Loading Deformation On Various Timescales Using Gps And Grace Measurements

Thesis (Ph.D.) University of Alaska Fairbanks, 2012Tidal, seasonal and long-term surface mass movements cause the earth to deform and the gravity field to change. Current geodetic satellites, GPS and GRACE, accurately measure these geophysical signals. I examine the effect on GPS solutions of using inconsistent reference frames to model ocean tidal loading (OTL). For seasonal loading, I choose two study areas, Nepal Himalaya and southern Alaska, and compare GPS-measured and GRACE-modeled seasonal hydrological ground loading deformation. Globally distributed stations are employed to compare GPS coordinate solutions with OTL corrections computed in different reference frames: center of mass of the solid Earth (CE), and center of mass of the Earth system (CM). A strong spectral peak at a period of ~14 days appears when inconsistent OTL models are applied along with smaller peaks at ~annual and ~semi-annual periods. Users of orbit/clock products must ensure to use OTL coefficients computed in the same frame as the OTL coefficients used by the analysis centers; otherwise, systematic errors will be introduced into position solutions. Continuous GPS measurements of seasonal deformation in Nepal Himalaya are compared with load model predictions derived from GRACE observations. The GPS seasonal height variation and GRACE-modeled seasonal vertical displacement due to the changing hydrologic load exhibit consistent results, for both amplitude and phase. GRACE indicates a long-term mass loss in the Himalaya region, which leads to crustal uplift since the earth behaves as an elastic body. We model this effect and remove it from GPS observed vertical rates. Then most GPS vertical rates can be explained by interseismic strain from the Main Himalayan Thrust. In southern Alaska, vertical seasonal loading deformation observed by GPS stations and modeled displacements due to seasonal hydrological loading inferred from GRACE are highly correlated. The effects of atmosphere and non-tidal ocean loading are important. Adding the AOD1B de-aliasing model to the GRACE solutions improves the correlation between these two geodetic measurements, because the displacements due to these loads are present in the GPS data. Weak correlations are found for some stations located in areas where the magnitude of the load changes over a short distance, due to GRACE's limited spatial resolution

The 2014 M_w 6.1 South Napa Earthquake: A Unilateral Rupture with Shallow Asperity and Rapid Afterslip

The Mw 6.1 South Napa earthquake occurred near Napa, California, on 24 August 2014 at 10:20:44.03 (UTC) and was the largest inland earthquake in northern California since the 1989 Mw 6.9 Loma Prieta earthquake. The first report of the earthquake from the Northern California Earthquake Data Center (NCEDC) indicates a hypocentral depth of 11.0 km with longitude and latitude of (122.3105° W, 38.217° N). Surface rupture was documented by field observations and Light Detection and Ranging (LiDAR) imaging (Brooks et al., 2014; Hudnut et al., 2014; Brocher et al., 2015), with about 12 km of continuous rupture starting near the epicenter and extending to the northwest. The southern part of the rupture is relatively straight, but the strike changes by about 15° at the northern end over a 6 km segment. The peak dextral offset was observed near the Buhman residence with right‐lateral motion of 46 cm, near the location where the strike of fault begins to rotate clockwise (Hudnut et al., 2014). The earthquake was well recorded by the strong‐motion network operated by the NCEDC, the California Geological Survey and the U.S. Geological Survey (USGS). There are about 12 sites within an epicentral distance of 15 km that had relatively good azimuthal coverage (Fig. 1). The largest peak ground velocity (PGV) of nearly 100  cm/s was observed on station 1765, which is the closest station to the rupture and lies about 3 km east of the northern segment (Fig. 1). The ground deformation associated with the earthquake was also well recorded by the high resolution COSMO–SkyMed (CSK) satellite and Sentinel-1A satellite, providing independent static observations

Earth’s Subdecadal Angular Momentum Balance from Deformation and Rotation Data

Length-of-Day (LOD) measurements represent variations in the angular momentum of the solid Earth (crust and mantle). There is a known ~6-year LOD signal suspected to be due to core-mantle coupling. If it is, then the core flow associated with the 6-year LOD signal may also deform the mantle, causing a 6-year signal in the deformation of the Earth’s surface. Stacking of Global Positioning System (GPS) data is found to contain a ~6-year radial deformation signal. We inverted the deformation signal for the outer core’s flow and equivalent angular momentum changes, finding good agreement with the LOD signal in some cases. These results support the idea of subdecadal core-mantle coupling, but are not robust. Interpretation of the results must also take into account methodological limitations. Gravitational field changes resulting from solid Earth deformation were also computed and found to be smaller than the errors in the currently available data

Tracking the weight of Hurricane Harvey’s stormwater using GPS data

On 26 August 2017, Hurricane Harvey struck the Gulf Coast as a category four cyclone depositing ~95 km3 of water, making it the wettest cyclone in U.S. history. Water left in Harvey’s wake should cause elastic loading and subsidence of Earth’s crust, and uplift as it drains into the ocean and evaporates. To track daily changes of transient water storage, we use Global Positioning System (GPS) measurements, finding a clear migration of subsidence (up to 21 mm) and horizontal motion (up to 4 mm) across the Gulf Coast, followed by gradual uplift over a 5-week period. Inversion of these data shows that a third of Harvey’s total stormwater was captured on land (25.7 ± 3.0 km3 ), indicating that the rest drained rapidly into the ocean at a rate of 8.2 km3 /day, with the remaining stored water gradually lost over the following 5 weeks at ~1 km3 /day, primarily by evapotranspiration. These results indicate that GPS networks can remotely track the spatial extent and daily evolution of terrestrial water storage following transient, extreme precipitation events, with implications for improving operational flood forecasts and understanding the response of drainage systems to large influxes of water

Automatic collateral quantification in acute ischemic stroke using U2-net

ObjectivesTo harness the U2-Net deep learning framework for automated quantification of collateral circulation in acute ischemic stroke (AIS) via computed tomography angiography (CTA) images, comparing its performance against traditional visual collateral scores (vCS).MethodsA cohort of 118 confirmed AIS cases was assembled and stratified into 94 development and 24 test cases. CTA images underwent preprocessing and annotation. The U2-Net was trained to segment collateral vessels, yielding a quantitative collateral score (qCS) based on vessel volume ratios between affected and healthy hemispheres. Performance was assessed via Dice Similarity Coefficient (DSC), Spearman correlation, Intraclass Correlation Coefficient (ICC), and accuracy, with comparisons to vCS (Tan and Menon score) and ground truth.ResultThe U2-Net demonstrated robust segmentation capabilities, achieving a mean DSC of 0.75 in the test set. The qCS showed a strong correlation with vCS with ρ ranging from 0.78 to 0.92. When compared to the more refined six-class Menon score, the qCS exhibited stronger consistency (development set: ICC = 0.83, test set: ICC = 0.93) than when compared to the four-class Tan score (development set: ICC = 0.76, test set: ICC = 0.79). In terms of classification accuracy, the AI model achieved 0.83 and 0.71 against ground truth and vCS, respectively, for four-class classification. This accuracy escalated to 0.88 and 0.83 for binary classification, emphasizing its proficiency in differentiating collateral status.ConclusionOur U2-Net AI model offers a reliable, objective tool for quantifying collateral circulation in AIS. The qCS aligns well with vCS and demonstrates the feasibility of automated collateral assessment, which may enhance diagnostic accuracy and therapeutic decision-making

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION

In order to solve the problem that temperature drift of silicon piezoresistive pressure sensor affects the accuracy of engineering measurement,proposed a temperature compensation strategy for BP neural network based on glowworm swarm optimization. The generalized BP neural network is used to optimize the weights and thresholds by using the firefly algorithm,thus improving the generalization performance and searching speed of the neural network,carried out temperature compensation of pressure sensor by optimized BP neural network. The temperature compensation performance of optimized BP neural network compares to that of conventional neural network and particle swarm optimization neural network. The results showed that compared with the conventional neural network and PSO optimization BP neural network,the optimized GSO optimization BP neural network is effective.The compensation error of GSO-BP neural network is 52% less than that of BP and 23% less than that of PSO-BP.Considering the time of compensation,the comprehensive performance of the BP neural network optimized by GSO is better.The compensated sensor data meet the experimental requirements of the subject.The compensation algorithm is feasible