Geophysics and Tectonics Seminar - winter-2024
Fracture Detection and Seismic Reflection Azimuthal Anisotropy
Jan. 17, 2024
noon - 1 p.m.
Zoom link: https://ucla.zoom.us/j/94377539122?pwd=bUJWWHBhSkx1UW8vZnEvdHBvVGlXdz09
Presented By:
- Dajani Abdulfattah -
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In naturally fractured reservoirs a detailed understanding and mapping of the subsurface fractures network are often necessary to optimize field development plans and optimize wells placements and designs in both conventional and unconventional reservoirs with a different objective in mind to fit purpose. This is true not only for hydrocarbon related applications but also for other resources development such as Geothermal and CO2 storage among others; whenever detecting fractures- both natural and/or induced via drilling and/or injection, is necessary for the target reservoirs. There are varieties of seismic technologies which attempt to map and detect fractures in the subsurface, one of which and one of the most common applied technologies, is the application of seismic reflection moveout. Detecting fractures using seismic reflections can be achieved as a post stack process and/or as a prestack process. In this presentation, we discuss the key ideas behind such technology with some applications and examples.
Art and Appetizers: Unveiling of art in memory of David Jackson
Jan. 24, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Kelly Rojas -
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Ribbon cutting and unveiling of art donated in memory of Dave Jackson by Kelly Jackson Rojas
Next-Generation Seismic Monitoring with Neural Operators
Jan. 31, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Hongyu Sun - California Institute of Technology
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Earthquake monitoring often involves measuring arrival times of P- and S-waves of earthquakes from continuous seismic data. With the advancement of artificial intelligence, state-of-the-art seismic monitoring methods use deep neural networks to examine seismic data from each station independently; this is in stark contrast to the way that human experts annotate seismic data, in which waveforms from the whole network containing multiple stations are examined simultaneously. With the performance gains of single-station algorithms approaching saturation, meaningful future advances will require algorithms that can naturally examine data for entire networks at once. Here we introduce a general-purpose network-wide phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our algorithm, called Phase Neural Operator, leverages the spatial-temporal information of earthquake signals from an input seismic network with arbitrary geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking many more seismic wave arrivals, yet also greatly improving measurement accuracy. Following similar trends being seen across the domains of artificial intelligence, our approach provides but a glimpse of the potential gains from fully utilizing the massive seismic datasets being collected around the world.
Experimental insights into the thermal conductivity of Earth’s mantle and core
Feb. 14, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Eric Edmund - Carnegie Science
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The Earth can be viewed as a giant heat engine, with ongoing interior and exterior dynamics which are enabled by the transfer of energy deep within the planet. However, the material parameters which govern heat transfer within the Earth’s interior remain controversial at the conditions of Earth’s lower mantle and core. Here we present a series of experiments using fast laser heating and dynamic temperature measurements to determine the thermal conductivity of the primary components of Earth’s lower mantle and core at high pressures and temperatures. Experimental results will be presented for bridgmanite and iron, at pressures ranging from 25 GPa to 135 GPa and at temperatures ranging from 1800 K to above 3500 K. These results are used to provide new insight into the coupled thermal evolution of Earth’s mantle and core over geologic time.
Stochastic Models of the Geodynamo
Feb. 21, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- William Davis - Scripps Institution of Oceanography
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Why is the geodynamo so complicated? In this talk, I will show work modeling the time variation of Earth’s magnetic field as a stochastic process. These processes, which comprise both deterministic and random elements, can give insights into the mechanisms of magnetic field generation in the outer core. I will show results from numerical geodynamo simulations and apply interpretations to the paleomagnetic record, with implications for field variability, reversal rates, superchrons, and the Earth's thermal evolution.
New Insights on P-T conditions in the Whipple Mountains Metamorphic Core Complex
Feb. 28, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Valeria Jaramillo Hernández - UCLA
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The North American Cordilleran metamorphic core complex belt has been extensively studied because it provides insights into the tectonometamorphic evolution of North America (Cassel et al., 2018; Coney, 1980; Davis & Coney, 1979; Dickinson, 2004; Dickinson et al., 2009; Hildebrand, 2013; Wernicke et al., 1987). Specifically, garnet-bearing assemblages are valuable recorders of the conditions experienced by rocks during metamorphism (Ferry & Spear, 1978; Berman, 1990; Chapman et al., 2021). In various studies, garnet-bearing assemblages have been used to generate pressure-temperature constraints on the metamorphic history of the North American Cordillera in its northern and central segments. However, such data sets are scarce in its southern segment of the North American Cordillera (Chapman et al., 2021). To address this issue, we evaluate the conditions and timing of when the Whipple detachment shear zone in southeastern California developed using conventional thermobarometers, isochemical phase diagrams, and isopleth thermobarometry (de Capitani & Brown, 1987; de Capitani & Petrakakis, 2010; Florence and Spear, 1991; Ganguly, 2010; Hoisch, 1990; Holland & Powell, 1998; Tracy et al., 1976). Conventional P-T estimates (Ferry and Spear, 1978; Hoisch, 1989; Berman, 1990) using the garnet rim and matrix minerals yield T = 700-800 °C and P = 7-9 kbar. U-Th dating of monazite inclusions in garnets from the same rock samples yields an older age of ~1.0 Ga and a range of younger ages from 80.6 Ma to 62.9 Ma. The results are consistent with garnet crystallizing at higher-grade conditions during the Paleocene-Early Eocene which provides insights into a thermal event not previously proposed.
