Earthquake triggered ground failure associated with the M7.1 2018 southcentral Alaska Earthquake (ver. 2.0, December 2023)

Dates

Publication Date: 2022-06-03
Time Period: 2018-11-30
Last Revision: 2023-12-29

Citation

Martinez, S.N., Allstadt, K.E., Thompson, E.M., Schaefer, L.N., Ellison, S., and Baxstrom, K., 2022, Earthquake triggered ground failure associated with the M7.1 2018 southcentral Alaska Earthquake (ver. 2.0, December 2023), https://doi.org/10.5066/P9SHA9M3.

Summary

The November 30, 2018, magnitude (Mw) 7.1 Anchorage, Alaska earthquake triggered substantial ground failure throughout Anchorage and surrounding areas (Grant and others, 2020; Jibson and others, 2020). The earthquake was an intraslab event with a focal depth of about 47 km and an epicenter about 16 km north of the city of Anchorage. Peak ground accelerations reached ∼30% g. Despite the relatively low severity of most of the ground failure occurrences, geotechnical damage to buildings and structures was widespread (Franke and others, 2019). Here, we present an inventory of the earthquake-triggered ground failure based on information compiled from numerous data sources. The inventory is comprised of 886 points that each correspond to [...]

Summary

The November 30, 2018, magnitude (Mw) 7.1 Anchorage, Alaska earthquake triggered substantial ground failure throughout Anchorage and surrounding areas (Grant and others, 2020; Jibson and others, 2020). The earthquake was an intraslab event with a focal depth of about 47 km and an epicenter about 16 km north of the city of Anchorage. Peak ground accelerations reached ∼30% g. Despite the relatively low severity of most of the ground failure occurrences, geotechnical damage to buildings and structures was widespread (Franke and others, 2019).

Here, we present an inventory of the earthquake-triggered ground failure based on information compiled from numerous data sources. The inventory is comprised of 886 points that each correspond to the location of earthquake triggered ground failure associated with the 2018 event (Figure 1). For each instance of ground failure, the direct observation is reported and, where possible, is categorized based on the process interpreted to have caused it. For example, an observation of a rock fall would be categorized as a landslide. Similarly, a sand boil would be categorized as evidence of liquefaction. Where possible, the features were more precisely mapped with an additional polygon (n=179) and or line feature (n=32). To determine the existence and location of ground failure features, we relied on a variety of mapping methods and data sources. As such, the certainty in the mapping of each feature within the inventory varies (Figure 2). Details on how uncertainty is reported as well as the methods and data sources used are discussed in the following sections.

Figure 1: Ground failure inventory for the 2018 Southcentral, AK M 7.1 earthquake. Inventory is overlain on the USGS ShakeMap (Atlas v4) associated with the earthquake event.

Data and Methods

The inventory was compiled using a variety of different sources and methods with varying levels of success (Martinez and others, 2021). To convey this, the overall mapping certainty for each feature within the inventory is determined by assigning a semi-quantitative grade for categories related to the certainty of the existence of the feature, the positional accuracy of the mapped feature, and the mapped delineation quality. Those categories are denoted as presence, location, and delineation certainty in the inventory attribute table. Confidence grade definitions, and all other inventory attributes, are described in Table 1.

Table 1: Attributes recorded for each ground failure inventory data point. Modified landslide classification used in this study is shown here as well as the semi-quantitative mapping confidence scheme used to assign a mapping certainty for each feature within the inventory.

Ground Failure Inventory Attributes
UID	Unique ID Number
Ground Failure Process	Landslide
	Liquefaction
	Liquefaction (Ambiguous)
	Unknown
Ground Failure Feature	Landslide Process Features			Liquefaction Process Features
	Soil Slump			Sand Boils
	Soil Slide			Settlement
	Soil Fall			Lateral Spread
	Rock Slide
	Rock Fall
	Rock Avalanche
	Debris Flow
	Debris Avalanche
	Snow Avalanche
	Surface Deformation
	Both Processes
	Rapid Soil Flow
	Ground Cracks
	Unknown
Condition	New
	Reactivated
Confidence	Category	Grade	Justification
	Presence	A	There is direct evidence of the feature (e.g., field observations) to confirm its existence and attribute it to the earthquake.
		B	There is some evidence to suggest the feature exists and/or can attributed to earthquake.
		C	It is uncertain if the feature exists and/or can be attributed to earthquake (typically assigned when a feature is mapped using only remotely sensed data with no secondary evidence to confirm).
	Location	A	Can be exactly located within 10 meters.
		B	Can be located within 10 meters to 1 kilometer.
		C	Can be located within 1 to 3 kilometers.
		D	Can be located to a general region.
	Delineation	A	Can exactly draw outline of feature.
		B	Can draw general shape of feature within tens of meters
		C	Can define boundary of feature.
		D	Cannot delineate feature
Mapping Method	Method(s) used to identify feature
Image Link	Associated Image File Name
Notes	Any pertinent notes related to the specific feature.

