ORIGINAL_ARTICLE
The transition energy and the beaming angle of converted LO-mode waves from 100 to 400 kHz through density gradient according to observations of kilometric continuum radiations in the plasmapause
The satellite observations such as the Cluster mission with four-point measurements show some local fluctuations in the density gradient in the vicinity of the plasmapause. These structures are found over a broad range of spatial scales, with a size from 20 to 5000 km. Also, the simultaneous observations of the kilometric continuum by IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) and GEOTAIL satellites have indicated another new evidence of a very broad emission. In this study, we considered the mode conversion of waves propagating under the presence of the density gradient in a scale length from 20 to 10,000 km, for a range of frequency from 100 to 400 kHz according to observations of the kilometric continuum. We calculated the transmitted energy flux as a function of the spatial scale lengths and the frequencies. We also calculated the resultant beaming angle for the frequency and the wave normal angle of incident waves. For these cases, results showed that the beaming angle becomes larger and smaller than the angle estimated by Jones’ formula. We suggest that the spatial scale length should be less than about 100 km for the efficient mode conversion and then that the beaming angle becomes consistent with the observed the kilometric continuum.
https://www.ijgeophysics.ir/article_34126_88a97f911d01ba37c54b81b8682bac85.pdf
2017-05-22
1
9
beaming angle
scale length
Cluster mission
kilometric continuum
Mohammad Javad
Kalaee
mjkalaee@ut.ac.ir
1
Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Yuto
Katoh
2
Graduate School of Science, Tohoku University, Sendai, Japan
AUTHOR
Boardsen, S. A., Green, J. L., and Reinisch, B. W., 2008, Comparison of kilometric continuum latitudinal radiation patterns with linear mode conversion theory: J. Geophys. Res., 113, A01219. http://dx.doi.org/10.1029/2007JA012319.
1
Budden, K. G., 1980, The theory of the radio window in the ionosphere and magnetosphere: J. Atm. Terr. Phys., 42, 287.
2
Budden, K. G., 1985, The propagation of the radio waves: Cambridge University Press.
3
Darrouzet, F., Decreau, P. M. E., De Keyser, J., Masson, A., Gallagher, D. L., Santolik, O., Sandel, B. R., Trotignon, J. G., Rauch, J. L., Le Guirriec, E., Canu, P., Sedgemore, F., Andre, M., and Lemaire, J. F., 2004, Density structures inside the plasmasphere: Cluster observations: Ann. Geophys., 22, 2577–2585.
4
Darrouzet, F., De Keyser, J., Décréau, P. M. E., Lemaire, J. F., and Dunlop, M. W., 2006, Spatial gradients in the plasmasphere from Cluster: Geophys. Res. Lett., 33, L08105, doi: 10.1029/2006GL025727.
5
Ellis, G. R., 1956, The Z propagation hole in the ionosphere: J. Atm. Terr. Phys., 8, 43.
6
Hashimoto, K., Green, J. L., Anderson, R. R., and Matsumoto, H., 2006, In: LaBelle, J. W., and Treumann, R. A., (Eds.), Review of Kilometric Continuum: in Lecture Notes in Physics, 687, Springer, New York, pp. 37–54.
7
Jones, D., 1980, Latitudinal beaming of planetary radio emissions: Nature, 288, 225–229.
8
Jones, D., 1981, Beaming of terrestrial myriametric radiation: Adv. Space Res., 1, 373–376.
9
Jones, D., 1988, Planetary radio emissions from low magnetic latitudes observations and theories: Planetary radio emissions II, edited by Rucker, H. O., Bauer, S. J., and Pedersen, B. M., Austrian Acad. of Sci., Vienna, Austria, 255.
10
Jones, D., Calvert, W., Gurnett, D. A., and Huff, R. L., 1987, Observed beaming of terrestrial myriametric radiation: Nature, 328, 391–395.
11
Kalaee, M. J., and Katoh, Y., 2014a, A simulation study on the mode conversion process from slow Z-mode to LO mode by the tunneling effect and variations of beaming angle: Adv. Space Res., 54, 2218–2223.
12
Kalaee, M. J., and Katoh, Y., 2014b, Effects of the angle between the density gradient and the external magnetic field on the linear mode conversion and resultant beaming angle of LO-mode radio emissions: Earth Moon and Planets, 114, 1–15.
13
Kalaee, M. J., and Katoh, Y., 2016, The role of deviation of magnetic field direction on the beaming angle: Extending of beaming angle theory: Atmospheric and Solar Terrestrial Phys., 142, 35–42.
14
Kalaee, M. J., Katoh, Y., Kumamoto, A., and Ono, T., 2010, Simulation of mode conversion from upper-hybrid waves to LO-mode waves in the vicinity of the Plasmapause: Ann. Geophys., 28, 1289–1297.
15
Kalaee, M. J., Ono, T., Katoh, Y., Iizima, M., and Nishimura, Y., 2009, Simulation of mode conversion from UHR-mode wave to LO-mode wave in an inhomogeneous plasma with different wave normal angles: Earth Planets and Space, 61, 1243–1254.
16
Mjulhus, E., 1984, Coupling to Z mode near critical angle: J. Plasma Phys., 31, 7–28.
17
Oya, H., 1971, Conversion of electrostatic plasma waves into electromagnetic waves: Numerical calculation of the dispersion relation for all wavelengths: Radio Sci., 12, 1131–1141.
18
Oya, H., 1974, Origin of Jovian decametric wave emissions — Conversion from the electron cyclotron plasma wave to the O-mode electromagnetic wave: Planet Space Sci., 22, 687–708.
19
Stix, T. H., 1992, Waves in Plasmas: American Institute of Physics, New York.
20
Warren, E. S., and Hagg, E. L., 1968, Observation of electrostatic resonances of the ionospheric plasma: Nature, 220, 466–468.
21
ORIGINAL_ARTICLE
Non-linear stochastic inversion of regional Bouguer anomalies by means of Particle Swarm Optimization: Application to the Zagros Mountains
Estimating the lateral depth variations of the Earth’s crust from gravity data is a non-linear ill-posed problem. The ill-posedness of the problem is due to the presence of noise in the data, and also the non-uniqueness of the problem. Particle Swarm Optimization (PSO) is a stochastic population-based optimizer, originally inspired by the social behavior of fish schools and bird flocks. PSO is a global search method, meaning that it has the ability to escape local minima. In addition, PSO is an iterative method, wherein an initial solution is chosen randomly and then improved iteratively until the algorithm finds a solution close enough to the global minimum. Herein, the inverse problem of estimating the thickness of the crust from gravity anomalies is formulated as a single objective optimization problem and is solved by PSO. The method is first tested on a realistic synthetic crustal model both with and without the presence of white Gaussian noise (WGN). Then it is applied to the gravity data from EIGEN-6c4, the latest combined global gravity model, in order to find the base of the crust in the Zagros Mountains (Iran) and compare the results with those of other geophysical methods. The assumed crustal model is one with a linear density gradient in which the densities at both the surface and the base of the crust are fixed. Results agree well with the previously published works including both seismic and potential field studies.
https://www.ijgeophysics.ir/article_42516_eb860151f1037168175d5f5ffa88f544.pdf
2017-05-22
10
21
gravity data
Particle Swarm Optimization (PSO)
Zagros mountains
Ali
Jamasb
ajamasb@ut.ac.ir
1
Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Seyed-Hani
Motavalli-Anbaran
motavalli@ut.ac.ir
2
Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Afonso, J. C., Fernández, M., Ranalli, G., Griffin, W., and Connolly, J., 2008, Integrated geophysical–petrological modeling of the lithosphere and sublithospheric upper mantle: Methodology and applications: Geochemistry, Geophysics, Geosystems, 9 (5), 1–36.
