Earth and Planetary Science: Prof. Ziyuan Ouyang to step down as Editor-in-Chief and Recruitment of New Editor-in-Chief
2nd series on Gravitation, Astrophysics and Cosmology Conference (ISGAC2024)
Advances on Geophysical Signal Processing
Dr. Indrajit G. Roy
Affiliation: Spaceage Geoconsulting, Banks, ACT, 2906, Australia
It is now more than seven decades since the first digital computer was used in geophysical time series analy-sis and in weather forecast modelling. Over this period there have been considerable developments in com-puter technology and also in understanding geophysical processes. Processing geophysical signal is an inte-gral part of numerical modelling of geophysical processes. But the geophysical signal, truly speaking, com-prises a wide range of physical domains, such as temporal, spatial, spatio-temporal etc. Similarly, there are ranges of classes of signal attributes applicable to geophysical signals. Therefore, the scope of geophysical signal processing remains hugely extensive, which enforces to remain focus a relatively narrower or restric-tive domains of geophysical signals. We attempt to define such restrictive domains of signal processing, alt-hough not fool-proof, by categorising into following sections.
1) Theory of sampling, reconstruction and compressed sensing, 2) Non-coherent and coherent noises and their removal, 3) Signal or anomaly enhancement techniques.
Although each section can be addressed with a big volume due to its enormous spread in dealing with range of geophysical signal and data, we, nevertheless, attempt to restrict only within a limited scope
1. Theory of sampling, reconstruction and compressed sensing
1.1 Sampling of non-bandlimited signal
1.2 Sampling of bandpass signal
1.3 Reconstruction of noisy signal and anomalies
1.4 Compressed sensing in seismic and ground penetrating radar
2. Non-coherent and coherent noises and their removal
2.1 Deterministic and stochastic processes of removal of non-coherent noise
2.2 Classifying coherent noise and techniques of removing such noise
2.3 Multiples and techniques of surface related multiple elimination (SRME)
2.4 Deep learning in noise removal
3. Signal or anomaly enhancement techniques
3.1 Anomaly enhancement in high frequency/wavenumber domain
3.2 Anomaly enhancement in low frequency/wavenumber domain
3.3 Signal enhancement through seismic migration technique.
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