Full moment tensor and source location inversion based on full waveform adjoint inversion: application at the Geysers geothermal field

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Final Report: Detection and Characterization of Underground Facilities by Stochastic Inversion and Modeling of Data from the New Generation of Synthetic Aperture Satellites

Many clandestine development and production activities can be conducted underground to evade surveillance. The purpose of the study reported here was to develop a technique to detect underground facilities by broad-area search and then to characterize the facilities by inversion of the collected data. This would enable constraints to be placed on the types of activities that would be feasible at each underground site, providing a basis the design of targeted surveillance and analysis for more complete characterization. Excavation of underground cavities causes deformation in the host material and overburden that produces displacements at the ground surface. Such displacements are often measurable by a variety of surveying or geodetic techniques. One measurement technique, Interferometric Synthetic Aperture Radar (InSAR), uses data from satellite-borne (or airborne) synthetic aperture radars (SARs) and so is ideal for detecting and measuring surface displacements in denied access regions. Depending on the radar frequency and the acquisition mode and the surface conditions, displacement maps derived from SAR interferograms can provide millimeter- to centimeter-level measurement accuracy on regional and local scales at spatial resolution of {approx}1-10 m. Relatively low-resolution ({approx}20 m, say) maps covering large regions can be used for broad-area detection, while finer resolutions ({approx}1 m) can be used to image details of displacement fields over targeted small areas. Surface displacements are generally expected to be largest during or a relatively short time after active excavation, but, depending on the material properties, measurable displacement may continue at a decreasing rate for a considerable time after completion. For a given excavated volume in a given geological setting, the amplitude of the surface displacements decreases as the depth of excavation increases, while the area of the discernable displacement pattern increases. Therefore, the ability to detect evidence for an underground facility using InSAR depends on the displacement sensitivity and spatial resolution of the interferogram, as well as on the size and depth of the facility and the time since its completion. The methodology development described in this report focuses on the exploitation of synthetic aperture radar data that are available commercially from a number of satellite missions. Development of the method involves three components: (1) Evaluation of the capability of InSAR to detect and characterize underground facilities ; (2) inversion of InSAR data to infer the location, depth, shape and volume of a subsurface facility; and (3) evaluation and selection of suitable geomechanical forward models to use in the inversion. We adapted LLNL's general-purpose Bayesian Markov Chain-Monte Carlo procedure, the 'Stochastic Engine' (SE), to carry out inversions to characterize subsurface void geometries. The SE performs forward simulations for a large number of trial source models to identify the set of models that are consistent with the observations and prior constraints. The inverse solution produced by this kind of stochastic method is a posterior probability density function (pdf) over alternative models, which forms an appropriate input to risk-based decision analyses to evaluate subsequent response strategies. One major advantage of a stochastic inversion approach is its ability to deal with complex, non-linear forward models employing empirical, analytical or numerical methods. However, while a geomechanical model must incorporate adequate physics to enable sufficiently accurate prediction of surface displacements, it must also be computationally fast enough to render the large number of forward realizations needed in stochastic inversion feasible. This latter requirement prompted us first to investigate computationally efficient empirical relations and closed-form analytical solutions. However, our evaluation revealed severe limitations in the ability of existing empirical and analytical forms to predict deformations from underground cavities with an accuracy consistent with the potential resolution and precision of InSAR data. We followed two approaches to overcoming these limitations. The first was to develop a new analytical solution for a 3D cavity excavated in an elastic half-space. The second was to adapt a fast parallelized finite element method to the SE and evaluate the feasibility of using in the stochastic inversion. To date we have demonstrated the ability of InSAR to detect underground facilities and measure the associated surface displacements by mapping surface deformations that track the excavation of the Los Angeles Metro system. The Stochastic Engine implementation has been completed and undergone functional testing

Mapping Public Engagement with Research in a UK University

Notwithstanding that ‘public engagement’ is conceptualised differently internationally and in different academic disciplines, higher education institutions largely accept the importance of public engagement with research. However, there is limited evidence on how researchers conceptualise engagement, their views on what constitutes engagement and the communities they would (or would not) like to engage with. This paper presents the results of a survey of researchers in the Open University that sought to gather data to fill these gaps. This research was part of an action research project designed to embed engagement in the routine practices of researchers at all levels. The findings indicate that researchers have a relatively narrow view of public engagement with research and the communities with which they interact. It also identified that very few strategically evaluate their public engagement activities. We conclude by discussing some of the interventions we have introduced with the aim of broadening and deepening future researcher engagement

Reaching across continents : engaging students through virtual collaborations

Business schools have the responsibility of preparing students for work in multicultural organizations and global markets. This paper examines a situated learning experience for undergraduates through a virtual collaboration between a UK university and a Brazilian university. This facilitated remote communication using social media and smart devices, allowing students from both institutions to enhance their cross-cultural management competencies. A qualitative approach was used for the research, drawing on the reflections of the tutors from both institutions, and feedback received from students in the UK and Brazil. This paper provides empirical observations regarding the use of this innovative pedagogic approach, generating discussion of the implications for teaching, thus contributing to the literature on international collaborations in cross-cultural management education

Coalition theories: empirical evidence for dutch municipalities

The paper analyzes coalition formation in Dutch municipalities. After discussing the main features of the institutional setting, several theories are discussed, which are classified as size oriented, policy oriented and actor oriented models. A test statistic is proposed to determine the predictive power of these models. The empirical analysis shows that strategic positions as well as some of the distinguished preferences are important in the setting of Dutch municipalities. Especially, the dominant minimum number principle yields highly significant results for coalition formations in the period 1978–1986

Seagrass spatial data synthesis from north-east Australia, Torres Strait and Gulf of Carpentaria, 1983 to 2022

The Gulf of Carpentaria and Torres Strait in north-eastern Australia support globally significant seagrass ecosystems that underpin fishing and cultural heritage of the region. Reliable data on seagrass distribution are critical to understanding how these ecosystems are changing, while managing for resilience. Spatial data on seagrass have been collected since the early 1980s, but the early data were poorly curated. Some was not publicly available, and some already lost. We validated and synthesized historical seagrass spatial data to create a publicly available database. We include a site layer of 48,612 geolocated data points including information on seagrass presence/absence, sediment, collection date, and data custodian. We include a polygon layer with 641 individual seagrass meadows. Thirteen seagrass species are identified in depths ranging from intertidal to 38 m below mean sea level. Our synthesis includes scientific survey data from 1983 to 2022 and provides an important evidence base for marine resource management