Nicolas Houlié

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Houlié

First Name

Nicolas

ORCID

0000-0001-7471-4020

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Publications 1 - 10 of 16

Lithosphere-Asthenosphere interactions near the San Andreas fault
Houlié, Nicolas; Chamberlain, Calum J.; Bentham, H.L.M.; et al. (2015)
AGU Fall Meeting Abstracts
Mapping Earth's interiors with GPS records
Kelevitz, Krisztina; Houlié, Nicolas; Rothacher, Markus; et al. (2013)
Abstract Volume 11th Swiss Geoscience Meeting, Cycles and Events in the Earth System, Lausanne, 15th – 16th November 2013
The potential of high-rate GPS forhazard and risk assessment
Michel, Clotaire; Houlié, Nicolas; Kelevitz, Krisztina; et al. (2016)
AGU Fall Meeting Abstracts
Real-time, reliable magnitudes for large earthquakes from 1 Hz GPS precise point positioning: The 2011 Tohoku-Oki (Japan) earthquake
Wright, Tim J.; Houlié, Nicolas; Hildyard, Mark; et al. (2012)
Geophysical Research Letters
The early warning issued after the onset of the Mw9.0 Tohoku-Oki earthquake significantly underestimated its magnitude, saturating, 120 seconds after the earthquake began, at Mw8.1. Here we investigate whether real-time deformation data from Japan's dense network of continuously-recording Global Positioning System (GPS) stations could have been used to provide a more reliable rapid estimate of the earthquake's magnitude, and ultimately a more robust tsunami forecast. We use precise point positioning in real-time mode with broadcast clock and orbital corrections to give station positions every 1 s. We then carry out a simple static inversion on a subset of stations to determine the portion of the fault that slipped and the earthquake magnitude. Unlike most previous methods, our method produces estimates of seismic moment before the earthquake rupture has completed. We find that the deformation data allow a robust magnitude estimate just ∼100 s after the earthquake onset. We also investigated the density of stations required for a robust moment magnitude estimate. Fewer than 1 station every 100 km are needed. We recommend that GPS data be incorporated into earthquake early warning systems for regions at threat from large magnitude earthquakes and tsunamis.
High-Rate GPS Source Solution of the 2004 Parkfield Earthquake
Houlié, Nicolas; Dreger, Douglas S.; Kim, Ahyi (2012)
Long-period surface motion of the multipatch Mw9.0 Tohoku-Oki earthquake
Psimoulis, Panos; Houlié, Nicolas; Michel, Clotaire; et al. (2014)
Increased tree-ring growth close to eruptive fissures prior to volcanic flank eruptions on Mount Etna (Sicily, Italy)
Seiler, Ruedi; Houlié, Nicolas; Kirchner, James W.; et al. (2015)
Tree-ring width reveals the preparation of the 1974 Mt. Etna eruption
Seiler, Ruedi; Houlié, Nicolas; Cherubini, Paolo (2017)
Scientific Reports
Reduced near-infrared reflectance observed in September 1973 in Skylab images of the western flank of Mt. Etna has been interpreted as an eruption precursor of the January 1974 eruption. Until now, it has been unclear when this signal started, whether it was sustained and which process(es) could have caused it. By analyzing tree-ring width time-series, we show that the reduced near-infrared precursory signal cannot be linked to a reduction in annual tree growth in the area. However, comparing the tree-ring width time-series with both remote sensing observations and volcano-seismic activity enables us to discuss the starting date of the pre-eruptive period of the 1974 eruption.
The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today
Houlié, Nicolas (2025)
Risks
I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.
Analysis of 1-Hz GPS data for the estimation of long-period surface motion of Tohoku Mw9.0 2011 earthquake
Psimoulis, Panos A.; Houlié, Nicolas; Meindl, Michael; et al. (2016)
Conference and Seminar Proceedings of the 3rd Joint International Symposium on Deformation Monitoring (JISDM)

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Nicolas Houlié

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