  Machine learning-based seismic processing models are typically trained
separately to perform specific seismic processing tasks (SPTs), and as a
result, require plenty of training data. However, preparing training data sets
is not trivial, especially for supervised learning (SL). Nevertheless, seismic
data of different types and from different regions share generally common
features, such as their sinusoidal nature and geometric texture. To learn the
shared features, and thus, quickly adapt to various SPTs, we develop a unified
paradigm for neural network-based seismic processing, called Meta-Processing,
that uses limited training data for meta learning a common network
initialization, which offers universal adaptability features. The proposed
Meta-Processing framework consists of two stages: meta-training and
meta-testing. In the meta-training stage, each SPT is treated as a separate
task and the training dataset is divided into support and query sets. Unlike
conventional SL methods, here, the neural network (NN) parameters are updated
by a bilevel gradient descent from the support set to the query set, iterating
through all tasks. In the meta-testing stage, we also utilize limited data to
fine-tune the optimized NN parameters in an SL fashion to conduct various SPTs,
such as denoising, interpolation, ground-roll attenuation, image enhancement,
and velocity estimation, aiming to converge quickly to ideal performance.
Comprehensive numerical examples are performed to evaluate the performance of
Meta-Processing on both synthetic and field data. The results demonstrate that
our method significantly improves the convergence speed and prediction accuracy
of the NN.
