Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Zhan, Chuanjun; Dai, Zhenxue; Soltanian, Mohamad Reza; Barros, Felipe P. J. de

doi:10.1029/2022wr033241

Cited by 60 publications

(14 citation statements)

References 103 publications

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“…This process will also accelerate the erosion process of the groundwater on shotcrete, resulting in an increase of concrete porosity and the alkali-aggregate reaction of concrete [ 51 , 52 , 53 ]. The karst type groundwater is rich in Ca 2+ and Mg 2+ , which directly provides a material source for crystallization in the drainage pipe and aggravates the crystalline blockage.…”

Section: Analysis Of Countermeasures For the Maintenance Of Transvers...mentioning

confidence: 99%

Fault Diagnosis and Maintenance Countermeasures of Transverse Drainage Pipe in Subway Tunnel Based on Fault Tree Analysis

Liu

Zhang

2022

IJERPH

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Transverse drainage pipe, one of the main channels of groundwater behind the lining of subway tunnels, plays an important role in the safety and stability of the tunnel lining structure. For the problem of blocked transverse drainage pipe in a subway tunnel, a fault tree model of blocked transverse drainage pipe in Chongqing subway tunnel was constructed in this paper, the quantitative and qualitative analysis of fault tree was conducted, and countermeasures for maintenance of transverse drainage pipe were proposed. The study finds that, (1) the chemical type of groundwater was mainly CaHCO3; most of the groundwater is strongly alkaline with pH greater than 8; the groundwater temperature is 20 ± 3 °C; (2) the basic events of blocked transverse drainage pipe have 3 minimum cut sets, and the basic events concrete slurry enters the drainage pipe; groundwater temperature, groundwater pH value, and concentration of anions and cations in groundwater were the main fault factors of blocked transverse drainage pipe; (3) preventive maintenance of transverse drainage pipe during tunnel construction includes construction quality control of drainage pipe and application of anti-crystallized blocking drainage pipe; preventive maintenance of transverse drainage pipe during tunnel operation includes monitoring of groundwater ion concentration, pH, and temperature; and maintenance treatment of transverse drainage pipe during tunnel operation includes physical treatment techniques, such as ultrasonic resonance, and chemical treatment techniques, such as acid-base neutralization reaction. The results of the study have certain guiding significance for the design, construction, and operation of transverse drainage pipe in subway tunnels.

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Section: Analysis Of Countermeasures For the Maintenance Of Transvers...mentioning

confidence: 99%

Fault Diagnosis and Maintenance Countermeasures of Transverse Drainage Pipe in Subway Tunnel Based on Fault Tree Analysis

Liu

Zhang

2022

IJERPH

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“…Landslide sensitivity analysis is a hot and difficult topic in landslide research (Feizizadeh and Blaschke, 2014). By analyzing the relationship between landslide influencing factors and landslide in the region, the distribution law of landslide can be determined, and the spatial distribution and occurrence probability of the existing or potential landslide can be analyzed qualitatively or quantitatively (Shi et al, 2005;Liu et al, 2021;Zhang et al, 2022a;Zhan et al, 2022). The causes of landslides are complex, and the influencing factors include the basic factors causing landslides (topography and landform, stratigraphic lithology, geological structure, traffic, and water system) and the inducing factors (rainfall, earthquake, and human engineering activities).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

et al. 2023

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The main purpose of this study is to analyze the main influencing factors of the landslide in the coal mine area and, on this basis, establish the sensitivity zoning model of the landslide. Considering the difficulty to obtain the expected results by using machine learning under the condition of lacking data, the typical landslide is used as the data basis, that is, the Fenxi coal mine and Xishan Bujiu coal mine are selected as the coal mining landslide points. Various factors, such as goaf, land subsidence, slope structure, formation lithology, and various indicators are used as input data sources, and artificial neural network (ANN) datasets are used for training to establish a pre-training model. Using the pre-training model, the mining landslide sensitivity evaluation model based on transfer learning is established. In order to demonstrate the performance of transfer learning more intuitively, the neural network is introduced to evaluate the evaluation model. The test results show that transfer learning can achieve a transfer effect higher than 0.95, and the regional distributions of highest landslide sensitivity calculated based on self-transfer learning, direct push transfer learning, and inductive transfer learning are 31.33, 35.50, and 33.75%, respectively, which further deduced that inductive transfer learning can be used for evaluating an LSP model.

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“…New classes of intelligent techniques, namely deep-learning framework 31 , deep reinforcement learning 32 , 33 , deep belief network 34 , dual-graph attention convolution network 35 are recently suggested to monitor (modeling, control, as well as classification) the behavior of even complicated problems. Therefore, the main problem addressed in this work involves selecting the most suitable EOR technique for the target reservoir using a novel deep learning-based classifier.…”

Section: Introductionmentioning

confidence: 99%

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Pandey

Gandomkar

Vaferi

et al. 2023

Sci Rep

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High oil prices and concern about limited oil reserves lead to increase interest in enhanced oil recovery (EOR). Selecting the most efficient development plan is of high interest to optimize economic cost. Hence, the main objective of this study is to construct a novel deep-learning classifier to select the best EOR method based on the reservoir’s rock and fluid properties (depth, porosity, permeability, gravity, viscosity), and temperature. Our deep learning-based classifier consists of a one-dimensional (1D) convolutional neural network, long short-term memory (LSTM), and densely connected neural network layers. The genetic algorithm has been applied to tune the hyperparameters of this hybrid classifier. The proposed classifier is developed and tested using 735 EOR projects on sandstone, unconsolidated sandstone, carbonate, and conglomerate reservoirs in more than 17 countries. Both the numerical and graphical investigations approve that the structure-tuned deep learning classifier is a reliable tool to screen the EOR scenarios and select the best one. The designed model correctly classifies training, validation, and testing examples with an accuracy of 96.82%, 84.31%, and 82.61%, respectively. It means that only 30 out of 735 available EOR projects are incorrectly identified by the proposed deep learning classifier. The model also demonstrates a small categorical cross-entropy of 0.1548 for the classification of the involved enhanced oil recovery techniques. Such a powerful classifier is required to select the most suitable EOR candidate for a given oil reservoir with limited field information.

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Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Abstract: Reliable characterization of heterogeneous subsurface structures is crucial for earth sciences and other related applications such as groundwater management and contamination, geological carbon storage, radioactive waste disposal, and geothermal applications (

Cited by 60 publications

References 103 publications

Fault Diagnosis and Maintenance Countermeasures of Transverse Drainage Pipe in Subway Tunnel Based on Fault Tree Analysis

Fault Diagnosis and Maintenance Countermeasures of Transverse Drainage Pipe in Subway Tunnel Based on Fault Tree Analysis

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

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