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The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non-ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar-based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre- and postevent mapping of liquefaction hazards. We describe liquefaction and ground-failure features in Trona and Argus, which produced lateral deformations and impacts on several single-story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction-related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.

Following the Ridgecrest earthquake sequence, consisting of an M 6.4 foreshock and M 7.1 mainshock along with many other events, the Geotechnical Extreme Events Reconnaissance association deployed a team to gather perishable data. The team focused their efforts on documenting ground deformations including surface fault rupture south of the Naval Air Weapons Station China Lake, and liquefaction features in Trona and Argus. The team published a report within two weeks of the M 7.1 mainshock. This article presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground-based observations using Global Positioning System trackers, digital cameras, and hand-measuring devices, as well as unmanned aerial vehicle-based imaging products using Structure from Motion to create point clouds and digital surface models. The article describes the data products, as well as tools available for interacting with the products.

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Soil liquefaction and resulting ground failure due to earthquakes presents a significant hazard to distributed infrastructure systems and structures around the world. Currently there is no consensus in liquefaction susceptibility or triggering models. The disagreements between models is a result of incomplete datasets and parameter spaces for model development. The Next Generation Liquefaction (NGL) Project was created to provide a database for advancing liquefaction research and to develop models for the prediction of liquefaction and its effects, derived in part from that database in a transparent and peer-reviewed manner, that provide end users with a consensus approach to assess liquefaction potential within a probabilistic framework. An online relational database was created for organizing and storing case histories which is available at http://nextgenerationliquefaction.org/ (https://www.doi.org/10.21222/C2J040, [1]). The NGL field case history database was recently expanded to include the results of laboratory testing programs because such results can inform aspects of liquefaction models that are poorly constrained by case histories alone. Data are organized by a schema describing tables, fields, and relationships among the tables. The types of information available in the database are test-specific and include processeddata quantities such as stress and strain rather than raw data such as load and displacement. The database is replicated in DesignSafe-CI [2] where users can write queries in Python scripts within Jupyter notebooks to interact with the data.