Automated fault detection without seismic processing

For hydrocarbon exploration, large volumes of data are acquired and used in physical modeling-based workflows to identify geologic features of interest such as fault networks, salt bodies, or, in general, elements of petroleum systems. The adjoint modeling step, which transforms the data into the model space, and subsequent interpretation can be very expensive, both in terms of computing resources and domain-expert time. We propose and implement a unique approach that bypasses these demanding steps, directly assisting interpretation. We do this by training a deep neural network to learn a mapping relationship between the data space and the final output (particularly, spatial points indicating fault presence). The key to obtaining accurate predictions is the use of the Wasserstein loss function, which properly handles the structured output — in our case, by exploiting fault surface continuity. The promising results shown here for synthetic data demonstrate a new way of using seismic data and suggest more direct methods to identify key elements in the subsurface

Incorporating unlabeled data into distributionally-robust learning

We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions. We illustrate a problem with current DRL formulations, which rely on an overly broad definition of allowed distributions for the adversary, leading to learned classifiers that are unable to predict with any confidence. We propose a solution that incorporates unlabeled data into the DRL problem to further constrain the adversary. We show that this new formulation is tractable for stochastic gradient-based optimization and yields a computable guarantee on the future performance of the learned classifier, analogous to—but tighter than—guarantees from conventional DRL. We examine the performance of this new formulation on 14 real data sets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither. Inspired by these results, we extend our DRL formulation to active learning with a novel, distributionally-robust version of the standard model-change heuristic. Our active learning algorithm often achieves superior learning performance to the original heuristic on real data sets.Accepted manuscrip

Heuristics for Automatically Decomposing a Stochastic Process for Factored Inference

Dynamic Bayesian networks are factored representations of stochastic processes. Despite their factoredness, exact inference in DBNs is generally intractable. One approach to approximate inference involves factoring the variables in the process into components. In this paper we study efficient methods for automatically decomposing a DBN into weakly-interacting components so as to minimize the error in inference entailed by treating them as independent. We investigate heuristics based on two views of weak interaction: mutual information and the degree of separability ([Pf01] and [Pf06]). It turns out, however, that measuring the degree of separability exactly is probably intractable. We present a method for estimating the degree of separability that includes a mechanism for trading off efficiency and accuracy. We use the aforementioned heuristics in two clustering frameworks to find weakly-interacting components, and we give an empirical comparison of the results in terms of the error encountered in the belief state across the whole system, as well as that in the belief states across single components.Engineering and Applied Science

WassRank: Listwise Document Ranking Using Optimal Transport Theory

Learning to rank has been intensively studied and has shown great value in many fields, such as web search, question answering and recommender systems. This paper focuses on listwise document ranking, where all documents associated with the same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learning the optimal ranking function that achieves the minimum Wasserstein distance. Specifically, given the query level predictions and the ground truth labels, we first map them into two probability vectors. Analogous to the optimal transport problem, we view each probability vector as a pile of relevance mass with peaks indicating higher relevance. The listwise ranking loss is formulated as the minimum cost (the Wasserstein distance) of transporting (or reshaping) the pile of predicted relevance mass so that it matches the pile of ground-truth relevance mass. The smaller the Wasserstein distance is, the closer the prediction gets to the ground-truth. To better capture the inherent relevance-based order information among documents with different relevance labels and lower the variance of predictions for documents with the same relevance label, ranking-specific cost matrix is imposed. To validate the effectiveness of WassRank, we conduct a series of experiments on two benchmark collections. The experimental results demonstrate that: compared with four non-trivial listwise ranking methods (i.e., LambdaRank, ListNet, ListMLE and ApxNDCG), WassRank can achieve substantially improved performance in terms of nDCG and ERR across different rank positions. Specifically, the maximum improvements of WassRank over LambdaRank, ListNet, ListMLE and ApxNDCG in terms of nDCG@1 are 15%, 5%, 7%, 5%, respectively