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Interactive analysis tools for the wide-angle seismic data for crustal structure study (Technical Report) (지각 구조 연구에서 광각 탄성파 자료를 위한 대화식 분석 방법들)

  • Fujie, Gou;Kasahara, Junzo;Murase, Kei;Mochizuki, Kimihiro;Kaneda, Yoshiyuki
    • Geophysics and Geophysical Exploration
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    • v.11 no.1
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    • pp.26-33
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    • 2008
  • The analysis of wide-angle seismic reflection and refraction data plays an important role in lithospheric-scale crustal structure study. However, it is extremely difficult to develop an appropriate velocity structure model directly from the observed data, and we have to improve the structure model step by step, because the crustal structure analysis is an intrinsically non-linear problem. There are several subjective processes in wide-angle crustal structure modelling, such as phase identification and trial-and-error forward modelling. Because these subjective processes in wide-angle data analysis reduce the uniqueness and credibility of the resultant models, it is important to reduce subjectivity in the analysis procedure. From this point of view, we describe two software tools, PASTEUP and MODELING, to be used for developing crustal structure models. PASTEUP is an interactive application that facilitates the plotting of record sections, analysis of wide-angle seismic data, and picking of phases. PASTEUP is equipped with various filters and analysis functions to enhance signal-to-noise ratio and to help phase identification. MODELING is an interactive application for editing velocity models, and ray-tracing. Synthetic traveltimes computed by the MODELING application can be directly compared with the observed waveforms in the PASTEUP application. This reduces subjectivity in crustal structure modelling because traveltime picking, which is one of the most subjective process in the crustal structure analysis, is not required. MODELING can convert an editable layered structure model into two-way traveltimes which can be compared with time-sections of Multi Channel Seismic (MCS) reflection data. Direct comparison between the structure model of wide-angle data with the reflection data will give the model more credibility. In addition, both PASTEUP and MODELING are efficient tools for handling a large dataset. These software tools help us develop more plausible lithospheric-scale structure models using wide-angle seismic data.

A Study on the Dimensions, Surface Area and Volume of Grains (곡립(穀粒)의 치수, 표면적(表面積) 및 체적(體積)에 관(關)한 연구(硏究))

  • Park, Jong Min;Kim, Man Soo
    • Korean Journal of Agricultural Science
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    • v.16 no.1
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    • pp.84-101
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    • 1989
  • An accurate measurement of size, surface area and volume of agricultural products is essential in many engineering operations such as handling and sorting, and in heat transfer studies on heating and cooling processes. Little information is available on these properties due to their irregular shape, and moreover very little information on the rough rice, soybean, barley, and wheat has been published. Physical dimensions of grain, such as length, width, thickness, surface area, and volume vary according to the variety, environmental conditions, temperature, and moisture content. Especially, recent research has emphasized on the variation of these properties with the important factors such as moisture content. The objectives of this study were to determine physical dimensions such as length, width and thickness, surface area and volume of the rough rice, soybean, barley, and wheat as a function of moisture content, to investigate the effect of moisture content on the properties, and to develop exponential equations to predict the surface area and the volume of the grains as a function of physical dimensions. The varieties of the rough rice used in this study were Akibare, Milyang 15, Seomjin, Samkang, Chilseong, and Yongmun, as a soybean sample Jangyeobkong and Hwangkeumkong, as a barley sample Olbori and Salbori, and as a wheat sample Eunpa and Guru were selected, respectively. The physical properties of the grain samples were determined at four levels of moisture content and ten or fifteen replications were run at each moisture content level and each variety. The results of this study are summarized as follows; 1. In comparison of the surface area and the volume of the 0.0375m diameter-sphere measured in this study with the calculated values by the formula the percent error between them showed least values of 0.65% and 0.77% at the rotational degree interval of 15 degree respectively. 2. The statistical test(t-test) results of the physical properties between the types of rough rice, and between the varieties of soybean and wheat indicated that there were significant difference at the 5% level between them. 3. The physical dimensions varied linearly with the moisture content, and the ratios of length to thickness (L/T) and of width to thickness (W/T) in rough rice decreased with increase of moisture content, while increased in soybean, but uniform tendency of the ratios in barley and wheat was not shown. In all of the sample grains except Olbori, sphericity decreased with increase of moisture content. 4. Over the experimental moisture levels, the surface area and the volume were in the ranges of about $45{\sim}51{\times}10^{-6}m^2$, $25{\sim}30{\times}10^{-9}m^3$ for Japonica-type rough rice, about $42{\sim}47{\times}10^{-6}m^2$, $21{\sim}26{\times}10^{-9}m^3$ for Indica${\times}$Japonica type rough rice, about $188{\sim}200{\times}10^{-6}m^2$, $277{\sim}300{\times}10^{-9}m^3$ for Jangyeobkong, about $180{\sim}201{\times}10^{-6}m^2$, $190{\sim}253{\times}10^{-9}m^3$ for Hwangkeumkong, about $60{\sim}69{\times}10^{-6}m^2$, $36{\sim}45{\times}10^{-9}m^3$ for Covered barley, about $47{\sim}60{\times}10^{-6}m^2$, $22{\sim}28{\times}10^{-9}m^3$ for Naked barley, about $51{\sim}20{\times}10^{-6}m^2$, $23{\sim}31{\times}10^{-9}m^3$ for Eunpamill, and about $57{\sim}69{\times}10^{-6}m^2$, $27{\sim}34{\times}10^{-9}m^3$ for Gurumill, respectively. 5. The increasing rate of surface area and volume with increase of moisture content was higher in soybean than other sample grains, and that of Japonica-type was slightly higher than Indica${\times}$Japonica type in rough rice. 6. The regression equations of physical dimensions, surface area and volume were developed as a function of moisture content, the exponential equations of surface area and volume were also developed as a function of physical dimensions, and the regression equations of surface area were also developed as a function of volume in all grain samples.

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