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Neuro-Fuzzy Classification of Felsic Lava Geomorphology at Alarcon Rise, Mexico Christina Hefron Maschmeyer University of South Carolina

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Neuro-Fuzzy Classification of Felsic Lava Geomorphology at Alarcon Rise, Mexico Christina Hefron Maschmeyer University of South Carolina University of South Carolina Scholar Commons Theses and Dissertations 2016 Neuro-Fuzzy Classification of Felsic Lava Geomorphology at Alarcon Rise, Mexico Christina Hefron Maschmeyer University of South Carolina Follow this and additional works at: https://scholarcommons.sc.edu/etd Part of the Geology Commons Recommended Citation Maschmeyer, C. H.(2016). Neuro-Fuzzy Classification of Felsic Lava Geomorphology at Alarcon Rise, Mexico. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/3566 This Open Access Thesis is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. NEURO-FUZZY CLASSIFICATION OF FELSIC LAVA GEOMORPHOLOGY AT ALARCON RISE, MEXICO by Christina Hefron Maschmeyer Bachelor of Science College of Charleston, 2014 Bachelor of Arts College of Charleston, 2014 Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Science in Geological Sciences College of Arts and Sciences University of South Carolina 2016 Accepted by: Scott White, Director of Thesis Michael Bizimis, Reader Brian Dreyer, Reader Lacy Ford, Senior Vice Provost and Dean of Graduate Studies © Copyright by Christina Hefron Maschmeyer, 2016 All Rights Reserved. ii DEDICATION This thesis is dedicated to Dr. Jim Carew for making me go to graduate school. iii ACKNOWLEDGEMENTS Data for this study were collected during cruises in 2012 aboard the R/V Zephyr and R/V Western Flyer and during 2015 on the R/V Rachel Carson and R/V Western Flyer from the Monterey Bay Aquarium Research Institute. I want to thank the captains, crews, ROV pilots and science parties for their work during these expeditions. Special thanks to Brian Dreyer, Dave Clague, and Jenny Paduan at the Monterey Bay Aquarium Research Institute for sharing their data, insights, and knowledge with me during this project. Support for this project was provided by the College of Arts and Sciences at the University of South Carolina. Special thanks to my primary advisor, Scott White, for his support and guidance during this project. I also want to acknowledge my committee members, Michael Bizimis and Brian Dreyer for their insight to my work. Thank you to my family, friends and mentors for their love and encouragement throughout my education and life. Finally, I want to thank my husband, Josh Maschmeyer, for his continual love, insight, and laughter as I pursued this degree. Thank you for being my best friend. iv ABSTRACT The Alarcon Rise is the only submarine oceanic spreading ridge setting where rhyolitic lavas have been found. This intermediate-rate spreading ridge provides a unique natural laboratory for studying the geomorphology of felsic submarine lava flows at oceanic spreading ridges. Seafloor observations of felsic lava indicate the flow morphology differs from typical submarine basaltic lava at the few other oceanic spreading ridges where differentiated compositions have been recorded. Morphologic variation between mafic and felsic lava flows, especially rhyolites, was also observed at Alarcon Rise. The Monterey Bay Aquarium Research Institute conducted mapping surveys with autonomous underwater vehicle D Allan B. in 2012 and 2015. The 1 m lateral resolution bathymetry produced from these surveys allowed sampling expeditions with the remotely operated vehicle Doc Ricketts in 2012 and 2015. We recovered all felsic lava samples along a ridge at the heavily-faulted north end of Alarcon, just south of the Pescadero Transform Fault. The ridge included a steep sloping, sub-rectangular rhyolitic complex. Angular, blocky spires at this complex are spaced ~10 m apart, appearing jagged in the 1 m resolution bathymetry. To determine if morphology can be used to identify compositional variation in lava, we produced a semi-automated pixel-based classification that identifies geomorphic characteristics we believe to be indicative of felsic lava flows. We constructed an v adaptive-neuro fuzzy inference system to distinguish between the jagged, rough lava flows produced by felsic lavas and smooth basaltic lava flows. To capture the steep sloping high-silica dome features, we derived local max slope over a 3 m distance from the 1 m resolution bathymetry. We also calculated bathymetric position index at a 0.5 km radius to distinguish the surface roughness in the felsic region from smoother basaltic flows at Alarcon Rise. Our classification is the first attempt at automating recognition of compositional variation of lava erupted at oceanic ridges. vi TABLE OF CONTENTS DEDICATION ....................................................................................................................... iii ACKNOWLEDGEMENTS ........................................................................................................ iv ABSTRACT ............................................................................................................................v LIST OF TABLES .................................................................................................................. ix LIST OF FIGURES ...................................................................................................................x CHAPTER 1 INTRODUCTION ...................................................................................................1 1.1 BACKGROUND .......................................................................................................1 1.2 GEOLOGIC SETTING ...............................................................................................2 1.3 RIDGE DESCRIPTION ..............................................................................................4 1.4 SUBMARINE LAVA MORPHOLOGY .........................................................................4 CHAPTER 2 DATA COLLECTION ............................................................................................9 2.1 SEAFLOOR MAPPING EXPEDITIONS ........................................................................9 2.2 IGNEOUS ROCK SAMPLE COLLECTION ...................................................................9 CHAPTER 3 DATA PROCESSING ...........................................................................................11 3.1 VISUAL INSPECTION ............................................................................................11 3.2 BATHYMETRIC PROCESSING ................................................................................12 3.3 TRAINING AND TESTING DATASETS .....................................................................14 CHAPTER 4 CLASSIFICATION METHODOLOGY .....................................................................17 CHAPTER 5 RESULTS ...........................................................................................................21 vii 5.1 ACCURACY ASSESSMENT ....................................................................................24 CHAPTER 6 DISCUSSION ......................................................................................................26 6.1 GEOMORPHIC IMPLICATIONS ...............................................................................26 6.2 PIXEL-BASED VERSUS OBJECT-BASED CLASSIFICATION .....................................28 6.3 FUTURE WORK ....................................................................................................28 CHAPTER 7 CONCLUSIONS ..................................................................................................35 REFERENCES .......................................................................................................................37 viii LIST OF TABLES Table 3.1 Training and testing datasets from 2012 and 2015 samples ..............................16 Table 4.1 Classification training results .............................................................................20 Table 5.1 Error matrix........................................................................................................25 Table 5.2 Accuracy matrix .................................................................................................25 ix LIST OF FIGURES Figure 1.1 Tectonic setting of the Alarcon Rise ..................................................................3 Figure 1.2 Bathymetry of the Alarcon Rise and study area locations .................................5 Figure 1.3 Submarine lava morphology for basaltic lava at Alarcon Rise ..........................7 Figure 1.4 Submarine lava morphologies of felsic lava at Alarcon .....................................8 Figure 3.1 BPI scale analysis .............................................................................................13 Figure 3.2 Side-scan sonar backscatter shadows and holidays ..........................................15 Figure 4.1 ANFIS workflow ..............................................................................................19 Figure 5.1 Determining mafic and felsic classes ...............................................................22 Figure 5.2 Classified lava flow at the Alarcon Rise ..........................................................23 Figure 6.1 Relative abundance of classified ground referenced samples ..........................27 Figure 6.2 Pixel-based classification of high-silica dome and basaltic mound .................30 Figure 6.3 Slope profile locations for felsic lava ...............................................................31 Figure 6.4 Locations of
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