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Abstract Topographic differencing measures landscape change by comparing multitemporal high-resolution topography data sets. Here, we focused on two types of topographic differencing: (1) Vertical differencing is the subtraction of digital elevation models (DEMs) that span an event of interest. (2) Three-dimensional (3-D) differencing measures surface change by registering point clouds with a rigid deformation. We recently released topographic differencing in OpenTopography where users perform on-demand vertical and 3-D differencing via an online interface. OpenTopography is a U.S. National Science Foundation–funded facility that provides access to topographic data and processing tools. While topographic differencing has been applied in numerous research studies, the lack of standardization, particularly of 3-D differencing, requires the customization of processing for individual data sets and hinders the community’s ability to efficiently perform differencing on the growing archive of topography data. Our paper focuses on streamlined techniques with which to efficiently difference data sets with varying spatial resolution and sensor type (i.e., optical vs. light detection and ranging [lidar]) and over variable landscapes. To optimize on-demand differencing, we considered algorithm choice and displacement resolution. The optimal resolution is controlled by point density, landscape characteristics (e.g., leaf-on vs. leaf-off), and data set quality. We provide processing options derived from metadata that allow users to produce optimal high-quality results, while experienced users can fine tune the parameters to suit their needs. We anticipate that the differencing tool will expand access to this state-of-the-art technology, will be a valuable educational tool, and will serve as a template for differencing the growing number of multitemporal topography data sets. 

The manufacture of flaked stone artifacts represents a major milestone in the technology of the human lineage. Although the earliest production of primitive stone tools, predating the genus Homo and emphasizing percussive activities, has been reported at 3.3 million years ago (Ma) from Lomekwi, Kenya, the systematic production of sharp-edged stone tools is unknown before the 2.58–2.55 Ma Oldowan assemblages from Gona, Ethiopia. The organized production of Oldowan stone artifacts is part of a suite of characteristics that is often associated with the adaptive grade shift linked to the genus Homo . Recent discoveries from Ledi-Geraru (LG), Ethiopia, place the first occurrence of Homo ∼250 thousand years earlier than the Oldowan at Gona. Here, we describe a substantial assemblage of systematically flaked stone tools excavated in situ from a stratigraphically constrained context [Bokol Dora 1, (BD 1) hereafter] at LG bracketed between 2.61 and 2.58 Ma. Although perhaps more primitive in some respects, quantitative analysis suggests the BD 1 assemblage fits more closely with the variability previously described for the Oldowan than with the earlier Lomekwian or with stone tools produced by modern nonhuman primates. These differences suggest that hominin technology is distinctly different from generalized tool use that may be a shared feature of much of the primate lineage. The BD 1 assemblage, near the origin of our genus, provides a link between behavioral adaptations—in the form of flaked stone artifacts—and the biological evolution of our ancestors. 

Observations of fault geometry and cumulative slip distribution serve as critical constraints on fault behavior over temporal scales ranging from a single earthquake to a fault’s complete history. The increasing availability of high-resolution topography (at least one observation per square meter) from air- and spaceborne platforms facilitates measuring geometric properties along faults over a range of spatial scales. However, manually mapping faults and measuring slip or scarp height is time-intensive, limiting the use of rich topography datasets. To substantially decrease the time required to analyze fault systems, we developed a novel approach for systematically mapping dip-slip faults and measuring scarp height. Our MATLAB algorithm detects fault scarps from topography by identifying regions of steep relief given length and slope parameters calibrated from a manually drawn fault map. We applied our algorithm to well-preserved normal faults in the Volcanic Tablelands of eastern California using four datasets: (1) structure-from-motion topography from a small uncrewed aerial system (sUAS; 20 cm resolution), (2) airborne laser scanning (25 cm), (3) Pléiades stereosatellite imagery (50 cm), and SRTM (30 m) topography. The algorithm and manually mapped fault trace architectures are consistent for primary faults, although can differ for secondary faults. On average, the scarp height profiles are asymmetric, suggesting fault lateral propagation and along-strike variations in the fault’s mechanical properties. We applied our algorithm to Arizona and Utah with a specific focus on the normal Hurricane fault where the algorithm mapped faults and other prominent topographic features well. This analysis demonstrates that the algorithm can be applied in a variety of geomorphic and tectonic settings.

 

Reproducibility of results from scientific studies is rarely demonstrated outside the laboratory. Measurements of fault slip rate underpin scientific models of active faulting and seismic hazard assessments widely used for policymaking and risk mitigation. We replicated a highly referenced study that measured the slip rate as 33.9 ± 2.9 mm/year along the San Andreas fault over the past ~3,700 years at Wallace Creek in the Carrizo Plain National Monument, USA. Our results provide a slip rate of slip 36 ± 1 mm/year since ~3,500 CalBP. We find that the late Holocene slip rate of the San Andreas fault, a key indicator of seismic hazard, is reproducible within measurement uncertainty. Geologic slip rate determinations are relatively insensitive to short‐term fluctuations and thus should be reproducible if the time interval of measurement is much greater than the average rupture interval.