  Seismic interpretation is now serving as a fundamental tool for depicting
subsurface geology and assisting activities in various domains, such as
environmental engineering and petroleum exploration. However, most of the
existing interpretation techniques are designed for interpreting a certain
seismic pattern (e.g., faults and salt domes) in a given seismic dataset at one
time; correspondingly, the rest patterns would be ignored. Interpreting all the
important seismic patterns becomes feasible with the aid of multiple
classification techniques. When implementing them into the seismic domain,
however, the major drawback is the low efficiency particularly for a large
dataset, since the classification need to be repeated at every seismic sample.
To resolve such limitation, this study first present a seismic pattern
interpretation dataset (SpiDat), which tentatively categorizes 12
commonly-observed seismic patterns based on their signal intensity and lateral
geometry, including these of important geologic implications such as faults,
salt domes, gas chimneys, and depositional sequences. Then we propose a seismic
pattern interpretation network (SpiNet) based on the state-of-the-art
deconvolutional neural network, which is capable of automatically recognizing
and annotating the 12 defined seismic patterns in real time. The impacts of the
proposed SpiNet come in two folds. First, applying the SpiNet to a seismic cube
allows interpreters to quickly identify the important seismic patterns as input
to advanced interpretation and modeling. Second, the SpiNet paves the
foundation for deriving more task-oriented seismic interpretation networks,
such as fault detection. It is concluded that the proposed SpiNet holds great
potentials for assisting the major seismic interpretation challenges and
advancing it further towards cognitive seismic data analysis.
