Development of homes, roads, and commercial buildings in northern Utah has grown significantly during the last several decades. Construction has expanded from the valley floor to higher elevations of benches, foothills, and other elevated regions of the Wasatch Mountain Front. Construction in the higher elevation areas are a concern due to potential for landslides, both new and reactivated. Landslides have been identified in this region and are dated as Pleistocene to historical in age. A possible landslide of about 0.5 km2 on the south slope of Traverse Mountain has been mapped by the Utah Geological Survey in 2005. Its surface exhibits hummocky topography and is comprised of Oligocene-age volcanic ash, block and ash flow tuffs, and andesite lava. Landslides along the Wasatch Mountain Front are complex features usually characterized by dense vegetation and poor outcrop and require a combination geological and geophysical methods to study their thickness, slope, lateral extent, and style of emplacement. Our study incorporates trenching, boreholes, and LiDAR aerial imagery. Unique to the study of landslides is our use of seismic reflection with a vibroseis source over the mapped landslide deposit. The seismic parameters of source, station spacing, and processing method provide a coherent, albeit low-resolution, image of the upper 500 m of the subsurface beneath the landslide. A major reflector boundary in our seismic profiles has an apparent dip of 4° to the south, approximately parallel with the surface topography. Its elevation and seismic character are indicative of a contact between the Oligocene-age volcanic rocks on top of a portion of the Pennsylvanian-age Bingham Mine Formation, a mixed carbonate and siliciclastic sequence. The reflector defines an asymmetric graben-like structure bounded by a north-northwest-trending normal fault system. Analysis of trenches, boreholes and local geology reveals a faulted, chaotic body of block and ash flow tuffs, surrounded by andesite lavas. Using LiDAR and surface geological reconnaissance, a possible toe or margin of a landslide has been interpreted in the north-west portion of the study area. The combination weakened block and ash flow tuffs and abundant clay production from this unit contribute to the likelihood of a coalescence of landslides in this mapped landslide area. The integration of LiDAR, trenching, boreholes and reflection seismology provides the range and resolution of data needed to assess the complex geology of landslides.

[pt] A interpretação de imagens sísmicas é uma tarefa essencial em diversas áreas das geociências, sendo um método amplamente utilizado na exploração de hidrocarbonetos. Porém, sua interpretação exige um investimento significativo de recursos, e nem sempre é possível obter um resultado satisfatório. A literatura mostra um número crescente de métodos de Deep Learning, DL, para detecção de horizontes, falhas e potenciais reservatórios de hidrocarbonetos, porém, os modelos para detecção de reservatórios de gás apresentam dificuldades de desempenho de generalização, ou seja, o desempenho fica comprometido quando utilizados em imagens sísmicas de novas explorações campanhas. Este problema é especialmente verdadeiro para levantamentos terrestres 2D, onde o processo de aquisição varia e as imagens apresentam muito ruído. Este trabalho apresenta três métodos para melhorar o desempenho de generalização de modelos DL de indicação de gás natural em imagens sísmicas 2D, para esta tarefa são utilizadas abordagens provenientes de Machine Learning, ML e DL. A pesquisa concentra-se na análise de dados para reconhecer padrões nas imagens sísmicas para permitir a seleção de conjuntos de treinamento para o modelo de inferência de gás com base em padrões nas imagens alvo. Esta abordagem permite uma melhor generalização do desempenho sem alterar a arquitetura do modelo DL de inferência de gás ou transformar os traços sísmicos originais. Os experimentos foram realizados utilizando o banco de dados de diferentes campos de exploração localizados na bacia do Parnaíba, no Nordeste do Brasil. Os resultados mostram um aumento de até 39 por cento na indicação correta do gás natural de acordo com a métrica de recall. Esta melhoria varia em cada campo e depende do método proposto utilizado e da existência de padrões representativos dentro do conjunto de treinamento de imagens sísmicas. Estes resultados concluem com uma melhoria no desempenho de generalização do modelo de inferência de gases DL que varia até 21 por cento de acordo com a pontuação F1 e até 15 por cento de acordo com a métrica IoU. Estes resultados demonstram que é possível encontrar padrões dentro das imagens sísmicas usando uma abordagem não