Artificial neural network-based framework for developing ground-motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas

This article puts forward an artificial neural network (ANN) framework to develop ground-motion models (GMMs) for natural and induced earthquakes in Oklahoma, Kansas, and Texas. The developed GMMs are mathematical equations that predict peak ground acceleration, peak ground velocity, and spectral accelerations at different frequencies given earthquake magnitude, hypocentral distance, and site condition. The motivation of this research stems from the recent increase in the seismicity rate of this particular region, which is mainly believed to be the result of the human activities related to petroleum production and wastewater disposal. Literature has shown that such events generally have shallow depths, leading to large-amplitude shaking, especially at short hypocentral distances. Thus, there is a pressing need to develop site-specific GMMs for this region. This study proposes an ANN-based framework to develop GMMs using a selected database of 4528 ground motions, including 376 seismic events with magnitudes of 3 to 5.8, recorded over the 4- to 500-km hypocentral distance range in these three states since 2005. The results show that the proposed GMMs lead to accurate estimations and have generalization capability for ground motions with a range of seismic characteristics similar to those considered in the database. The sensitivity of the equations to predictive parameters is also presented. Finally, the attenuation of ground motions in this particular region is compared with those in other areas of North America.