Imaging the mantle beneath Iceland using integrated seismological techniques

Using a combination of body wave and surface wave data sets to reveal the mantle plume and plume head, this study presents a tomographic image of the mantle structure beneath Iceland to 400 km depth. Data comes primarily from the PASSCAL-HOTSPOT deployment of 30 broadband instruments over a period of 2 years, and is supplemented by data from the SIL and ICEMELT networks. Three sets of relative teleseismic body wave arrival times are generated through cross correlation: S and SKS arrivals at 0.03–0.1 Hz, and P and PKIKP arrivals at 0.03–0.1 and 0.8–2.0 Hz. Prior to inversion the crustal portion of the travel time anomalies is removed using the crustal model ICECRTb. This step has a significant effect on the mantle velocity variations imaged down to a depth of ∼250 km. Inversion of relative arrival times only provides information on lateral velocity variations. Surface waves are therefore used to provide absolute velocity information for the uppermost mantle beneath Iceland. The average wave number for the Love wave fundamental mode at 0.020 and 0.024 Hz is measured and used to invert for the average S velocity. Combination of the body wave and surface wave information reveals a predominantly horizontal low-velocity anomaly extending from the Moho down to ∼250 km depth, interpreted as a plume head. Below the plume head a near-cylindrical low-velocity anomaly with a radius of ∼100 km and peak VP and VS anomalies of −2% and −4%, respectively, extends down to the maximum depth of resolution at 400 km. Within the plume head, in the uppermost mantle above the core of the plume, there is a relatively high velocity with a maximum VP and VS anomaly of +2%. This high-velocity anomaly may be the result of the extreme degree of melt extraction necessary to generate the thick (46 km) crust in central Iceland. Comparison of the plume volumetric flux implied by our images, the crustal generation rate, and the degree of melting suggested by rare earth element inversions, suggests that (1) mantle material must be flowing horizontally away from the plume core faster than the overlying lithosphere and (2) the bulk of the plume material does not participate in melting beneath Iceland

A global corporate census: publicly traded and close companies in 1910

In 1910 the world had almost half a million corporations, only one-hundredth of today's total. About one-fifth—with over half of corporate capital—were publicly tradable, higher portions than today. Most publicly quoted corporations traded in Europe and the British Empire, but most close (private) corporations operated in the US, which, until the 1940s, had more corporations per capita than anywhere else. The 83 countries surveyed here differed markedly in company numbers, corporate capital/GDP ratios, and average corporate size. Enclave economies—dominated by quoted (and often foreign-owned) companies—had the largest average sizes, while other nations had more varied mixes of large quoted corporations and close company small and medium enterprises

Oceanic Residual Depth Measurements, the Plate Cooling Model and Global Dynamic Topography

Convective circulation of the mantle causes deflections of the Earth's surface that vary as a function of space and time. Accurate measurements of this dynamic topography are complicated by the need to isolate and remove other sources of elevation, arising from flexure and lithospheric isostasy. The complex architecture of continental lithosphere means that measurement of present-day dynamic topography is more straightforward in the oceanic realm. Here, we present an updated methodology for calculating oceanic residual bathymetry, which is a proxy for dynamic topography. Corrections are applied that account for the effects of sedimentary loading and compaction, for anomalous crustal thickness variations, for subsidence of oceanic lithosphere as a function of age, and for non-hydrostatic geoid height variations. Errors are formally propagated to estimate measurement uncertainties. We apply this methodology to a global database of 1,936 seismic surveys located on oceanic crust and generate 2,297 spot measurements of residual topography, including 1,161 with crustal corrections. The resultant anomalies have amplitudes of ±1 km and wavelengths of ∼1,000 km. Spectral analysis of our database using cross-validation demonstrates that spherical harmonics up to and including degree 30 (i.e. wavelengths down to 1,300 km) are required to accurately represent these observations. Truncation of the expansion at a lower maximum degree erroneously increases the amplitude of inferred long-wavelength dynamic topography. There is a strong correlation between our observations and free-air gravity anomalies, magmatism, ridge seismicity, vertical motions of adjacent rifted margins, and global tomographic models. We infer that shorter wavelength components of the observed pattern of dynamic topography may be attributable to the presence of thermal anomalies within the shallow asthenospheric mantle.This research is supported by a BP-Cambridge collaboration

