DOI: 10.5066/p1cwhewt
title: Rock mass quality and structural geology observations in Glacier Bay National Park and Preserve, Alaska from the summers of 2021 and 2022

description: Subaerial and submarine landslides adjacent to, and within, Glacier Bay, Glacier Bay National Park and Preserve (GBNPP), Alaska pose a threat to the public because of their potential to generate ocean waves that could impact marine activities. Although historical records of tsunamis generated by landslides in GBNPP are uncommon, there are records that document the destructive power of at least three landslide-generated tsunamis in Lituya Bay on the west side of the park (Miller, 1960; Fritz, 2001).  Additionally, the threat of a rapid failure of a slow-moving subaerial bedrock landslide in Tidal Inlet off the West Arm of Glacier Bay has drawn attention because of its tsunamigenic potential that could affect boat and cruise-ship traffic in the West Arm (Wieczorek and others, 2007). The largest historical subaerial landslide in GBNPP was the June 28, 2016 Lamplugh rock avalanche with a volume of about 70 M m3 (Bessette-Kirton and others, 2018; Dufresne and others, 2019). This rock avalanche did not enter the water of Glacier Bay, but instead was deposited on the Lamplugh Glacier.
 
As part of a broad effort by the U.S. Geological Survey and National Park Service to evaluate landslide and tsunamigenic potential throughout GBNPP (for example, Coe and others, 2018; Coe and others, 2019; Avdievitch and Coe, 2022; Kim and others, 2022; Hults and others, 2023), we assessed rock mass quality and collected structural geology data in a large part of Glacier Bay National Park and Preserve in the summers of 2021 and 2022. The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes.
 
Our fieldwork was primarily boat-based and was therefore conducted at sites along and near the coastline. A small number of sites were accessed by hiking. At each field site, we made our measurements at rock outcrops that were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2.
 
We visited a total of 57 sites in the field. Sites occurred within a variety of geologic units (Brew, 2008; Wilson and others, 2015; National Park Service, 2020). Specific geologic units mentioned in our data files are from a geologic map compilation by National Park Service (2020).  Of the 57 sites, we collected rock mass quality data and structural data at 54 sites, and only strike and dip of bedding or fractures at 3 sites. At each of the 54 sites, we collected data that we later used to classify rock mass quality according to four commonly used classiﬁcation schemes; Rock Mass Quality (Q, for example, Barton and others, 1974, Coe and others, 2005); Rock Mass Rating (RMR, for example, Bieniawski, 1989); Slope Mass Rating (SMR, for example, Romana, 1995, Moore and others, 2009) and Geologic Strength Index (GSI, for example, Marinos and Hoek, 2000, Marinos and others, 2005). We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989, Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type N hammer (see RatingsReadMe.pdf for details). Schmidt hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For non-limey sedimentary and metasedimentary rocks, rebound values from the Type N Schmidt hammer were converted to UCS by first converting Type N rebound values to Type L rebound values using equation 25 in Asteris and others (2021), then using these Type L values in the equation shown in Table 3 and Figure 3 of Morales and others (2004). For intrusive igneous rocks, marble, and limestone, UCS values were calculated using Type N rebound values in equation 2 of Katz and others (2000). For extrusive igneous rocks, UCS values were calculated using Type L rebound values in the equation for igneous rocks listed in Table 2 of Karaman and Keismal (2015). Additionally, we collected strikes and dips of any observed bedding, fractures, and cleavage. 
 
All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of ﬁlling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. A file of a blank field data collection sheet (FieldDataCollectionSheet) is included in this data release. Numerical ratings for each of these factors are assigned based on the correlation of ﬁeld measurements and observations with descriptive rankings. The rankings used for Q, RMR, SMR, and GSI classification schemes are shown in Table 1, Table 2, Table 3, Figure 1, and Figure 2 (available in a zip file named FiguresandTables.zip). Additional details regarding descriptive rankings and numerical ratings not shown in the tables and figures are given in the RatingsReadMe.pdf.
 
