This study describes a deep convolutional neural network (CNN) based technique to predict
intensity measurements (IMs) of earthquake ground shaking. The input data to the CNN …

In this work, different types of artificial neural networks are investigated for the estimation of
the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural …

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the
life-cycle of technical appliances. This is the case of mechanical-related stress, either due to …

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand
active faults or other causes of earthquakes, thereby facilitating the preparation of early …

The past several decades have seen an exponential increase in the volume of available
seismic data, and with it has come the need to develop fast, automatic earthquake detection …

Monitoring of non-structural elements is not usually implemented, despite the seismic
vulnerability of these components and the significant cost associated with their replacement …

Reliable earthquake detection algorithms are necessary to properly analyze and catalog the
continuously growing seismic records. We report the results of applying a deep …

Non-stationary signals are often analyzed using raw waveform data or spectrograms of
those data; however, the possibility of alternative time–frequency representations being …

Automatic detection of seismic events is a useful tool for routine data processing. Effective
detection saves time and effort in phase picking and events' location, especially in areas …

The number and efficiency of seismic networks have steadily increase over time delivering
large datasets to be analyzed for earthquake occurrence. Automatic tools for accurate …