Estimation of Subsurface Temperatures in the Tattapani Geothermal Field, Central India, from Limited Volume of Magnetotelluric Data and Borehole Thermograms Using a Constructive Back-Propagation Neural Network

Earth Interactions

Anthony E.Akpan¹ 

关键词: SLFFNN;    Magnetotellurics;    Resistivity;    Borehole thermograms;    CBP;    Performance;    Tattapani;    India;   

DOI  :  10.1175/2013EI000539.1

学科分类：地球科学(综合)

来源: American Geophysical Union

【 摘 要 】

AbstractA constructive back-propagation code that was designed to run as a single-hidden-layer, feed-forward neural network (SLFFNN) has been adapted and used to estimate subsurface temperature from a small volume of magnetotelluric (MT)-derived electrical resistivity data and borehole thermograms. The code was adapted to use a looping procedure in searching for better initialization conditions that can optimally solve nonlinear problems using the random weight initialization approach. Available one-dimensional (1D) MT-derived resistivity data and borehole temperature records from the Tattapani geothermal field, central India, were collated and digitized at 10-m intervals. The two datasets were paired to form a set of input–output pairs. The paired data were randomized, standardized, and partitioned into three mutually exclusive subsets. The various subsets had 52% (later increased to 61%), 30%, and 18% (later reduced to 9%) for training, validation, and testing, respectively, in the first and second trai...

【 授权许可】

Unknown