Machine-learning algorithm helps geoscientists create a 3-D picture of a fault zone, generating new insight into seismic processes.
A naturally occurring injection of underground fluids drove a four-year-long earthquake swarm near Cahuilla, California, according to a new seismological study that utilizes advances in earthquake monitoring with a machine-learning algorithm. In contrast to mainshock/aftershock sequences, where a large earthquake is followed by many smaller aftershocks, swarms typically do not have a single standout event.
The study, published in the journal Science, illustrates an evolving understanding of how fault architecture governs earthquake patterns. “We used to think of faults more in