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A Deep Learning Approach for Acoustic Emission Event Detection
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 80th EAGE Conference and Exhibition 2018, Jun 2018, Volume 2018, p.1 - 5
Abstract
Over the last decades, the volume of rock physics experiment data has increased exponentially, creating a need for efficient algorithms to reliably detect the data produce during the rock physics experiment. Today’s most elaborate methods scan through the plethora of continuous waveform records, searching for the valid signals. In this work, we leverage the recent advances in artificial intelligence and present a highly scalable convolutional neural network for valid signals detection from a single waveform. We apply our method to study the sandstone hydraulic fracturing rock physics experiment. Our approach has a higher degree of accuracy than the traditional approach, and can distinguish the data type from valid data.