1
Aitken, A. R. A., Salmon, M. L., and Kennett, B. L. N., 2013, Australia's Moho: A test of the usefulness of gravity modelling for the determination of Moho depth: Tectonophysics, 609, 468–479.
2
Al-Lazki, A. I., Al-Damegh, K. S., El-Hadidy, S. Y., Ghods, A., and Tatar, M., 2014, Pn-velocity structure beneath Arabia–Eurasia Zagros collision and Makran subduction zones: Geological Society, London, Special Publications, 392, 45–60.
3
Al-Lazki, A. I., Sandvol, E., Seber, D., Barazangi, M., Turkelli, N., and Mohamad, R., 2004, Pn tomographic imaging of mantle lid velocity and anisotropy at the junction of the Arabian, Eurasian and African plates: Geophys. J. Int., 158, 1024–1040.
4
Alavi, M., 1994, Tectonics of the Zagros orogenic belt of Iran: new data and interpretations: Tectonophysics, 229, 211–238.
5
Angeline, P. J., 1998, Using selection to improve particle swarm optimization: paper presented at Proceedings of IEEE International Conference on Evolutionary Computation, Citeseer.
6
Bäck, T., 1996, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms: Oxford University Press, 328 pp.
7
Bäck, T., and Schwefel, H.-P., 1993, An overview of evolutionary algorithms for parameter optimization: Evolutionary Computation, 1, 1–23.
8
Barazangi, M., Sandvol, E., and Seber, D., 2006, Structure and tectonic evolution of the Anatolian plateau in eastern Turkey: Geological Society of America Special Papers, 409, 463–473.
9
Berberian, M., 1995, Master “blind” thrust faults hidden under the Zagros folds: active basement tectonics and surface morphotectonics: Tectonophysics, 241, 193–224.
10
Bott, M., 1960, The use of rapid digital computing methods for direct gravity interpretation of sedimentary basins: Geophys. J. Int., 3, 63–67.
11
Bott, M., 1965, The upper mantle beneath Iceland: Geophys. J. Int., 9, 275–277.
12
Corchete, V., Chourak, M., and Khattach, D., 2010, A methodology for filtering and inversion of gravity data: an example of application to the determination of the Moho undulation in Morocco: Engineering, 2, 149–159.
13
Cordell, L., 1973, Gravity analysis using an exponential density-depth function—San Jacinto Graben, California: Geophysics, 38, 684–690.
14
Čuma, M., Wilson, G. A., and Zhdanov, M. S., 2012, Large-scale 3D inversion of potential field data: Geophys. Prospect., 60, 1186–1199.
15
Dewey, J., Hempton, M., Kidd, W., Saroglu, F. T., and Şengör, A., 1986, Shortening of continental lithosphere: the neotectonics of Eastern Anatolia—a young collision zone: Geological Society, London, Special Publications, 19, 1–36.
16
Dorigo, M., Maniezzo, V., and Colorni, A., 1996, Ant system: optimization by a colony of cooperating agents: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26, 29–41.
17
Eberhart, R. C., and Kennedy, J., 1995, A new optimizer using particle swarm theory: Proceedings of the 6th International Symposium on Micro Machine and Human Science, New York, NY.
18
Förste, C., Bruinsma, S., Abrikosov, O., Flechtner, F., Marty, J.-C., Lemoine, J.-M., Dahle, C., Neumayer, H., Barthelmes, F., and König, R., 2014, EIGEN-6C4 The latest combined global gravity field model including GOCE data up to degree and order 1949 of GFZ Potsdam and GRGS Toulouse: EGU General Assembly Conference Abstracts.
19
Gallardo-Delgado, L. A., Pérez-Flores, M. A., and Gómez-Treviño, E., 2003, A versatile algorithm for joint 3D inversion of gravity and magnetic data: Geophysics, 68, 949–959.
20
Garnier, S., Gautrais, J., and Theraulaz, G., 2007, The biological principles of swarm intelligence: Swarm Intelligence, 1, 3–31.
21
Gómez-Ortiz, D., Agarwal, B., Tejero, R., and Ruiz, J., 2011, Crustal structure from gravity signatures in the Iberian Peninsula: Geological Society of America Bulletin, 123, 1247–1257.
22
Gonzalez, T. F., 2007, Handbook of Approximation Algorithms and Metaheuristics: CRC Press, 1432 pp.
23
Gould, S. J., 2002, The Structure of Evolutionary Theory: Harvard University Press, 1464 pp.
24
Grad, M., and Tiira, T., 2009, The Moho depth map of the European Plate: Geophys. J. Int., 176, 279–292.
25
Grad, M., and Tiira, T., 2012, Moho depth of the European Plate from teleseismic receiver functions: J. Seismology, 16, 95–105.
26
Hafkenscheid, E., Wortel, M., and Spakman, W., 2006, Subduction history of the Tethyan region derived from seismic tomography and tectonic reconstructions: J. Geophys. Res., Solid Earth, 111, B08401, doi: 10.1029/2005JB003791.
27
Hatzfeld, D., Tatar, M., Priestley, K., and Ghafory-Ashtiany, M., 2003, Seismological constraints on the crustal structure beneath the Zagros Mountain belt (Iran): Geophys. J. Int., 155, 403–410.
28
Kennedy, J., Kennedy, J. F., Eberhart, R. C., and Shi, Y., 2001, Swarm Intelligence: Morgan Kaufmann, 512 pp.
29
Liu, S., Hu, X., and Liu, T., 2014, A stochastic inversion method for potential field data: ant colony optimization: Pure and Applied Geophysics, 171, 1531–1555.
30
Manaman, N. S., and Shomali, H., 2010, Upper mantle S-velocity structure and Moho depth variations across Zagros belt, Arabian–Eurasian plate boundary: Physics of the Earth and Planetary Interiors, 180, 92–103.
31
Manaman, N. S., Shomali, H., and Koyi, H., 2011, New constraints on upper-mantle S-velocity structure and crustal thickness of the Iranian plateau using partitioned waveform inversion: Geophys. J. Int., 184, 247–267.
32
Molinaro, M., Zeyen, H., and Laurencin, X., 2005, Lithospheric structure beneath the south-eastern Zagros Mountains, Iran: recent slab break-off?: Terra Nova, 17, 1–6.
33
Monteiro Santos, F. A., 2010, Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization: Computers & Geosciences, 36, 1185–1190.
34
Motavalli-Anbaran, S.-H., Zeyen, H., and Ardestani, V. E., 2013, 3D joint inversion modeling of the lithospheric density structure based on gravity, geoid and topography data—Application to the Alborz Mountains (Iran) and South Caspian Basin region: Tectonophysics, 586, 192–205.
35
Motavalli-Anbaran, S.-H., Zeyen, H., and Jamasb, A., 2016, 3D crustal and lithospheric model of the Arabia–Eurasia collision zone: J. Asian Earth Sciences, 122, 158–167.
36
Motavalli-Anbaran, S. H., Zeyen, H., Brunet, M. F., and Ardestani, V. E., 2011, Crustal and lithospheric structure of the Alborz Mountains, Iran, and surrounding areas from integrated geophysical modeling: Tectonics, 30, TC5012, doi: 10.1029/2011TC002934.
37
Mutlu, A. K., and Karabulut, H., 2011, Anisotropic Pn tomography of Turkey and adjacent regions: Geophys. J. Int., 187, 1743–1758.
