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By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
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If quantitative estimates of the seismic properties do not exist at a location of interest then the site response spectral amplifications must be estimated from data collected at other locations. Currently, the most common approach employs correlations of site class with maps of surficial geology. Analogously, correlations of site class with topographic slope can be employed where the surficial geology is unknown. Our goal is to identify and validate a method to estimate site response with greater spatial resolution and accuracy for regions where additional effort is warranted. This method consists of three components: region-specific data collection, a spatial model for interpolating seismic properties, and a theoretical method for computing spectral amplifications from the interpolated seismic properties. We consider three spatial interpolation schemes: correlations with surficial geology, termed the geologic trend (GT), ordinary kriging (OK), and kriging with a trend (KT). We estimate the spectral amplifications from seismic properties using the square root of impedance method, thereby linking the frequency-dependent spectral amplifications to the depth-dependent seismic properties. Thus, the range of periods for which this method is applicable is limited by the depth of exploration. A dense survey of near-surface S-wave slowness (Ss) throughout Kobe, Japan shows that the geostatistical methods give more accurate estimates of Ss than the topographic slope and GT methods, and the OK and KT methods perform equally well. We prefer the KT model because it can be seamlessly integrated with geologic maps that cover larger regions. Empirical spectral amplifications show that the region-specific data achieve more accurate estimates of observed median short-period amplifications than the topographic slope method. ?? 2010 Elsevier B.V.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
Introduction Abandoned railroads and infrastructure from the anthracite coal-mining industry are significant features in abandoned mine lands and are an important part of history; however, these features are often lost and masked by the passage of time and the regrowth of forests. The application of modern light detection and ranging (lidar) topographic analysis, combined with field verification, enabled the mapping of these historical features. Waste rock piles and abandoned mine lands from historical mining locally appear as distinct features on the landscape depicted on the percent-slope base map. Abandoned, and in many places demolished, infrastructure such as breakers, turntables, rail beds, water tanks, tram piers, and bridge abutments, for example, were identified in the field and located with a Global Positioning System (GPS) receiver. This percent-slope map shows the locations of many of the abandoned features from the coal-mining industry near Forest City, Pennsylvania, and preserves a time that was an important part of the industrial revolution and a way of life that has been quiet for over half a century.
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Introduction When magma accumulates or migrates, it can cause pressurization and related ground deformation. Characterization of surface deformation provides important constraints on the potential for future volcanic activity, especially in combination with seismic activity, gas emissions, and other indicators. A wide variety of techniques and instrument types have been applied to the study of ground deformation at volcanoes (sidebar, p. 2; Dzurisin, 2000, 2003, 2007). Geodetic instruments include continuously recording Global Navigation Satellite System (GNSS; of which the United States’ Global Positioning System is one example) stations (fig. D1), borehole tiltmeters, and interferometric synthetic aperture radar (InSAR) measurements (from satellites, occupied and unoccupied aircraft systems, and ground-based sensors). Additional geodetic measurements like continuous- and survey-mode gravity (fig. D2) can contribute substantially to interpreting these data. Borehole strainmeters (see chapter K , this volume, by Hurwitz and Lowenstern, 2024) also have outstanding utility for monitoring deformation, although because of cost and permitting challenges, we do not include them as part of standard volcano monitoring networks for U.S. volcanoes. Still other techniques like light detection and ranging (lidar), structure from motion, and optical satellite data can be used to derive gross topographic changes, which can be used to map volcanic deposits, infer eruption rates, and gain insights into the source processes associated with eruptive activity (see chapter G , this volume, on tracking surface changes caused by volcanic activity; Orr and others, 2024). Experience has shown that no single geodetic monitoring technique is adequate to detect and track the entire range of ground-motion patterns that occur at volcanoes, primarily because of the temporal and spatial diversity of volcano deformation (fig. D3). Similarly, the magnitude of surface deformation varies widely. Geodetic monitoring strategies should therefore include multiple techniques and instrument types to cover a wide range of spatial and temporal scales. In identifying recommendations for geodetic instrumentation for volcano monitoring networks, we attempted to maximize the diversity of instrument types to measure the full range of deformation signals and minimize their expense and number; thus, we do not include several well-known deformation-monitoring techniques in our recommendations. Extensometers, for example, measure strains over distances of a few meters and have an excellent record of success in detecting changes in preeruptive localized ground motion