M. Colin Marvin

All Publications

• Automated determination of transport and depositional environments in sand and sandstones. Proceedings of the National Academy of Sciences of the United States of America Hasson, M., Marvin, M. C., Lapôtre, M. G. 2024; 121 (40): e2407655121

  Abstract

  As sand moves across Earth's landscapes, the shapes of individual grains evolve, and microscopic textures accumulate on their surfaces. Because transport processes vary between environments, the shape and suite of microtextures etched on sand grains provide insights into their transport histories. For example, previous efforts to link microtextures to transport environments have demonstrated that they can provide important information about the depositional environments of rocks with few other indicators. However, such analyses rely on 1) subjective human description of microtextures, which can yield biased, error-prone results; 2) nonstandard lists of microtextures; and 3) relatively large sample sizes (>20 grains) to obtain reliable results, the manual documentation of which is extremely labor intensive. These drawbacks have hindered broad adoption of the technique. We address these limitations by developing a deep neural network model, SandAI, that classifies scanning electron microscope images of modern sand grains by transport environment with high accuracy. The SandAI model was developed using images of sand grains from modern environments around the globe. Training data encompass the four most common terrestrial environments: fluvial, eolian, glacial, and beach. We validate the model on quartz grains from modern sites unknown to it, and Jurassic-Pliocene sandstones of known depositional environments. Next, the model is applied to two samples of the Cryogenian Bråvika Member (of contested origin), yielding insights into periglacial systems associated with Snowball Earth. Our results demonstrate the robustness and versatility of the model in quickly and automatically constraining the transport histories recorded in individual grains of quartz sand.

  View details for DOI 10.1073/pnas.2407655121

  View details for PubMedID 39284038

• A depositional model for meandering rivers without land plants SEDIMENTOLOGY Hasson, M., Marvin, M., Gunn, A., Ielpi, A., Lapotre, M. A. 2023

  View details for DOI 10.1111/sed.13121

  View details for Web of Science ID 001041940500001

• Dune interactions record changes in boundary conditions GEOLOGY Marvin, M., Lapotre, M. A., Gunn, A., Day, M., Soto, A. 2023

  View details for DOI 10.1130/G51264.1

  View details for Web of Science ID 001046897700001

• A Physical Geography Lab's Online Transition: Student and Instructor Insights Using iGEO Video Games during the Pandemic JOURNAL OF GEOGRAPHY Heintzman, R., Brandi, A., Kelley, M., Marvin, M. 2023

  View details for DOI 10.1080/00221341.2023.2216705

  View details for Web of Science ID 000999580100001

• Regional piedmont incision during base-level rise in the northeastern Sonoran Desert, Arizona, USA PHYSICAL GEOGRAPHY Gonzalez, C., Kelley, M., Marvin, M., Lopez-Castaneda, N., Dorn, R. I., Schmeeckle, M. 2022; 43 (1): 67-97

  View details for DOI 10.1080/02723646.2021.1934964

  View details for Web of Science ID 000679261200001

• Assessing Geomorphic Change in Restored Coastal Dune Ecosystems Using a Multi-Platform Aerial Approach REMOTE SENSING Hilgendorf, Z., Marvin, M., Turner, C. M., Walker, I. J. 2021; 13 (3)

  View details for DOI 10.3390/rs13030354

  View details for Web of Science ID 000615481400001

• The Fieldwork of Shared Experiences The Geographical Bulletin Shandonay, K., Moll, H., Marvin, M. C., López-Castañeda, N., Kelley, M. M., Hilgendorf, Z., Heintzman, R., Dorn, R. I. 2021; 62 (2): 82-88