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We present an approach based on machine learning (ML) to distinguish eruptionand precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisyenvironments. 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, whichhas relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. MLmethods such as Random Forests (RF) are known to be robust multi-classclassifiers and perform well under unfavorable noisy conditions. However, theextent of the RF method's accuracy is poorly understood for this$\mathrm{CO}_2$-driven geysering application. The current study aims toquantify the performance of RF-classifiers to discern the geyser state. Towardsthis goal, we first present the data collected from the seismometer that isinstalled near the Chimay\'{o} geyser. The seismic signals collected at thissite contain different types of noises such as daily temperature variations,seasonal trends, animal movement near the geyser, and human activity. First, wefilter the signals from these noises by combining the Butterworth-Highpassfilter and an Autoregressive method in a multi-level fashion. We show that bycombining these filtering techniques, in a hierarchical fashion, leads toreduction in the noise in the seismic data without removing the precursors anderuption event signals. We then use RF on the filtered data to classify thestate of geyser into three classes -- remnant noise, precursor, and eruptionstates. We show that the classification accuracy using RF on the filtered datais greater than 90\%.These aspects make the proposed ML framework attractivefor event discrimination and signal enhancement under noisy conditions, withstrong potential for application to monitoring leaks in $\mathrm{CO}_2$sequestration.

Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico