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At least 181 records · Page 10Linked to original sources

USGS2001

Dating the Vostok ice core record by importing the Devils Hole chronology

The development of an accurate chronology for the Vostok record continues to be an open research question because these invaluable ice cores cannot be dated directly. Depth-to-age relationships have been developed using many different approaches, but published age estimates are inconsistent, even for major paleoclimatic events. We have developed a chronology for the Vostok deuterium paleotemperature record using a simple and objective algorithm to transfer ages of major paleoclimatic events from the radiometrically dated 500,000-year δ 18 O-paleotemperature record from Devils Hole, Nevada. The method is based only on a strong inference that major shifts in paleotemperature recorded at both locations occurred synchronously, consistent with an atmospheric teleconnection. The derived depth-to-age relationship conforms with the physics of ice compaction, and internally produces ages for climatic events 5.4 and 11.24 which are consistent with the externally assigned ages that the Vostok team needed to assume in order to derive their most recent chronology, GT4. Indeed, the resulting V-DH chronology is highly correlated with GT4 because of the unexpected correspondence even in the timing of second-order climatic events that were not constrained by the algorithm. Furthermore, the algorithm developed herein is not specific to this problem; rather, the procedure can be used whenever two paleoclimate records are proxies for the same physical phenomenon, and paleoclimatic conditions forcing the two records can be considered to have occurred contemporaneously. The ability of the algorithm to date the East Antarctic Dome Fuji core is also demonstrated.

Journal of Geophysical Research D: Atmospheres↗

USGS2003

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Journal of Geophysical Research E: Planets↗

USGS2019

Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations

The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

Oregon↗

USGS2007

Evaluation of an experimental LiDAR for surveying a shallow, braided, sand-bedded river

Reaches of a shallow (<1.0m), braided, sand-bedded river were surveyed in 2002 and 2005 with the National Aeronautics and Space Administration's Experimental Advanced Airborne Research LiDAR (EAARL) and concurrently with conventional survey-grade, real-time kinematic, global positioning system technology. The laser pulses transmitted by the EAARL instrument and the return backscatter waveforms from exposed sand and submerged sand targets in the river were completely digitized and stored for postflight processing. The vertical mapping accuracy of the EAARL was evaluated by comparing the ellipsoidal heights computed from ranging measurements made using an EAARL terrestrial algorithm to nearby (<0.5m apart) ground-truth ellipsoidal heights. After correcting for apparent systematic bias in the surveys, the root mean square error of these heights with the terrestrial algorithm in the 2002 survey was 0.11m for the 26 measurements taken on exposed sand and 0.18m for the 59 measurements taken on submerged sand. In the 2005 survey, the root mean square error was 0.18m for 92 measurements taken on exposed sand and 0.24m for 434 measurements on submerged sand. In submerged areas the waveforms were complicated by reflections from the surface, water column entrained turbidity, and potentially the riverbed. When applied to these waveforms, especially in depths greater than 0.4m, the terrestrial algorithm calculated the range above the riverbed. A bathymetric algorithm has been developed to approximate the position of the riverbed in these convolved waveforms and preliminary results are encouraging. ?? 2007 ASCE.

Journal of Hydraulic Engineering↗

USGS1994

Neural networks for river flow prediction

The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model. However, such analytic models may not adequately represent the flow process, because they are based on many simplifying assumptions about the natural phenomena that influence the river flow. This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor. Issues such as selecting an appropriate neural network architecture and a correct training algorithm as well as presenting data to neural networks are addressed using a constructive algorithm called the cascade‐correlation algorithm. The neural‐network approach is applied to the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich. Empirical comparisons are performed between the predictive capability of the neural network models and the most commonly used analytic nonlinear power model in terms of accuracy and convenience of use. Our preliminary results are quite encouraging. An analysis performed on the structure of the networks developed by the cascade‐correlation algorithm shows that the neural networks are capable of adapting their complexity to match changes in the flow history and that the models developed by the neural‐network approach are more complex than the power model.

Journal of Computing in Civil Engineering↗

USGS2020

Quantifying model structural uncertainty using airborne electromagnetic data

The ability to quantify structural uncertainty in geological models that incorporate geophysical data is affected by two primary sources of uncertainty: geophysical parameter uncertainty and uncertainty in the relationship between geophysical parameters and geological properties of interest. Here, we introduce an open-source, trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm GeoBIPy—Geophysical Bayesian Inference in Python—for robust uncertainty analysis of time-domain or frequency-domain airborne electromagnetic (AEM) data. The McMC algorithm provides a robust assessment of geophysical parameter uncertainty using a trans-dimensional approach that lets the AEM data inform the level of model complexity necessary by allowing the number of model layers itself to be an unknown parameter. Additional components of the Bayesian algorithm allow the user to solve for parameters such as data errors or corrections to the measured instrument height above ground. Probability distributions for a user-specified number of lithologic classes are developed through posterior clustering of McMC-derived resistivity models. Estimates of geological model structural uncertainty are thus obtained through the joint probability of geophysical parameter uncertainty and the uncertainty in the definition of each class. Examples of the implementation of this algorithm are presented for both time-domain and frequency-domain AEM data acquired in Nebraska, USA.

Nebraska↗

USGS2009

TinyOS-based quality of service management in wireless sensor networks

Previously the cost and extremely limited capabilities of sensors prohibited Quality of Service (QoS) implementations in wireless sensor networks. With advances in technology, sensors are becoming significantly less expensive and the increases in computational and storage capabilities are opening the door for new, sophisticated algorithms to be implemented. Newer sensor network applications require higher data rates with more stringent priority requirements. We introduce a dynamic scheduling algorithm to improve bandwidth for high priority data in sensor networks, called Tiny-DWFQ. Our Tiny-Dynamic Weighted Fair Queuing scheduling algorithm allows for dynamic QoS for prioritized communications by continually adjusting the treatment of communication packages according to their priorities and the current level of network congestion. For performance evaluation, we tested Tiny-DWFQ, Tiny-WFQ (traditional WFQ algorithm implemented in TinyOS), and FIFO queues on an Imote2-based wireless sensor network and report their throughput and packet loss. Our results show that Tiny-DWFQ performs better in all test cases. ?? 2009 IEEE.

Conference Paper↗

USGS2007

Time-optimum packet scheduling for many-to-one routing in wireless sensor networks

This paper studies the WSN application scenario with periodical traffic from all sensors to a sink. We present a time-optimum and energy-efficient packet scheduling algorithm and its distributed implementation. We first give a general many-to-one packet scheduling algorithm for wireless networks, and then prove that it is time-optimum and costs max(2N(u1) - 1, N(u 0) -1) time slots, assuming each node reports one unit of data in each round. Here N(u0) is the total number of sensors, while N(u 1) denotes the number of sensors in a sink's largest branch subtree. With a few adjustments, we then show that our algorithm also achieves time-optimum scheduling in heterogeneous scenarios, where each sensor reports a heterogeneous amount of data in each round. Then we give a distributed implementation to let each node calculate its duty-cycle locally and maximize efficiency globally. In this packet scheduling algorithm, each node goes to sleep whenever it is not transceiving, so that the energy waste of idle listening is also eliminated. Finally, simulations are conducted to evaluate network performance using the Qualnet simulator. Among other contributions, our study also identifies the maximum reporting frequency that a deployed sensor network can handle. ??2006 IEEE.

Conference Paper↗

USGS2009

Atmospheric correction at AERONET locations: A new science and validation data set

This paper describes an Aerosol Robotic Network (AERONET)-based Surface Reflectance Validation Network (ASRVN) and its data set of spectral surface bidirectional reflectance and albedo based on Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50 ?? 50 km 2 ; subsets of MODIS level 1B (L1B) data from MODIS adaptive processing system and AERONET aerosol and water-vapor information. Then, it performs an atmospheric correction (AC) for about 100 AERONET sites based on accurate radiative-transfer theory with complex quality control of the input data. The ASRVN processing software consists of an L1B data gridding algorithm, a new cloud-mask (CM) algorithm based on a time-series analysis, and an AC algorithm using ancillary AERONET aerosol and water-vapor data. The AC is achieved by fitting the MODIS top-of-atmosphere measurements, accumulated for a 16-day interval, with theoretical reflectance parameterized in terms of the coefficients of the Li SparseRoss Thick (LSRT) model of the bidirectional reflectance factor (BRF). The ASRVN takes several steps to ensure high quality of results: 1) the filtering of opaque clouds by a CM algorithm; 2) the development of an aerosol filter to filter residual semitransparent and subpixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing the requirement of the consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of a seasonal backup spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixel. The ASRVN products include three parameters of the LSRT model (kL, kG, and kV), surface albedo, normalized BRF (computed for a standard viewing geometry, VZA = 0, SZA = 45??), and instantaneous BRF (or one-angle BRF value derived from the last day of MODIS measurement for specific viewing geometry) for the MODIS 500-m bands 17. The results are produced daily at a resolution of 1 km in gridded format. We also provide a cloud mask, a quality flag, and a browse bitmap image. The ASRVN data set, including 6 years of MODIS TERRA and 1.5 years of MODIS AQUA data, is available now as a standard MODIS product (MODASRVN) which can be accessed through the Level 1 and Atmosphere Archive and Distribution System website ( http://ladsweb.nascom.nasa.gov/data/search.html). It can be used for a wide range of applications including validation analysis and science research. ?? 2006 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2013

An automated cross-correlation based event detection technique and its application to surface passive data set

In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

Geophysical Prospecting↗

USGS2018

Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.

