Interactive Seismic Stratigraphic Analysis: User-Guided Visual Enhancement and AI-Driven Depositional Element Extraction

While crucial to understand the subsurface, interpreting 3D depositional elements from seismic data is often challenged by issues such as effectively visualising subtle stratigraphic features and accurately transforming them into 3D objects that reflect geological reality. This paper introduces an interactive framework that directly addresses both through an innovative three-step workflow. The process begins with chronostratigraphic colour blending on stratal slices, enabling rapid visualisation of depositional patterns and sequence relationships. Next, to enhance feature separation, interpreters interactively place seed points within target elements and background facies, allowing for the automatic determination of optimal spectral decomposition frequencies. Finally, the interpreter guides an image segmentation foundation model with simple point prompts, facilitating the precise extraction of depositional elements without the bottleneck of geological-specific training data. Application of the method to fields, namely channel systems from the Maui Field, New Zealand, and carbonate build-ups and reefs from the Poseidon Field, Australia demonstrates a reduction in interpretation time from weeks to just days, all while maintaining high accuracy. This chronostratigraphic-centric approach guarantees that extracted objects represent true depositional architecture rather than arbitrary seismic anomalies.

Authors

Publication Details

Journal
First Break
Published
2025-09-01
DOI
https://doi.org/10.3997/1365-2397.fb2025069
Citations
1
Primary Topic
Geological Modeling and Analysis
Type
article
Field-Weighted Citation Impact
1.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interactive Seismic Stratigraphic Analysis: User-Guided Visual Enhancement and AI-Driven Depositional Element Extraction

J. Razza, Rémi Leblond, M. Etchebes, Laurent Souche et al.
1 citations
First Break
Geological Modeling and Analysis
1.34
article

Interactive Seismic Stratigraphic Analysis: User-Guided Visual Enhancement and AI-Driven Depositional Element Extraction

J. Razza, Rémi Leblond, M. Etchebes, Laurent Souche, Étienne Legeay, Nasser Olleik
article en
1 citations

Abstract

While crucial to understand the subsurface, interpreting 3D depositional elements from seismic data is often challenged by issues such as effectively visualising subtle stratigraphic features and accurately transforming them into 3D objects that reflect geological reality. This paper introduces an interactive framework that directly addresses both through an innovative three-step workflow. The process begins with chronostratigraphic colour blending on stratal slices, enabling rapid visualisation of depositional patterns and sequence relationships. Next, to enhance feature separation, interpreters interactively place seed points within target elements and background facies, allowing for the automatic determination of optimal spectral decomposition frequencies. Finally, the interpreter guides an image segmentation foundation model with simple point prompts, facilitating the precise extraction of depositional elements without the bottleneck of geological-specific training data. Application of the method to fields, namely channel systems from the Maui Field, New Zealand, and carbonate build-ups and reefs from the Poseidon Field, Australia demonstrates a reduction in interpretation time from weeks to just days, all while maintaining high accuracy. This chronostratigraphic-centric approach guarantees that extracted objects represent true depositional architecture rather than arbitrary seismic anomalies.

First BreakVol. 43(9)
Openalex Percentile: Top 17%
Geological Modeling and Analysis
1.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.