In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

to be done in this area. Face recognition is a problem that scarcely clamoured for attention before the computer age but, having surfaced, has involved a wide range of techniques and has attracted the attention of some fine minds (David Mumford was a Fields Medallist in 1974). This singular application of mathematical modelling to a messy applied problem of obvious utility and importance but with no unique solution is a pretty one to share with students: perhaps, returning to the source of our opening quotation, we may invert Duncan's earlier observation, 'There is an art to find the mind's construction in the face!'.

Linear and nonlinear waves, by G. B. Whitham. Pp.636. £50. 1999. ISBN 0 471 35942 4 (Wiley).

The boundary layer equations for plane, incompressible, and steady flow are 

$$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Boundary Layer Theory

We have created a digital age grid of the ocean floor with a grid node interval of 6 arc min using a self-consistent set of global isochrons and associated plate reconstruction poles. The age at each grid node was determined by linear interpolation between adjacent isochrons in the direction of spreading. Ages for ocean floor between the oldest identified magnetic anomalies and continental crust were interpolated by estimating the ages of passive continental margin segments from geological data and published plate models. We have constructed an age grid with error estimates for each grid cell as a function of (1) the error of ocean floor ages identified from magnetic anomalies along ship tracks and the age of the corresponding grid cells in our age grid, (2) the distance of a given grid cell to the nearest magnetic anomaly identification, and (3) the gradient of the age grid: i.e., larger errors are associated with high age gradients at fracture zones or other age discontinuities. Future applications of this digital grid include studies of the thermal and elastic structure of the lithosphere, the heat loss of the Earth, ridge-push forces through time, asymmetry of spreading, and providing constraints for seismic tomography and mantle convection models.

Digital isochrons of the world's ocean floor

Submarine sediment density flows are one of the most important processes for moving sediment across our planet, yet they are extremely difficult to monitor directly. The speed of long run-out submarine density flows has been measured directly in just five locations worldwide and their sediment concentration has never been measured directly. The only record of most density flows is their sediment deposit. This article summarizes the processes by which density flows deposit sediment and proposes a new single classification for the resulting types of deposit. Colloidal properties of fine cohesive mud ensure that mud deposition is complex, and large volumes of mud can sometimes pond or drain-back for long distances into basinal lows. Deposition of ungraded mud (TE-3) most probably finally results from en masse consolidation in relatively thin and dense flows, although initial size sorting of mud indicates earlier stages of dilute and expanded flow. Graded mud (TE-2) and finely laminated mud (TE-1) most probably result from floc settling at lower mud concentrations. Grain-size breaks beneath mud intervals are commonplace, and record bypass of intermediate grain sizes due to colloidal mud behaviour. Planar-laminated (TD) and ripple cross-laminated (TC) non-cohesive silt or fine sand is deposited by dilute flow, and the external deposit shape is consistent with previous models of spatial decelerating (dissipative) dilute flow. A grain-size break beneath the ripple cross-laminated (TC) interval is common, and records a period of sediment reworking (sometimes into dunes) or bypass. Finely planar-laminated sand can be deposited by low-amplitude bed waves in dilute flow (TB-1), but it is most likely to be deposited mainly by high-concentration near-bed layers beneath high-density flows (TB-2). More widely spaced planar lamination (TB-3) occurs beneath massive clean sand (TA), and is also formed by high-density turbidity currents. High-density turbidite deposits (TA, TB-2 and TB-3) have a tabular shape consistent with hindered settling, and are typically overlain by a more extensive drape of low-density turbidite (TD and TC,). This core and drape shape suggests that events sometimes comprise two distinct flow components. Massive clean sand is less commonly deposited en masse by liquefied debris flow (DCS), in which case the clean sand is ungraded or has a patchy grain-size texture. Clean-sand debrites can extend for several tens of kilometres before pinching out abruptly. Up-current transitions suggest that clean-sand debris flows sometimes form via transformation from high-density turbidity currents. Cohesive debris flows can deposit three types of ungraded muddy sand that may contain clasts. Thick cohesive debrites tend to occur in more proximal settings and extend from an initial slope failure. Thinner and highly mobile low-strength cohesive debris flows produce extensive deposits restricted to distal areas. These low-strength debris flows may contain clasts and travel long distances (DM-2), or result from more local flow transformation due to turbulence damping by cohesive mud (DM-1). Mapping of individual flow deposits (beds) emphasizes how a single event can contain several flow types, with transformations between flow types. Flow transformation may be from dilute to dense flow, as well as from dense to dilute flow. Flow state, deposit type and flow transformation are strongly dependent on the volume fraction of cohesive fine mud within a flow. Recent field observations show significant deviations from previous widely cited models, and many hypotheses linking flow type to deposit type are poorly tested. There is much still to learn about these remarkable flows.

