Machine learning-detected signal predicts time to earthquake

Machine-learning research published in two related papers today in Nature Geoscience reports the detection of seismic signals accurately predicting the Cascadia fault's slow slippage, a type of failure observed to precede ...

Study reveals how sand dunes alter seismic waves

Sand dunes may be lovely to behold, but they have long been seismic troublemakers to geophysicists trying to detect what lies underground nearby. A new study takes a stab at figuring out just how dunes are fouling seismic ...

Geologists simulate deep earthquakes in the laboratory

More than 20 years ago, geologist Harry Green, now a distinguished professor of the graduate division at the University of California, Riverside, and colleagues discovered a high-pressure failure mechanism that they proposed ...

Scientists use solar power to study elephants in Africa

A team of elephant researchers from Stanford University has transformed a remote corner of southern Africa into a high-tech field camp run entirely on sunlight. The seasonal solar-powered research camp gives scientists a ...

Lahar detection system upgraded for Mount Rainier

In the shadow of Washington State's Mount Rainier, about 90,000 people live in the path of a potential large lahar—a destructive, fluid and fast-moving debris flow associated with volcanic slopes.

page 2 from 4