Enhancement of seismic imaging: An innovative deep learning approach

TL;DR

This paper introduces a deep learning method to enhance seismic data resolution by expanding frequency bandwidth, improving detail extraction, and reducing artifacts, while incorporating multi-well geospatial information for accurate inversion.

Contribution

A novel deep learning approach that extracts reflections from well logs to broaden seismic spectrum bandwidth and improve data resolution with geological consistency.

Findings

Enhanced seismic resolution and artifact reduction demonstrated.

Seismic data aligns well with well log information.

Uncertainty analysis quantifies seismic response variability.

Abstract

Enhancing the frequency bandwidth of the seismic data is always the pursuance at the geophysical community. High resolution of seismic data provides the key resource to extract detailed stratigraphic knowledge. Here, a novel approach, based on deep learning model, is introduced by extracting reflections from well log data to broaden spectrum bandwidth of seismic data through boosting low and high frequencies. The corresponding improvement is observed from the enhancement of resolution of seismic data as well as elimination of sidelobe artifacts from seismic wavelets. During the training stage of deep learning model, geo-spatial information by taking consideration of multiple wells simultaneously is fully guaranteed, which assures that laterally and vertically geological information are constrained by and accurate away from the well controls during the inversion procedure. Extensive experiments prove that the enhanced seismic data is consistent with well log information, and honors rock property relationships defined from the wells at given locations. Uncertainty analysis could also be quantitatively assessed to determine the possibilities of a range of seismic responses by leveraging the outputs from the proposed approach.