Results: Eccentricity Analysis Results: Physical Cones Cinder Cones with Complex Original Forms and Implications for Morphologic Dating – REU 2014 Ryan.

Slides:



Advertisements
Similar presentations
Beth Schreck Northern Arizona University Mentor: Dr. Nancy Riggs
Advertisements

Grey Level Enhancement Contrast stretching Linear mapping Non-linear mapping Efficient implementation of mapping algorithms Design of classes to support.
Isfahan University of Technology Department of Mechanical Engineering May 7 th 2011 Course Instructor: Dr. S. Ziaei Rad Mode Indicator Functions.
Geologic Maps Enter. Geologic Maps Geologic maps show the areal distribution of rocks of the various geologic ages. Depending on the map scale, it could.
Statistics for the Behavioral Sciences Frequency Distributions
Reading Graphs and Charts are more attractive and easy to understand than tables enable the reader to ‘see’ patterns in the data are easy to use for comparisons.
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
Why does eccentricity only vary between 0 and 1?
The Growth and Erosion of Cinder Cones An Article Summary Kelsii Dana.
Degradation of Quaternary cinder cones in the Cima volcanic field, Mojave Desert, California Overview by Richard Fletcher.
More Raster and Surface Analysis in Spatial Analyst
Series Resonance ET 242 Circuit Analysis II
Morphometric Analysis of Cinder Cone Degradation
From: McCune, B. & J. B. Grace Analysis of Ecological Communities. MjM Software Design, Gleneden Beach, Oregon
The structure and emplacement of cinder cone fields. An overview by Richard Fletcher.
1 Pertemuan 06 Sebaran Normal dan Sampling Matakuliah: >K0614/ >FISIKA Tahun: >2006.
Volcanoes as Possible Indicators of Tectonic Stress Orientation – Principal and Proposal D. Dziekan.
Analysis of Research Data
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-1 Chapter 6 The Normal Distribution and Other Continuous Distributions.
Data observation and Descriptive Statistics
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 6-1 Chapter 6 The Normal Distribution Business Statistics: A First Course 5 th.
Chapter 2 CREATING AND USING FREQUENCY DISTRIBUTIONS.
DEVELOPMENT OF TOPOGRAPHIC ASYMMETRY: INSIGHTS FROM DATED CINDER CONES IN THE WESTERN UNITED STATES MCGUIRE ET ALL., 2014 ARTICLE SUMMARY BY KAITLYN HUGMEYER.
Chapter 2: Statistics of One Variable
Timescales of quartz crystallization estimated from glass inclusion faceting using 3D propagation phase-contrast x-ray tomography: examples from the Bishop.
Graphical Summary of Data Distribution Statistical View Point Histograms Skewness Kurtosis Other Descriptive Summary Measures Source:
1 DATA DESCRIPTION. 2 Units l Unit: entity we are studying, subject if human being l Each unit/subject has certain parameters, e.g., a student (subject)
Copyright © Cengage Learning. All rights reserved. 2 Descriptive Analysis and Presentation of Single-Variable Data.
Photogrammetric and LIDAR surveys on the Sciara del Fuoco to monitor the 2007 Stromboli eruption Bernardo E. 1, Coltelli M. 2, Marsella M. 1, Proietti.
Discussion of High Thermal Inertia Craters on Mars in the Isidis and Syrtis Major Regions Jordana Friedman Arizona State University.
GRAPHING AND RELATIONSHIPS. GRAPHING AND VARIABLES Identifying Variables A variable is any factor that might affect the behavior of an experimental setup.
Numerical simulations of optical properties of nonspherical dust aerosols using the T-matrix method Hyung-Jin Choi School.
So, what’s the “point” to all of this?….
TWELFTH EUROPEAN SPACE WEATHER WEEK (ESWW12) OOSTENDE, BELGIUM, NOVEMBER, 2015 Alessandro Settimi (1)*, Michael Pezzopane (1), Marco Pietrella.
NOTES #9 CREATING DOT PLOTS & READING FREQUENCY TABLES.
m/sampling_dist/index.html.
Statistics Josée L. Jarry, Ph.D., C.Psych. Introduction to Psychology Department of Psychology University of Toronto June 9, 2003.
