Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

Frequency filtering is widely used in routine processing of seismic data to improve the signal-
to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In …

Attenuation of seismic random noise is considered an important processing step to enhance
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …

Earthquake signal detection is at the core of observational seismology. A good detection
algorithm should be sensitive to small and weak events with a variety of waveform shapes …

Regardless of successful applications of the convolutional neural networks (CNNs) in
different fields, its application to seismic waveform classification and first-break (FB) picking …

Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …

Effective and efficient arrival picking plays an important role in microseismic and earthquake
data processing and imaging. Widely used short-term-average long-term-average ratio …

It is well known that time-frequency analysis (TFA) characterises signals in time-frequency
plane. Theoretically, traditional non-parameterised TFA can analyze any signal, but it is …