38
Paul, A., Kaviani, A., Hatzfeld, D., Vergne, J., and Mokhtari, M., 2006, Seismological evidence for crustal-scale thrusting in the Zagros mountain belt (Iran): Geophys. J. Int., 166, 227–237.
39
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K., 2012, The development and evaluation of the Earth Gravitational Model 2008 (EGM2008): J. Geophys. Res., 117, B04406, doi: 10.1029/2011JB008916.
40
Rao, C. V., Raju, M., and Chakravarthi, V., 1995, Gravity modelling of an interface above which the density contrast decreases hyperbolically with depth: J. Applied Geophys., 34, 63–67.
41
Rao, D. B., 1990, Analysis of gravity anomalies of sedimentary basins by an asymmetrical trapezoidal model with quadratic density function: Geophysics, 55, 226–231.
42
Sen, M. K., and Stoffa, P. L., 2013, Global Optimization Methods in Geophysical Inversion: Cambridge University Press, 302 pp.
43
Shaw, R., and Srivastava, S., 2007, Particle swarm optimization: A new tool to invert geophysical data: Geophysics, 72, F75–F83.
44
Shi, Y., and Eberhart, R., 1998, A modified particle swarm optimizer: Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference.
45
Silva, J. B., Santos, D. F., and Gomes, K. P., 2014, Fast gravity inversion of basement relief: Geophysics, 79, G79–G91.
46
Srivastava, S., and Agarwal, B. N. P., 2010, Inversion of the amplitude of the two-dimensional analytic signal of the magnetic anomaly by the particle swarm optimization technique: Geophys. J. Int., 182, 652–662.
47
Wessel, P., Smith, W. H., Scharroo, R., Luis, J., and Wobbe, F., 2013, Generic mapping tools: improved version released: Eos. Trans. AGU, 94, 409–410.
48
Zeyen, H., and Pous, J., 1993, 3-D joint inversion of magnetic and gravimetric data with a priori information: Geophys. J. Int., 112, 244–256.
49
Zhdanov, M. S., 2002, Geophysical Inverse Theory and Regularization Problems: Elsevier, 633 pp.
50
ORIGINAL_ARTICLE
Subsurface structural characterization of the Chooman Dam site using geoelectric method
A recent evaluation of Chooman Dam highlighted the potential for dam failure due to either seepage or an earthquake on nearby faults. Unfortunately, this dam suffers from infiltration or leakage problems related mainly to different geological and tectonic factors. In response to these concerns, electrical resistivity surveys employing vertical electric sounding (VES) method were carried out at the dam site, located in Kurdistan Province in the west of Iran in order to delineate potential pathways of leakage occurring thorough the subsurface structure close to the dam body, bed rock depth and lateral discontinuities in the study area. The VES surveys were conducted using the Schlumberger electrode array in 400 points or stations along 28 profiles at a station interval of 20 to 50 m in up- and downstream sides of the dam embankment. For data acquisition, a terrameter SAS 4000 resistivity system, made by Swedish ABEM Company, was used. Maximum separation of current electrodes in the Schlumberger VES surveys was considered 430 m. These geoelectrical studies revealed a thick package of andesite basement in the eastern part and vitriform tuff basement in the western part of the study area. Considering the results and the detection of a strike-slip fault on the downstream side of the dam embankment, it is evident that fractures are the main causative factor responsible for the leakage in the Chooman Dam.
https://www.ijgeophysics.ir/article_46953_8bd0683c1ffed5e14909d31deb675d26.pdf
2017-05-22
22
30
Electrical resistivity
vertical electric sounding (VES) method
Schlumberger array
Chooman Dam
Kurdistan province
Iran
Sadegh
Moghaddam
sadegh136789@yahoo.com
1
Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Abolghasem
Kamkar Rouhani
kamkarr@yahoo.com
2
School of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Alireza
Arab Amiri
alirezaarabamiri@yahoo.com
3
School of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Aina, A., Olurunfemi, M. O., and Ojo, J. S., 1996. An integration of aeromagnetic and electrical resistivity methods in dam site investigation: Geophysics, 61, 349–356.
1
Banton, O., Seguin, M. K. and Cimon, M. A., 1997, Mapping field-scale physical properties of soil with electrical resistivity: Soil Science Society of America Journal, 61, 1010–1017.
2
Batayneh, A. T., Abdallah, S. A. Z., and Abdelruhman, A. A., 2001, Geophysical investigations for the location of a proposed dam in Al Bishriyya (Al Aritayn) area, northeast Badia of Jordan: Environmental Geology, 40, 918–922.
3
Bogoslovsky, V. A., and Ogilvy, A. A., 1970, Application of geophysical methods for studying the technical status of earth dams: Geophysical Prospecting, 18, 758–773.
4
Bronner, N, Fagerström, H, and Stille H., 1988, Bedrock cracks as a possible cause of leakage in two Swedish dams: Proceedings of International Commission on Large Dams (ICOLD), 16th Congress, San Francisco, Q.61, R.55.
5
Buselli, G., and Lu, K., 2001, Groundwater contamination monitoring with multichannel electrical and electromagnetic methods: Journal of Applied Geophysics, 48, 11–23.
6
Butler, D. K., 1984, Geophysical methods for seepage detection, mapping and monitoring: SEG Expanded Abstracts, 3, 157–160.
7
Cho, I.-K., and Yeom, J.-Y., 2007, Crossline resistivity tomography for the delineation of anomalous seepage pathways in an embankment dam: Geophysics, 72, 31–38.
8
Egbai, J. C. and Asokhia, M. B., 1998, Correlation between resistivity survey and well logging in Delta State, Nigeria: Journal of the Nigerian Association of Mathematical Physics, 2, 163–175.
9
Ezomo, F. O. and Akujieze, C. N., 2011, Geophysical study of limestone attributes at Abudu area of Edo State, Nigeria: J. of Emerging Trends in Engineering and Applied Sciences, 2, 795–800.
10
Morgan, F. D., 2001, Self-Potential and Resistivity for the Detection and Monitoring of Earthen Dam Seepage: Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences, Earth Resources Laboratory.
11
Panthulu, T. V., Krishnaiah, C., and Shirke, J. M., 2001, Detection of seepage paths in earth dams using self-potential and electrical resistivity methods: Engineering Geology, 59, 281–295.
12
Telford, W. M., Geldart, L. P. and Sheriff, R. E., 1990, Applied Geophysics: Cambridge University Press, London.
13
Van Tuyen, D., Canh, T., and Weller, A., 2000, Geophysical investigations of river dikes in Vietnam: European Journal of Environmental and Engineering Geophysics, 4, 195–206.
14
Voronkov, O. K., Kagan, A. A., Krivonogova, N. F., Glagovsky, V. B., and Prokopovich, V. S., 2004, Geophysical methods and identification of embankment dam parameters: Proceedings of the 2nd International Conference on Site Characterization (ISC), Porto, Portugal, 19–22 September, pp. 593–599.