As previously mentioned, we relied on a variety of methods and data sources to develop the ground failure inventory. We primarily relied on field observation data and information shared from federal, state, and local agencies. These data sources were particularly helpful where remotely sensed data were not available or insufficient for mapping ground failure. Here, we summarize how remotely sensed data were used and in which ways they were advantageous or insufficient. High resolution digital elevation models (DEMs) are typically used to develop high-quality ground failure inventories. We began our ground failure mapping efforts by differencing high resolution pre- and post-earthquake lidar derived DEMs from the Alaska Department of Natural Resources (AK DNR) Division of Geological and Geophysical Surveys (DGGS) elevation portal (DGGS Elevation Portal (alaska.gov)). The pre- and post-earthquake DEMs used to identify and map the extent of ground failure in this study had an areal overlap of approximately 321 km². These data cover only a small portion of the area that experienced moderate to severe shaking during the earthquake (60,690 km²) as estimated from the USGS ShakeMap.

In regions lacking sufficient DEM coverage we used Normalized Difference Vegetation Index (NDVI) differencing to identify and map ground failure. NDVI maps are derived from multispectral imagery and display the relative health or existence of vegetation in a landscape. For this study, NDVI maps were derived using imagery from the European Space Agency’s Sentinel-2 (S2) Multispectral Instrument (MSI) (Drusch and others, 2012). Ground failure that is severe enough to disturb vegetation can be mapped by differencing pre- and post- earthquake NDVI maps. However, efforts to create NDVI maps of the landscape following the earthquake were hindered due to snowfall that began to accumulate shortly after the earthquake (2 December 2018). To overcome this limitation, we used Google Earth Engine (Gorelick and others, 2017) to create composites of multispectral images of the summers (May 1 to July 30) before and after the earthquake instead. The image composites were then used to create NDVI maps that were differenced to determine where there were any significant changes in vegetation that could potentially correspond to ground failure. While the resolution of the data used for the NDVI differencing (10-m) is lower than that used for DEM differencing (1-m) it is advantageous as it is available globally, and thus, for the entire earthquake affected region of Southcentral, Alaska.

Despite the advantage of globally available NDVI data, the method is still limited in its ability to detect smaller and more subtle vegetation changes. Our comparisons may also include some landslides that were not triggered by the earthquake but happened in the time spanned by the images. When possible, satellite and aerial imagery alone were also used to map ground failure features. However, in most instances the imagery alone was inadequate for mapping because many features were not visible in available satellite imagery. In addition, the ground surface was obscured in satellite imagery as a result of snow fall that occurred shortly after the earthquake. Some features, such as large road cracks, were not visible following snowmelt as they were quickly repaired by The Alaska Department of Transportation and Public Facilities. Other ephemeral features, such as sand boils or cracks on tidal flats, were no longer visible in satellite imagery after snowmelt and several tidal cycles.

Remotely sensed data alone were found to be insufficient for mapping ground failure associated with this event (refer to Martinez and others, 2021). In addition to the limitations of remotely sensed data, the aforementioned snowfall that occurred immediately after the earthquake obscured large swaths of land and subsequently hindered efforts to gather ground failure observations while in the field. Thus, in addition to the remotely sensed data, the inventory was supplemented with field observations gathered by U.S. Geological Survey scientists shortly after the event (Grant and others, 2020), information on earthquake damage supplied from federal (FEMA Region X), state (The Alaska Department of Transportation and Public Facilities) and local agencies (Municipality of Anchorage, Kenai Peninsula Borough, Matanuska-Susitna Borough Department of Emergency Services, 673 Civil Engineers Squadron at the Joint Base Elmendorf-Richardson, Anchorage Fire Station 12) as well as compiled social media information and data from literature on the 2018 earthquake event.