supervisionada, e estas podem ser usadas para recomendar o conjunto de treinamento DL de acordo com o padrão na imagem sísmica alvo; Além disso, demonstra que o conjunto de treinamento afeta diretamente o desempenho de generalização do modelo DL para imagens sísmicas. / [en] Interpreting seismic images is an essential task in diverse fields of geosciences, and it s a widely used method in hydrocarbon exploration. However, its interpretation requires a significant investment of resources, and obtaining a satisfactory result is not always possible. The literature shows an increasing number of Deep Learning, DL, methods to detect horizons, faults, and potential hydrocarbon reservoirs, nevertheless, the models to detect gas reservoirs present generalization performance difficulties, i.e., performance is compromised when used in seismic images from new exploration campaigns. This problem is especially true for 2D land surveys where the acquisition process varies, and the images are very noisy. This work presents three methods to improve the generalization performance of DL models of natural gas indication in 2D seismic images, for this task, approaches that come from Machine Learning, ML, and DL are used. The research focuses on data analysis to recognize patterns within the seismic images to enable the selection of training sets for the gas inference model based on patterns in the target images. This approach allows a better generalization of performance without altering the architecture of the gas inference DL model or transforming the original seismic traces. The experiments were carried out using the database of different exploitation fields located in the Parnaíba basin, in northeastern Brazil. The results show an increase of up to 39 percent in the correct indication of natural gas according to the recall metric. This improvement varies in each field and depends on the proposed method used and the existence of representative patterns within the training set of seismic images. These results conclude with an improvement in the generalization performance of the DL gas inference model that varies up to 21 percent according to the F1 score and up to 15 percent according to the IoU metric. These results demonstrate that it is possible to find patterns within the seismic images using an unsupervised approach, and these can be used to recommend the DL training set according to the pattern in the target seismic image; Furthermore, it demonstrates that the training set directly affects the generalization performance of the DL model for seismic images.

Depuis la mise en place en 2010, d’une nouvelle tarification française sur le tarif de l’énergie géothermique, l’Alsace est la région de France la plus dynamique quant à la réalisation de forages géothermiques profonds à haute température (>150°C). Ainsi, l’approche géologique, qui a été primordiale pour les forages de Rittershoffen, le sera encore davantage pour les projets à venir compte tenu de la méconnaissance géologique de certaines zones profondes du bassin rhénan. Cette étude propose d’appréhender la compréhension de l’architecture stratigraphique et séquentielle des formations de la couverture sédimentaire rhénane. 15 puits profonds ont été réinterprétés et corrélés à travers l’ensemble du bassin, permettant l’élaboration d’une colonne stratigraphique complète incluant le sommet et la base de chaque formation. Ces réinterprétations ont également mis en lumière le signal caractéristique de la diagraphie gamma-ray (GR) de chacune des formations de la colonne stratigraphique rhénane. De plus, la caractérisation lithostratigraphique du passage entre le socle et la couverture sédimentaire a fait l’objet d’un axe important de recherche. / Since the introduction of a new French pricing system for geothermal energy in 2010, Alsace has been the most dynamic region in France for deep geothermal drilling at high temperatures (>150°C). Thus, the geological approach, which has been essential for the Rittershoffen boreholes, will be even more for future projects given the lack of geological knowledge of some deep parts of the URG. This study aims at understanding the stratigraphic and sequential architecture of the formations of the URG sedimentary cover. 15 deep wells were reinterpreted and correlated throughout the basin, allowing the development of a complete stratigraphic column including the top and base of each formation. These reinterpretations also highlighted the characteristic gamma-ray signal (GR) of each of the formations in the URG stratigraphic column. In addition, the lithostratigraphic characterization of the transition between the basement and the sedimentary cover was the subject of an important research focus.