Coevolution of organic matter burial in sediments and granite geochemistry
March 3, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Claire Bucholz - California Institute of Technology
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Phosphorus plays a critical role in both surface biological cycles as an essential nutrient and in magmatic systems as the backbone for trace element-rich phosphate minerals. Further, phosphorus cycling between sedimentary and igneous systems throughout Earth history has had potentially profound effects on biogeochemical cycles. I will discuss my recent work on phosphorus concentrations in strongly peraluminous granites (SPGs) from the past 3.5 billion years. SPGs are generated by the partial melting of sedimentary rocks and can thus provide a novel archive to reveal secular trends in Earth’s environmental history that integrate large volumes of siliciclastic sediments. I find that phosphorus contents of SPGs systematically increase across the Precambrian-Phanerozoic boundary, mirroring a previously documented increase in the phosphorus contents of marine siliciclastic sediments. After consideration of the effects of metamorphic, partial melting, and igneous differentiation processes on SPG phosphorus contents, I conclude that low phosphorus contents in Precambrian SPGs are most parsimoniously explained as resulting from low phosphorus contents in their source rocks. This increase is also mirrored in nitrogen (N) contents of SPGs across the same time interval. Collectively, this data is most parsimoniously explained by an absolute increase in biomass burial in the late Proterozoic or early Phanerozoic. I will also discuss the implications of this work for the use of trace elements in detrital zircon as a tool to fingerprint for the nature of their source rocks. Relevant references: Bucholz, C.E., 2022. Coevolution of sedimentary and strongly peraluminous granite phosphorus records. Earth and Planetary Science Letters, 596, p.117795. Bucholz, C.E., Liebmann, J. and Spencer, C.J., 2022. Secular variability in zircon phosphorus concentrations prevents simple petrogenetic classification. Geochemical Perspectives Letters, 24, pp.12-16. Mikhail, S., Stüeken, E.E., Boocock, T.J., Athey, M., Mappin, N., Boyce, A.J., Liebmann, J., Spencer, C.J. and Bucholz, C.E., 2024. Strongly peraluminous granites provide independent evidence for an increase in biomass burial across the Precambrian–Phanerozoic boundary. Geology, 52(1), pp.87-91.
Geophysical and Geo-AI-based evaluation and modelling of earthquake-induced landslides
March 7, 2024
2 p.m. - 3 p.m.
3814 Geology
Presented By:
- Ashok Dahal - University of Twente- ITC, Netherlands
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Earthquake-induced landslides are one of the most significant cascading hazards during and after an earthquake, with the potential to cause severe damage to infrastructure and people. Those landslides are mainly conditional to the ground motion and other environmental variables. The past five decades of research on earthquake-induced landslides have focused primarily on understanding the behavior of landslides with respect to the ground motion intensity parameters such as peak ground velocity and acceleration. However, the ground motion varies in amplitude and phase, both in space and time. Therefore, reasoning landslide occurrence and their size, as well as modelling them based on single-intensity parameters, does not respect the physical nature of the system. This presentation will explore the steps towards using ground motion simulations (which solve wave equations in space and time, thus producing detailed estimates of the shaking time series) to understand and model the co-seismic and post-seismic landslide hazards using explainable artificial intelligence models. To achieve that, we will go through the complexities of ground motion simulations, the role of topographic amplification on landslide occurrence, explainable AI to model and understand the process, transformer neural networks to relate ground motion to landslides, post-seismic landslide models and probabilistic landslide hazard modelling approach using extreme value theory. Finally, providing an outlook towards the influence of large earthquakes on surface deformation and physics-informed neural networks to model the co-seismic landslides.
The increasingly dominant role of climate change on the Earth’s variable rotation
March 13, 2024
noon - 1 p.m.
Geology 1707
Presented By:
- Surendra Adhikari - Jet Propulsion Laboratory
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Space geodetic techniques have provided high-precision measurement of daily variations in the Earth’s spin rate -- characterized by the Length of Day (LOD) -- and the spin axis position relative to the crust, termed Polar Motion (PM). Before the advent of the space era, astronomers and geophysicists have inferred the change in LOD for the past three millennia from eclipse records and the PM for over a century from star catalogs. In this talk, I will summarize our ongoing efforts to understand and disentangle various driving mechanisms of the Earth’s rotation parameters across timescales and highlight the increasingly dominant role of ongoing climate change. By leveraging observations of Earth’s surface mass distribution, state-of-the-art climatological and geophysical models, and a novel approach of the Physics Informed Neural Networks (PINNs), we show that the sustained melting of glaciers and ice sheets not only alters the long-term drift direction of the spin axis but also exhibits a strong control in slowing down the spin rate. These findings have broad implications for constraining various geophysical and climatological processes and exploring their possible interactions.