A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simula...A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process.展开更多
Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enha...Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enhancing the exploration and production process.Deep learning-based seismic inversion does not need a known physical system and can give satisfactory results with sufficient training data.The acquisition of such datasets for seismic inversion poses a significant challenge due to the exorbitant costs associated with drilling activities.Integrating domain knowledge,physical systems,and well log data into a deep learning-based seismic inversion framework is crucial for improving its efficiency and effectiveness.Nevertheless,existing data-driven approaches do not adequately exploit such information,thereby constraining their overall performance and applicability.Therefore,we develop a double dual neural network structure built upon the closed-loop neural network framework,which incorporates both physics and model information to mitigate the dependency on extensive labeled datasets.The information from the different domains is linked through a loss function,where one dual network is responsible for constraining the inversion results using physics information to ensure the physics consistency of the predictions,and the other dual network is responsible for constraining the inversion results using a priori model information to enhance the reliability of the predictions.The method makes full use of well-log data for network training when wells are available,as well as providing unsupervised learning and inversion under well-free conditions.The integration of qualitative and quantitative analyses proves instrumental in demonstrating the effectiveness of the proposed methodology through the use of synthetic and field pre-stack examples.展开更多
Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequ...Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequential extreme learning machine （OS-ELM） on DPFNN, we derive the online sequential double parallel extreme learning machine algorithm （OS-DPELM）. Compared to other similar algorithms, our algorithms can achieve approximate learning performance with fewer numbers of hidden units, as well as the parameters to be determined. The experimental results show that the proposed algorithm has good generalization performance for real world classification problems, and thus can be a necessary and beneficial complement to OS-ELM.展开更多
文摘A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process.
基金supported in part by the National Natural Science Foundation of China under Grant 42204108,42374166 and42374149in part by National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum,Beijing under Grant PRE/open-2305in part by Research on Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010。
文摘Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enhancing the exploration and production process.Deep learning-based seismic inversion does not need a known physical system and can give satisfactory results with sufficient training data.The acquisition of such datasets for seismic inversion poses a significant challenge due to the exorbitant costs associated with drilling activities.Integrating domain knowledge,physical systems,and well log data into a deep learning-based seismic inversion framework is crucial for improving its efficiency and effectiveness.Nevertheless,existing data-driven approaches do not adequately exploit such information,thereby constraining their overall performance and applicability.Therefore,we develop a double dual neural network structure built upon the closed-loop neural network framework,which incorporates both physics and model information to mitigate the dependency on extensive labeled datasets.The information from the different domains is linked through a loss function,where one dual network is responsible for constraining the inversion results using physics information to ensure the physics consistency of the predictions,and the other dual network is responsible for constraining the inversion results using a priori model information to enhance the reliability of the predictions.The method makes full use of well-log data for network training when wells are available,as well as providing unsupervised learning and inversion under well-free conditions.The integration of qualitative and quantitative analyses proves instrumental in demonstrating the effectiveness of the proposed methodology through the use of synthetic and field pre-stack examples.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1140107661473328+1 种基金1117136761473059)
文摘Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequential extreme learning machine （OS-ELM） on DPFNN, we derive the online sequential double parallel extreme learning machine algorithm （OS-DPELM）. Compared to other similar algorithms, our algorithms can achieve approximate learning performance with fewer numbers of hidden units, as well as the parameters to be determined. The experimental results show that the proposed algorithm has good generalization performance for real world classification problems, and thus can be a necessary and beneficial complement to OS-ELM.