Using the data mentioned above, we inferred which ground failure process to assign to each ground failure location based on contextual information such as the environment and presence of evidence that suggests ground failure may have occurred. We assigned the “landslide” process to locations where there are indications of the downhill movement of material. A modified classification scheme based on Keefer (1984) was used to classify the observed landslide features within our inventory (refer to Table 1 for landslide classification scheme). Evidence that liquefaction processes may have occurred include the presence of ground cracks, sand boils, lateral spreads, the settlement of structures, and rapid soil flows. In cases where direct evidence of liquefaction is lacking (i.e., sand boils), the classification is based on proximity to identified sand boils. Liquefaction was interpreted to be the process for observed lateral spreading features parallel to water bodies. Low-lying features on flat slopes (e.g., settlement alone) for which there is no direct evidence to suggest that liquefaction occurred are given the label “Liquefaction (Ambiguous)” to reflect the uncertain nature in their process designation.

Despite the use of supplemental data, the inventory is still considered incomplete, with varying levels of completeness throughout the region. Adverse environmental conditions including snow fall, limited daylight hours, a large earthquake-affected area, and accessibility constraints limited the extent to which both field and remotely sensed observations could be reliably used to compile a ground failure inventory. To communicate this variability in inventory completeness and quality, Figure 2 displays the region, highlighted in white, in which direct observations of ground failure were made in addition to where high-resolution pre- and post-earthquake lidar data are available. This higher-confidence mapping region displays where the inventory is considered complete and high quality due to the nature of the data (i.e., direct observations, high-quality lidar) used for mapping in this region. To minimize human error, two co-authors carefully reviewed the inventory for inconsistencies and errors.

Figure 2: Regions in white show areas of high confidence in inventory data. Refer to Grant and others (2020) for location of ground and flight tracks. Direct ground and aerial observations, along with high-resolution (1-m) pre- and post-earthquake lidar for a portion of the area, all contribute to the higher confidence within this region. Individual points within the inventory have been assigned semi-quantitative certainty values (Table 1). Shown here is the presence certainty which corresponds to the certainty the mapper has that the feature exists and that it can be confidently attributed to the earthquake.

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

References Cited

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P., 2012, Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services: Remote Sensing of Environment, v. 120, p. 25–36. https://doi.org/10.1016/j.rse.2011.11.026

Franke, K.W., Koehler, R., Beyzaei, C.Z., Cabas, A., Christie, S., Dickenson, S., Pierce, I., Stuedlein, A., and Yang, Z., 2019, Geotechnical Engineering Reconnaissance of the 30 November 2018 Mw 7.1 Anchorage, Alaska Earthquake, Version 2.0: Geotechnical Extreme Events Reconnaissance Association.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone: Remote Sensing of Environment, v. 202, p. 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Grant, A.R., Jibson, R.W., Witter, R.C., Allstadt, K., Thompson, E., Bender, A.M and Schmitt, R.G., 2020, Field reconnaissance of ground failure triggered by shaking during the 2018 M7.1 Anchorage, Alaska, earthquake: U.S. Geological Survey data release, https://doi.org/10.5066/P99ONUNM

Jibson, R.W., R. Grant, A.R., Witter, R.C., Allstadt, K.E., Thompson, E.M., and Bender, A.M., 2020, Ground Failure from the Anchorage, Alaska, Earthquake of 30 November 2018: Seismological Research Letters, v. 91, no. 1, p. 19–32. https://doi.org/10.1785/0220190187

Keefer, D.K., 1984, Landslides caused by earthquakes. Geological Society of America Bulletin, 95(4), pp.406-421.

