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Olivier Cavalié

Publications and source records attributed to Olivier Cavalié.

3 recordsLinked to original sources

What Do EROIs Measure? Implications for Energy Transition Assessment

Multiple formulations of the Energy Return on Investment (EROI) coexist in the literature, differing mainly in their treatment of self-consumption and external direct energy inputs. This article shows that these differences are not merely conventional: they determine whether EROI measures the net energy surplus available to society or the internal conversion efficiency of the production process. By benchmarking three established formulations against theoretical limit cases, we demonstrate that only the external variant (EXT), which excludes self-consumed energy from the investment term, correctly measures the net energy surplus available to society. The internal (INT) and standard (STD) variants instead converge toward measures of process efficiency. We further show that, in multi-source systems exchanging direct energy flows, consistency requires converting these flows into their upstream embodied energy equivalents before including them in the energy investment term. A generic reallocation formula is derived and shown to uniquely preserve aggregation consistency. Applied to U.S. oil and gas production (1919--2007) and China's fossil fuel sectors (1995--2010), the revised framework yields systematically higher EROI values than standard formulations, potentially placing the same physical system on opposite sides of the net energy cliff, with direct implications for energy transition assessment and policy.

physics.soc-ph↗

Distribution of interseismic coupling along the North and East Anatolian Faults inferred from InSAR and GPS data

The North Anatolian Fault (NAF) has produced numerous major earthquakes. After decades of quiescence, the $\rm{M_w}$ 6.8 Elazığ earthquake (January 24, 2020) has recently reminded us that the East Anatolian Fault (EAF) is also capable of producing significant earthquakes. To better estimate the seismic hazard associated with these two faults, we jointly invert Interferometric Synthetic Aperture Radar (InSAR) and GPS data to image the spatial distribution of interseismic coupling along the eastern part of both the North and East Anatolian Faults. We perform the inversion in a Bayesian framework, enabling to estimate uncertainties on both long-term relative plate motion and coupling. We find that coupling is high and deep (0-20 km) on the NAF and heterogeneous and superficial (0-5 km) on the EAF. Our model predicts that the Elazığ earthquake released between 200 and 250 years of accumulated moment, suggesting a bi-centennial recurrence time.

physics.geo-ph↗

Allan variance computed in space domain: Application to InSAR data to characterize noise and geophysical signal

The Allan variance was introduced fifty years ago for analyzing the stability of frequency standards. Beside its metrological interest, it is also an estimator of the large trends of the power spectral density (PSD) of frequency deviation. For instance, the Allan variance is able to discriminate different types of noise characterized by different power laws in the PSD. But, it was also used in other fields: accelerometry, geophysics, geodesy, astrophysics and even finances! However, it seems that up to now, it has been exclusively applied for time series analysis. We propose here to use the Allan variance onto spatial data. Interferometric synthetic aperture radar (InSAR) is used in geophysics to image ground displacements in space (over the SAR image spatial coverage) and in time thank to the regular SAR image acquisitions by dedicated satellites. The main limitation of the technique is the atmospheric disturbances that affect the radar signal while traveling from the sensor to the ground and back. In this paper, we propose to use the Allan variance for analyzing spatial data from InSAR measurements. The Allan variance was computed in XY mode as well as in radial mode for detecting different types of behavior for different space-scales, in the same way as the different types of noise versus the integration time in the classical time and frequency application. We found that radial AVAR is the more appropriate way to have an estimator insensitive to the spatial axis and we applied it on SAR data acquired over eastern Turkey for the period 2003-2011. Space AVAR allowed to well characterize noise features, classically found in InSAR such as phase decorrelation producing white noise or atmospheric delays, behaving like a random walk signal. We finally applied the space AVAR to an InSAR time series to detect when the geophysical signal, here the ground motion, emerges from the noise.

physics.data-an↗