AI visualization of underground seismic data interpretation.

Unlock Buried Treasures: How AI is Revolutionizing 3D Seismic Interpretation

Avery Sinclair in Tech & Innovation August 2025 • 4 min read.

"Discover how genetic algorithms, inspired by the Human Genome Project, are transforming the analysis of vast 3D seismic datasets, leading to more accurate subsurface models and smarter exploration decisions."

For over two decades, geoscientists have amassed an enormous collection of 3D seismic data, covering both land and sea, in the hopes of finding minerals and fuels. This data, when combined with geological and geophysical information, helps create models of what lies beneath the surface of Earth. These models are the foundation for decisions about where to explore, assess potential, and develop resources.

Since the 1980s, big steps forward in how we gather, process, and understand this data have led to a flood of high-quality information. Today's geoscientists face the challenge of thoroughly studying and using this ever-growing amount of data, often spread across multiple areas. The problem is, the time they have to carefully look at and integrate all of this information is limited and shrinking.

Typically, geoscientists spend much of their time carefully studying smaller parts of these datasets, thinking deeply about what the results mean. Fortunately, they are not the only ones dealing with analyzing huge amounts of data. Borrowing ideas from the Human Genome Project (HGP), this article explores how to use algorithms to automatically divide and identify all the different underground layers within a 3D block of data. The results are then stored in a visual database.

Automated Seismic Interpretation: Inspired by the Human Genome Project

AI visualization of underground seismic data interpretation.

The method presented leverages genetic algorithms, drawing inspiration from the Human Genome Project (HGP). Genetic algorithms are mathematical procedures that simulate the biological process of evolution. The evaluation of a potential solution hinges on predefined criteria that assess its 'goodness of fit.' The more closely a solution aligns with these criteria, the higher its likelihood of survival into subsequent evaluation rounds. This innovative strategy enables the identification of unique waveform segments that correspond to subsurface layers or horizons, referred to as GeoPopulations™.

To map GeoPopulations™ comprehensively, these algorithms utilize natural selection principles to cultivate genetically related individuals from scattered seed points. Genetic algorithms have demonstrated effectiveness across diverse optimization challenges, including applications in medicine, airline logistics, and financial markets.

A chromosome is similar to a seismic trace.
The seismic volume, then, is many chromosomes.
Every chromosome has a set of genes, like a seismic trace has a set of waveforms. So, seismic waveforms are like genes.
Every gene (waveform) has unique features (location, strength, shape of the neighbor, etc.).

Initially, the seismic volume undergoes automated segmentation into a collection of individual waveforms. Individuals within this collection are randomly selected as new populations. Each gene then searches both locally and globally for other genes with the most similar genetic characteristics (amplitude values, trace shapes, frequency or any combination of attributes that are associated to each sample).

Streamlining Processes

By automating the surface extraction process and providing one or two orders of magnitude higher quality surfaces than conventional interpretation techniques, more time can be spent developing an understanding of the results instead of losing time in the mechanics of the extraction process. In areas where a horizon is noisy or subject to coherent interference, the ability to rapidly have an objective horizon for critical review by the interpreter can significantly improve the reliability of the interpretation. Moreover, when this analysis is incorporated during the processing of the data, additional information can be used to optimise the processing of the data and get useful data to the interpreter at a much earlier stage.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

How are genetic algorithms used in 3D seismic interpretation?

Genetic algorithms, inspired by the Human Genome Project (HGP), are used to automate the interpretation of 3D seismic data. They simulate the biological process of evolution to identify unique waveform segments that correspond to subsurface layers or horizons, called GeoPopulations™. These algorithms use natural selection principles to cultivate genetically related individuals from scattered seed points within the seismic volume. This method allows geoscientists to analyze vast datasets more efficiently and accurately, leading to better subsurface models and exploration decisions.

What is a GeoPopulation™ and how does it relate to seismic data analysis?

A GeoPopulation™ represents subsurface layers or horizons identified through the use of genetic algorithms in 3D seismic interpretation. The algorithms identify unique waveform segments, which correspond to these GeoPopulations™. These are essentially the building blocks for understanding the structure beneath the Earth's surface. By identifying and mapping GeoPopulations™, geoscientists can create more accurate models of the subsurface, which is critical for resource exploration and development.

Can you explain the analogy between seismic data and genetics in the context of this AI method?

In this AI method, there's a clear analogy between seismic data and genetics. A seismic volume is akin to many chromosomes. Each chromosome, like a seismic trace, comprises numerous genes. These genes are seismic waveforms, each possessing unique features like location, strength, and shape. The algorithm analyzes these 'genes' (waveforms) within the seismic data to identify patterns and characteristics of the subsurface layers, mirroring how genetic analysis identifies traits and characteristics in biological organisms.

How does automating the interpretation process improve the work of geoscientists?

Automating the interpretation process, using techniques like genetic algorithms, significantly improves the efficiency and accuracy of geoscientists' work. It allows them to analyze massive datasets more quickly, saving time and enabling more in-depth analysis. By providing higher-quality surfaces compared to traditional techniques, the automated method allows geoscientists to focus on understanding the results rather than the mechanics of data extraction. This streamlined process enhances the reliability of interpretations, especially in complex areas with noisy data or interference, and enables the integration of analysis during data processing for further optimization.

What are the practical implications of using AI-powered genetic algorithms for subsurface resource exploration?

The practical implications of using AI-powered genetic algorithms are substantial. By automating the analysis of 3D seismic data, geoscientists can create more accurate subsurface models, which is fundamental for making informed decisions about resource exploration and development. This leads to improved identification of potential reserves, better assessment of resource potential, and more effective planning for development activities. Moreover, the ability to analyze data more efficiently allows companies to make faster, more informed decisions, potentially reducing costs and increasing the success rate of exploration projects. The integration of this analysis during data processing can further optimize the quality of data used by the interpreter at an earlier stage.