Economic essays on wildlife-aircraft conflict in the United States

2019 Summer.Includes bibliographical references.Wildlife-aircraft conflict poses a substantial economic and safety threat in the United States (US). Dolbeer, Wright, Weller, Anderson, and Begier (2014) estimates direct costs related to wildlife strikes burdened the US economy by approximately 157 million annually between 1990 and 2014. In 1995, the Federal Aviation Administration (FAA) collaborated on a project with the United States Department of Agriculture's (USDA) Wildlife Services to investigate the magnitude and nature of the wildlife strike problem, ultimately resulting in the creation of the National Wildlife Strike Database (NWSD). However, reporting strikes (and associated information, such as repair costs) to the NWSD is not mandatory, and information used to calculate economic damage estimates from wildlife strikes in the US relies on voluntarily reported cost data. This dissertation focuses on the direct costs of wildlife strikes in the US and the associated disclosure behaviors of large domestic American airlines. Chapter 1 investigates the relationship between the likelihood of voluntary repair cost disclosure after a wildlife-strike event by such airlines and market competitiveness and idiosyncratic firm profits. Results show changes in competitiveness and profitability impact the voluntary disclosure of wildlife-strike repair costs by major US airlines to the NWSD. Chapter 2 similarly examines airline voluntary disclosure accuracy, employing emerging methods from economics and accounting literature that test the accuracy of self-reported data based on a statistical property exhibited by large datasets, known as Benford's Law (de Marchi & Hamilton, 2006; Dumas & Devine, 2000; Nigrini, 1996; Zahran, Iverson, Weiler, & Underwood, 2014). Analogous to Chapter 1, findings indicate the accuracy of repair costs American air carriers report to the NWSD is linked to market competition and profits. Chapter 3 relates to developing a method for interpolating missing repair costs in the NWSD using machine learning techniques. Results show that a neural network outperforms both linear regression and random forest models when predicting out-of-sample data, and furthermore, interpolating missing costs in the NWSD with a neural network delivers an average annual estimate of the direct costs of wildlife strikes in the US that is approximately 75 million, significantly less than prior estimates. Specifically, the neural network approach yields estimates $19 and$82 million lower, respectively, than when using mean cost assignment and Dolbeer et al. (2014)'s reported estimate derived using a variation of the same method

Detection of cropland field parcels from Landsat imagery

A slowdown in global agricultural expansion, spurred by land limitations, improved technologies, and demand for specific crops has led to increased agricultural intensification. While agricultural expansion has been heavily scrutinized, less attention has been paid to changes within cropland systems. Here we present a method to detect individual cropland field parcels from temporal Landsat imagery to improve cropland estimates and better depict the scale of farming across South America. The methods consist of multi-spectral image edge extraction and multi-scale contrast limited adaptive histogram equalization (CLAHE) and adaptive thresholding using Landsat Surface Reflectance Climate Data Record (CDR) products. We tested our methods across a South American region with approximately 82% of the 2000/2001 total cropland area, using a Landsat time series composite with a January 1, 2000 to August 1, 2001 timeframe. A thematic accuracy assessment revealed an overall cropland f-score of 91%, while an object-based assessment of 5,480 fields showed low geometric errors. The results illustrate that Landsat time series can be used to accurately estimate cropland in South America, and the low geometric errors of the per-parcel estimates highlight the applicability of the proposed methods over a large area. Our approach offers a new technique of analyzing agricultural changes across a broad geographic scale. By using multi-temporal Landsat imagery with a semi-automatic field extraction approach, we can monitor within-agricultural changes at a high degree of accuracy, and advance our understanding of regional agricultural expansion and intensification dynamics across South America.Arts, Faculty ofNon UBCLiu Institute for Global IssuesUnreviewedFacult

Increasing expansion of large-scale crop production onto deforested land in sub-Andean South America

A combination of high commodity crop prices, rising global food demand, and technological advances has transformed the scale of global crop production. Farming in South America is a prime example, where large-scale cash crops, such as soy, have transformed the land use dynamics at the forest frontier. We evaluate this transformation in sub-Andean South America by estimating crop and forest cover and detecting individual cropland field parcels using Landsat imagery in 5 year intervals over a 24 year period. From 1990 to 2014, cropland expansion onto deforested land was increasingly driven by large fields (>50 ha), whose contribution increased from 32% to 48% (+16% increase), while the contribution of smaller fields