicated and/or tedious tasks. We present strategies for automating the processing of time-domain induced polarization (IP) data using ML. An IP data set from Grindsted in Denmark is used to investigate the applicability of neural networks for processing such data. The Grindsted data set consists of eight profiles, with approximately 2000 data curves per profile, on average. Each curve needs to be processed, which, using the manual approach, can take 1–2 hr per profile. Around 20 per cent of the curves were manually processed and used to train and validate an artificial neural network. Once trained, the network could process all curves, in 6–15 s for each profile. The accuracy of the neural network, when considering the manual processing as a reference, is 90.8 per cent. At first, the network could not detect outlier curves, that is where entire chargeability curves were significantly different from their spatial neighbours. Therefore, an outlier curve detection algorithm was developed and implemented to work in tandem with the network. The automatic processing approach developed here, involving the neural network and the outlier curve detection, leads to similar inversion results as the manual processing, with the two significant advantages of reduced processing times and enhanced processing consistency.

中文翻译：

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使用监督人工神经网络自动处理时域感应极化数据

概要

地球物理数据的处理是一项耗时的工作，涉及许多不同的步骤。加速和自动化地球物理数据处理的一种方法是着眼于机器学习（ML）。ML包含各种工具，可用于自动化复杂和/或繁琐的任务。我们提出了使用ML自动处理时域感应极化（IP）数据的策略。来自丹麦Grindsted的IP数据集用于研究神经网络处理此类数据的适用性。Grindsted数据集由八个轮廓组成，每个轮廓平均大约有2000条数据曲线。每条曲线都需要进行处理，使用手动方法，每个轮廓可能需要1-2小时。大约20％的曲线是手动处理的，用于训练和验证人工神经网络。训练后，网络可以在6-15 s内处理每个曲线的所有曲线。如果将人工处理作为参考，则神经网络的准确性为90.8％。起初，网络无法检测到异常曲线，即整个充电曲线与空间邻域明显不同。因此，开发并实施了离群曲线检测算法以与网络协同工作。此处开发的自动处理方法涉及神经网络和离群曲线检测，可产生与手动处理类似的反演结果，具有减少处理时间和增强处理一致性的两个显着优点。