Journal of the American Water Resources Associatio↗

USGS2014

Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model

The phenology of arctic ecosystems is driven primarily by abiotic forces, with temperature acting as the main determinant of growing season onset and leaf budburst in the spring. However, while the plant species in arctic ecosystems require differing amounts of accumulated heat for leaf-out, dynamic vegetation models simulated over regional to global scales typically assume some average leaf-out for all of the species within an ecosystem. Here, we make use of air temperature records and observations of spring leaf phenology collected across dominant groupings of species (dwarf birch shrubs, willow shrubs, other deciduous shrubs, grasses, sedges, and forbs) in arctic and boreal ecosystems in Alaska. We then parameterize a dynamic vegetation model based on these data for four types of tundra ecosystems (heath tundra, shrub tundra, wet sedge tundra, and tussock tundra), as well as ecotonal boreal white spruce forest, and perform model simulations for the years 1970 -2100. Over the course of the model simulations, we found changes in ecosystem composition under this new phenology algorithm compared to simulations with the previous phenology algorithm. These changes were the result of the differential timing of leaf-out, as well as the ability for the groupings of species to compete for nitrogen and light availability. Regionally, there were differences in the trends of the carbon pools and fluxes between the new phenology algorithm and the previous phenology algorithm, although these differences depended on the future climate scenario. These findings indicate the importance of leaf phenology data collection by species and across the various ecosystem types within the highly heterogeneous Arctic landscape, and that dynamic vegetation models should consider variation in leaf-out by groupings of species within these ecosystems to make more accurate projections of future plant distributions and carbon cycling in Arctic regions.

Global Change Biology↗

USGS2013

Validating predictions from climate envelope models

Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

PLoS ONE↗

USGS1994

Problems of stock definition in estimating relative contributions of Atlantic striped bass to the coastal fishery

Stock contribution studies of mixed‐stock fisheries rely on the application of classification algorithms to samples of unknown origin. Although the performance of these algorithms can be assessed, there are no guidelines regarding decisions about including minor stocks, pooling stocks into regional groups, or sampling discrete substocks to adequately characterize a stock. We examined these questions for striped bass Morone saxatilis of the U.S. Atlantic coast by applying linear discriminant functions to meristic and morphometric data from fish collected from spawning areas. Some of our samples were from the Hudson and Roanoke rivers and four tributaries of the Chesapeake Bay. We also collected fish of mixed‐stock origin from the Atlantic Ocean near Montauk, New York. Inclusion of the minor stock from the Roanoke River in the classification algorithm decreased the correct‐classification rate, whereas grouping of the Roanoke River and Chesapeake Bay stock into a regional (“southern”) group increased the overall resolution. The increased resolution was offset by our inability to obtain separate contribution estimates of the groups that were pooled. Although multivariate analysis of variance indicated significant differences among Chesapeake Bay substocks, increasing the number of substocks in the discriminant analysis decreased the overall correct‐classification rate, Although the inclusion of one, two, three, or four substocks in the classification algorithm did not greatly affect the overall correct‐classification rates, the specific combination of substocks significantly affected the relative contribution estimates derived from the mixed‐stock sample, Future studies of this kind must balance the costs and benefits of including minor stocks and would profit from examination of the variation in discriminant characters among all Chesapeake Bay substocks.

Maryland↗

USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗

USGS2018

Development of a geodetic component for the U.S. West Coast Earthquake Early Warning System

