## The Machine Learning Techniques in Seismic Data Processing and Interpretation Problems

**Maxim Ryabinskiy (speaker), Dr. Dmitriy Finikov,**

Dr. Nikita Zelinsky

Dr. Nikita Zelinsky

*Russia, Yandex.Terra*

There are several most frequent challenges in modern seismic explora- tion that demand the use of machine learning techniques: adjusting the forward and inverse filtering parameters during seismic data processing, pattern recognition during geological strata tracking, the data regular- ization problem, fields interpolation during geological interpretation

and geological structures classification at the final interpretation stage. Solving these problems involves a great variety of artificial intelligence techniques: different modifications of regression in first-type problems, the kriging method and artificial neural networks and fuzzy logic systems in second- and third-type problems accordingly.

and geological structures classification at the final interpretation stage. Solving these problems involves a great variety of artificial intelligence techniques: different modifications of regression in first-type problems, the kriging method and artificial neural networks and fuzzy logic systems in second- and third-type problems accordingly.

The parameters adjusting problem demands the construction of learning sets from the modeled data, then we use iterative routines to realize the learning procedure and try to use the established parameters on real data. Commonly we use different regression models, especially the power regression technique.

Machine learning is more popular in geological interpretation of seismic surveys data. Geological interpretation is an attempt to solve the inverse geophysical problem using all the available a priori information: well-log data, regional geological maps, etc. The a priori information is always insufficient, so the inverse problem is poorly conditioned and so we have to use either geologists’ experience or artificial intelligence. The main aim of the oil geologist is locating hydrocarbon traps; this problem seems like facial recognition and involves the same kits to solve it, but otherwise, the number of parameters in geological interpretation reaches several hundred. Therefore, the dimension reduction of the feature space also is an actual problem.

So, applying machine learning techniques in seismic data processing and interpretation is a modern and actual branch of geology.