气候变化领域集成服务门户(CCPortal): High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data

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DOI	10.5194/hess-23-2561-2019
	High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
	Jiang Z.; Mallants D.; Peeters L.; Gao L.; Soerensen C.; Mariethoz G.
发表日期	2019
ISSN	1027-5606
起始页码	2561
结束页码	2580
卷号	23 期号:6
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159669
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
推荐引用方式 GB/T 7714	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],2019,23(6).
APA	Jiang Z.,Mallants D.,Peeters L.,Gao L.,Soerensen C.,&Mariethoz G..(2019).High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data.Hydrology and Earth System Sciences,23(6).
MLA	Jiang Z.,et al."High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data".Hydrology and Earth System Sciences 23.6(2019).