Seismic sensors collect massive quantities of data that contain a wealth of information about processes within the earth. Seismologists are increasingly adopting data mining and machine learning techniques to identify previously unknown earthquakes in large seismic data sets. Our new earthquake detection method, Fingerprint and Similarity Thresholding (FAST), enables waveform-similarity-based e...

The main aim of this study, is to evaluate the seismic reliability of steel concentrically braced frame (SCBF) structures optimally designed in the context of performance-based design. The Monte Carlo simulation (MCS) method and neural network (NN) techniques were utilized to conduct the reliability analysis of the optimally designed SCBFs. Multi-layer perceptron (MLP) trained by back propagati...

We proposed an economic, cost-constrained optimal design of a water distribution system (WDS) that maximizes seismic reliability while satisfying pressure constraints. The model quantifies the seismic reliability of a WDS through a series of procedures: stochastic earthquake generation, seismic intensity attenuation, determination of the pipe failure status (normal, leakage, and breakage), pipe...

Abstract In both industry and academic, the seismic exploration does not yet have the capability of illuminating the physical dynamics with high resolution and in real-time, as it involves collecting the raw seismic data from sensors to data loggers then manually retrieving data for post processing which may take months to complete. This study presents InsightTomo, a In-Situ Seismic Tomographic...

The increased availability and fidelity of broadband seismic instruments have played a key role in bridging the gap between exploration and global seismic applications. Long gone are the days when global structure mapping and source characterization/monitoring relied on the manual digitization of WWSSN (World-Wide Standard Seismographic Network) analogue records or hours in front of microfiche ...