Author(s):  
YOSHUA BENGIO

The task discussed in this paper is that of learning to map input sequences to output sequences. In particular, problems of phoneme recognition in continuous speech are considered, but most of the discussed techniques could be applied to other tasks, such as the recognition of sequences of handwritten characters. The systems considered in this paper are based on connectionist models, or artificial neural networks, sometimes combined with statistical techniques for recognition of sequences of patterns, stressing the integration of prior knowledge and learning. Different architectures for sequence and speech recognition are reviewed, including recurrent networks as well as hybrid systems involving hidden Markov models.


2020 ◽  
Vol 221 (3) ◽  
pp. 1484-1498 ◽  
Author(s):  
Runhai Feng

SUMMARY Lithofacies is one of the most important reservoir parameters, which could provide a qualitative description for hydrocarbon and geothermal reservoirs. Various techniques, such as artificial neural networks (ANN) and hidden Markov models (HMM), have been applied to extract this information, with the well log suites as inputs. However, both of these methods have their own limitations, such as no geological priors in ANN, since log samples along the depth direction are treated independently. While in HMM, a simple Gaussian assumption is usually adopted, which may not be sufficient for complex data distributions. In order to address these problems, a new method is proposed, which combines ANN and HMM into a hybrid system. This new technique allows for a more flexible approach to the probability distributions of rock properties, without any Gaussian assumption being made. At the same time, the geological dependence between adjacent samples is introduced by a representative transition matrix of lithofacies. The output probability from ANN must be reformulated to an emission function before it can be fed into the HMM, which is achieved via the Bayes’ rule. Then the Viterbi algorithm in HMM is applied for the decoding of sequential patterns in the subsurface. In this fashion, the classification process can be proceeded statistically and geologically. Better performance of the proposed approach, compared with other classification methods, is demonstrated in two case studies.


Sign in / Sign up

Export Citation Format

Share Document