Fault activity studies in the Lower Tagus valley and Lisbon region using geophysical data

he Metropolitan Area of Lisbon and the Lower Tagus Valley (LTV) region are located in central Portugal and inhabited by nearly 4 million people. The region has suffered throughout its history the effect of destructive earthquakes caused by hidden faults, possibly related to the plate boundary, which is sited approximately 400 km south of the region (Figure 1). In spite of low slip-rates and big recurrence times that have been estimated for these local, regional faults, they can produce moderate-to-large earthquakes that cause large damage and loss of life, as in 1344, 1531, or 1909 (e.g. Justo and Salwa, 1998; Cabral et al., 2003; 2013). The shorter occurrence time of the earthquakes might be owing to the existence of multiple active faults and/or time clustering owing to stress drop caused by proximal faults (e.g. Carvalho et al., 2006). Therefore, the seismic hazard and risk evaluation of the region has long been a reason of concern. Geological outcrop and geomorphologic mapping identified several regional faults in the LTV region that could be the source the historical earthquakes, but some of them do not affect. Quaternary sediments and lacked the proofs that they were active faults. On the other side, in the vast quaternary alluvial plains that cover the region, it was difficult to identify active faults, as the sedimentation/erosion rates erase any possible surface rupture caused by the low slip-rate faults (<0,35 mm/y). By the late-20th century, seismic reflection data that had been acquired for the oil-industry till the beginning of the 1980s began to be used to identify the major hidden fault zones (e.g. Cabral et al., 2003; Vilanova and Fonseca, 2004; Carvalho et al., 2006). Potential field data was also used to locate active faults in the areas where no seismic data is available (Carvalho et al., 2008; 2011). Though a few more active faults have been proposed, the vast majority of authors agree that the following active faults threaten the region: Nazaré-Caldas da Rainha, Lower Tagus Valley, Ota, Azambuja, Vila Franca de Xira (VFX), Pinhal Novo and Porto Alto faults (Garcia-Mayordomo et al., 2012; Vilanova et al., 2014). In this work, we discuss the acquisition, processing and interpretation of near surface geophysical works carried out over three of these faults — the VFX, Porto Alto and Azambuja faults — in order to confirm they have had activity during the Holoceneera. Their location is shown in Figure 2. We further estimate some of its fault parameters (vertical displacement, slip-rate, length, etc.) and respective implications in terms of seismic hazard

Learning models of plant behavior for anomaly detection and condition monitoring

Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

Understanding earthquake hazards in southern California - the "LARSE" project - working toward a safer future for Los Angeles

The Los Angeles region is underlain by a network of active faults, including many that are deep and do not break the Earth’s surface. These hidden faults include the previously unknown one responsible for the devastating January 1994 Northridge earthquake, the costliest quake in U.S. history. So that structures can be built or strengthened to withstand the quakes that are certain in the future, the Los Angeles Region Seismic Experiment (LARSE) is locating hidden earthquake hazards beneath the region to help scientists determine where the strongest shaking will occur

Disaggregation bands as an indicator for slow creep activity on blind faults

Hidden, blind faults have a strong seismic hazard potential. Consequently, there is a great demand for a robust geological indicator of neotectonic activity on such faults. Here, we conduct field measurements of disaggregation bands above known underlying blind faults at several locations in Central Europe. We observe that the disaggregation bands have the same orientation as that of the faults, indicating their close connection. Disaggregation bands develop in unconsolidated, near-surface, sandy sediments. They form by shear-related reorganization of the sediment fabric, as a consequence of grain rolling and sliding processes, which can reduce the porosity. Using an analogue shearing experiment, we show that disaggregation bands can form at a velocity of 2 cm h−1, which is several orders of magnitude slower than seismogenic fault-slip velocities. Based on the field data and the experiments, we infer that disaggregation bands can form in the process zone of active blind faults and serve as an indicator of neotectonic activity, even if the fault creeps at very low slip velocity. Disaggregation bands could open a new path to detect hidden active faults undergoing aseismic movements. © 2022, The Author(s)

Disaggregation bands as an indicator for slow creep activity on blind faults

Hidden, blind faults have a strong seismic hazard potential. Consequently, there is a great demand for a robust geological indicator of neotectonic activity on such faults. Here, we conduct field measurements of disaggregation bands above known underlying blind faults at several locations in Central Europe. We observe that the disaggregation bands have the same orientation as that of the faults, indicating their close connection. Disaggregation bands develop in unconsolidated, near-surface, sandy sediments. They form by shear-related reorganization of the sediment fabric, as a consequence of grain rolling and sliding processes, which can reduce the porosity. Using an analogue shearing experiment, we show that disaggregation bands can form at a velocity of 2 cm h−1, which is several orders of magnitude slower than seismogenic fault-slip velocities. Based on the field data and the experiments, we infer that disaggregation bands can form in the process zone of active blind faults and serve as an indicator of neotectonic activity, even if the fault creeps at very low slip velocity. Disaggregation bands could open a new path to detect hidden active faults undergoing aseismic movements.publishedVersio

ARTIFICIAL NEURAL NETWORK BASED FAULT LOCATION FOR TRANSMISSION LINES

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances