The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

With the increased size and complexity of seismic surveys, manual labeling of seismic facies
has become a significant challenge. Application of automatic methods for seismic facies …

目前叠加速度的获取主要是通过人工拾取速度谱, 存在着效率低, 耗时长且易受人为因素影响的
缺点. 本文提出了一种基于自适应阈值约束的无监督聚类智能速度拾取方法 …

Salt bodies are important subsurface structures with significant implications for hydrocarbon
accumulation and sealing in offshore petroleum reservoirs. This study presents an …

Seismic facies classification takes a two-step approach: attribute extraction and seismic
facies analysis by using clustering algorithms, sequentially. In general, it is clear that the …

Traditional seismic facies analysis, which depends on manual interpretation of seimic
amplitude, encounters difficulties because of the complexity, volume, and limited resolution …

In seismic interpretation, stacking velocity is mainly acquired by manual picking from velocity
spectra, which is time-consuming and highly susceptible to human experience. To improve …

Abstract Machine learning (ML) algorithms, such as principal component analysis,
independent component analysis, self-organizing maps, and artificial neural networks, have …

Given the high number and diversity of events in a typical cryoseismic dataset, in particular
those recorded on ice sheet margins, it is desirable to use a semi-automated method of …

Multifrequency attribute blending is a highly effective tool for characterizing hydrocarbon
reservoirs. It begins by extracting multifrequency attributes of seismic data based on time …