CC8.EE.6 Use similar triangles to explain why the slope m is the same between any two distinct points on a non‐vertical line in the coordinate plane; derive.
Graphs with SPSS Aravinda Guntupalli. Bar charts  Bar Charts are used for graphical representation of Nominal and Ordinal data  Height of the bar is.
Graphs Another good way to organize this data is with a Graph. Graph – a diagram that shows a relationship between two sets of numbers. So do we have two.
Date of download: 6/7/2016 Copyright © 2016 SPIE. All rights reserved. Placement of the optical fiber for tone-on-light masking experiments. An ex vivo.
Date of download: 6/9/2016 Copyright © 2016 SPIE. All rights reserved. Schematic showing the spatially modulated NIR illumination system. Figure Legend:
Comparison of Temperature Data from HIPPO-1 Flights Using COSMIC and Microwave Temperature Profiler Kelly Schick 1,2,3 and Julie Haggerty 4 1 Monarch High.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Multiplicity, average transverse momentum and azimuthal anisotropy in U+U at √s NN = 200 GeV using AMPT model Md. Rihan Haque 1 Zi-Wei Lin 2 Bedangadas.
DISPLAYING DATA DIAGRAMMATICALLY. The Aim By the end of this lecture, the students will be aware of graphical representation of data and by using SPSS.
Types of variables Discrete VS Continuous Discrete Continuous
of Temperature in the San Francisco Bay Area
Assessment of data acquisition parameters, and analysis techniques for noise reduction in spinal cord fMRI data  R.L. Bosma, P.W. Stroman  Magnetic Resonance.
of Temperature in the San Francisco Bay Area
Research Statistics Objective: Students will acquire knowledge related to research Statistics in order to identify how they are used to develop research.
Assessment of Base-isolated CAP1400 Nuclear Island Design
Optimization of Elliptical SRF Cavities where
In this section you will:
How to describe a graph Otherwise called CUSS
Volume 101, Issue 4, Pages (August 2011)
R.A. Yingst, F.C. Chuang, D.C. Berman, S.C. Mest
Example Histogram c) Interpret the following histogram that captures the percentage of body-fat in a testgroup [4]:  
Platelet Adhesive Dynamics
6.1 Introduction to Chi-Square Space
E. Olofsen, J.W. Sleigh, A. Dahan  British Journal of Anaesthesia 
Effect of Expected Reward Magnitude on the Response of Neurons in the Dorsolateral Prefrontal Cortex of the Macaque  Matthew I. Leon, Michael N. Shadlen 
Raft Formation in Lipid Bilayers Coupled to Curvature
Direct Visualization Reveals Kinetics of Meiotic Chromosome Synapsis
Volume 82, Issue 4, Pages (April 2002)
Quantitative Data Who? Cans of cola. What? Weight (g) of contents.
Stephen V. David, Benjamin Y. Hayden, James A. Mazer, Jack L. Gallant 
Pier Francesco Palamara, Todd Lencz, Ariel Darvasi, Itsik Pe’er 
Section 13.6 The Normal Curve
Phase Equilibria in DOPC/DPPC-d62/Cholesterol Mixtures
Presentation transcript:

Results: Eccentricity Analysis Results: Physical Cones Cinder Cones with Complex Original Forms and Implications for Morphologic Dating – REU 2014 Ryan Till 1, Ramon Arrowsmith 2, Fabrizio Alfano 2, Amanda Clarke 2, Mattia de’Michieli Vitturi 3, Joanmarie Del Vecchio 4, Kristin Pearthree 5, James Muirhead 6, Brett Carr 2 1.The State University of New York at Buffalo, Buffalo, New York, USA. 2. Arizona State University, Tempe, Arizona, USA. 3. Instituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Italy. 4. Pomona College, Claremont, California, USA. 5. Oberlin College, Oberlin, Ohio, USA. 6. University of Idaho, Moscow, Idaho, U.S.A. Fig. 1: Base map of San Francisco Volcanic Field. Fig. 5: Flow of methods for producing slope histograms from target cones. Fig. 6: Flying the drone over SP Crater for SfM. Fig. 7: Flying the Balloon over SP Crater for SfM. Fig. 2: Diagram demonstrating functionality of slope histograms with theoretical cone. Numbers on aerial view of cone represent regions of similar slope, and correspond to numbered regions on histogram. Fig. 11: SP Crater; 10 m, 1 m (LAStools), 0.1 m (Points2Grid) Fig. 15: Vents ; 10 m, 0.6 m (Agisoft), 0.1 m (Points2Grid) Fig. 14: Southern Doney Craters; 10 m, 0.6 m (Agisoft), 0.1 m (Points2Grid) Fig. 12: Sunset Crater; 10 m, 1 m (LiDAR) Fig. 13: Crater 173; 10 m, 1 m (LAStools), 0.1 m (Points2Grid) Fig. 3: Crater 173, example of elongate cone. Fig. 4: Doney Craters, example of cone series. Table 1: MATLAB runs on synthetic cones. Results from purple rows not plotted. Fig. 16: Single cone with increasing morphologic age Fig. 19: Cone series at same age with decreasing spacing Fig. 18: Single elongate cone with increasing morphologic age Fig. 17: Single cone at 0 age, Increasing elongation Fig. 8: Histogram of results from cone eccentricity analysis in survey area of figure 1. Illustration depicting control of eccentricity on ellipse shape. Fig. 9 Fig. 10 Introduction Methods Results: Synthetic Cones Acknowledgements This research was funded by NSF grant EAR and EAR Results from analyses of slope histograms of the target cones follow results from the simulated cones. The numerical models indicate that slope abundances vary based on cone form, but cone form alone has little effect on styles and rates of degradation. Therefore, slope histograms provide a suitable method for morphologically dating cinder cones regardless of form. Remote sensing analyses reveal that all cones in the central San Francisco Volcanic Field exhibit eccentricity, which ranges from 0.17 to 0.98, with a mean eccentricity of Main axis orientations are NNW-SSE and NNE-SSW. These trends likely reflect the orientations of dikes feeding the volcanic cones (similar to fault strike), indicating a structural control on initial cone morphology which has important implications on morphologic dating using slope analysis. Diffusion modeling of volcanic cones shows that initial plan eccentricity has an effect on slope distributions. A unimodal slope histogram represents a single conical form, or a conical series with close spacing, whereas a bimodal slope histogram represents a single elongate cone, or a conical series with broad spacing. Increasing cone elongation results in a higher separation of the slope histogram modes, which does not occur in cone series regardless of the cone separation. Discussion & Conclusions Special thanks to Nancy Riggs of NAU and the other REU Participants IRIS 3D Quadcopter by 3drobotics Notice similar trends between fault and cone axis orientations P-value from 2 tailed type 3 t-test = Run DescriptionChanging VariableResults 1 cone, 700 m base, conical kt = 0 Conical model to act as control; decrease in max slope over time, change from exponential curve to more parabolic (Fig. 16) kt = 1 kt = 10 kt = 100 kt = m initial base, 1kt 1:1 axis ratio Test to see how ellipticity affects slope; addition of bi- modality at onset of elongation which moves to lower slopes as ratio increases (Fig. 17) 3:1 axis ratio 5:1 axis ratio 7:1 axis ratio 700 m initial base, 7:1 axis ratio kt = 0 Test to compare elongate cone with conical cone (run 1); bi- modal, higher slopes become less parabolic, lower slopes become more parabolic (Fig. 18) kt = 1 kt = 10 kt = 100 kt = m base, 4 cones, kt 900 m spacing Test to see if cone spacing in linear sequence affects slope; change from parabolic to more exponential (Fig. 19) 700 m spacing 500 m spacing 300 m spacing 100 m spacing 1 cone, 350 m base, conical kt = 0 Attempt to see if smaller cone erodes differently than larger cone; Same as run for 700m base cone when grid resolution is increased to match smaller size kt = 1 kt = 10 kt = 100 kt = m base, 300m center-to- center spacing (100m overlap), kt 1 cone Test to see if number of cones in linear sequence affects slope; generally stays the same except increase in frequency 2 cones 3 cones 4 cones Cinder Cone Orientations in the Central San Francisco Volcanic Field Elevation plots (zero values (dark blue) are masked), and slope histograms Fault Orientations in the Central San Francisco Volcanic Field 0 m 2 1 m 2 10 m m m 2 0 m 2 1 m 2 10 m m m m 2 Balloon and camera setup