<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JSE</journal-id><journal-title-group><journal-title>Journal of Seismic Exploration</journal-title></journal-title-group><issn>0963-0651</issn><eissn/><publisher><publisher-name>AccScience Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi"/><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Building a heterogeneous Q model: an approach using surface reflection data</title><url>https://geophysical-press.com/journal/JSE/articles/206</url><author>WANGJIN,LIUWEI,ZHANGJIANFENG,ZHAOZHONGHUA</author><pub-date pub-type="publication-year"><year>2017</year></pub-date><volume>26</volume><issue>4</issue><history><date date-type="pub"><published-time>2017-08-01</published-time></date></history><abstract>Wang, J., Liu, W., Zhang, J. and Zhao, Z., 2017. Building a heterogeneous Q model: an approach using surface reflection data. Journal of Seismic Exploration, 26: 293-310. The anelasticity of a subsurface medium will cause dissipation of seismic energy. It is challenging to derive an interval Q model in the absence of VSP data and cross-well data. In this paper, we propose a method to derive the Q model using surface reflection data by introducing an effective Q model. Considering the existence of various types of noise, we estimate and evaluate the Q value in terms of compensation effects along with imaging resolution and noise level. Finally, we obtain an optimal compensation result with better resolution and wider bandwidth. Specifically, the effective Q model can be estimated using scanning technology at selected CDP locations to avoid the difficulties of determining a reference event and the thin-bed tuning effect in the conventional spectrum ratio method. The whole Q model can be obtained by a type of interpolation algorithm constrained by geological interfaces, which can be used in the de-absorption prestack time migration directly or in the de-absorption prestack depth migration with the proper time-to-depth conversion. Finally we demonstrate the effectiveness of the proposed approach using a field data example from eastern China. A high-resolution image is obtained.</abstract><keywords>Q model, Q scanning, compensation effects, interpolation algorithm</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Aki, K. and Richards, P.G., 1980. Quantitative Seismology. W.H. Freeman &amp; Co, San Francisco.Bickel, S.H. and Natarajan, R.R., 1985. Plane-wave Q deconvolution. Geophysics, 50: 1426-1437.Bleistein, N., 1984. Mathematical Methods for Wave Phenomena. Academic Press Inc., New York.Futterman, LW., 1962. Dispersive body waves. J. Geophys. Res., 67: 5279-5291.Hargreaves, N.D. and Calvert, A.J., 1991. Inverse Q filtering by Fourier transform. Geophysics,56: 519-527.Kjartansson, E. 1979. Constant Q-wave propagation and attenuation. J. Geophys. Res., 84:4737-4748.Lu, W.K., Zhang, W.P. and Liu, D.Q., 2006. Local linear coherent noise attenuation based on localpolynomial approximation. Geophysics, 71: V163-V169.310 WANG, LIU, ZHANG &amp; ZHAOCameron, M., Fomel, S. and Sethian, J., 2008. Time-to-depth conversion and seismic velocityestimation using time-migration velocity. Geophysics, 73: VE205-VE210.Mittet, R., Sollie, R. and Hokstak, K., 1995. Prestack depth migration with compensation forabsorption and dispersion. Geophysics, 60: 1485-1494.Youli, Q. and Harris, J.M., 1997. Seismic attenuation tomography using the frequency shift method.Geophysics, 62: 895-905.Dasgupta, R. and Clark,R.A., 1998. Estimation of Q from surface seismic reflection data.Geophysics, 63: 2120-2128.Rainer, T., 1989. Comparison of seven methods for the computation of Q. Phys. Earth Planet.Inter., 55: 259-268.Wang, Y., 2002. A stable and efficient approach of inverse Q filtering. Geophysics, 67: 657-663.Wang, S., Yang, D., Li, D.F. and Song, H.J., 2015. Q factor estimation based on the method oflogarithmic spectral area difference. Geophysics, 80: V157-V171.Waters, K.H., 1978. Reflection Seismology: A tool for Energy Resource Exploration. John Wileyand Sons, New York.Zhang, C.J. and Ulrych, T.J., 2002. Estimation of quality factors from CMP records. Geophysics,67: 1542-1547.Zhang, J.F. and Wapenaar, C.P.A.. 2002. Wavefield extrapolation and prestack depth migration inanelastic inhomogeneous media. Geophys. Prosp., 50: 629-643.Zhang, J.F., Wu, J.Z. and Li, X.Y., 2013. Compensation for absorption and dispersion in prestackmigration. An effective Q approach. Geophysics, 78: S1-S14.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
