What Should We Learn? Subsurface Exploration: Recent Advances in Geo-Signal Processing, Interpretation, and Learning

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What Should We Learn? Subsurface Exploration: Recent Advances in Geo-Signal Processing, Interpretation, and Learning

Yang Li

For centuries, humans have been exploring the subsurface structure of planet Earth. Several Earth geophysical applications, such as mining, earthquake studies, and oil and gas exploration, have driven research that produced, over the years, ground-breaking theories and innovative technologies that image Earth’s subsurface. The pursuit is ongoing with an increasing desire to have higher-resolution subsurface models and images. Signal processing, data interpretation, and modeling have been the cornerstones of such innovations. In recent years, there have been advances in technologies and requirements that demand the utilization of advanced signal processing and machine-learning theories and algorithms. For example, the wide- and full-azimuth acquisition technologies have proven to be instrumental in providing high-resolution subsurface images. Similarly, geophones and hydrophones are becoming smaller, cableless, wireless, and mobile and, soon, with processing capabilities. Further, recent experiments to deploy autonomous nodes have proven the viability of automating part of or the entire acquisition process. This becomes necessary when the number of sensors increases rapidly to be in the range of hundreds of thousands per survey.

Deployment requires these sensors to be wireless and more intelligent with limited processing in the field. Besides such advances in sensing, the target reservoirs are getting deeper with complex wave propagation effects and poor target illumination. With all of these added complexities, the interpretation task becomes more challenging. Recent advances in machine learning have proven effective in automating part of the interpretation process. This special issue of IEEE Signal Processing Magazine (SPM) provides an introduction to the role of signal processing in areas where automation and learning are being deployed in Earth exploration. We envision that such technologies will also be essential for the exploration of other planets including Mars.

The articles are diverse in their coverage and discuss a wide range of problems in geophysics. Some of these problems range from near-surface monitoring to well-log data estimation and from compression of seismic data to interpretation of seismic volumes using machine learning.

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