Fabio Boschetti

Inversion of seismic refraction data using genetic algorithms

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the nonlinearity inherent in such applications. However, in their application to specific problems, as with all algorithms, problems of implementation arise. After extensive numerical tests, we implemented a genetic algorithm to efficiently invert several sets of synthetic seismic refraction data. In particular, we aimed at overcoming one of the main problems in the application of genetic algorithms to geophysical problems: i.e., high dimensionality. The addition of a pseudo-subspace method to the genetic algorithm, whereby the complexity and dimensionality of a problem is progressively increased during the inversion, improves the convergence of the process. The method allows the region of the solution space containing the global minimum to be quickly found. The use of local optimization methods at the last stage of the search further improves the quality of the inversion. The genetic algorithm has been tested on a field data set to determine the structure and base of the weathered layer (regolith) overlaying a basement of granite and greenstones in an Archaean terrain of Western Australia.

A fractal-based algorithm for detecting first arrivals on seismic traces

A new algorithm is proposed for the automatic picking of seismic first arrivals that detects the presence of a signal by analyzing the variation in fractal dimension along the trace. The “divider-method” is found to be the most suitable method for calculating the fractal dimension. A change in dimension is found to occur close to the transition from noise to signal plus noise, that is the first arrival. The nature of this change varies from trace to trace, but a detectable change is always found to occur. The algorithm has been tested on real data sets with varying S/N ratios and the results compared to those obtained using previously published algorithms. With an appropriate tuning of its parameters, the fractal-based algorithm proved more accurate than all these other algorithms, especially in the presence of significant noise. The fractal method proved able to tolerate noise up to 80% of the average signal amplitude. However, the fractal-based algorithm is considerably slower than the other methods and hence is intended for use only on data sets with low S/N ratios.

Interactive inversion in geosciences

Inversion algorithms numerically evaluate the mismatch between model and data to guide the search for minima in parameter spaces. In an alternative approach, the numerical evaluation of data misfit can be replaced by subjectively judging the solution’s quality. This widens the class of problems that can be treated within the framework of formal inverse theory—in particular, various geophysical/geological/geodynamic applications in which structural similarity between model and data determines the quality of the fit. In this situation, prior knowledge, experience, and even personal intuition are crucial. This approach also provides a simple way to include such expertise in more traditional numeric applications, e.g., to treat ambiguous problems and disregard geologically unfeasible solutions from the inverse search.

Inverse modelling in geology by interactive evolutionary computation

Inverse modelling of geological processes, in the absence of established numerical criteria to act as inversion targets, requires an approach that uses human interaction to assess forward model results. The method of interactive evolutionary computation provides for the inclusion of qualitative geological expertise within a rigorous mathematical inversion scheme, by simply asking an expert user to evaluate a sequence of forward geological models. The traditional numerical misfit is replaced by a human appraisal of misfit. We use this interactive technique to successfully invert a geodynamic model for a conceptual pattern of fault spacing during crustal extension.

A Turing Test for Emergence

In the parlance of complex systems science, the global outcomes arising from broad guidelines on a system’s components, including robustness and adaptivity, are often defined as emergent features. Because design inevitably requires a trial and error process, it is natural to expect that our community will need to develop methods to: detect emergent features when they arise; categorize them in order to understand what classes of processes arise as a result of different initial conditions; experiment with various configurations in order to optimize the emergent processes.

Interactive modelling for natural resource management

We present a modelling approach to rapidly assess the effect of management decisions on ecological problems and demonstrate its use in fishery management. Each stage of the approach is controlled by a Graphical User Interface which allows a team of non-expert modellers to compare the outcomes of multiple model simulations and to decide what further simulation is needed. A distinguishing feature of the method is that it allows the goal of the management strategies to arise as a result of the interaction between the user and the model, rather than being defined a priori, as well as to change during the process in response to the information and the insight such modelling may provide. We envisage that a management team, rather than a single user, may also employ the method as an avenue for communication, in order to discuss the potentially conflicting aspects of different model outcomes along the path to finding workable compromises. We discuss an application of the approach to the sustainable management of a recreational fishery in a marine park in Western Australia.

Inversion in geology by interactive evolutionary computation

We present the first step in the development of a system that would allow geological models to evolve backwards in time. The method of interactive evolutionary computation provides for the inclusion of geological knowledge and expertise in a rigorous mathematical inversion scheme, by simply asking an expert user to visually evaluate different geological models. We demonstrate the potential of the technique for the cases of folding and faulting.

ATTITUDES, IDEOLOGIES AND SELF-ORGANIZATION: INFORMATION LOAD MINIMIZATION IN MULTI-AGENT DECISION MAKING

Sophisticated models of human social behavior are fast becoming highly desirable in an increasingly complex and interrelated world. Here, we propose that rather than taking established theories from the physical sciences and naively mapping them into the social world, the advanced concepts and theories of social psychology should be taken as a starting point, and used to develop a new modeling methodology. In order to illustrate how such an approach might be carried out, we attempt to model the low elaboration attitude changes of a society of agents in an evolving social context. We propose a geometric model of an agent in context, where individual agent attitudes are seen to self-organize to form ideologies, which then serve to guide further agent-based attitude changes. A computational implementation of the model is shown to exhibit a number of interesting phenomena, including a tendency for a measure of the entropy in the system to decrease, and a potential for externally guiding a population of agents toward a new desired ideology.

Automatic detection of particle aggregation in particle code simulations of rock deformation

We explore whether it is possible to determine the initial stages of localization of deformation during a numerical experiment on a “virtual” rock purely through observation and interpretation of geometrical rearrangements of the particles. We study the spatial arrangements of particle via two tools: first, by viewing the virtual rock as a network, we look for local clusters of edges (particle contacts); second, by analyzing the distribution of the directions of motion of the particles, we recognize that the main particle aggregations are characterized by directions which are statistically meaningful. More important, the locations of edge clusters and particle aggregations coincide, suggesting that the tools presented could be routinely used to detect emergent structures in particle assemblages.

A call for empirically based guidelines for building trust among stakeholders in environmental sustainability projects

‘‘The scientific community must rapidly reorganize to focus on global sustainability solutions. We must develop a new strategy for creating and rapidly translating knowledge into action, which will form part of a new contract between science and society’’ (UN State of the Planet Declaration).