Automatic Interpretative Image-Focusing Analysis

TL;DR

This paper introduces an automatic, data-driven CNN-based method for seismic image focusing analysis that accurately estimates velocity errors and enhances fault interpretation, outperforming traditional approaches.

Contribution

The paper presents a novel convolutional neural network approach for automating seismic image focusing analysis using both spatial and offset/angle information.

Findings

Accurately estimates velocity errors in seismic images.

Outperforms traditional semblance-based methods.

Improves fault interpretation within seismic images.

Abstract

The focusing of a seismic image is directly linked to the accuracy of the velocity model. Therefore, a critical step in a seismic imaging workflow is to perform a focusing analysis on a seismic image to determine velocity errors. While the offset/aperture-angle axis is frequently used for focusing analysis, the physical (i.e., midpoint) axes of seismic images tend to be ignored as focusing analysis of geological structures is highly interpretative and difficult to automate. We have developed an automatic data-driven approach using convolutional neural networks to automate image-focusing analysis. Using focused and unfocused geological faults, we show that our method can make use of both spatial and offset/angle focusing information to robustly estimate velocity errors within seismic images. We demonstrate that our method correctly estimates velocity errors from a 2D Gulf of Mexico limited-aperture image where a traditional semblance-based approach fails. We also show that our method has the added benefit of improving the interpretation of faults within the image.