@conference {4242, title = {Deep Recurrent Architectures for Seismic Tomography}, booktitle = {81st EAGE Conference and Exhibition 2019}, year = {2019}, month = {06/2019}, abstract = {

This paper introduces novel deep recurrent neural network architectures for Velocity Model Building (VMB), which is beyond what Araya-Polo et al 2018 pioneered with the Machine Learning-based seismic tomography built with convolutional non-recurrent neural network. Our investigation includes the utilization of basic recurrent neural network (RNN) cells, as well as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Performance evaluation reveals that salt bodies are consistently predicted more accurately by GRU and LSTM-based architectures, as compared to non-recurrent architectures. The results take us a step closer to the final goal of a reliable fully Machine Learning-based tomography from pre-stack data, which when achieved will reduce the VMB turnaround from weeks to days.

}, author = {Amir Adler and Mauricio Araya-Polo and Tomaso Poggio} } @inbook {4198, title = {Fast and Accurate Seismic Tomography via Deep Learning}, booktitle = {Deep Learning: Algorithms and Applications}, year = {2019}, publisher = {SPRINGER-VERLAG}, organization = {SPRINGER-VERLAG}, author = {Mauricio Araya-Polo and Amir Adler and Stuart Farris and Joseph Jennings} } @article {3269, title = {Deep-learning tomography}, journal = {The Leading Edge}, volume = {37}, year = {2018}, month = {01/2018}, pages = {58 - 66}, abstract = {

Velocity model building (VMB) is a key step in hydrocarbon exploration; The VMB main product is an initial model of the subsurface that is subsequently used in seismic imaging and interpretation workflows. Reflection or refraction Tomography and full waveform inversion (FWI) are the most commonly used techniques in VMB. On one hand, Tomography is a time-consuming activity that relies on successive updates of highly human-curated analysis of gathers. On the other hand, FWI is very computationally demanding with no guarantees of global convergence.

We propose and implement a novel concept that bypasses these demanding steps, directly producing an accurate gridding or layered velocity model from shot gathers. Our approach relies on training deep neural networks; the resulting predictive model maps relationships between the data space and the final output (particularly, the presence of high velocity segments that might indicate salt formations). In term of time, the training task takes a few hours for 2D data, but the inference step (predicting a model from previously unseen data) takes only seconds.

The promising results shown here for synthetic 2D data demonstrate a new way of using seismic data and suggests fast turnaround of workflows that now make use of machine learning approaches to identify key structures in the subsurface.

}, keywords = {algorithm, full waveform inversion, Neural Networks, NMO, tomography Read More: https://library.seg.org/doi/abs/10.1190/tle37010058.1}, issn = {1070-485X}, doi = {10.1190/tle37010058.1}, url = {https://library.seg.org/doi/10.1190/tle37010058.1}, author = {Mauricio Araya-Polo and Joseph Jennings and Amir Adler and Dahlke, Taylor} } @conference {1574, title = {Learning with a Wasserstein Loss}, booktitle = {Advances in Neural Information Processing Systems (NIPS 2015) 28}, year = {2015}, abstract = {

Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissimilarity for probability measures. Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is efficiently computed. We describe an efficient learning algorithm based on this regularization, as well as a novel extension of the Wasserstein distance from prob- ability measures to unnormalized measures. We also describe a statistical learning bound for the loss. The Wasserstein loss can encourage smoothness of the predic- tions with respect to a chosen metric on the output space. We demonstrate this property on a real-data tag prediction problem, using the Yahoo Flickr Creative Commons dataset, outperforming a baseline that doesn{\textquoteright}t use the metric.

}, url = {http://arxiv.org/abs/1506.05439}, author = {Charlie Frogner and Chiyuan Zhang and Hossein Mobahi and Mauricio Araya-Polo and Tomaso Poggio} } @conference {216, title = {Machine Learning Based Automated Fault Detection in Seismic Traces}, booktitle = {EAGE Conference and Exhibition 2014}, year = {2014}, month = {06/2014}, address = {The Netherlands}, abstract = {

Introduction:

The Initial stages of velocity model building (VMB) start off from smooth models that capture geological assumptions of the subsurface region under analysis. Acceptable velocity models result from successive iterations of human intervention (interpreter) and seismic data processing with in complex workflows. The interpreters ensure that any additions or corrections made by seismic processing are compliant with geological and geophysical knowledge. The information that seismic processing adds to the model consists of structural elements, faults are one of the most relevant of those events since they can signal reservoir boundaries or hydrocarbon traps. Faults are excluded in the initial models due to their local scale. Bringing faults into the model in early stages can help to steer the VMB process.

This work introduced a tool whose purpose is to assist the interpreters during the initial stages of the VMB, when no seismic data has been migrated. Our novel method is based on machine learning techniques and can automatically identify and localize faults from not migrated seismic data. Comprehensive research has targeted the fault localization problem, but most of the results are obtained using processed seismic data or images as input (Admasu and Toennies (2004); Tingdahl et al. (2001); Cohen et al. (2006); Hale (2013), etc). Our approach suggests an additional tool that can be used to speed up the
VMB process.

Fully automated VMB has not been achieved because the human factor is difficult to formalize in a way that can be systematically applied. Nonetheless, if our framework is extended to other seismic events or attributes, it might become a powerful tool to alleviate interpreters{\textquoteright} work.

}, url = {http://cbcl.mit.edu/publications/eage14.pdf}, author = {Chiyuan Zhang and Charlie Frogner and Mauricio Araya-Polo and Detlef Hohl} }