Under operator, matrix and inverse theory, seismic-wave imaging can be considered a unified process—mapping from data space to model space. The main topics in seismic-wave imaging include (1) seismic-data interpolation, regularization and redatuming, which mainly decrease the imaging noise; (2) seismic-wave illumination analysis, which predicts whether a target reflector can be imaged and evalu...

S U M M A R Y Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. In addition to manual data inspection, seismogram interpretation requires therefore new processing utilities for event detection, signal classification and data visualization. The use of machine learning techniques automatises decision processes and r...

An application of a wavelet transform and a Bayesian statistical method to seismic event detection and classiication has been studied using data from the New England region. A wavelet expansion forms a new basis set for picking out, from a data stream, important features of a seismic event: time, energy and predominant period of the rst, peak and last waveforms. From these informative features ...

When accompanied by the appropriate algorithmic approach, seismic imaging is an application that can take advantage of massively parallel computer systems. Three-dimensional (3D) prestack time migration (PSTM) and pre-stack depth migration (PSDM) are key components of seismic imaging and require very large computing resources. In this paper, we show that execution of these algorithms can be dra...