15
ORIGINAL_ARTICLE
The regional estimates of the GPS satellite and receiver differential code biases
The Differential Code Biases (DCB), which are also termed hardware delay biases, are the frequency-dependent time delays of the satellite and receiver. Possible sources of these delays are antennas and cables, as well as different filters used in receivers and satellites. These instrumental delays affect both code and carrier measurements. These biases for satellites and some IGS stations tend to be obtained from the Center for Orbit Determination in Europe (CODE) as daily or monthly constants, which are based on the global ionospheric total electron content (TEC) modeling in the solar-geomagnetic frame. These biases are not provided for regional and local network receivers, and need to be computed by the user. In this study, the regional approach by the spherical Slepian function was used to estimate the GPS satellite and receiver DCBs. Validations using real data showed that this method has significant potential and the ability to yield reliable results, even for a single station DCB estimate.
https://www.ijgeophysics.ir/article_46954_b221322f44a3ad82da67c3d89f374beb.pdf
2017-05-22
31
41
DCB
GPS
Slepian function
regional modeling
Saeed
Farzaneh
farzaneh@ut.ac.ir
1
Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mohammad Ali
Sharifi
sharifi@ut.ac.ir
2
Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Arikan, F., Nayir, H., Sezen, U., and Arikan, O., 2008, Estimation of single station interfrequency receiver bias using GPS-TEC: Radio Science, 43, RS4004. Doi: 10.1029/2007RS003785
1
Chen, W., Hu, C., Gao, S., Chen, Y., and Ding, X., 2004, Absolute ionospheric delay estimation based on GPS PPP and GPS active network: International Symposium on GNSS/GPS, Sydney, Australia, 6–8 Dec, 2004.
2
Ciraolo, L., Azpilicueta, F., Brunini, C., Meza, A., and Radicella, S., 2007, Calibration errors on experimental slant total electron content (TEC) determined with GPS: Journal of Geodesy, 81, 111–120. Doi: 10.1007/s00190-006-0093-1
3
Coco, D., Coker, C., Dahlke, S., and Clynch, J., 1991, Variability of GPS satellite differential group delay biases: IEEE Transactions on Aerospace and Electronic Systems., 27, 931– 938.
4
Conte, J., Azpilicueta, F., and Brunini, C., 2011, Accuracy assessment of the GPS-TEC calibration constants by means of a simulation technique: Journal of Geodesy, 85, 1–8. Doi: 10.1007/s00190-011-0477-8
5
Dach, R., Hugentobler, U., Fridez, P., and Meindle, M., 2007, Bernese GPS software version 5.0: Astronomical Institute, University of Bern, Switzerland.
6
Dettmering, D., Heinkelmann, R., and Schmidt, M., 2011, Systematic differences between VTEC obtained by different space-geodetic techniques during CONT08: Journal of Geodesy, 85, 443–451.
7
Durmaz, M., and Karslioglu, M. O., 2014, Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS): Journal of Geodesy, 89, 347–360. Doi: 10.1007/s00190-014-0779-8
8
Gao, Y., Heroux, P., and Kouba, J., 1994, Estimation of GPS receiver and satellite L1/L2 signal delay biases using data from CACS: Proceedings of the International Symposium on Kinematic Systems in Geodesy, Geomatics and Navigation, 109–117, August 30–September 2, Banff, Canada.
9
Hofmann-Wellenhof, B., Lichtenegger, H., and Wasle, E., 2008, GNSS – Global Navigation Satellite Systems – GPS, GLONASS, Galileo & more: Springer-Verlag, Wien.
10
Jakowski, N., Sardon, E., Engler, E., Jungstand, A., and Klahn, D., 1996, Relationships between GPS-signal propagation errors and EISCAT observations: Annals of Geophysics, 14, 1429–1436.
11
Jin, S. G., Luo, O. F., and Park, P., 2008, GPS observations of the ionospheric F2-layer behavior during the 20th November 2003 geomagnetic storm over South Korea: Journal of Geodesy, 82, 883–892. doi:10.1007/ s00190-008-0217-x
12
Jin, R., Jin, S., and Feng, G., 2012, M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases: GPS Solutions, 16, 541–548.
13
Kee, C., and Yun, D., 2002, Extending coverage of DGPS by considering atmospheric models and corrections: Journal of Navigation, 55, 305–322. doi:10.1017/S0373463302001741
14
Komjathy, A., Sparks, L., Wilson, B. D., and Mannucci, A. J., 2005, Automated daily processing of more than 1000 ground-based GPS receivers for studying intense ionospheric storms: Radio Science, 40, S6006. Doi: 10.1029/2005RS003279
15
Lanyi, G. E., and Roth, A., 1998, Comparison of mapped and measured total ionospheric electron content using global positioning system and beacon satellite observation: Radio Science, 23, 483–292.
16
Lin, L. S., 2001, Remote sensing of ionosphere using GPS measurements: the 22nd Asian Conference on Remote Sensing, 5–9 Nov., Singapore, 2001.
17
Liu, Z., and Gao, Y., 2003, Ionospheric TEC predictions over a local area GPS reference network: GPS Solutions, 8, 23–29.
18
Ma, G., and Maruyama, T., 2003, Derivation of TEC and estimation of instrumental biases from GEONET in Japan: Annals of Geophysics, 21, 2083–2093.
19
Mannucci, A. J., Iijima, B. A., Lindqwister, U. J., Pi, X., Sparks, L., and Wilson, B. D., 1999, GPS and ionosphere: Review of Radio Science, 1996–1999: Oxford University Press, New York.
20
Misra, P., and Enge, P., 2003, Global Positioning System: Signals, Measurements, and Performance: Ganga–Jamuna Press, Massachusetts.
21
Nohutcu, M., Karslioglu, M. O., and Schmidt, M., 2010, B-spline modeling of VTEC over Turkey using GPS observations: Journal of Atmospheric and Solar-Terrestrial Physics, 72, 617–624.
22
Otsuka, Y., Ogawa, T., Saito, A., Tsugawa, T., Fukao, S., and Miyazaki, S., 2002, A new technique for mapping of total electron content using GPS network in Japan: Earth Planets Space, 54, 63–70.
23
Ray, J., and Senior, K., 2005, Geodetic techniques for time and frequency comparisons using GPS phase and code measurements: Metrologia 42, 215–232. doi:10.1088/0026-1394/42/4/005
24
Schaer, S., 1999, Mapping and predicting the Earth’s ionosphere using the Global Positioning System: Ph.D. Thesis, Astronomical Institute, University of Berne, Switzerland.
25
Schaer, S., Beutler, G., Mervart, L., Rothacher, M., and Wild, U., 1995, Global and regional ionosphere models using the GPS double difference phase observable: In Gendt G., and Dick, G., (Eds.), Proceedings of the IGS Workshop 1995 on Special Topics and New Directions, 15–18 May 1995, GFZ, Potsdam, Germany, pp. 77–92.
26
Seeber, G., 2003, Satellite Geodesy: Foundations, Methods and Application: Walter de Gruyter, Berlin and New York, 589 pp.
27
Sharifi, M. A., and Farzaneh, S., 2013, The spatio–spectral localization approach to modelling VTEC over the western part of the USA using GPS observations: Advances in Space Research, 54, 908–916.
28
Sharifi, M. A., and Farzaneh, S., 2015, Regional TEC dynamic modeling based on Slepian functions: Advances in Space Research, 56, 907–915.
29
Wen, D., Liu, S., and Tang, P., 2010, Tomographic reconstruction of ionospheric electron density based on constrained algebraic reconstruction technique: GPS Solutions, 14, 375–380. Doi: 10.1007/s10291-010-0161-0
30
Wielgosz, P., Grejner-Brzezinska, D., and Kashani, I., 2003, Regional ionosphere mapping with kriging and multiquadratic methods: Journal of Global Positioning System: 2, 48–55.
31
Wilson, B. D., Mannucci, A. J., 1993, Instrumental biases in ionospheric measurement derived from GPS data: In paper presented at proceedings of the ION GPS-93, Salt Lake City, UT, September 22–24, pp 1343–1351.
32
Wilson, B. D., Yinger, C. H., Feess, W. A., and Shank, C., 1999, Newand improved: the broadcast interfrequency biases: GPS World, 10, 56–66.