All field measurements, numerical ranking values, and calculated Q, RMR, SMR, GSI, and RQD values are given in the RMQMeasurements_Ratings_Values20212022 file (.csv and .xlsx). Site names beginning with “JAC”, followed by numbers, are locations where both rock mass quality and structural data were collected. Site names beginning with “JACSD” are locations where only the strike and dip of bedding was measured. Question marks in the data files indicate a lack of certainty in field observations. Abbreviations of rating parameters (for example, R4e, Jw, etc.) for the RMR, SMR, and Q classification systems used in column headings are defined in more detail in Tables 1 and 2. All structural measurements are given in the StructuralData20212022 file (.csv and .xlsx). The planar and toppling calculations used for determining SMR values are given in the SMRCalculationsWorksheet20212022 file (.csv and .xlsx). Final Q, RMR, SMR, GSI, and RQD values for each site are presented in a separate file (FinalRockStength_QualityValues20212022, .csv and .xlsx). All rock mass quality values are positively correlated with rock quality. That is, as Q, RMR, SMR, GSI, and RQD values increase, rock quality increases.

Photos from each site are included in a separate folder (20212022PhotosbySiteName), organized by the individual site names and the names of the photographers. A Google Earth file, GBSiteNameCoords20212022.kml, showing site locations, site names, and geographic coordinates is also included.
 
We thank National Park Service research vessel captain Justin Smith for his expert guidance and patience during ﬁeldwork in the summers of 2021 and 2022.

Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

References

Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.-T., Kardani, N., and Armaghani, D.J., 2021, Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks: Transportation Geotechnics, v. 29, p. 100588, https://doi.org/10.1016/j.trgeo.2021.100588

Avdievitch, N.N. and Coe, J.A., 2022, Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska: Frontiers in Earth Science, v. 10, 821188,  https://doi.org/10.3389/feart.2022.821188

Barton, N., Lien, R., and Lunde, J., 1974, Engineering classiﬁcation of rock masses for the design of tunnel support: Rock Mechanics, v. 6, p. 189-236. https://doi.org/10.1007/BF01239496

Bessette-Kirton, E.K., Coe, J.A., and Zhou, W., 2018, Using stereo satellite imagery to account for ablation, entrainment, and compaction in volume calculations for rock avalanches on glaciers: Application to the 2016 Lamplugh rock avalanche in Glacier Bay National Park, Alaska: Journal of Geophysical Research - Earth Surface, v. 123, no. 4, p. 622-641.  https://doi.org/10.1002/2017JF004512
 
Bieniawski, Z.T., 1989, Engineering rock mass classifications a complete manual for engineers and geologist in mining, civil, and petroleum engineering: John Wiley & Sons, New York, 251 p.
 
Brew, D. A., 2008, Delineation of Landform and Lithologic Units for Ecological Landtype-Association Analysis in Glacier Bay National Park, Southeast Alaska. U.S. Geological Survey Scientiﬁc Investigations Report 2008-5183. https://pubs.usgs.gov/sir/2008/5183/

Coe, J.A., Harp, E.L., Tarr, A.C., and Michael, J.A., 2005, Rock-fall hazard assessment of Little Mill campground, American Fork Canyon, Uinta National Forest, Utah: U.S. Geological Survey Open File Report 2005-1229, 48 p., two 1:3000-scale plates. http://pubs.usgs.gov/of/2005/1229/     
 
Coe, J.A., Bessette-Kirton, E.K., and Geertsema, M., 2018, Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery: Landslides, v. 15, no. 3, p. 393-407, https://link.springer.com/article/10.1007/s10346-017-0879-7 
 
Coe, J.A., Schmitt, R.G., and Bessette-Kirton, E.K., 2019, An initial assessment of areas where landslides could enter the West Arm of Glacier Bay, Alaska and implications for tsunami hazards: Alaska Park Science, v. 18, no. 1, p. 26-37, https://www.nps.gov/articles/aps-18-1-4.htm 

Deere, D.U., and Deere, D.W., 1989, Rock Quality Designation (RQD) after twenty years: Contract Report GL-89-1, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss., 25 p.
  
