使用机器学习在嘈杂环境中识别喷发：新墨西哥州奇马约的CO2-driven冷水间歇泉案例研究
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
摘要：
我们提出了一种基于机器学习（ML）的方法来区分火山喷发和火山喷发信号的奇马约间歇泉（新墨西哥州，美国）在嘈杂的环境。该间歇泉可被视为$\mathrm{CO}_2$侵入浅水含水层的天然模拟。通过研究这个间歇泉，我们可以了解从深处的$\mathrm{CO}_2$丰富的流体，这与泄漏监测$\mathrm{CO}_2$封存项目。ML方法，如随机森林（RF）是已知的强大的多类分类器，并在不利的噪声条件下表现良好。然而，对于这种$\mathrm{CO}_2$驱动的间歇泉应用，人们对RF方法的准确性程度知之甚少。目前的研究旨在量化的RF分类器的性能，以辨别间歇泉状态。为了实现这一目标，我们首先介绍了从安装在Chimayó间歇泉附近的地震仪收集的数据。在这个地点收集的地震信号包含不同类型的噪音，如每日温度变化，季节趋势，间歇泉附近的动物运动和人类活动。首先，我们通过结合蝶形高通滤波器和自回归方法在多层次的方式从这些噪声中过滤的信号。我们表明，通过结合这些滤波技术，在一个层次的方式，导致减少地震数据中的噪声，而不删除的前兆和喷发事件信号。然后，我们使用RF的过滤数据，间歇泉的状态分为三类-残余噪音，前兆，和喷发状态。实验结果表明，基于RF的分类准确率大于90%，这使得所提出的ML框架在噪声条件下的事件识别和信号增强方面具有很大的吸引力，在$\mathrm{CO}_2$封存泄漏监测中具有很强的应用潜力.
英文摘要：
We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this $\mathrm{CO}_2$-driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimayó geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -- remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90\%.These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.
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