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

Journal Article · Wed Feb 13 00:00:00 EST 2019 · Seismological Research Letters

DOI:https://doi.org/10.1785/0220180306· OSTI ID:1544684

Yuan, Baichuan ^[1]; Tan, Yen Joe ^[2]; Mudunuru, Maruti Kumar  ^[3]; Marcillo, Omar Eduardo  ^[3]; Delorey, Andrew A.  ^[3]; Roberts, Peter Morse  ^[3]; Webster, Jeremy David  ^[3]; Gammans, Christine Naomi Louise  ^[3]; Karra, Satish  ^[3]; Guthrie, George Drake ^[3]; Johnson, Paul Allan  ^[3]

---

1. Univ. of California, Los Angeles, CA (United States)
2. Columbia Univ., Palisades, NY (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

In this paper, we present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, U.S.A.) under noisy environmental conditions. This geyser can be considered a natural analog of CO₂ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of CO₂-rich fluids from depth, which has relevance to leak monitoring in a CO₂ sequestration project. ML methods such as random forests (RFs) are known to be robust multiclass classifiers and perform well under unfavorable, noisy conditions. However, the extent of the RF method’s accuracy is poorly understood for this CO₂-driven geysering application. The current study aims to quantify the performance of RF classifiers to discern the geyser state. Toward 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, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth high-pass (BH) filter and an autoregressive (AR) method in a multilevel fashion. We show that by combining these filtering techniques in a hierarchical fashion leads to a reduction in 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. RF classifier is constructed based on the comprehensive features extracted using the Tsfresh Python package. We show that the classification accuracy using RF on the filtered data is greater than 90%. We also evaluate the accuracy of other classical time-series methods such as dynamic time warping (DTW) on filtered data along with RF on partially filtered data in which we remove the daily trends. Classification accuracy shows that DTW performs poorly (44%) and RF on partially filtered data performs decently (87%). Denoising seismic signals from both daily trends and human activity enhances RF classifier performance by 7%. Finally, these aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitor leaks in CO₂ sequestration.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1544684
• Report Number(s): LA-UR-18-29261
• Journal Information: Seismological Research Letters, Vol. 90, Issue 2A; ISSN 0895-0695
• Publisher: Seismological Society of America
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
Computer Science
Earth Sciences
Energy Sciences
Information Science
Mathematics
CO2-driven cold-water geysers
carbon sequestration
monitoring
seismicity
signal processing
precursor
eruption
machine learning
event classification
feature extraction
random forests