Marco Broccardo

Induced seismicity closed-form traffic light system for actuarial decision-making during deep fluid injections

The rise in the frequency of anthropogenic earthquakes due to deep fluid injections is posing serious economic, societal, and legal challenges to many geo-energy and waste-disposal projects. Existing tools to assess such problems are still inherently heuristic and mostly based on expert elicitation (so-called clinical judgment). We propose, as a complementary approach, an adaptive traffic light system (ATLS) that is function of a statistical model of induced seismicity. It offers an actuarial judgement of the risk, which is based on a mapping between earthquake magnitude and risk. Using data from six underground reservoir stimulation experiments, mostly from Enhanced Geothermal Systems, we illustrate how such a data-driven adaptive forecasting system could guarantee a risk-based safety target. The proposed model, which includes a linear relationship between seismicity rate and flow rate, as well as a normal diffusion process for post-injection, is first confirmed to be representative of the data. Being integrable, the model yields a closed-form ATLS solution that is both transparent and robust. Although simulations verify that the safety target is consistently ensured when the ATLS is applied, the model from which simulations are generated is validated on a limited dataset, hence still requiring further tests in additional fluid injection environments.

A compositional demand/supply framework to quantify the resilience of civil infrastructure systems (Re-CoDeS)

Abstract Disaster resilience of civil infrastructure systems is essential to security and economic stability of communities. The novel compositional demand/supply resilience framework named Re-CoDeS (Resilience-Compositional Demand/Supply) generalizes the concept of disaster resilience across the spectrum of civil infrastructure systems by accounting not only for the ability of the civil infrastructure system to supply its service to the community, but also for the community demand for such service in the aftermath of a disaster. A Lack of Resilience is consequently observed when the demand for service cannot be fully supplied. Normalized resilience measures are proposed to allow for direct comparisons between different civil infrastructure systems at component and system levels. A scheme is introduced to classify component and system configurations with respect to their resilience. In addition to quantify the resilience of civil infrastructure systems, Re-CoDeS can be used to evaluate or design community risk mitigation strategies and to optimize post-disaster recovery.

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a non-homogeneous Poisson process (NHPP) with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters, and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.