33
Yuan, Y., and Ou, J., 1999, The effects of instrumental bias in GPS observations on determining ionospheric delays and the methods of its calibration: Acta Geodaetica et Geophysica. Cartogr. Sin., 2, 110–114. Doi: cnki:ISSN: 1001-1595.0.1999-02-002
34
Zhang, Y., Wu, F., Kubo, N., and Yasuda, A., 2003, TEC measurement by single dual-frequency GPS receiver: paper presented at International Symposium on GPS/GNSS, Tokyo, 15–18, Nov, 2003.
35
ORIGINAL_ARTICLE
Improving seismic image in complex structures by new solving strategies in the CO-CRS and the CO-CDS methods
Conventional seismic imaging possesses problem in exposing structural detail in complex geological media. Nevertheless, some recently introduced methods reduce this ambiguity to some extent, by using data based imaging operator or emancipation from the macro-velocity model. The zero offset common reflection surface (ZO-CRS) stack method is a velocity independent imaging technique which is frequently used in seismic imaging. Various modifications of this method were introduced through its development. The ZO diffraction stacking operator, the common offset CRS (CO-CRS) and anisotropic CRS methods were introduced to enhance the final seismic image. As diffraction events are carriers of structural details information, we adhere to improve response diffraction to obtain more structural details in the final image. Thus we combined advantages of the CO-CRS method by the diffraction operator to make the CO-CDS stack operator. The parameters of the reflection operator were changed to fulfill conditions of a diffraction response in CO domain. Meanwhile, to resolve the problem of conflicting dips, the solving strategy was modified in order to consider all possible angles and make a contribution to them in their related operators. Thus it was expected that the CO-CDS stack reveals weak diffraction events in the stacked section, in favor of further depth migration. The introduced method was applied to a synthetic and land data. Utilizing the CO-CDS method on the synthetic data brings out as much as diffraction in the stacked result. For land data set, the CO-CDS operator boosted the share of diffraction in the stack section which was further underwent depth migration procedure by the robust Gaussian Beam algorithm with a smooth velocity model. Outstanding enhancement in the final result compared to the conventional and the CRS methods were depicted by depth imaging of the CO-CDS result, which was a consequence of improved diffraction based operator of the CRS method.
https://www.ijgeophysics.ir/article_46955_561625d290b9d4c93ca559e35d356b61.pdf
2017-05-22
42
56
seismic imaging
CRS
CDS
diffraction imaging
Gaussian Beam migration
Ali
Pahlavanloo
alipahlavanloo@gmail.com
1
, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mehrdad
Soleimani monfared
mehrdad.soleimani2005@gmail.com
2
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Claudio
Gallo
fender@crs4.it
3
Imaging and Numerical Geophysics Program, Centre for Advanced Studies, Research and Development in Sardinia, Pula, Italy
AUTHOR
Baykulov, M., and Gajewski, D., 2009, Prestack seismic data enhancement with partial common-reflection-surface (CRS) stack: Geophysics, 74, 49-58. http://dx.doi.org/10.1190/1.3106182
1
Bergler, S., 2001, The Common-Reflection-Surface stack for common offset-theory and application: M. Sc. Thesis, Universität Karlsruhe, Karlsruhe, Germany.
2
Bergler, S., Hubral, P., Marchetti, P., Cristini, A., and Cardone, G., 2002, 3D common-reflection-surface stack and kinematic wavefield attributes: The Leading Edge, 21, 1010-1015.
3
Biondi, B., 2006, 3D seismic imaging: Society of Exploration Geophysicists.
4
Bonomi, E., Cristini, A.M., Theis, D., and Marchetti, P., 2009, 3D CRS analysis: a new data-driven optimization strategy for the simultaneous estimate of the eight stacking parameters: In Expanded Abstracts, 79th SEG Technical Program.
5
Bonomi, E., Tomas, C., Marchetti, P., and Caddeo, G., 2014, Velocity-independent and data-driven prestack time imaging: It is possible: The Leading Edge, 33, 1008-1014.
6
Bortfeld, R., 1989, Geometrical ray theory; Rays and traveltimes in seismic systems second-order approximations of the traveltimes: Geophysics, 54, 342-349.
7
Chandrakala, K., Mall, D.M., Sarkar, D., and Pandey O.P., 2013, Seismic imaging of the Proterozoic Cuddapah basin, South India and regional geodynamics: Precambrian Research, 231, 277– 289.
8
Cristini, A., Cardone, G., Chira, P., Hubral, P., and Marchetti, P., 2001, 3D zero offset-common reflection surface stack for land data: Presented at the SEG Workshop Velocity Model Independent Imaging in Complex Media. San Antonio, USA.
9
Duveneck, E., 2004, Velocity model estimation with data-derived wavefront attributes: Geophysics, 69, 265–274.
10
Fomel, S., 2007, Velocity-independent time-domain seismic imaging using local event slopes: Geophysics, 72, 139-147.
11
Garabito, G., Oliva, P. C., and Cruz, J. C. R., 2011, Numerical analysis of the finite-offset common-reflection-surface traveltime approximations: Journal of Applied Geophysics, 74, 89–99.
12
Garabito, G., Cruz, J. C. R., and Soellner, W., 2016, Finite-offset common reflection surface stack using global optimization for parameter estimation: a land data example: Geophysical Prospecting, Published Online.
13
Gelchinsky, B., Berkovitch, A., and Keydar, S., 1999, Multifocusing homeomorphic imaging: Part 1. Basic concepts and formulas: Journal of Applied Geophysics, 42, 229-242.
14
Hedin, P., Almqvist, B., Berthet, T., Juhlin, C., Buske, S., Simon, H., Giese, R., Krauss, F., Rosberg, J. E., and Alm, P. G., 2015, 3D reflection seismic imaging at the 2.5 km deep COSC-1 scientific borehole, central Scandinavian Caledonides: Tectonophysics, 689, 40-55.
15
Hertweck, T., Schleicher, J., and Mann, J., 2007, Data stacking beyond CMP, The Leading Edge, 26: 818-827.
16
Höcht, G., de Bazelaire, E., Majer, P., and Hubral, P., 1999, Seismic and optics: hyperbolae and curvatures: Journal of Applied Geophysics, 42, 261–281.
17
Iidaka, T., Kurashimo, E., Iwasaki, T., Arai, R., Kato, A., Katao, H., and Yamazaki, F., 2015, Large heterogeneous structure beneath the Atotsugawa Fault, central Japan, revealed by seismic refraction and reflection experiments: Tectonophysics, 657, 144-154.
18
Jäger, R., 1999, The common reflection surface stack: theory and application: M. Sc. Thesis, Universität Karlsruhe, Karlsruhe, Germany.
19
Juhlin, C., Dehghannejad, M., Lund, B., Malehmir, A., and Pratt, G., 2010, Reflection seismic imaging of the end-glacial Pärvie Fault system, northern Sweden: Journal of Applied Geophysics, 70, 307–316.
20
Landa, E., Fomel, S., and Moser, T., 2006, Path-integral seismic imaging: Geophysical Prospecting, 54, 491–503.
21
Leite, L. W. B., Lima, H. M., Heilmann, B. Z., and Mann, J., 2010, CRS-based seismic imaging in complex marine geology: In Expanded Abstract, 72nd EAGE Conference & Exhibition, Barcelona.
22
Mann, J., Jäger, R., Müller, T., Höcht, G., and Hubral, P., 1999, Common-reflection-surface stack - a real data example: Journal of Applied Geophysics 42, 301-318.
23
Mann, J., 2002, Extensions and applications of the common-reflection-surface stack method: Ph. D. Thesis, Universität Karlsruhe, Karlsruhe, Germany.