An earthquake early warning (EEW) system, ShakeAlert, is under development for the West Coast of the United States. This system currently uses the first few seconds of waveforms recorded by seismic instrumentation to rapidly characterize earthquake magnitude, location, and origin time; ShakeAlert recently added a seismic line source algorithm. For large to great earthquakes, magnitudes estimated from the earliest seismic data alone generally saturate. Real‐time Global Navigation Satellite System (GNSS) data can directly measure large displacements, enabling accurate magnitude estimates for M w 7 + "> M w 7 + events, possibly before rupture termination. GNSS‐measured displacements also track evolving slip and, alone or in combination with seismic data, constrain finite‐fault models. Particularly for large‐magnitude, long‐rupture events, GNSS‐based magnitude and rupture extent estimates can improve updates to predicted shaking and thus alert accuracy. GNSS data processing centers at ShakeAlert partner institutions provide real‐time streams to the EEW system, and three geodetic EEW algorithms have been developed through the ShakeAlert collaboration. These algorithms will undergo initial testing within ShakeAlert’s computational architecture using a suite of input data that includes simulated real‐time displacements from synthetic earthquakes and GNSS recordings from recent earthquakes worldwide. Performance will be evaluated using metrics and standards consistent with those adopted for ShakeAlert overall. This initial assessment will guide method refinement and synthesis of the most successful features into a candidate geodetic algorithm for the ShakeAlert production system. In parallel, improvements to geodetic networks and streamlining approaches to data processing and exchange will ensure robust geodetic data availability in the event of an earthquake.

West coast↗

USGS1980

A survey of image processing developments in support of remote sensing

New algorithm developments for image processing (IP) will occur throughout the 1980's, resulting from evolution in computer hardware and sensors as well as continuing research. This report will describe the areas of algorithm development that are occurring in applications, research, and operational environments. Included is an overview of image processing activities at institutions which are generally regarded as leaders in IP algorithm development and implementation. Finally, this report addresses directions in IP algorithm development that are being proposed for the EROS Data Center (EDC). The major applications of IP at EDC are developed for use in the processing, analysis, and extraction of remote sensing information from Landsat and aircraft data (platforms).

Report↗

USGS2001

hypoDD-A Program to Compute Double-Difference Hypocenter Locations

HypoDD is a Fortran computer program package for relocating earthquakes with the double-difference algorithm of Waldhauser and Ellsworth (2000). This document provides a brief introduction into how to run and use the programs ph2dt and hypoDD to compute double-difference (DD) hypocenter locations. It gives a short overview of the DD technique, discusses the data preprocessing using ph2dt, and leads through the earthquake relocation process using hypoDD. The appendices include the reference manuals for the two programs and a short description of auxiliary programs and example data. Some minor subroutines are presently in the c language, and future releases will be in c. Earthquake location algorithms are usually based on some form of Geiger’s method, the linearization of the travel time equation in a first order Taylor series that relates the difference between the observed and predicted travel time to unknown adjustments in the hypocentral coordinates through the partial derivatives of travel time with respect to the unknowns. Earthquakes can be located individually with this algorithm, or jointly when other unknowns link together the solutions to indivdual earthquakes, such as station corrections in the joint hypocenter determination (JHD) method, or the earth model in seismic tomography. The DD technique (described in detail in Waldhauser and Ellsworth, 2000) takes advantage of the fact that if the hypocentral separation between two earthquakes is small compared to the event-station distance and the scale length of velocity heterogeneity, then the ray paths between the source region and a common station are similar along almost the entire ray path (Fréchet, 1985; Got et al., 1994). In this case, the difference in travel times for two events observed at one station can be attributed to the spatial offset between the events with high accuracy. DD equations are built by differencing Geiger’s equation for earthquake location. In this way, the residual between observed and calculated travel-time difference (or double-difference) between two events at a common station are a related to adjustments in the relative position of the hypocenters and origin times through the partial derivatives of the travel times for each event with respect to the unknown. HypoDD calculates travel times in a layered velocity model (where velocity depends only on depth) for the current hypocenters at the station where the phase was recorded. The double-difference residuals for pairs of earthquakes at each station are minimized by weighted least squares using the method of singular value decomposition (SVD) or the conjugate gradients method (LSQR, Paige and Saunders, 1982). Solutions are found by iteratively adjusting the vector difference between nearby hypocentral pairs, with the locations and partial derivatives being updated after each iteration. Details about the algorithm can be found in Waldhauser and Ellsworth (2000). When the earthquake location problem is linearized using the double-difference equations, the common mode errors cancel, principally those related to the receiver-side structure. Thus we avoid the need for station corrections or high-accuracy of predicted travel times for the portion of the raypath that lies outside the focal volume. This approach is especially useful in regions with a dense distribution of seismicity, i.e. where distances between neighboring events are only a few hundred meters. The improvement of double-difference locations over ordinary JHD locations is shown in Figure 1 for about 10,000 earthquakes that occurred during the 1997 seismic crisis in the Long Valley caldera, California. While the JHD locations (left panel) show a diffuse picture of the seismicity, double-difference locations (right panel) bring structural details such as the location of active fault planes into sharp focus.

Open-File Report↗