globalchange  > 气候变化与战略
DOI: 10.5194/hess-23-2561-2019
论文题名:
High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
作者: Jiang Z.; Mallants D.; Peeters L.; Gao L.; Soerensen C.; Mariethoz G.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期:6
起始页码: 2561
结束页码: 2580
语种: 英语
Scopus关键词: Aquifers ; Digital instruments ; E-learning ; Electric conductivity ; Electromagnetic logging ; Geomorphology ; Groundwater resources ; Hydrogeology ; Interpolation ; Landforms ; Lithology ; Magnetometers ; Neural networks ; Surveys ; Airborne electromagnetic ; Bicubic interpolation ; Connectivity structures ; Convolutional neural network ; Digital elevation model ; Digital elevation model data ; Electrical conductivity ; Neural network training ; Deep neural networks ; aquifer ; artificial neural network ; data set ; groundwater ; imaging method ; pixel ; spatial analysis ; survey method ; Australia ; South Australia
英文摘要: Paleovalleys are buried ancient river valleys that often form productive aquifers, especially in the semiarid and arid areas of Australia. Delineating their extent and hydrostratigraphy is however a challenging task in groundwater system characterization. This study developed a methodology based on the deep learning super-resolution convolutional neural network (SRCNN) approach, to convert electrical conductivity (EC) estimates from an airborne electromagnetic (AEM) survey in South Australia to a high-resolution binary paleovalley map. The SRCNN was trained and tested with a synthetic training dataset, where valleys were generated from readily available digital elevation model (DEM) data from the AEM survey area. Electrical conductivities typical of valley sediments were generated by Archie's law, and subsequently blurred by down-sampling and bicubic interpolation to represent noise from the AEM survey, inversion and interpolation. After a model training step, the SRCNN successfully removed such noise, and reclassified the low-resolution, converted unimodal but skewed EC values into a high-resolution paleovalley index following a bimodal distribution. The latter allows us to distinguish valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the paleovalley was predicted when compared with borehole lithology logs and a valley bottom flatness indicator. Overall the methodology permitted us to better constrain the three-dimensional paleovalley geometry from AEM images that are becoming more widely available for groundwater prospecting. 2019. This work is distributed under the Creative Commons Attribution 4.0 License. © Author(s) 2019.
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被引频次[WOS]:26   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162959
Appears in Collections:气候变化与战略

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作者单位: Jiang, Z., Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of Environment and Resources, Jilin University, Changchun, 130021, China, CSIRO Land and Water, Locked Bag 2, Glen Osmond, SA 5064, Australia; Mallants, D., CSIRO Land and Water, Locked Bag 2, Glen Osmond, SA 5064, Australia; Peeters, L., CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia; Gao, L., CSIRO Land and Water, Locked Bag 2, Glen Osmond, SA 5064, Australia; Soerensen, C., CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia; Mariethoz, G., University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Lausanne, Switzerland

Recommended Citation:
Jiang Z.,Mallants D.,Peeters L.,et al. High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data[J]. Hydrology and Earth System Sciences,2019-01-01,23(6)
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