Natural gas and oil from characteristically complex unconventional reservoirs, such as organic shale, tight gas and oil, coal-bed methane; are transforming the global energy market. These conventional reserves exist in complex geologic formations where conventional seismic techniques have been challenged to successfully image the subsurface. To acquire maximum benefits from these unconventional reserves, seismic anisotropy must be at the center of our modeling and inversion workflows. I present algorithms for fast traveltime computations in anisotropic media. Both ray-based and finite-difference solvers of the anisotropic eikonal equation are developed. The proposed algorithms present novel techniques to obtain accurate traveltime solutions for anisotropic media in a cost-efficient manner. The traveltime computation algorithms are then used to invert for anisotropy parameters. Specifically, I develop inversion techniques by using diffractions and diving waves in the seismic data. The diffraction-based inversion algorithm can be combined with an isotropic full-waveform inversion (FWI) method to obtain a high-resolution model for the anellipticity anisotropy parameter. The inversion algorithm based on diving waves is useful for building initial anisotropic models for depth-migration and FWI. I also develop the idea of 'effective elliptic models' for obtaining solutions of the anisotropic two-way wave equation. The proposed technique offers a viable alternative for wavefield computations in anisotropic media using a computationally cheaper wave propagation operator. The methods developed in the thesis lead to a direct cost savings for imaging and inversion projects, in addition to a reduction in turn-around time. With an eye on the next generation inversion methods, these techniques allow us to incorporate more accurate physics into our modeling and inversion framework.

In my thesis, I have focused on composing by way of adapting motifs from solos I have transcribed, in order to find strong melodies and basslines. I had a hard time selecting the material I would make use of and struggled with balancing what I created myself and what I drew upon from outside sources. Hence the thesis' title, Internal and external influence on a composition process, where I use the terms “internal” and “external” to illustrate two sources I based my composing upon. At the beginning of the process, I was heavily emphasizing the use of external material, to detach myself from my habits and hopefully write innovative music. The result was a harmony rich with dissonance that conflicted with my personal taste. I realized that I needed to abandon this principle and find a balance where external material merely inspired me but was directed by my internal inclinations. / <p>Langørgen, Anders, "Iwazaru"; 2019. Langørgen, Anders, "Ankh"; 2020. Langørgen, Anders, "Baron Wry"; 2020. Langørgen, Anders, "Plasma Cherub"; 2020.</p><p>Sebastian Jonsson – sopran- och altsaxofon, Mattias Olofsson – gitarr, Max Agnas – piano, Edvin Fridolfsson – trummor, Anders Langørgen – kontrabas.</p>

Sedimentary rock-hosted deposits are a major source of copper and cobalt, with the Neoproterozoic central African Copperbelt being among the largest Cu-Co provinces in the world, accounting for around 15% of its copper resource. The deposits occur primarily in the carbonates and siliciclastic sediments overlying the basement, and formed during early diagenesis (around 820 Ma) and late diagenesis/metamorphism during the Pan-African Orogeny (580-520 Ma). The northwest province of Zambia hosts three major copper deposits, amongst which is Kansanshi: the focus of this study. The deposit, which lies north of the Solwezi dome, is hosted within the Katangan Supergroup, particularly within the carbonaceous phyllites and porphyroblastic schists of the Mshwaya subgroup and lower Nguba Group and extends along the strike length of the North-West trending Kansanshi antiform. In this study, tomographic inversion is applied to first arrival refraction data collected at the Kansanshi Copper Mine with the aim of locating potential copper-bearing structures.  The survey was carried out using both dynamite and VIBSIST sources along 3 profiles; 2 trending North-East across the Kansanshi anticline and 1 trending north-west parallel to it. Seismic refraction tomography is an excellent tool for investigating the shallow subsurface, providing a velocity distribution. Unlike conventional refraction seismics, it allows for the velocity calculation of each cell in a non-homogeneous earth model, rather than just the average velocity of individual layers - allowing us to map structure and infer geological units and weathering profiles. The data highlights abundant faulting and varying depth to fresh bedrock. The various lithologies have also been interpreted.

In this paper, new variations of some well-known permutation statistics are introduced and studied. Firstly, a double-descent of a permutation π is defined as a position i where πi ≥ 2πi+1. By proofs by induction and direct proofs, recursive and explicit expressions for the number of n-permutations with k double-descents are presented. Also, an expression for the total number of double-descents in all n-permutations is presented. Secondly, a double-inversion of a permutation π is defined as a pair (πi,πj) where i&lt;j but πi ≥ 2πj. The total number of double-inversions in all n-permutations is presented.