Dufresne, A., Wolken, G., Hilbert, C., Bessette-Kirton, E.K., Coe, J.A., Geertsema, M., and Ekstrom, G., 2019, The 2016 Lamplugh landslide: Alaska: deposit structures and emplacement dynamics: Landslides, v. 16, no. 12, p. 2301-2319, https://doi.org/10.1007/s10346-019-01225-4 

Fritz, H.M., Hager, W.H., and Minor, H-E., 2001, Lituya Bay Case: Rockslide Impact and Wave Run-Up: Science of Tsunami Hazards, v. 19, no. 1, p. 3–22.
 
Hults, C.P., Coe, J.A., and Avdievitch, N.N., 2023, Fractures, Scarps, Faults, and Landslides Mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska: National Park Service Natural Resource Report. NPS/GLBA/NRR—2023/2605. National Park Service. Fort Collins, Colorado. https://doi.org/10.36967/2300706
 
Karaman, K. and Keismal, A., 2015, A comparative study of Schmidt hammer test methods for estimating the uniaxial compressive strength of rocks: Bulletin of Engineering Geology and the Environment, v. 74, p. 507–520. https://doi.org/10.1007/s10064-014-0617-5  

Katz, O., Reches, Z., and Roegiers, J.-C., 2000, Evaluation of mechanical rock properties using a Schmidt hammer: International Journal of Rock Mechanics and Mining Sciences, v. 37, p. 723-728. https://doi.org/10.1016/S1365-1609(00)00004-6 
 
Kim, J., Coe, J.A., Lu, Z., Avdievitch, N.N., and Hults, C.P., 2022, Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia: Remote Sensing of Environment, v. 281, 113231, https://doi.org/10.1016/j.rse.2022.113231

Marinos, P., and Hoek, E., 2000, GSI: a geologically friendly tool for rock mass strength estimation. In: Proceedings of the GeoEng2000 at the international conference on geotechnical and geological engineering, Melbourne, Technomic publishers, Lancaster, pp. 1422–1446.
 
Marinos, V., Marinos, P., and Hoek, E., 2005, The geological strength index: applications and limitations: Bulletin of Engineering Geology and the Environment,  v. 64, p. 55-65 https://doi.org/10.1007/s10064-004-0270-5
 
Miller, D. J., 1960, Giant waves in Lituya Bay Alaska: U.S. Geological Survey Professional Paper 354-C, p. 51–86. https://doi.org/10.3133/pp354C

Moore, J.R., Sanders, J.W., Dietrich, W.E., and Glaser, S.D., 2009, Influence of rock mass strength on the erosion rate of alpine cliffs: Earth Surface Processes and Landforms, v. 34, p. 1339-1352, https://doi.org/10.1002/esp.1821
 
Morales, T., Uribe-Etxebarria, G., Uriarte, J.A., and Fernández de Valderrama, I., 2004, Geomechanical characterisation of rock masses in Alpine regions: the Basque Arc (Basque-Cantabrian basin, Northern Spain): Engineering Geology, v. 71, p. 343–362, https://doi.org/10.1016/S0013-7952(03)00160-1.

National Park Service, 2020, Digital Geologic-GIS Map of Glacier Bay National Park and Preserve and Vicinity, Alaska (NPS, GRD, GRI, GLBA, GLBA digital map) adapted from a U.S. Geological Survey Scientific Investigations Map by Wilson, Hults, Mull and Karl (2015). National Park Service (NPS) Geologic Resources Inventory (GRI) program, https://irma.nps.gov/DataStore/Reference/Profile/2278825  

Palmström, A., 1982, The volumetric joint count—A useful and simple measure of the degree of jointing: Proceedings of the 4th Congress of the International Association of Engineering Geologists, New Delhi, India, p. 221–228.
 
Romana, M., 1995, The geomechanical classification SMR for slope correction: Proceedings of the Eighth International Congress on Rock Mechanics, Tokyo, Japan, p. 1085–1092.
  
Wieczorek G.F., Geist E.L., Motyka R.J., and Jakob M., 2007, Hazard assessment of thetidal inlet landslide and potential subsequent tsunami, Glacier Bay National Park, Alaska: Landslides, v. 4, p. 205–215. https://doi.org/10.1007/s10346-007-0084-1

Wilson, F.H., Hults, C.P., Mull, C.G, and Karl, S.M, 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, pamphlet 196 p., 2 sheets, scale 1:1,584,000, http://dx.doi.org/10.3133/sim3340.