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The concept of neural networks has been around for decades and was successfully applied already in the 1990s to applications in different areas. However, network models back then where limited in complexity, due to their demand in memory and computational resources for training, which directly translated into a limitation in performance and thus practical use.  

With the rise of programmable graphics processing pipelines in consumer-grade graphics processors (GPUs) it became possible to utilize the capability of massive parallel processing found in those GPUs for arbitrary calculations. Training and productive use of neural networks are by their nature largely parallel computation tasks and hence GPUs are an ideal platform for their implementation. Using the processing abilities of modern GPUs it is possible today to build much larger network models and these so called Deep Neural Networks were found to be surprisingly powerful compared to their much smaller siblings. A prominent example is deep neural networks that challenge human performance in object recognition.

Deep Learning for pattern recognition is a trending topic today. We conducted an experiment to evaluate this hype and asses if this technology could be of use in oil and gas exploration.


Related Publication(s)

Detecting Geological Structures in Seismic Volumes Using Deep Convolutional Neural Networks


Master Thesis by Ying Jiang, submitted to the Rheinische-Westfälische Technische Hochschule Aachen on February 22, 2017



Detecting prospective structures in volumetric geo-seismic data using deep convolutional neural Networks


Poster by Ying Jiang and Benjamin Wulff presented on November 15, 2016  at the annual foundation council meeting of the Bonn-Aachen International Center for Information Technology (b-it)


TO DOWNLOAD: DeepGeo Demonstration for Non-members on July 20, 2017


Presentation by Benjamin Wulff, Fraunhofer IAIS, 70.8 MB, MP4-Video, about 60 min.