کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
507436 | 865122 | 2012 | 9 صفحه PDF | دانلود رایگان |

MARD 1.0 is a computer program for generating smoothed rose diagrams by using a moving average, which is designed for use across the wide range of disciplines encompassed within the Earth Sciences. Available in MATLAB®, Microsoft® Excel and GNU Octave formats, the program is fully compatible with both Microsoft® Windows and Macintosh operating systems. Each version has been implemented in a user-friendly way that requires no prior experience in programming with the software. MARD conducts a moving average smoothing, a form of signal processing low-pass filter, upon the raw circular data according to a set of pre-defined conditions selected by the user. This form of signal processing filter smoothes the angular dataset, emphasising significant circular trends whilst reducing background noise. Customisable parameters include whether the data is uni- or bi-directional, the angular range (or aperture) over which the data is averaged, and whether an unweighted or weighted moving average is to be applied. In addition to the uni- and bi-directional options, the MATLAB® and Octave versions also possess a function for plotting 2-dimensional dips/pitches in a single, lower, hemisphere. The rose diagrams from each version are exportable as one of a selection of common graphical formats. Frequently employed statistical measures that determine the vector mean, mean resultant (or length), circular standard deviation and circular variance are also included. MARD's scope is demonstrated via its application to a variety of datasets within the Earth Sciences.
Figure optionsDownload as PowerPoint slideHighlights
► MARD is an application for generating moving average rose diagrams.
► Moving average rose diagrams are more informative than conventional binned equivalents.
► MARD is available in three easy-to-use versions: MATLAB, GNU Octave, and Microsoft Excel.
► MARD includes frequently used circular statistical measures.
Journal: Computers & Geosciences - Volume 49, December 2012, Pages 112–120