A geostatistical analysis of seismicity in Oklahoma using regression trees and neural networks

Larson, J. D.; Kramar, David; Leonard, Karl

doi:10.1080/02723646.2020.1762982

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Wang et al (2022a) and Kemna et al (2022) used a tree-based ML approach to identify critical factors (including operational parameters of hydraulic fracturing) controlling induced earthquakes. Larson et al (2021) used classification/ regression trees and neural networks for a similar problem. Szafranski and Duan (2022) trained ML models of random forest, bagging, and k-neighbors regression algorithms using numerical simulation results and used the resultant model for Bayesian inversion analysis to estimate subsurface conditions.…”

Section: Risk Evaluation Of Induced Earthquakesmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract

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Section: Risk Evaluation Of Induced Earthquakesmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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“…They found that injection depth relative to basement most strongly correlates with seismic moment release. Larson et al (2020) modeled the frequency of induced seismicity in Oklahoma using Euclidean distance from earthquakes to the nearest disposal well, the nearest fault, and average fluid injection rate to develop nonparametric regressions. They found that proximity to wastewater disposal sites, fluid injection rates, and adjacency to subsurface faults were sufficient to model seismicity in northcentral Oklahoma.…”

Section: 103mentioning

confidence: 99%

Machine Learning in Earthquake Seismology

Mousavi

Beroza

2023

Annual Review of Earth and Planetary Sciences

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Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data mining techniques can significantly improve our capability for seismic data processing. In this review we provide a comprehensive overview of ML applications in earthquake seismology, discuss progress and challenges, and offer suggestions for future work. ▪ Conceptual, algorithmic, and computational advances have enabled rapid progress in the development of machine learning approaches to earthquake seismology. ▪ The impact of that progress is most clearly evident in earthquake monitoring and is leading to a new generation of much more comprehensive earthquake catalogs. ▪ Application of unsupervised approaches for exploratory analysis of these high-dimensional catalogs may reveal new understanding of seismicity. ▪ Machine learning methods are proving to be effective across a broad range of other seismological tasks, but systematic benchmarking through open source frameworks and benchmark data sets are important to ensure continuing progress. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 51 is May 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…They showed that cumulative injection volume and bottom-hole pressures were the most significant variables associated with induced seismicity increases. Larson et al (2020) modeled earthquake magnitude using various classification and regression methods, and found that earthquake frequency correlated with wastewater disposal location, fault location, injection volumes, and injection pressure. Sinha et al (2018) used the hierarchical and K-means clustering method to group wells into clusters that conform with However, the above-described studies did not evaluate the significance of individual wells within specific well groups.…”

Section: Data-driven Models Of Injection-induced Seismicitymentioning

confidence: 99%

“…Larson et al. (2020) modeled earthquake magnitude using various classification and regression methods, and found that earthquake frequency correlated with wastewater disposal location, fault location, injection volumes, and injection pressure. Sinha et al.…”

Section: Introductionmentioning

confidence: 99%

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Dempsey

Rinaldi³

et al. 2023

JGR Solid Earth

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Understanding injection‐induced microseismicity in geothermal systems can provide insight into reservoir connectedness. However, fault and reservoir complexity are difficult to represent in simple analytical models, which makes it difficult to discern clear relationships from incidental associations. Here, we have used data‐driven models to study how fluid injection and microseismicity are related in the Rotokawa (New Zealand) and Hellisheiði (Iceland) geothermal fields. We tested two classes of model: (a) lagged linear regression of seismicity rate as a function of well injection rates; and (b) systematic extraction of injection time series features that are then evaluated for associations with the seismicity. These models allowed us to determine which wells had the greatest correlation with microseismicity and to explain this association in a reservoir context. Finally, exploring different data types and transformations, we were unable to establish a link between rapid changes in injection rate and seismicity spikes, as suggested by some theoretical models.

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A geostatistical analysis of seismicity in Oklahoma using regression trees and neural networks

Cited by 4 publications

References 32 publications

Recent advances in earthquake seismology using machine learning

Recent advances in earthquake seismology using machine learning

Machine Learning in Earthquake Seismology

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

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