Data-Driven Microseismic Event Localization: An Application to the Oklahoma Arkoma Basin Hydraulic Fracturing Data

Wang, Hanchen; Alkhalifah, Tariq; Waheed, Umair bin; Birnie, Claire

doi:10.1109/tgrs.2021.3120546

Cited by 14 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these methods work well, observation data are generally incomplete, limiting the effectiveness for events with hypocenter locations and focal mechanisms not included in the training data. To overcome this challenge, some studies have attempted training based on synthetic waveforms (Wang et al 2022c;Sugiyama et al 2021;Wamriew et al 2022;Vinard et al 2022), including data generated with semi-supervised GAN (Feng et al 2022a).…”

Section: Earthquake Locationmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

View full text Add to dashboard Cite

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

show abstract

Section: Earthquake Locationmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

View full text Add to dashboard Cite

show abstract

“…Therefore, it is no surprise that several localization approaches have recently been proposed to harness the potential of supervised machine learning. These methods typically train a convolutional neural network (CNN) using historical or synthetically generated datasets [10,11]. Once the CNN model is trained, it can be used to infer locations in real-time.…”

Section: Introductionmentioning

confidence: 99%

Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty

Izzatullah¹,

Yıldırım²,

Alkhalifah³

et al. 2022

Mach. Learn.: Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of real-time microseismic monitoring possible. Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and versatile framework for solving partial differential equations (PDEs) along with the associated initial or boundary conditions. We develop HypoPINN -- a PINN-based inversion framework for hypocenter localization and introduce an approximate Bayesian framework for estimating its predictive uncertainties. This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN's weights and biases using the Laplace approximation. We train HypoPINN to obtain the optimized weights for predicting hypocenter location. Next, we approximate the covariance matrix at the optimized HypoPINN's weights for posterior sampling with the Laplace approximation. The posterior samples represent various realizations of HypoPINN's weights. Finally, we predict the locations of the hypocenter associated with those weights' realizations to investigate the uncertainty propagation that comes from those realisations. We demonstrate the features of this methodology through several numerical examples, including using the Otway velocity model based on the Otway project in Australia.

show abstract

“…Therefore, it is no surprise that several localization approaches have recently been proposed to harness the potential of supervised machine learning. These methods typically train a convolutional neural network (CNN) using historical or synthetically generated datasets [17,30]. Once the CNN model is trained, it can be used to infer locations in real-time.…”

Section: Introductionmentioning

confidence: 99%

Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty

Izzatullah¹,

Yıldırım²,

Waheed³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of real-time microseismic monitoring possible. Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and versatile framework for solving partial differential equations (PDEs) along with the associated initial or boundary conditions. We develop HypoPINN -a PINN-based inversion framework for hypocenter localization and introduce an approximate Bayesian framework for estimating its predictive uncertainties. This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN's weights and biases using the Laplace approximation. We train HypoPINN to obtain the optimized weights for predicting hypocenter location. Next, we approximate the covariance matrix at the optimized HypoPINN's weights for posterior sampling with the Laplace approximation. The posterior samples represent various realizations of HypoPINN's weights. Finally, we predict the locations of the hypocenter associated with those weights' realizations to investigate the uncertainty propagation that comes from those realisations. We demonstrate the features of this methodology through several numerical examples, including using the Otway velocity model based on the Otway project in Australia.

show abstract

Data-Driven Microseismic Event Localization: An Application to the Oklahoma Arkoma Basin Hydraulic Fracturing Data

Cited by 14 publications

References 55 publications

Recent advances in earthquake seismology using machine learning

Recent advances in earthquake seismology using machine learning

Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty

Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty

Contact Info

Product

Resources

About