Abstract “The most distinctive characteristic of the Los Angeles basin“The most distinctive characteristic of the Los Angeles basin is its structural relief and complexity in relation to its age and size” (Yerkes et aI., 1965, p. AI6); however, its very complexity caused no small amount of discussion in designing and naming this volume of the AAPG World Petroleum Basin Memoirs. (See the Foreword for a discussion of the scope of these memoirs.) The series coordinators decided early that the Los Angeles basin should be included in the World Petroleum Basins project because of its interesting geology and importance as a hydrocarbon producer. Initially, the Los Angeles basin was considered for a convergent-margin volume, presumably in recognition of the late-stage shortening that has taken place in the Los Angeles region of southern California. There is little doubt, however, that the Los Angeles basin has formed and deformed within the evolving San Andreas transform system (Atwater, 1970, 1989; Campbell and Yerkes, 1976; Blake et al., 1978; Engebretson et al., 1985; Wright, this volume). There is also little doubt among those who have worked in the area that the initial subsidence of the Neogene Los Angeles basin was caused by extension (Yeats, 1968; Crowell, 1974, 1976, 1987; Wright, this volume). The series coordinators decided, therefore, that to portray the Los Angeles basin as a model for basins formed in convergent-margin settings would be misleading. The title of this volume, Active Margin Basins , is a compromise, but, like many compromises, this title falls short of completely describing its subject

Abstract “The most distinctive characteristic of the Los Angeles basin“The most distinctive characteristic of the Los Angeles basin is its structural relief and complexity in relation to its age and size” (Yerkes et aI., 1965, p. AI6); however, its very complexity caused no small amount of discussion in designing and naming this volume of the AAPG World Petroleum Basin Memoirs. (See the Foreword for a discussion of the scope of these memoirs.) The series coordinators decided early that the Los Angeles basin should be included in the World Petroleum Basins project because of its interesting geology and importance as a hydrocarbon producer. Initially, the Los Angeles basin was considered for a convergent-margin volume, presumably in recognition of the late-stage shortening that has taken place in the Los Angeles region of southern California. There is little doubt, however, that the Los Angeles basin has formed and deformed within the evolving San Andreas transform system (Atwater, 1970, 1989; Campbell and Yerkes, 1976; Blake et al., 1978; Engebretson et al., 1985; Wright, this volume). There is also little doubt among those who have worked in the area that the initial subsidence of the Neogene Los Angeles basin was caused by extension (Yeats, 1968; Crowell, 1974, 1976, 1987; Wright, this volume). The series coordinators decided, therefore, that to portray the Los Angeles basin as a model for basins formed in convergent-margin settings would be misleading. The title of this volume, Active Margin Basins , is a compromise, but, like many compromises, this title falls short of completely describing its subject

ABSTRACT The use of artificial intelligence (AI) and machine learning (ML) methods in the geosciences can be categorized into three types, those that: (1) accelerate computationally expensive Earth system models; (2) fill the vacuum where numerical and physics-based models struggle; and (3) enable and enlighten data-driven discoveries. To achieve these tasks, many cyberinfrastructure (CI) systems are required. This chapter reviews the cutting-edge CI aiding the implementation of AI in the geosciences. Each technique presented is evaluated to assist geoscientists in determining how appropriate it is. Use cases in the subdomains of seismology, hydrology, and climatology are introduced to help readers understand the workflows. Challenges and future opportunities for CI development center on big data, provenance, interoperability, and heterogeneity due to the scale and complexity that future AI models in the geosciences will require.