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

Seismic velocity is one of the most important parameters used in seismic exploration.
Accurate velocity models are the key prerequisites for reverse time migration and other high …

Compared with traditional seismic noise attenuation algorithms that depend on signal
models and their corresponding prior assumptions, removing noise with a deep neural …

Mapping fault planes using seismic images is a crucial and time-consuming step in
hydrocarbon prospecting. Conventionally, this requires significant manual efforts that …

Seismic interpretations are, by definition, subjective and often require significant time and
expertise from the interpreter. We are convinced that machine-learning techniques can help …

The challenging task of automatic seismic fault detection recently gained in quality with the
emergence of deep learning techniques. Those methods successfully take advantage of a …

Machine learning (ML) systems can automatically mine data sets for hidden features or
relationships. Recently, ML methods have become increasingly used within many scientific …

The development and wide application of high-precision seismic exploration technology
puts forward new requirements for fault interpretation. The problems of poor fault continuity …

In this work we describe a machine learning pipeline for facies classification based on
wireline logging measurements. The algorithm has been designed to work even with a …

高精度地震勘探技术的发展与广泛应用对断层解释提出了新的要求, 在构造复杂地区断层连续
性差, 识别难度大等问题严重限制了复杂地区油气藏勘探开发. 深度学习作为一种新兴技术 …