24
Müller, T., 1999, The Common Reflection Surface Stack Method–Seismic imaging without explicit knowledge of the velocity model: Ph. D. Thesis, Universität Karlsruhe, Karlsruhe, Germany.
25
Pu, R., Zhang, Y., and Luo, J., 2012, Seismic reflection, distribution, and potential trap of Permian volcanic rocks in the Tahe field: Journal of Earth Science 23, 421–430.
26
Robein, E., 2010, Seismic imaging—A review of the techniques, their principles, merits and limitations, EAGE publication, Amsterdam, Netherlands.
27
Schwarz, B., Vanelle, C., Gajewski, D., and Kashtan, B., 2014, Curvatures and inhomogeneities: An improved common-reflection-surface approach: Geophysics 79, 231–240.
28
Soleimani, M., 2015, Seismic imaging of mud volcano boundary in the east of Caspian Sea by common diffraction surface stack method: Arabian Journal of Geoscience, 8, 3943–3958.
29
Soleimani, M., 2016a, Seismic imaging by 3D partial CDS method in complex media: Journal of Petroleum Science and Engineering, 143, 54–64.
30
Soleimani, M., 2016b, Seismic image enhancement of mud volcano bearing complex structure by the CDS method, a case study in SE of the Caspian Sea shoreline: Russian Geology and Geophysics, 57, 1757–1768.
31
Soleimani, M., Jodeiri-Shokri, B., and Rafiei, M., 2016, Improvement of seismic structural interpretation of Zagros fold-thrust belt by dip scanning in common diffraction surface imaging method: Acta Geodaetica et Geophysica, published online.
32
Spinner, M., Tomas, C., Marchetti, P., Gallo, C., and Arfeen, S., 2012, Common-Offset CRS for advanced imaging in complex geological settings: In Expanded Abstract, 82nd SEG Technical Program.
33
Vieth, K. U., 2001, Kinematic wavefield attributes in seismic imaging: Logos Verlag, Berlin.
34
Xu, B., Xiao, A., Wu, L., Mao, L., Dong, Y., and Zhou, L., 2014, 3D seismic attributes for structural analysis in compressional context: A case study from western Sichuan Basin: Journal of Earth Science, 25, 985–990.
35
Yang, K., Chen, B. S., Wang, X. J., Yang, X. C., and Liu, J. R., 2012, Handling dip discrimination phenomenon in common‐reflection‐surface stack via combination of output‐imaging‐scheme and migration/demigration: Geophysical Prospecting 60, 255-269.
36
Zhang, Y., Bergler, S., and Hubral, P., 2001, Common‐reflection‐surface (CRS) stack for common offset: Geophysical Prospecting, 49, 709-718.
37
ORIGINAL_ARTICLE
Analysis of updraft velocity in mesoscale convective systems using satellite and WRF model simulations
Updraft vertical velocity is an important dynamical quantity which is strongly related to storm intensity and heavy precipitation. It can be calculated by direct observations, NWP model, and geostationary satellites which can provide the possibility of measuring this quantity with high temporal resolution. This research analyzed updraft velocity based on six derived parameters from INSAT3-D and high temporal and spatial resolution simulations of WRF model in the west and southwest of Iran. The interrelationship among the derived variables was investigated from the immature to mature stages of convective cells in Mesoscale Convective Systems (MCS). Updraft velocity was calculated based on a theoretical framework and real observations. The was a large results discrepancy among the results. This finding was in company with previous studies which concluded that updraft velocity is the resultant of other bulk buoyancy forces and environmental variables. Also, the estimated updraft velocities showed a positive correlation with height. The authors proposed linear regression, as a parametric, and Random Forest (RF), as a non-parametric, machine learning methods for estimation of updraft velocity based on satellite variables. A forward–backward method was applied to reach the best modeling in both methods. In linear regression modeling, the cloud-top cooling rate was the most significant factor, and in the RF, band difference of water vapor, thermal infrared 1, and elevation data had the maximum importance. Results showed that the RF could better estimate updraft velocity.
https://www.ijgeophysics.ir/article_46956_90a76c7f9dcd03ce76c2e2fa42df4829.pdf
2017-05-22
57
70
MCS
updraft velocity
NWP
geostationary satellite
CAPE
Reza
Khandan
rs.reza_khandan@ut.ac.ir
1
Faculty of Geography, University of Tehran, Tehran, Iran
AUTHOR
Seyed Kazem
Alavipanah
salavipa@ut.ac.ir
2
Faculty of Geography, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Arastoo
Pour Biazar
biazar@nsstc.uah.edu
3
Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, USA
AUTHOR
Maryam
Gharaylou
gharaylo@ut.ac.ir
4
Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Ackerman, SA., 1996, Global Satellite Observations of Negative Brightness Temperature Differences between 11 and 6.7 µm: Journal of the Atmospheric Sciences, 53(19), 2803-2812.
1
Adlerman, EJ., & Droegemeier, KK., 2005, The Dependence of Numerically Simulated Cyclic Mesocyclogenesis Upon Environmental Vertical Wind Shear: Monthly Weather Review, 133(12), 3595-3623.
2
Ahmadi Givi, F., Mohebalhojeh, AR., & Gharaylou, M., 2006, The Dynamics of Cyclonic Systems over Iran Using Potential Vorticity Diagnostics; A Case Study for Nov-Dec 2003: Earth and Space Physics, 32(1), 13.
3
Cohen, C, & McCaul Jr, EW.2006. The Sensitivity of Simulated Convective Storms to Variations in Prescribed Single-Moment Microphysics Parameters That Describe Particle Distributions, Sizes, and Numbers. Monthly Weather Review, 134(9), 2547-2565.
4
Ellrod, GP.2004. Impact on Volcanic Ash Detection Caused by the Loss of the 12.0 Μm “Split Window” Band on Goes Imagers. Journal of volcanology and geothermal research, 135(1), 91-103.
5
Giangrande, SE, Toto, T, Jensen, MP, Bartholomew, MJ, Feng, Z, Protat, A, . . . Machado, L.2016. Convective Cloud Vertical Velocity and Mass‐Flux Characteristics from Radar Wind Profiler Observations During Goamazon2014/5. Journal of Geophysical Research: Atmospheres, 121(21).
6
Hamada, A, & Takayabu, YN.2016. Convective Cloud Top Vertical Velocity Estimated from Geostationary Satellite Rapid‐Scan Measurements. Geophysical Research Letters, 43(10), 5435-5441.
7
Inoue, T.1987. An Instantaneous Delineation of Convective Rainfall Areas Using Split Window Data of Noah-7 Avhrr. Journal of the Meteorological Society of Japan. Ser. II, 65(3), 469-481.
8
James, G, Witten, D, Hastie, T, & Tibshirani, R. 2013. An Introduction to Statistical Learning (Vol. 6): Springer.
9
Jensen, MP, Petersen, WA, Bansemer, A, Bharadwaj, N, Carey, L, Cecil, D, Gerlach, J.2016. The Midlatitude Continental Convective Clouds Experiment (Mc3e). Bulletin of the American Meteorological Society, 97(9), 1667-1686.
10
Kain, JS.2004. The Kain–Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology, 43(1), 170-181.
11
Kirkpatrick, C, McCaul Jr, EW, & Cohen, C.2009. Variability of Updraft and Downdraft Characteristics in a Large Parameter Space Study of Convective Storms. Monthly Weather Review, 137(5), 1550-1561.
12
Luo, ZJ, Jeyaratnam, J, Iwasaki, S, Takahashi, H, & Anderson, R.2014. Convective Vertical Velocity and Cloud Internal Vertical Structure: An a‐Train Perspective. Geophysical Research Letters, 41(2), 723-729.
