Machine Learning in Earthquake Seismology

Instrumentation in Earthquake Seismology

Implementing Machine Learning in Earthquake Engineering

The use of machine learning across many fields has seen a rise in recent years, from life and physical sciences to finance and athletics. Within the physical sciences, it is just starting to see some implementation in the field of Earthquake Engineering. The objective of this paper is to implement machine learning to Earthquake Engineering data to create more literature in the field. In particu...

Real-time Seismology and Earthquake Damage Mitigation

■ Abstract Real-time seismology refers to a practice in which seismic data are collected and analyzed quickly after a significant seismic event, so that the results can be effectively used for postearthquake emergency response and early warning. As the technology of seismic instrumentation, telemetry, computers, and data storage facility advances, the real-time seismology for rapid postearthqua...

Is Earthquake Seismology a Hard, Quantitative Science?

Our purpose is to analyze the causes of recent failures in earthquake forecasting, as well as the difficulties in earthquake investigation. We then propose that more rigorous methods are necessary in earthquake seismology research. First, we discuss the failures of direct earthquake forecasts and the poor quantitative predictive power of theoretical and computer simulation methods in explaining...

The 2007 Pisco, Peru, earthquake (M8.0): seismology and geodesy

S U M M A R Y We investigate the 2007 Pisco, Peru, earthquake (M8.0) using seismological and geodetic data. Analysis of teleseismic records indicates that the earthquake is characterized by a steadily increasing moment rate, with the maximum occurring between about 55 and 70 s and the associated slip occurring about 40 km south of the epicentre. We combine InSAR data from L-band (ALOS) and C-ba...