# Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective

## TL;DR

This paper reviews recent advances in analyzing Earth's subsurface structures through seismic volume interpretation using image processing, computer vision, and machine learning techniques, highlighting challenges and future directions.

## Contribution

It provides a comprehensive summary of recent methods combining seismic data analysis with advanced image processing and machine learning, emphasizing emerging techniques and challenges.

## Key findings

- Summarizes recent image processing and computer vision methods for seismic interpretation.
- Discusses challenges and potential solutions using machine learning algorithms.
- Highlights future research directions in subsurface structure analysis.

## Abstract

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms.