Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas

2018 ◽  
Vol 90 (2A) ◽  
pp. 604-613 ◽  
Author(s):  
Farid Khosravikia ◽  
Patricia Clayton ◽  
Zoltan Nagy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Induced Earthquakes ◽  
Motion Models ◽  
Artificial Neural ◽  
Ground Motion Models

Prediction of Ground Motion Intensity Measures Using an Artificial Neural Network

2021 ◽  
Author(s):  
K. P. Sreejaya ◽  
Jahnabi Basu ◽  
S. T. G. Raghukanth ◽  
D. Srinagesh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Intensity Measures ◽  
Artificial Neural

scholarly journals A New Procedure for Evaluating Ground-Motion Models, with Application to Hydraulic-Fracture-Induced Seismicity in the United Kingdom

2020 ◽  
Vol 110 (5) ◽  
pp. 2380-2397 ◽  
Author(s):  
Gemma Cremen ◽  
Maximilian J. Werner ◽  
Brian Baptie
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Shale Gas ◽  
Hydraulic Fracture ◽  
Induced Seismicity ◽  
Ground Motions ◽  
Induced Earthquakes ◽  
Statistical Tool ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT An essential component of seismic hazard analysis is the prediction of ground shaking (and its uncertainty), using ground-motion models (GMMs). This article proposes a new method to evaluate (i.e., rank) the suitability of GMMs for modeling ground motions in a given region. The method leverages a statistical tool from sensitivity analysis to quantitatively compare predictions of a GMM with underlying observations. We demonstrate the performance of the proposed method relative to several other popular GMM ranking procedures and highlight its advantages, which include its intuitive scoring system and its ability to account for the hierarchical structure of GMMs. We use the proposed method to evaluate the applicability of several GMMs for modeling ground motions from induced earthquakes due to U.K. shale gas development. The data consist of 195 recordings at hypocentral distances (R) less than 10 km for 29 events with local magnitude (ML) greater than 0 that relate to 2018/2019 hydraulic-fracture operations at the Preston New Road shale gas site in Lancashire and 192 R<10  km recordings for 48 ML>0 events induced—within the same geologic formation—by coal mining near New Ollerton, North Nottinghamshire. We examine: (1) the Akkar, Sandikkaya, and Bommer (2014) models for European seismicity; (2) the Douglas et al. (2013) model for geothermal-induced seismicity; and (3) the Atkinson (2015) model for central and eastern North America induced seismicity. We find the Douglas et al. (2013) model to be the most suitable for almost all of the considered ground-motion intensity measures. We modify this model by recomputing its coefficients in line with the observed data, to further improve its accuracy for future analyses of the seismic hazard of interest. This study both advances the state of the art in GMM evaluation and enhances understanding of the seismic hazard related to U.K. shale gas development.

scholarly journals Comparing Artificial Neural Networks with Traditional Ground-Motion Models for Small-Magnitude Earthquakes in Southern California

2021 ◽  
Author(s):  
Alexis Klimasewski ◽  
Valerie Sahakian ◽  
Amanda Thomas
Keyword(s):  
Machine Learning ◽  
Ground Motion ◽  
Southern California ◽  
Small Magnitude ◽  
Motion Models ◽  
Input Parameters ◽  
Ann Models ◽  
Artificial Neural ◽  
A Site ◽  
Ground Motion Models

ABSTRACT Traditional, empirical ground-motion models (GMMs) are developed by prescribing a functional form between predictive parameters and ground-motion intensity measures. Machine-learning techniques may serve as a fully data-driven alternative to widely used regression techniques, as they do not require explicitly defining these relationships. Although, machine-learning methods offer a nonparametric alternative to regression methods, there are few studies that develop and assess performance of traditional versus machine-learning GMMs side by side. We compare the performance and behavior of these two approaches: a mixed-effects maximum-likelihood (MEML) model and a feed-forward artificial neural network (ANN). We develop and train both models on the same dataset from southern California. We subsequently test both models on a dataset from the 2019 Ridgecrest sequence, in a new region and on magnitudes outside the range of the training dataset, to examine model portability. Our models estimate horizontal peak ground acceleration, and the input parameters include moment magnitude (M) and hypocentral distance (Rhyp), and some include a site parameter, either VS30 or κ0. We find that, with our small set of input parameters, the ANN generally shows more site-specific predictions than the MEML model with more variation between sites, and, performs better than their corresponding MEML model, when applied “blind” to our testing dataset (in which the MEML random effects cannot be considered). Although, previous studies have found that κ0 may be a better predictor of site effects than VS30, we found similar performance, suggesting that including a site parameter may be more important than the physical meaning of the parameter. Finally, when applying our models to our Ridgecrest dataset, we find that both methods perform well; however, the MEML models perform better with the new dataset than the ANN models, suggesting that future applications of ANN models may need to consider how to accommodate model portability.

Ground Motion Prediction Model Using Artificial Neural Network

2017 ◽  
Vol 175 (3) ◽  
pp. 1035-1064 ◽  
Author(s):  
J. Dhanya ◽  
S. T. G. Raghukanth
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Artificial Neural

scholarly journals Observations on the application of artificial neural network to predicting ground motion measures

2012 ◽  
Vol 25 (2) ◽  
pp. 161-175 ◽  
Author(s):  
Hanping Hong ◽  
Taojun Liu ◽  
Chien-Shen Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Artificial Neural

Are Ground‐Motion Models Derived from Natural Events Applicable to the Estimation of Expected Motions for Induced Earthquakes?

2017 ◽  
Vol 88 (2A) ◽  
pp. 430-441 ◽  
Author(s):  
Gail M. Atkinson ◽  
Karen Assatourians
Keyword(s):  
Ground Motion ◽  
Induced Earthquakes ◽  
Motion Models ◽  
Ground Motion Models

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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