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Linus Pasasa
Physics Of Complex System, Institute Of Technology Bandung, Bandung, Indonesia
Humanities Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Physics Public Health Social Sciences Other

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Journal : Journal of Engineering and Technological Sciences

 1 
The Use of GPR in Delineating an Iron Sand Boundary and the Determination of Its Electromagnetic Wave Velocity: A Case Study in Jepara, Central Java Bijaksana, S.; Rusyanti, I.; T. Taib, M. I.; Pasasa, L. A.; Andreas, A. S.
Journal of Engineering and Technological Sciences Vol 37, No 2 (2005)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1138.177 KB) | DOI: 10.5614/itbj.eng.sci.2005.37.2.1

Abstract

 Exploring the vertical extent of iron sand deposit is challenging as conventional geophysical methods (electrical resistivity, geomagnetic, and seismic refraction) are inappropriate and unsuccessful in delineating the iron sand deposit from the bedrock. Ground-penetrating radar (GPR) offers a solution to the above problem as radar is not affected negatively by the physical properties of iron sand. In the year 2003, a RAMAC’s GPR survey was carried out in the coast of Bayuran in the Regency of Jepara, Central Java to map the distribution of sub-bottom iron sand. The sand is highly magnetic. The survey used 100 MHz antennas. The survey is also complimented by a novel method in determining the electromagnetic (EM) wave velocity of iron sand. Combination of reflection profiling and CMP sounding was deployed. Results of CMP sounding were processed using CMP-semblance analysis that produces the RMS velocity in velocity-time spectra. The RMS velocity is then converted to interval velocity using Dix’s formula and is found to be about 135 mm/ns. Meanwhile, combination of magnetic susceptibility, relative permittivity, and dissipation factors produces radiowaves velocity of iron sand as a function of frequency. The velocities of radiowaves estimated from laboratory match that estimated from CMP analysis.
Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide Sompotan, Armstrong F.; Pasasa, Linus A.; Sule, Rachmat
Journal of Engineering and Technological Sciences Vol 43, No 3 (2011)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.02 KB) | DOI: 10.5614/itbj.eng.sci.2011.43.3.1

Abstract

Detailed  investigations  of  landslides  are  essential  to  understand fundamental landslide  mechanisms.  Seismic  refraction  method  has been  proven as a useful geophysical tool for investigating shallow landslides. The objective of this  study  is  to  introduce  a  new  workflow  using  neural  network  in  analyzing seismic  refraction  data  and  to  compare  the  result  with  some  methods;  that  are general  reciprocal  method  (GRM)  and  refraction  tomography.  The  GRM  is effective when the velocity structure is relatively simple and refractors are gently dipping.  Refraction  tomography  is  capable  of  modeling  the  complex  velocity structures  of  landslides.  Neural  network  is  found  to  be  more  potential  in application  especially  in  time  consuming  and  complicated  numerical  methods. Neural network  seem to have the  ability to establish a relationship between an input  and  output  space  for  mapping  seismic  velocity.  Therefore,  we  made  a preliminary attempt to evaluate the applicability of neural network to determine velocity  and  elevation  of  subsurface  synthetic  models  corresponding  to  arrival times.  The  training  and  testing  process  of  the  neural  network  is  successfully accomplished  using  the  synthetic  data.  Furthermore,  we  evaluated  the  neural network  using  observed  data.  The  result  of  the  evaluation  indicates  that  the neural  network  can  compute  velocity  and  elevation  corresponding  to  arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.
Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide Armstrong F. Sompotan; Linus A. Pasasa; Rachmat Sule
Journal of Engineering and Technological Sciences Vol. 43 No. 3 (2011)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.eng.sci.2011.43.3.1

Abstract

Detailed  investigations  of  landslides  are  essential  to  understand fundamental landslide  mechanisms.  Seismic  refraction  method  has been  proven as a useful geophysical tool for investigating shallow landslides. The objective of this  study  is  to  introduce  a  new  workflow  using  neural  network  in  analyzing seismic  refraction  data  and  to  compare  the  result  with  some  methods;  that  are general  reciprocal  method  (GRM)  and  refraction  tomography.  The  GRM  is effective when the velocity structure is relatively simple and refractors are gently dipping.  Refraction  tomography  is  capable  of  modeling  the  complex  velocity structures  of  landslides.  Neural  network  is  found  to  be  more  potential  in application  especially  in  time  consuming  and  complicated  numerical  methods. Neural network  seem to have the  ability to establish a relationship between an input  and  output  space  for  mapping  seismic  velocity.  Therefore,  we  made  a preliminary attempt to evaluate the applicability of neural network to determine velocity  and  elevation  of  subsurface  synthetic  models  corresponding  to  arrival times.  The  training  and  testing  process  of  the  neural  network  is  successfully accomplished  using  the  synthetic  data.  Furthermore,  we  evaluated  the  neural network  using  observed  data.  The  result  of  the  evaluation  indicates  that  the neural  network  can  compute  velocity  and  elevation  corresponding  to  arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.
The Use of GPR in Delineating an Iron Sand Boundary and the Determination of Its Electromagnetic Wave Velocity: A Case Study in Jepara, Central Java S. Bijaksana; I. Rusyanti; M. I. T. Taib; L. A. Pasasa; A. S. Andreas
Journal of Engineering and Technological Sciences Vol. 37 No. 2 (2005)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.eng.sci.2005.37.2.1

Abstract

 Exploring the vertical extent of iron sand deposit is challenging as conventional geophysical methods (electrical resistivity, geomagnetic, and seismic refraction) are inappropriate and unsuccessful in delineating the iron sand deposit from the bedrock. Ground-penetrating radar (GPR) offers a solution to the above problem as radar is not affected negatively by the physical properties of iron sand. In the year 2003, a RAMAC's GPR survey was carried out in the coast of Bayuran in the Regency of Jepara, Central Java to map the distribution of sub-bottom iron sand. The sand is highly magnetic. The survey used 100 MHz antennas. The survey is also complimented by a novel method in determining the electromagnetic (EM) wave velocity of iron sand. Combination of reflection profiling and CMP sounding was deployed. Results of CMP sounding were processed using CMP-semblance analysis that produces the RMS velocity in velocity-time spectra. The RMS velocity is then converted to interval velocity using Dix's formula and is found to be about 135 mm/ns. Meanwhile, combination of magnetic susceptibility, relative permittivity, and dissipation factors produces radiowaves velocity of iron sand as a function of frequency. The velocities of radiowaves estimated from laboratory match that estimated from CMP analysis.
Co-Authors A. S. Andreas A. S. Andreas Alamta Singarimbun Alamta Singarimbun Andhika, Benedictus Widy Armstrong F. Sompotan Baskoro, Gading Bun Sucento EFRATA D. S. YUNUS Harapan Marpaung Hendro Hendro, Hendro Hidayat, Nila Khrisnawati I. Rusyanti I. Rusyanti Irfan Dwi Adita Khumaedi Sastrawiharja Lilik Hendrajaya M. I. T. Taib M. I. T. Taib Marselina Irasonia Tan Mitra Djamal Muhammad Ikhsan Ni Ketut Lasmia Nila K. Hidayat Nila K. Hidayat, Nila K. Nurhendiarni, Sri Padang, Elohansen Rachmat Sule Rachmat Sule RINALDI RAYMOND RR. Ella Evrita Hestiandari S. Bijaksana S. Bijaksana Satria Bijaksana Singarimbun, Alamta Umar Fauzi Wahyu Srigutomo Wahyudi Parnadi Wahyudi Parnadi