Frequency trend attribute analysis for stratigraphic division and correlation

Abstract

Stratigraphic sequence interpretation and correlation are part of basic geologic research, but present frequent problems such as subjective and accurate division and correlation of sequence cycles, and a multiplicity of solutions to highfrequency sequences. We developed a novel method, termed frequency trend attribute analysis (FTAA), to solve these problems and improve the accuracy of division. The method was based on maximum entropy spectrum analysis data, built on theoretical foundations, and tested on geologic models as well as empirical data. We developed examples of how FTAA can improve stratigraphic division and correlation. We extracted frequency trend lines from well logging data (using all or a selected part of a facies-sensitive log such as the natural gamma-ray log) whereby the FTAA outcome reflected the overlay series and cycle structures. The resulting frequency trend lines also indirectly reflected changes to the sedimentary environment and base level, and the precise stratigraphic division and isochronous comparisons were automatically deduced from the frequency trend lines. According to the practical comparison with wells in the field, the frequency trend lines were found to be more accurate than using outcrop data, and the method proved to be effective and convenient in use. The FTAA significantly improved the precision and accuracy of automatic division and correlation of sequence cycles.

Reconstruction of meander‐bend migration from associated channel‐belt architecture recorded in successions of ancient meandering rivers: A case study from the Cretaceous Songliao Basin, China

“…To carry out stratigraphic correlations and interpretations, 11 well‐to‐well cross‐sections (see lines L1–L3, T4–T9 and S10–S11 in Figure 1C) were examined, including longitudinal cross‐sections L1–L3 (parallel to average palaeoflow direction) extending from south‐west to north‐east, transverse cross‐sections T4–T9 (perpendicular to palaeoflow direction) extending from north‐west to south‐east and oblique cross‐sections S10–S11 extending west‐east and south‐north. The cross‐sections were used to establish the sequence stratigraphic framework (Li et al, 2012; Shan et al, 2015c, 2018b; Xu et al, 2014) for the study area. A pseudo‐3D fence diagram was produced to examine the 3D stratigraphic framework (Figure ).…”

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

“…In seismic exploration, signals recorded by near‐surface sensors are usually contaminated by random noise, which causes poor performance in subsequent processing procedures, such as absorption compensation, seismic attribute analysis, and generating high‐resolution image (Xu et al ., 2015; Du et al ., 2018; Ma et al ., 2019, 2020; Wang et al ., 2019). Therefore, removing random noise from received noisy data is an essential procedure.…”