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ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses. 

Abstract Establishing an extensive and highly durable, long-term, seafloor network of autonomous broadband seismic stations to complement the land-based Global Seismographic Network has been a goal of seismologists for decades. Seismic signals, chiefly the vibrations from earthquakes but also signals generated by storms and other environmental processes, have been processed from land-based seismic stations to build intriguing but incomplete images of the Earth’s interior. Seismologists have mapped structures such as tectonic plates and other crustal remnants sinking deep into the mantle to obtain information on their chemical composition and physical state; but resolution of these structures from land stations is not globally uniform. Because the global surface is two-thirds ocean, increasing the number of seismic stations located in the oceans is critical for better resolution of the Earth’s interior and tectonic structures. A recommendation for a long-term seafloor seismic station pilot experiment is presented here. The overarching instrumentation goal of a pilot experiment is performance that will lead to the installation of a large number of long-term autonomous ocean-bottom seismic stations. The payoff of a network of stations separated from one another by a few hundred kilometers under the global oceans would be greatly refined resolution of the Earth’s interior at all depths. A second prime result would be enriched understanding of large-earthquake rupture processes in both oceanic and continental plates. The experiment would take advantage of newly available technologies such as robotic wave gliders that put an affordable autonomous prototype within reach. These technologies would allow data to be relayed to satellites from seismometers that are deployed on the seafloor with long-lasting, rechargeable batteries. Two regions are presented as promising arenas for such a prototype seafloor seismic station. One site is the central North Atlantic Ocean, and the other high-interest locale is the central South Pacific Ocean.