Subaqueous sediment density flows: Depositional processes and deposit types

Huge landslides, mobilizing hundreds to thousands of km3 of sediment and rock are ubiquitous in submarine settings ranging from the steepest volcanic island slopes to the gentlest muddy slopes of submarine deltas. Here, we summarize current knowledge of such landslides and the problems of assessing their hazard potential. The major hazards related to submarine landslides include destruction of seabed infrastructure, collapse of coastal areas into the sea and landslide-generated tsunamis. Most submarine slopes are inherently stable. Elevated pore pressures (leading to decreased frictional resistance to sliding) and specific weak layers within stratified sequences appear to be the key factors influencing landslide occurrence. Elevated pore pressures can result from normal depositional processes or from transient processes such as earthquake shaking; historical evidence suggests that the majority of large submarine landslides are triggered by earthquakes. Because of their tsunamigenic potential, ocean-island flank collapses and rockslides in fjords have been identified as the most dangerous of all landslide related hazards. Published models of ocean-island landslides mainly examine ‘worst-case scenarios’ that have a low probability of occurrence. Areas prone to submarine landsliding are relatively easy to identify, but we are still some way from being able to forecast individual events with precision. Monitoring of critical areas where landslides might be imminent and modelling landslide consequences so that appropriate mitigation strategies can be developed would appear to be areas where advances on current practice are possible.

Submarine landslides: processes, triggers and hazard prediction

http://folk.uib.no/ngljm/PDF_files/Bondevik%20et%20al%202005,%20Storegga.pdf

The Storegga Slide tsunami—comparing field observations with numerical simulations

Model simulations of tsunamis generated by the Storegga Slides

Wave formation and propagation due to submarine landslides are complex phenomena that may be divided into four parts: Landslide dynamics, energy transfer from landslide motion to water motion, wave propagation in open water, and wave run-up along the shores. This paper focuses on the energy transfer from landslide motion to water motion, i.e. the generation of tsunamis by submarine landslides.

/pdf/mechanisms-of-tsunami-generation-by-submarine-landslides-a-287lur9w2g.pdf

Mechanisms of tsunami generation by submarine landslides - a short review

A number of examples are presented to substantiate that submarine landslides have occurred along most continental margins and along several volcano flanks. Their properties of importance for tsunami generation (i.e. physical dimensions, acceleration, maximum velocity, mass discharge, and travel distance) can all gain extreme values compared to their subaerial counterparts. Hence, landslide tsunamis may also be extreme and have regional impact. Landslide tsunami characteristics are discussed explaining how they may exceed tsunamis induced by megathrust earthquakes, hence representing a significant risk even though they occur more infrequently. In fact, submarine landslides may cause potentially extreme tsunami run-up heights, which may have consequences for the design of critical infrastructure often based on unjustifiably long return periods. Giant submarine landslides are rare and related to climate changes or glacial cycles, indicating that giant submarine landslide tsunami hazard is in most regions negligible compared to earthquake tsunami hazard. Large-scale debris flows surrounding active volcanoes or submarine landslides in river deltas may be more frequent. Giant volcano flank collapses at the Canary and Hawaii Islands developed in the early stages of the history of the volcanoes, and the tsunamigenic potential of these collapses is disputed. Estimations of recurrence intervals, hazard, and uncertainties with today’s methods are discussed. It is concluded that insufficient sampling and changing conditions for landslide release are major obstacles in transporting a Probabilistic Tsunami Hazard Assessment (PTHA) approach from earthquake to landslide tsunamis and that the more robust Scenario-Based Tsunami Hazard Assessment (SBTHA) approach will still be most efficient to use. Finally, the needs for data acquisition and analyses, laboratory experiments, and more sophisticated numerical modelling for improved understanding and hazard assessment of landslide tsunamis are elaborated.

/pdf/submarine-landslide-tsunamis-how-extreme-and-how-likely-4penzlul7b.pdf

Carl B. Harbitz

Papers

Submarine landslides: processes, triggers and hazard prediction

The Storegga Slide tsunami—comparing field observations with numerical simulations

Model simulations of tsunamis generated by the Storegga Slides

Mechanisms of tsunami generation by submarine landslides - a short review

Submarine landslide tsunamis: how extreme and how likely?