This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …

Recent advances in neural networks have provided computers (and machines) with intuition-
the ability to produce a reasonable result to a problem which is intractable, or unreasonably …

Seismic horizons are essential for structural analysis, inversion, time-to-depth conversion,
and seismic attribution analysis. However, seismic horizons are obtained commonly by …

Lacustrine-fan deltas feature high reservoir-quality lithounits that are critical targets to
hydrocarbon exploration and development. However, depicting their intricate sedimentary …

Steeply dipping prograding fan deltas possess high reservoir quality facies that could be
excellent targets while exploring for hydrocarbons. Due to their complex stacking nature …

Seismic horizon tracking is a fundamental aspect of seismic data interpretation. However,
seismic horizons are typically obtained using manual tracking or a combination of manual …

State-of-the-art processing of the data of petroleum seismology require8 tedious and
expensive interpretation of velocity spectra. Currently this interpretation is gener-dly …

In seismic data interpretation, horizon picking is important for structural analysis, feature
recognition, and site appraisal. However, horizon picking is still commonly done by hand, a …

Picking the reflection horizon is an important step in velocity inversion and seismic
interpretation. Manual picking is time-consuming and no longer suitable for current large …