across existing cracks, including at Mount St. Helens, Washington (Iwatsubo and others, 1992), and Piton de la Fournaise, Réunion Island (Peltier and others, 2006). Despite being relatively inexpensive, extensometers are best used primarily when localized ground displacements (for example, ground cracks) need to be tracked, and are not necessary at all volcanoes. In considering volcano deformation monitoring strategies, two complicating factors are deserving of special attention. First, not all deformation is driven by subsurface magmatic activity—for example, at many large stratovolcanoes (for example, Mount Rainier), flank collapses and landslides are significant geologic hazards (Reid and others, 2001) that may occur even in the absence of magmatic activity. Monitoring the stability of volcanoes is thus another critical application of geodetic monitoring networks to inform hazard assessment. One of the most famous examples of edifice instability is the large flank collapse that initiated the May 18, 1980, eruption of Mount St. Helens. Deformation monitoring had detected a bulge on the north flank of the mountain in April 1980 that was expanding by several meters per day (Lipman and others, 1981). Given that flank collapses can happen at any time during a period of volcanic unrest (or even outside a period of unrest), the capability to assess edifice stability is critical. Second, although volcanoes are commonly treated as idealized structures that erupt from single points, like centralvent stratovolcanoes, many are characterized by long rift zones from which eruptions may originate, and distributed volcanic fields are characterized by broadly spaced vents. For example, linear dikes are common at Kīlauea, Mauna Loa, and between Mount Shasta and Medicine Lake in California. At Kīlauea, one of these linear dikes emerged more than 40 kilometers (km) away from the summit of the volcano during the lower East Rift Zone eruption in 2018. Other volcanic fields, like Lassen volcanic center, California, or the San Francisco Volcanic Field, Arizona, have many small vents spread over a wide area. Although the instrumentation guidelines presented in this chapter remain phrased for central-vent volcanoes, they should be modified as needed in the context of the eruptive characteristics of each individual volcanic system. Spatial analysis of geodetic network coverage could help to ensure adequate instrumentation in areas where volcanism can occur over a broad area as opposed to a central vent. As an example, consider the adjacent volcanoes Mount Shasta and Medicine Lake. If station locations are chosen based only on the distance from the centers of the volcanoes, then any geodetic anomalies between the two volcanoes—an area of potential volcanism as indicated by the presence of volcanic features—may remain undetected by ground-based instrumentation. The spatial analysis is accomplished via a grid of pressure point sources (Mogi, 1958) evenly distributed across the map area, at a depth of 5 km in this example (fig. D4). Each source is inflated until predicted deformations exceed the GNSS white noise uncertainty estimates at one site (Langbein, 2017; Murray and Svarc, 2017). This volume of detectable magma provides a measure of the quality of the coverage (fig. D4). The results indicate that, as of 2022, there is a large area between Mount Shasta and Medicine Lake volcano with existing mapped dikes in which a substantial amount of magma could intrude without being detected geodetically. Applying this style of analysis to individual volcanic systems can provide a guide for designing network geometry given the expected locations of future eruptions.
The Wasatch fault zone (WFZ) is a 220-mile-long (350-km) fault zone divided into 10 structural segments extending from southeastern Idaho to central Utah. The central five segments of the WFZ underlie the densely populated Wasatch Front region, where the majority of Utah’s population and economy are proximal to the fault zone. The West Valley fault zone (WVFZ) is an antithetic structure related to the WFZ and runs through the Salt Lake Valley. Communities on or adjacent to the WFZ are at risk of earthquake damage, due to their proximity to the fault zones. During 2016–2018, the Utah Geological Survey and a U.S. Geological Survey collaborator performed updated fault mapping of 39 7.5' quadrangles along the WFZ using recently acquired high-resolution topographic data derived from airborne light detection and ranging (lidar) elevation data. Previous geologic mapping, paleoseismic investigations, historical aerial photography, and field investigations were also used to identify and map surface fault traces and infer fault locations. Special study zones were delineated around fault traces to facilitate understanding of the surface-rupturing hazard and associated risk. Defining these special study zones encourages the creation and implementation of municipal and county geologic-hazard ordinances dealing with hazardous faults. We identified potential paleoseismic investigation sites where fault scarps appear relatively pristine, are located in geologically favorable settings, and where additional earthquake timing data would be beneficial to the continued earthquake research of the WFZ. The fault geometries, attributes, and special study zones were published in the online Utah Geologic Hazards Portal simultaneously with this Report of Investigation (RI). This report contains supplementary material describing the data and methods used to perform the mapping and in locating potential paleoseismic investigation sites in the study area. This work is critical to raise awareness of earthquake hazards in areas of Utah experiencing rapid growth.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
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Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.