13
McCaul Jr, EW, & Cohen, C.2002. The Impact on Simulated Storm Structure and Intensity of Variations in the Mixed Layer and Moist Layer Depths. Monthly Weather Review, 130(7), 1722-1748.
14
McCaul Jr, EW, Cohen, C, & Kirkpatrick, C.2005. The Sensitivity of Simulated Storm Structure, Intensity, and Precipitation Efficiency to Environmental Temperature. Monthly Weather Review, 133(10), 3015-3037.
15
McCaul Jr, EW, & Weisman, ML.1996. Simulations of Shallow Supercell Storms in Landfalling Hurricane Environments. Monthly Weather Review, 124(3), 408-429.
16
Mecikalski, JR, Jewett, CP, Apke, JM, & Carey, LD.2016. Analysis of Cumulus Cloud Updrafts as Observed with 1-Min Resolution Super Rapid Scan Goes Imagery. Monthly Weather Review, 144(2), 811-830.
17
Mohammadi, H, Fattahi, E, Shamsi pour, AA, & Akbari, M.2012. Dynamic Analysis of Sudan Low-Pressure Systems and Torrents in Southwest of Iran.
18
Morrison, H.2016. Impacts of Updraft Size and Dimensionality on the Perturbation Pressure and Vertical Velocity in Cumulus Convection. Part Ii: Comparison of Theoretical and Numerical Solutions and Fully Dynamical Simulations. Journal of the Atmospheric Sciences, 73(4), 1455-1480.
19
Nazaripour, H, Dostkamiyan, M, & Alizadeh, S.2015. The Spatial Distribution Patterns of Temperature, Precipitation, and Humidity Using Geostatistical Exploratory Analysis (Case Study: Central Area of Iran).
20
Parodi, A, & Emanuel, K.2009. A Theory for Buoyancy and Velocity Scales in Deep Moist Convection. Journal of the Atmospheric Sciences, 66(11), 3449-3463.
21
Prata, A.1989. Observations of Volcanic Ash Clouds in the 10-12 Μm Window Using Avhrr/2 Data. International Journal of Remote Sensing, 10(4-5), 751-761.
22
Schmetz, J, Tjemkes, S, Gube, M, & Van de Berg, L.1997. Monitoring Deep Convection and Convective Overshooting with Meteosat. Advances in Space Research, 19(3), 433-441.
23
Schumacher, C, Stevenson, SN, & Williams, CR.2015. Vertical Motions of the Tropical Convective Cloud Spectrum over Darwin, Australia. Quarterly Journal of the Royal Meteorological Society, 141(691), 2277-2288.
24
Skamarock, WC, Klemp, JB, Dudhia, J, Gill, DO, Barker, DM, Wang, W, & Powers, JG. (2005). A Description of the Advanced Research Wrf Version 2. Retrieved from
25
Soden, BJ, & Bretherton, FP.1993. Upper Tropospheric Relative Humidity from the Goes 6.7 Μm Channel: Method and Climatology for July 1987. Journal of Geophysical Research: Atmospheres, 98(D9), 16669-16688.
26
Tang, S, Xie, S, Zhang, Y, Zhang, M, Schumacher, C, Upton, H, . . . Ahlgrimm, M.2016. Large-Scale Vertical Velocity, Diabatic Heating and Drying Profiles Associated with Seasonal and Diurnal Variations of Convective Systems Observed in the Goamazon2014/5 Experiment. Atmospheric Chemistry and Physics, 16(22), 14249.
27
Thompson, G, Rasmussen, RM, & Manning, K.2004. Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis. Monthly Weather Review, 132(2), 519-542.
28
Tian, Y, & Kuang, Z.2016. Dependence of Entrainment in Shallow Cumulus Convection on Vertical Velocity and Distance to Cloud Edge. Geophysical Research Letters, 43(8), 4056-4065.
29
Walker, JR, MacKenzie Jr, WM, Mecikalski, JR, & Jewett, CP.2012. An Enhanced Geostationary Satellite–Based Convective Initiation Algorithm for 0–2-H Nowcasting with Object Tracking. Journal of Applied Meteorology and Climatology, 51(11), 1931-1949.
30
Wang, X, & Zhang, M.2014. Vertical Velocity in Shallow Convection for Different Plume Types. Journal of Advances in Modeling Earth Systems, 6(2), 478-489.
31
Weisman, ML, & Klemp, JB.1982. The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy. Monthly Weather Review, 110(6), 504-520.
32
Weisman, ML, & Klemp, JB.1984. The Structure and Classification of Numerically Simulated Convective Stormsin Directionally Varying Wind Shears. Monthly Weather Review, 112(12), 2479-2498.
33
Xu, K-M, & Randall, DA.2001. Updraft and Downdraft Statistics of Simulated Tropical and Midlatitude Cumulus Convection. Journal of the Atmospheric Sciences, 58(13), 1630-1649.
34
Yang, J, Wang, Z, Heymsfield, AJ, & French, JR.2016. Characteristics of Vertical Air Motion in Isolated Convective Clouds. Atmospheric Chemistry and Physics, 16(15), 10159-10173.
35
ORIGINAL_ARTICLE
A closer look at rock physics models and their assisted interpretation in seismic exploration
Subsurface rocks and their fluid content along with their architecture affect reflected seismic waves through variations in their travel time, reflection amplitude, and phase within the field of exploration seismology. The combined effects of these factors make subsurface interpretation by using reflection waves very difficult. Therefore, assistance from other subsurface disciplines is needed if we intend to make a more accurate image of the subsurface. In this regard, rock physics acts as an integrated tool to combine subsurface information from different disciplines in a set of relationships between engineering (petrophysical) properties and their relevant geophysical variations, or more specifically, elastic variations. As a matter of fact, rock physics is required for a better understanding of rock properties if we intend to have a full understanding of our reservoir properties and their fluid content. This paper reviews some of the most important rock physics models and their application within the field of seismic exploration. These models are generally valid for the given conditions in which they are derived, and as a result, having a good understanding of their physical and geological limitations can help a lot with accurate rock physics modeling and interpretation. In this regard, this paper is an attempt to create a better understanding of such models, using different references and my personal experiences with these models. The application contexts of the models presented in this paper are not limited to the discussed scenarios. These scenarios are the ones that are commonly used and have shown a good prediction power in practice.
https://www.ijgeophysics.ir/article_46957_a3a1f00530313478c4d0865bf3125525.pdf
2017-05-22
71
84
Rock Physics
seismic velocities
elastic rock properties
rock properties
exploration seismology
Mohammad Reza
Saberi
saberi.rp@gmail.com
1
CGG, Bordewijklaan 58, 2591 XR, The Hague, The Netherlands
LEAD_AUTHOR
Anselmetti, F. S. and Eberli, G. P., 1999, The velocity deviation log: A tool to predict pore type and permeability trends in carbonate drill holes from sonic and porosity or density logs: American Association of Petroleum Geologist, 83, 450–466.
1
Avseth, P., Mukerji, T. and Mavko, G., 2005, Quantitative seismic interpretation: Applying Rock Physics Tool to Reduce Interpretation Risk (First Edition): Cambridge University Press, Cambridge, UK.
2
Backus, G. E., 1962, Long-wave elastic anisotropy produced by horizontal layering: Journal of Geophysical Research, 67, 4427–4440.
3
Batzle, M. and Wang, Z., 1992, Seismic properties of pore fluids: Geophysics, 57, 1396–1408.
