Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

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Extreme Learning Machine-Based Traffic Incidents Detection with Domain Adaptation Transfer Learning

Journal of Intelligent Systems ◽

10.1515/jisys-2016-0028 ◽

2017 ◽

Vol 26 (4) ◽

pp. 601-612

Author(s):

Chaimae Elhatri ◽

Mohammed Tahifa ◽

Jaouad Boumhidi

Keyword(s):

Extreme Learning Machine ◽

Transfer Learning ◽

Domain Adaptation ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Incident Detection ◽

Target Domain ◽

Traffic Incidents ◽

Learning Machine ◽

Past Experiences

AbstractTraffic incidents in big cities are increasing alongside economic growth, causing traffic delays and deteriorating road safety conditions. Thus, developing a universal freeway automatic incident detection (AID) algorithm is a task that took the interest of researchers. This paper presents a novel automatic traffic incident detection method based on the extreme learning machine (ELM) algorithm. Furthermore, transfer learning has recently gained popularity as it can successfully generalise information across multiple tasks. This paper aimed to develop a new approach for the traffic domain-based domain adaptation. The ELM was used as a classifier for detection, and target domain adaptation transfer ELM (TELM-TDA) was used as a tool to transfer knowledge between environments to benefit from past experiences. The detection performance was evaluated by common criteria including detection rate, false alarm rate, and others. To prove the efficiency of the proposed method, a comparison was first made between back-propagation neural network and ELM; then, another comparison was made between ELM and TELM-TDA.

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Unsupervised Domain Adaptation Using Maximum Mean Discrepancy Optimization for Lithology identification

Geophysics ◽

10.1190/geo2020-0391.1 ◽

2020 ◽

pp. 1-84

Author(s):

Ji Chang ◽

Jing Li ◽

Yu Kang ◽

Wenjun Lv ◽

Ting Xu ◽

...

Keyword(s):

Machine Learning ◽

Probability Distribution ◽

Domain Adaptation ◽

Sedimentary Environment ◽

Bohai Bay ◽

Multiple Perspectives ◽

Maximum Mean Discrepancy ◽

Reservoir Evaluation ◽

Unsupervised Domain Adaptation ◽

Lithology Identification

Lithology identification plays an essential role in geological exploration and reservoir evaluation. In recent years, machine learning-based logging lithology identification has received considerable attention due to its ability to fit complex models. Existing work develops machine learning models under the assumption that the data gathered from different wells are from the same probability distribution, so that the model trained on data from old wells can be directly applied to predict the lithologies of a new well without losing accuracy. In fact, due to variations in sedimentary environment and well-logging technique, the data from different wells may not have the same probability distribution. Therefore, such a direct application is unreliable. To prevent the accuracy from being reduced by the distribution difference, we integrate the unsupervised domain adaptation method into lithology identification, under the assumption that no lithology labels are available on a new well. Specifically, we propose a two-flow multi-layer neural network. We train our network with a maximum mean discrepancy optimization, and the training process is interrupted by an early-stopping criterion. These methods ensure that the feature representations learned by our network are both domain-invariant and discriminative. Our method is evaluated from multiple perspectives on a total of 21 wells located in the Jiyang Depression, Bohai Bay Basin. The experimental results demonstrate that our method effectively mitigates the performance degradation caused by data distribution differences and outperforms the baselines by about 10%.

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