Full Waveform Inversion (FWI) is a non-linear optimization problem aimed to estimating subsurface parameters by minimizing the misfit between modeled and recorded seismic data using gradient descent methods, which are the only practical choice because of the size of the problem. Due to the high non-linearity of the problem, gradient methods will converge to a local minimum if the starting model is not close to the true one. The accuracy of the long-wavelength components of the initial model controls the level of non-linearity of the inversion. In order for FWI to converge to the global minimum, we have to obtain the long wavelength components of the model before inverting for the short wavelengths. Ultra-low temporal frequencies are sensitive to the smooth (long wavelength) part of the model, and can be utilized by waveform inversion to resolve that part. Unfortunately, frequencies in this range are normally missing in field data due to data acquisition limitations. The lack of low frequencies can be compensated for by utilizing wide-aperture data, as they include arrivals that are especially sensitive to the long wavelength components of the model. The higher the scattering angle of a 5 recorded event, the higher the model wavelength it can resolve. Based on this property, a scattering-angle filtering algorithm is proposed to start the inversion process with events corresponding to the highest scattering angle available in the data, and then include lower scattering angles progressively. The large scattering angles will resolve the smooth part of the model and reduce the non-linearity of the problem, then the lower ones will enhance the resolution of the model. Recorded data is first migrated using Pre-stack Exploding Reflector Migration (PERM), then the resulting pre-stack image is transformed into angle gathers to which an angle filtering process is applied to remove events below a certain cut-off angle. The filtered pre-stack image cube is then demigrated (forward modeled) to produce filtered surface data that can be used in waveform inversion. Numerical tests confirm the feasibility of the proposed filtering algorithm. However, the accuracy of the filtered section is limited by PERM’s singularity for horizontally-traveling waves, which in turn is dependent on the velocity model used for migration and demigration

Terrestrial iron oxides, such as hematite, goethite and magnetite, form specifically cemented concretions in sandstones using an aqueous diagenetic solution. According to the new results, the timeline on which concretions are formed could be much larger than current estimates. The basis for this claim is a magnetic record of the Jurassic Navajo Sandstone in Utah. The record contains fluctuations of remanent magnetization, which could prove the inversion of the Earth's magnetic field. This observation suggests that the time axis of the accumulation of iron concretions is comparable to the time axis of the accumulation of marine iron-manganese nodules, which contain a complex magnetic record. The occurrence of extremely slow changes in iron deposition in sandstones also helps to better understand the origin of concretions. In addition, terrestrial concretions provide an analogy for the "blueberries" found by the Mars rover Opportunity on Mars. These concretions probably underwent similar formation processes and could record inversions of the early magnetic dynamo on Mars.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19014号 / 工博第4056号 / 新制||工||1624(附属図書館) / 31965 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 杉野目 道紀, 教授 澤本 光男, 教授 辻 康之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20951号 / 理博第4403号 / 新制||理||1632(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 大須賀 篤弘, 教授 丸岡 啓二, 教授 依光 英樹 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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Recent research shows that machine learning algorithms are highly susceptible to attacks trying to extract sensitive information about the data used in model training. These attacks called privacy attacks, exploit the model training process. Contemporary defense techniques make alterations to the training algorithm. Such defenses are computationally expensive, cause a noticeable privacy-utility tradeoff, and require control over the training process. This thesis presents a data-centric approach using data augmentations to mitigate privacy attacks. We present privacy-focused data augmentations to change the sensitive data submitted to the model trainer. Compared to traditional defenses, our method provides more control to the individual data owner to protect one's private data. The defense is model-agnostic and does not require the data owner to have any sort of control over the model training. Privacypreserving augmentations are implemented for two attacks namely membership inference and model inversion using two distinct techniques. While the proposed augmentations offer a better privacy-utility tradeoff on CIFAR-10 for membership inference, they reduce the reconstruction rate to ≤ 1% while reducing the classification accuracy by only 2% against model inversion attacks. This is the first attempt to defend model inversion and membership inference attacks using decentralized privacy protection. / Master of Science / Privacy attacks are threats posed to extract sensitive information about the data used to train machine learning models. As machine learning is used extensively for many applications, they have access to private information like financial records, medical history, etc depending on the application. It has been observed that machine learning models can leak the information they contain. As models tend to 'memorize' training data to some extent, even removing the data from the training set cannot prevent privacy leakage. As a result, the research community has focused its attention on developing defense techniques to prevent this information leakage. However, the existing defenses rely heavily on making alterations to the way a machine learning model is trained. This approach is termed as a model-centric approach wherein the model owner is responsible to make changes to the model algorithm to preserve data privacy. By doing this, the model performance is degraded while upholding data privacy. Our work introduces the first data-centric defense which provides the tools to protect the data to the data owner. We demonstrate the effectiveness of the proposed defense in providing protection while ensuring that the model performance is maintained to a great extent.