Martinez, S.N., Schaefer, L.N., Allstadt, K.E., and Thompson, E.M., 2021, Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake: Frontiers in Earth Science, v. 9. https://doi.org/10.3389/feart.2021.673137

Contacts

Point of Contact :: Sabrina N Martinez
Originator :: Sabrina N Martinez, Kate E Allstadt, Eric M Thompson, Lauren N Schaefer, Sonia M Ellison, Kelli W Baxstrom
Metadata Contact :: Sabrina N Martinez
Publisher :: U.S. Geological Survey
Distributor :: U.S. Geological Survey - ScienceBase
SDC Data Owner :: Geologic Hazards Science Center
USGS Mission Area :: Natural Hazards

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v2_AK18_GF_Summary_Metadata.xml Original FGDC Metadata	View	52.58 KB	application/fgdc+xml
v2_Inventory_Photos.zip		432.79 MB	application/zip
v2_shapefiles.zip		505.48 KB	application/zip
v2_summary_of_changes.txt		19.81 KB	text/plain
v2_Table1.csv		1.56 KB	text/csv
Version History.txt		1.79 KB	text/plain
v1_AK18_Files.zip		459.15 MB	application/zip
v2_Figure1.jpg		9.01 MB	image/jpeg
v2_Figure2.jpg		10.13 MB	image/jpeg

Related External Resources

Type: Web Link

The Division of Geological and Geophysical Survey (DGGS) Elevation Portal	https://elevation.alaska.gov/
Matanuska-Susitna Borough Aerial Imagery	https://data1-msb.opendata.arcgis.com/pages/msb-aerial-imagery
Earthquake Engineering Research Institute's (EERI) Earthquake Clearinghouse	http://www.learningfromearthquakes.org/2018-11-30-anchorage-alaska/index.php?option=com_content&view=article&id=55

Type: Web Application

MAXAR Global Enhanced GEOINT Delivery (G-EGD) Portal	https://evwhs.digitalglobe.com/myDigitalGlobe/login

Type: ArcGIS Web Services

ArcGIS - Ground Failure Inventory for the M 7.1 2018 Southcentral, Alaska Earthquake	https://doi.org/10.5066/P9457YU7

Type: Citation

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P., 2012, Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services: Remote Sensing of Environment, v. 120, p. 25–36. https://doi.org/10.1016/j.rse.2011.11.026	https://doi.org/10.1016/j.rse.2011.11.026
Franke, K.W., Koehler, R., Beyzaei, C.Z., Cabas, A., Christie, S., Dickenson, S., Pierce, I., Stuedlein, A., and Yang, Z., 2019, Geotechnical Engineering Reconnaissance of the 30 November 2018 Mw 7.1 Anchorage, Alaska Earthquake, Version 2.0: Geotechnical Extreme Events Reconnaissance Association.	https://geerassociation.org/components/com_geer_reports/geerfiles/Version2.1_Alaska.pdf
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone: Remote Sensing of Environment, v. 202, p. 18–27. https://doi.org/10.1016/j.rse.2017.06.031	https://doi.org/10.1016/j.rse.2017.06.031
Grant, A.R., Jibson, R.W., Witter, R.C., Allstadt, K., Thompson, E., Bender, A.M and Schmitt, R.G., 2020, Field reconnaissance of ground failure triggered by shaking during the 2018 M7.1 Anchorage, Alaska, earthquake: U.S. Geological Survey data release, https://doi.org/10.5066/P99ONUNM	https://doi.org/10.5066/P99ONUNM
Jibson, R.W., R. Grant, A.R., Witter, R.C., Allstadt, K.E., Thompson, E.M., and Bender, A.M., 2020, Ground Failure from the Anchorage, Alaska, Earthquake of 30 November 2018: Seismological Research Letters, v. 91, no. 1, p. 19–32. https://doi.org/10.1785/0220190187	https://doi.org/10.1785/0220190187
Keefer, D.K., 1984, Landslides caused by earthquakes. Geological Society of America Bulletin, 95(4), pp.406-421.	https://pubs.usgs.gov/publication/70014049
Martinez, S.N., Schaefer, L.N., Allstadt, K.E., and Thompson, E.M., 2021, Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake: Frontiers in Earth Science, v. 9.	https://doi.org/10.3389/feart.2021.673137

Purpose

The data were collected to create an inventory of the ground failure associated with the 2018 Southcentral, Alaska earthquake.

Earthquake triggered ground failure associated with the M7.1 2018 southcentral Alaska Earthquake (ver. 2.0, December 2023)

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