4
Brie, A., Pampuri, F., Marsala, A. F., and Meazza, O., 1995, Shear sonic interpretation in gas-bearing sands: SPE 30595, 701–710.
5
Berryman, J. G., 1980a, Long-wavelength propagation in composite elastic media I. Spherical inclusions: Journal of Acoustic Society of America, 68, 1809–1819.
6
Berryman, J. G., 1980b, Long-wavelength propagation in composite elastic media II. Ellipsoidal inclusions: Journal of Acoustic Society of America, 68, 1820–1831.
7
Biot, M. A., 1956, Theory of propagation of elastic waves in a fluid saturated porous solid. I. Low frequency range and II. Higher frequency range: Journal of Acoustical Society of America, 28, 168–191.
8
Brown, R. and Korringa, J., 1975, On the dependence of the elastic properties of a porous rock on the compressibility of the pore fluid: Geophysics, 40, 608–616.
9
Castagna, J. P., Batzle, M. L. and Eastwood, R. L., 1985, Relationships between compressional wave and shear wave velocities in clastic silicate rocks: Geophysics, 50, 571–581.
10
Ciz, R. and Shapiro, S., 2007, Generalization of Gassmann equations for porous media saturated with a solid material: Geophysics, 72, A75–A79.
11
Deng, J. X., Han, D. and Liu, J., 2006, The effects of geologic parameter variation on the A-B Cross-plot of sand reservoir: Fluid/DHI Annual Meeting.
12
Digby, P. J., 1981, The effective elastic moduli of porous granular rocks: Journal of Applied Mechanics, 48, 803–808.
13
Dræge, A., 2006, Impact of Diagenesis on Seismic Properties of Siliciclastic Rocks: Ph. D. dissertation, University of Bergen, Norway.
14
Dvorkin, J. and Nur, A., 1993, Dynamic poroelasticity: a unified model with the squirt and the Biot mechanisms: Geophysics, 58, 524–533.
15
Dvorkin, J. and Nur, A., 1996, Elasticity of high-porosity sandstones, Theory for two North Sea data sets: Geophysics, 61, 559–564.
16
Eberhart-Phillips, D. M., 1989, Investigation of Crustal Structure and Active Tectonic Processes in the Coast Ranges, Central California: Ph. D. dissertation, Stanford University, USA.
17
Gassmann, F., 1951, Uber die Elastizitat poroser Medien: Vier. der Natur. Gesellschaft Zurich, 96, 1–23.
18
Greenberg, M. L. and Castagna, J. P., 1992, Shear-wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification and applications: Geophysical Prospecting, 40, 195–209.
19
Gurevich, B. and Lopatnikov S. L., 1995, Velocity and attenuation of elastic waves in finely layered porous rocks: Geophysical Journal International, 121, 933–947.
20
Hashin, Z. and Shtrikman, S., 1963, A variational approach to the elastic behavior of multiphase materials: Journal of Mechanics and Physics of Solids, 11, 127–140.
21
Hill, R., 1952, The elastic behavior of crystalline aggregate: Proceeding of Physical Society, 65, 349–354.
22
Hudson, J. A., 1980, Overall properties of a cracked solid: Mathematical Proceedings of the Cambridge Philosophical Society, 88, 371–384.
23
Hossain, Z., Mukerji, T., Dvorkin, J. and Fabricius, I. L., 2011, Rock physics model of glauconitic greensand from the North Sea: Geophysics, 76, E199-E209.
24
Jakobsen, M., Hudson, J. A., and Johansen, T. A., 2003a, T-matrix approach to shale acoustics: Geophysical Journal International, 154, 533–558.
25
Jakobsen, M., Johansen, T. A., and McCann, C., 2003b, The acoustic signature of fluid flow in complex porous media: Journal of Applied Geophysics, 54, 219–246.
26
King, M. S., 2005, Rock-physics developments in seismic exploration: A personal 50-year perspective: Geophysics, 70, 3ND–8ND.
27
Krief, M., Garat, J., Stellingwerff, J., and Ventre, J., 1990, A petrophysical interpretation using the velocities of P and S waves (full-waveform sonic): Log Analyst, 31, 355–369.
28
Kuster, G. T., and Toksöz, M. N., 1974, Velocity and attenuation of seismic waves in two phase media: Part I. Theoretical formulations: Geophysics, 39, 587–606.
29
Marion, D., 1990, Acoustical, Mechanical and Transport Properties of Sediments and Granular Materials: Ph. D. dissertation, Stanford University.
30
Mavko, G., Mukerji, T. and Dvorkin, J., 1998, The rock physics handbook: Cambridge University Press, Cambridge, UK.
31
Mindlin, R. D., 1949, Compliance of elastic bodies in contact: Journal of Applied Mechanics, 16, 259–268.
32
Nishizawa, O., 1982, Seismic velocity anisotropy in a medium containing oriented cracks transversely isotropic case: Journal of Physics of the Earth, 30, 331–347.
33
Nur, A., Mavko, G., Dvorkin, J. and Gal, D., 1995, Critical porosity: the key to relating physical properties to porosity in rocks: In Proceeding of 65th Annual International Meeting, Society Exploration Geophysicist, 878.
34
Pride, S. R., Berryman J. G. and Harris J. M., 2004, Seismic attenuation due to wave-induced flow: Journal of Geophysical Research, 109, B01201.
35
Raymer, L. L., Hunt, E. R., and Gardner, J. S., 1980, An improved sonic transit time-to-porosity transform: Transcript for Society of Professional Well Log Analysts, 21st Annual Logging Symposium, Paper P.
36
Reuss, A., 1929, Berechnung der Fliessgrenzen vonMischkristallen aufGrund der Plastizita¨tsbedingung fu¨r Einkristalle: Z. Ang. Math. Mech., 9, 49–58.
37
Richa R., 2010, Preservation of Transport Properties Trends: Computational Rock Physics Approach: Ph. D. dissertation, Stanford University, USA.
38
Saberi, M. R., 2010: An Integrated Approach for Seismic Characterization of Carbonates, Ph. D. dissertation, University of Bergen, Norway.
39
Saberi, M. R., 2016, Modeling an elastic stiffness tensor in a transverse isotropic subsurface medium: International application Patent No: WO 2016/083893 A1.
40
Sams M. S. and Andera M. A., 2001, The effect of clay distribution on the elastic properties of sandstones: Geophysical Prospecting, 49, 128–150.
41
Sayer, C., 2013, Introduction: Rock Physics for Reservoir Exploration, Characterisation and Monitoring: Geophysical Prospecting, 61, 251–253.
42
Smith, G. C. and Gidlow, P. M., 1987, Weighted stacking for rock property estimation and detection of gas: Geophysical Prospecting, 35, 993–1014.
43
Thomas, E. C. and Stieber, S. J., 1975, The distribution of shale in sandstones and its effect upon porosity: In Transcripts of 16th Annual Logging Symposium of the SPWLA, paper T.
44
Voigt, W., 1890, Bestimmung der Elastizita¨tskonstanten des brasilianischen Turmalines: Annual Review of Physical Chemistry, 41, 712–729.
45
Walton, K., 1987, The effective elastic moduli of a random packing of spheres: Journal of Mechanics and Physics of Solids, 35, 213–226.
46
Wang, Z., 2001, Fundamentals of seismic rock physics: Geophysics, 66, 398–412.
47
Xu, S. and White, R. E., 1995, A new velocity model for clay-sand mixtures: Geophysical Prospecting, 43, 91–118.
48
Xu, S. and Payne, M. A., 2009, Modeling elastic properties in carbonate rocks: The Leading Edge, 28, 66–74.
49