OrthoSeisnet: Seismic Inversion through Orthogonal Multi-scale Frequency Domain U-Net for Geophysical Exploration

TL;DR

OrthoSeisnet is a novel deep learning model that enhances seismic inversion by integrating multi-scale frequency domain transforms within a U-Net, improving detection of sparse thin hydrocarbon layers in seismic data.

Contribution

The paper introduces OrthoSeisnet, a new approach combining frequency domain transforms and orthogonal basis functions within U-Net to better handle data sparsity in seismic inversion.

Findings

Outperforms traditional methods in synthetic and real datasets

Improves interpretability and resolution of seismic images

Effectively detects sparse hydrocarbon layers

Abstract

Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic datasets presents a significant challenge due to the ill-posed nature and poor non-linearity of the problem. While data-driven deep learning algorithms have shown promise, effectively addressing sparsity remains a critical area for improvement. To overcome this limitation, we propose OrthoSeisnet, a novel technique that integrates a multi-scale frequency domain transform within the U-Net framework. OrthoSeisnet aims to enhance the interpretability and resolution of seismic images, enabling the identification and utilization of sparse frequency components associated with hydrocarbon-bearing layers. By leveraging orthogonal basis functions and decoupling frequency components, OrthoSeisnet effectively improves data sparsity. We evaluate the performance of OrthoSeisnet using synthetic and real datasets obtained from the Krishna-Godavari basin. Orthoseisnet outperforms the traditional method through extensive performance analysis utilizing commonly used measures, such as mean absolute error (MAE), mean squared error (MSE), and structural similarity index (SSIM) https://github.com/supriyo100/Orthoseisnet.