The term "gradient descent" is something you will almost certainly encounter if you open any machine learning textbook. But honestly, does the explanation of "the image of descending a mountain" ...
Chemistry, mathematics and physics are central to our understanding of nature. Physics explores the fundamental laws of mechanics, electromagnetism, quantum mechanics and relativity. Chemistry studies ...
(4) estimate user-defined unknown parameters (offsets in data, systematic errors in data caused by non-seismic sources, e.g., block motion/rotation, spatial linear trends, etc.) simultaneously with ...
Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...
Deep learning has been highly successful in recent years and has led to dramatic improvements in multiple domains. Deep-learning algorithms often generalize quite well in practice, namely, given ...
Abstract: Sparsity-regularized linear inverse problem has served as the base in many disciplines, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed ...
Least-squares reverse-time migration (LSRTM) can overcome the problems of low resolution and unbalanced amplitude energy of deep formation imaging in reverse-time migration (RTM); hence, it can obtain ...
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