MARD - A moving average rose diagram application for the geosciences

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^(R), Microsoft^(R) Excel and GNU Octave formats, the program is fully compatible with both Microsoft^(R) 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^(R) 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.

[1]  P. Currie,et al.  Stratigraphy, sedimentology, and taphonomy of the Albertosaurus bonebed (upper Horseshoe Canyon Formation; Maastrichtian), southern Alberta, CanadaThis article is one of a series of papers published in this Special Issue on the theme Albertosaurus. , 2010 .

[2]  M. Voorhies.  Taphonomy and population dynamics of an early Pliocene vertebrate fauna, Knox County, Nebraska , 1969 .

[3]  Jaco H. Baas,et al.  EZ-ROSE: a computer program for equal-area circular histograms and statistical analysis of two-dimensional vectorial data , 2000 .

[4]  Graham J. Borradaile,et al.  Statistics of Earth Science Data: Their Distribution in Time, Space and Orientation , 2003 .

[5]  M. Klausen.  Geometry and mode of emplacement of dike swarms around the Birnudalstindur igneous centre, SE Iceland , 2006 .

[6]  A. Behrensmeyer.  Time resolution in fluvial vertebrate assemblages , 1982, Paleobiology.

[7]  N. Wells.  Are There Better Alternatives to Standard Rose Diagrams , 2000 .

[8]  Y. Ben‐Zion,et al.  Evolving geometrical heterogeneities of fault trace data , 2010 .

[9]  W. Nemec,et al.  The shape of the rose , 1988 .

[10]  Scott E. Johnson,et al.  Sequential porphyroblast growth during progressive deformation and low-P high-T (LPHT) metamorphism, Cooma Complex, Australia: the use of microstructural analysis to better understand deformation and metamorphic histories , 1992 .

[11]  Thomas A. Jones,et al.  MATLAB functions to analyze directional (azimuthal) data - I: Single-sample inference , 2006, Comput. Geosci..

[12]  Neil A. Wells,et al.  ASTRA.BAS: a program in QuickBasic 4.5 for exploring rose diagrams, circular histograms and some alternatives , 1999 .

[13]  N. Hayward,et al.  Microstructural analysis of the classical spiral garnet porphyroblasts of south‐east Vermont: evidence for non‐rotation , 1992 .

[14]  John Starkey,et al.  PhotoLin: a program to identify and analyze linear structures in aerial photographs, satellite images and maps , 2001 .

[15]  T. Blenkinsop,et al.  Tabular intrusion and folding of the late Archaean Murehwa granite, Zimbabwe, during regional shortening , 2001, Journal of the Geological Society.

[16]  D. Aerden.  Correlating deformation in Variscan NW-Iberia using porphyroblasts; implications for the Ibero-Armorican Arc , 2004 .

[17]  R. Reyment,et al.  Statistics and Data Analysis in Geology. , 1988 .

[18]  D. Aerden.  Preferred orientation of planar microstructures determined via statistical best-fit of measured intersection-lines: the 'FitPitch' computer program , 2003 .

[19]  Philipp Berens,et al.  CircStat: AMATLABToolbox for Circular Statistics , 2009, Journal of Statistical Software.

[20]  T. Blenkinsop,et al.  Steep extrusion of late Archaean granulites in the Northern Marginal Zone, Zimbabwe: evidence for secular change in orogenic style , 2005, Geological Society, London, Special Publications.

[21]  M. Hendrix,et al.  Taphonomy of a Petrified Forest in the Two Medicine Formation (Campanian), Northwest Montana: Implications for Palinspastic Restoration of the Boulder Batholith and Elkhorn Mountains Volcanics , 2000 .

[22]  D. Aerden.  Porphyroblast non-rotation during crustal extension in the Variscan Lys-Caillaouas Massif, Pyrenees , 1995 .

[23]  Arthur Pewsey,et al.  The large-sample joint distribution of key circular statistics , 2004 .

[24]  M. Sayab,et al.  From Adria- to Africa-driven orogenesis: Evidence from porphyroblasts in the Betic Cordillera, Spain , 2008 .

[25]  Ryan T Tucker,et al.  Taphonomy of Sheridan College Quarry 1, Buffalo, Wyoming: Implications for reconstructing historic dinosaur localities including Utterback's 1902–1910 Morrison dinosaur expeditions , 2011 .

[26]  R. Armstrong,et al.  Sedimentology and depositional environments of the Red Sandstone Group, Rukwa Rift Basin, southwestern Tanzania: new insight into Cretaceous and Paleogene terrestrial ecosystems and tectonics in sub-equatorial Africa , 2010 .

[27]  N. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[28]  Gyozo Jordan.  Adaptive smoothing of valleys in DEMs using TIN interpolation from ridgeline elevations: An application to morphotectonic aspect analysis , 2007, Comput. Geosci..

[29]  A. Behrensmeyer.  Vertebrate preservation in fluvial channels , 1988 .

[30]  T. S. Kutty,et al.  Rose.C—a program in “C” for producing high-quality Rose diagrams , 1992 .