Seismic data interpolation is essential in a seismic data processing workflow, recovering
data from sparse sampling. Traditional and deep-learning-based methods have been widely …

Physical and budget constraints often result in irregular sampling, which complicates
accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot …

Seismic data interpolation of irregularly missing traces plays a crucial role in subsurface
imaging, enabling accurate analysis and interpretation throughout the seismic processing …

Regular sampled seismic data is important for seismic data processing. However, seismic
data is often missing due to natural or economic reasons. Especially, when encountering big …

With the development of seismic surveys and the decline of shallow petroleum resources,
high resolution and high signal-to-noise ratio have become more important in seismic …

The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing
the integrity of subsurface structures and serves as a primary parameter for evaluating the …

How to suppress the background noise and also recover signals is a widely-concerned and
long standing problem in the field of seismic data processing. Effective seismic denoising …

Los levantamientos sísmicos usualmente se ven afectados por obstáculos o restricciones
ambientales que impiden el muestreo regular en la adquisición sísmica. Por lo tanto, se han …

Seismic data reconstruction is an inverse problem in the geophysical community. Deep
learning-based methods directly learn the projection between undersampled and complete …

Data quality is of utmost importance in assessing seismic hazards and risk mitigation related
to geohazards. Erroneous data incorporation may lead to inaccurate observations and …