000			02248 a2200229 4500
005			20250416160004.0
008			240425b2018|||||||| |||| 00| 0 eng d
020			_a9780128135556
041			_aEnglish
082			_a551 M46, 4
100			_aMenke, William _eAuthor _93848
245			_aGeophysical data analysis: discrete inverse theory
250			_a4th ed.
260			_bAcademic Press (Elsevier), _c2018. _aUK:
300			_axxx, 322p.; 23cms.
500			_aGeophysical Data Analysis: Diverse Inverse Theory, Fourth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. It demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. Using problems and case studies, along with MATLAB computer code and summaries of methods, the book provides data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory. Key features: Includes material on probability, including Bayesian influence, probability density function and metropolis algorithm Offers detailed discussion of the application of inverse theory to tectonic, gravitational and geomagnetic studies Contains numerous examples, color figures and end-of-chapter homework problems to help readers explore and further understand presented ideas Includes MATLAB examples and problem sets Updated and refined throughout to bring the text in line with current understanding and improved examples and case studies Expanded sections to cover material, such as second-derivation smoothing and chi-squared tests not covered in the previous edition
650			_aSome comments on probability theory _93849
650			_aNonuniqueness and localized averages _93850
650			_aApplications of vector spaces _93851
650			_aLinear inverse problems and non-gaussian statistics _93853
942			_cBK
999			_c1120 _d1120