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Artificial Intelligence for Automating Seismic Horizon Picking

By Norman Mark, Oil and Gas Exploration Consultant, San Diego, CA USA


Fully-automated seismic horizon picking dramatically shortens the time between acquiring seismic data and picking drilling targets. Oil and gas exploration companies worldwide spend millions of tedious man-hours picking horizons on seismic data to produce inventories of drilling targets. Freeing up that time to better incorporate the geologic and geophysical properties within mapped structures guarantees more drilling success.

Artificial intelligence is frequently mentioned in today’s news: self-driving cars, vehicle identification, robot vacuums, fingerprint identification, facial recognition, Alexa - are just a few examples. Why not seismic data interpretation! Here, it is proved automated seismic horizon picking is possible.

From a 2-column text file of time-amplitude pairs my algorithm produces a reflection-time-ordered text file of all continuous series of connected pixel coordinates easily-read by mapping software. These connected pixels are plotted separately on the input seismic test line’s peaks and troughs. Over ten thousand connected segments were written in less than a minute of computer time, saving many hours of manual labor. This time-saving extrapolated to 3D seismic surveys will reduce the time it takes to find drilling targets by months.


An inordinate amount of the time generating subsurface maps from seismic data is spent defining horizons by clicking a mouse on connected pixels, connected being the key word.

Before beginning this project I was an oil exploration geophysicist and worked with major and minor oil companies for many years interpreting and processing seismic data using some of the most advanced software for interpretation and processing.

Seismic data is extremely expensive to acquire, which drives oil companies to want to get their money’s worth from seismic data – not to mention drilling costs! Great improvements in numerical solutions to the wave equation, computer processing speed, computer network efficiency and lower computer costs have led to seismic images looking more and more like real geology over the last 30 years. An effective seismic processing geophysicist is continuously challenged by new technology.

The appearance of seismic interpretation software in the ‘80’s sped up map making compared to early when maps were made using sepias and drafting sets. But today, it is still a tedious process using a mouse to connect the continuous pixels of a geologic horizon reflection. Improvements in the time-consuming process of picking horizons on those seismic images have not kept pace with image quality improvements.

Most oil companies require a candidate for employment to have an MS in Geoscience. Sadly, the possibly over-qualified new hire will spend exorbitant amounts of time – perhaps most of his/her career “connecting the dots” like a kindergartner would in a coloring book.

Extensive internet searches showed no evidence that horizon picking had been automated. There were apparently no web-published algorithms that seemed to have any relevance, but literature on facial recognition and fingerprint identification software gave confidence that automating horizon picking is possible. That was the motivation for this project. At start time, identifying faces and matching finger prints to people seemed far more complex tasks. See references.

Keys to the development of the horizon detection algorithm was developing an understanding of how seismic horizon pixels are connected and of how to sort the horizon coordinates into depth-order.

Oil exploration and geophysical service companies combined have millions of seismic line miles partially interpreted at best. What if a human connecting the dots became unnecessary? What if software could make the hundreds or thousands of often tedious and time-consuming decisions in advance for each seismic image by connecting the adjacent pixels? Then those who interpret seismic data would have much more time available to truly interpret: have more time to better understand the geologic and geophysical properties of the structures they have mapped

More good maps mean more discovered structures. More time spent on integrating the geophysical and geological properties into those maps mean fewer dry holes. There could be fewer poorly-mapped prospects without the blurry-eyed, mind-numbing tedium in finding drilling prospects.

Conventional mapping software

Current commercial geophysical mapping software such as Petrel, 3DCanvas, and Kingdom have the built-in facility to extend and propagate individual horizons picked with a mouse along the seismic section being picked and into the third dimension by following coded rules for adjacency. The commercial packages do not pick more than one horizon at a time. Most of them have the facility to extend the picked horizon on a 2D image into the third dimension. A 3D surface can be formed by the software but its “seed” must be manually picked.

At the beginning of the project I was shown an example of detailed automated horizon-tracking but without any ordered text output. Finding peaks and troughs is a far much simpler task than finding them in depth-order. A simple for-loop will find every peak pixel but only in top-to-bottom, side-to-side order.

Input Data

Figure 1. The public domain proof-of-concept input seismic section is a public domain vibroseis line from Rankin Springs, Australia. It is a relative amplitude plot with 600 traces by 750x4ms time samples. It shows faulted turnover with several potential hydrocarbon fault traps - possible 4-way closure with many possible fault traps. It’s image quality is middle-of-the-road which makes it an ideal test line.

Worldwide, seismic data quality ranges from looking like a complete waste of money to showing such detail that one can “almost see” fluid movement within the horizons. If I chose the latter data type for the test case it would be said by some that the data set made it easy. That is why an older vintage, noisy but structurally interesting line was selected for this study. As it turned out, the algorithm is not prejudiced: it is blind to overall image quality.

The test line shows enough detail to understand the motivation for acquiring the data in the first place – possible fault traps and possible four-way closure – possible dome or plunging dome. There are patches of long, short, and zero-continuity segments, clear fault terminations, zones of conflicting dip and zones of mostly random noise in the most potentially hydrocarbon-rich, or at least most structurally complex part of the section.

The data were normalized after removing the overall mean from each trace. The brightest red signifies an amplitude of 1. The brightest blue signifies an amplitude of -1. No-color implies an amplitude close to zero - typical for a relative amplitude display.

The data input format is a text file of a trace numbers followed by two columns - time in milliseconds and seismic amplitude - 600 traces by 750 time samples for each trace.