BibTex Citation Data :
@article{YPJ14093, author = {Frysca Muviana and Agus Setyawan and Rahmat Gernowo}, title = {PEMODELAN BAWAH PERMUKAAN DAERAH PANASBUMI KALIULO BERDASARKAN DATA RESISTIVITAS KONFIGURASI SCHLUMBERGER DENGAN ALGORITMA JARINGAN SYARAF TIRUAN-BACKPROPAGATION}, journal = {Youngster Physics Journal}, volume = {5}, number = {4}, year = {2016}, keywords = {Schlumberger, artificial neural networks, inversion, MSE, manifestation Kaliulo}, abstract = { This research used secondary data configuration Schlumberger geoelectric method in the area of geothermal manifestations Kaliulo Mount Ungaran to implement the use of artificial neural network algorithm in geophysical this case to obtain the actual value of the thickness and resistivity. In this artificial neural networks do two processes, namely the training and testing, the training using synthetic data and on testing using field data Then in training the neural network produced the best architectural which is used train resilient propagation (train rp) with three hidden layers with each neuron in the hidden layer consist of 300 neuron, this architecture will be used in testing. The output of the test data is value of the thickness and true resistivity which can be modeled. Result modeling of data processing from ANN is almost the same with IPI2WIN, MSE value obtained is equal to 0.10519 and 0.088304 respectively on the thickness and resistivity actually. The result of 3D model shows the lower part of the earth's subsurface its rock consists as following: topsoil, clay, volcanic breccias, tuff and limestone. }, issn = {2302-7371}, pages = {311--318} url = {https://ejournal3.undip.ac.id/index.php/bfd/article/view/14093} }
Refworks Citation Data :
This research used secondary data configuration Schlumberger geoelectric method in the area of geothermal manifestations Kaliulo Mount Ungaran to implement the use of artificial neural network algorithm in geophysical this case to obtain the actual value of the thickness and resistivity. In this artificial neural networks do two processes, namely the training and testing, the training using synthetic data and on testing using field data Then in training the neural network produced the best architectural which is used train resilient propagation (train rp) with three hidden layers with each neuron in the hidden layer consist of 300 neuron, this architecture will be used in testing. The output of the test data is value of the thickness and true resistivity which can be modeled. Result modeling of data processing from ANN is almost the same with IPI2WIN, MSE value obtained is equal to 0.10519 and 0.088304 respectively on the thickness and resistivity actually. The result of 3D model shows the lower part of the earth's subsurface its rock consists as following: topsoil, clay, volcanic breccias, tuff and limestone.
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