•FWI thresholds for 4 fire danger classes are identified, adopted to Greek conditions.•Weather and fire data at municipality level on a daily basis for 17-yrs are used.•Predictive performance of new ...FWI thresholds vs the EFFIS ones is highly improved.•Overestimation of danger decreases, reliability of danger classification increases.
The objective of the following paper is to identify weather-related thresholds for wildfire danger, i.e., the potential extent of fire, based on local weather conditions. The target area is Greece, a wildfire prone Mediterranean country which experienced on average 2000 wildfires annually over the last two decades. Initially, the Fire Weather Index (FWI) component of the Canadian Fire Weather Index (FWI) System (FWI System) adopted by the European Forest Fire Information System (EFFIS), is evaluated with respect to its wildfire danger predictive ability. Hence, weather and wildfire data at municipality level and on a daily basis, for the period 2000–2016 are exploited. The analysis showed that the FWI thresholds proposed by EFFIS for assessing the level of fire danger in Europe are too low for the case of Greece and, therefore, are not representative of the country's fire weather conditions. Two statistical approaches, cluster analysis and non-linear least-squares regression, are subsequently applied to determine the most appropriate FWI thresholds for discrete levels of wildfire danger. The results are presented in 4 sets of FWI thresholds and they are further evaluated on the basis of verification measures. All sets of FWI thresholds were found to significantly improve the predictability of wildfire danger compared to the EFFIS fire danger class thresholds. In particular, two of them were found to meet selected performance requirements for a balanced predictive performance, namely a reduced overestimation of wildfire danger and increased reliability in danger classification. The results are expected to have significant practical implications for wildfire prevention and risk mitigation strategies implemented by the forest fire control agencies of the country.
This article utilizes the spectral analysis method to investigate the influence of 2-D transceiver array aperture size and polarization on 3-D qualitative microwave imaging of subsurface objects. The ...investigation is performed in three steps. First, in the framework of the integral equation with Born approximation (BA), we derive the analytical relationship between the 2-D spectra of the scattered electric fields and the reconstructable 3-D object spectrum, which is weighted by a vector coefficient related to transceiver polarization. The theoretically reconstructable 3-D spectrum is analyzed. Second, to obtain the 3-D spectrum for more practical microwave imaging measurement with the wave attenuation, the evanescent mode contribution, and the transceiver polarization considered, we perform the singular value decomposition (SVD) to compute the discretized integral operator right-singular function whose spectrum reflects the reconstructable 3-D spectrum. It is shown that the mutual coupling in two orthogonal horizontal directions leads to the vertical "allpass" feature and the horizontal "lowpass" feature in one vertical 2-D plane if the array aperture size in the orthogonal horizontal direction is large enough. Meanwhile, the transceiver polarization also has obvious effects on the reconstructable spectrum. Third, numerical experiments are implemented to verify the relationship between the transceiver array configuration and the reconstructable spectrum.
Key message
Wildfire danger and burnt areas should increase over the century in southern Europe, owing to climate warming. Fire-prone area expansion to the north and to Mediterranean mountains is a ...concern, while climate-induced burnt area increase might be limited by fuel availability in the most arid areas. Further studies are needed to both assess and reduce uncertainties on future trends.
Context
Wildfire is the main disturbance in forested ecosystems of southern Europe. Warmer and drier conditions projected in this region are expected to profoundly affect wildfire regimes.
Aims
In this review, we pursue a twofold objective: (i) report the trends in wildfire danger and activity projected under warming climate in southern Europe and (ii) discuss the limitations of these projections.
Methods
We reviewed 23 projection studies that examined future wildfire danger or wildfire activity at local, regional or continental scale.
Results
Under the scenarios with the highest greenhouse gas emissions, we found that projection studies estimate an increase in future fire danger and burnt areas varying, on average, from 2 to 4% and from 5 to 50% per decade, respectively. Further comparisons on the magnitude of increase remained challenging because of heterogeneous methodological choices between projection studies. We then described three main sources of uncertainty that may affect the reliability of wildfire projections: climate projections, climate-fire models and the influences of fuels, fire-vegetation feedbacks and human-related factors on the climate-fire relationships.
Conclusion
We suggest research directions to address some of these issues for the purpose of refining fire danger and fire activity projections in southern Europe.
The theory and practice of multisource full waveform inversion of marine supergathers are described with a frequency-selection strategy. The key enabling property of frequency selection is that it ...eliminates the crosstalk among sources, thus overcoming the aperture mismatch of marine multisource inversion. Tests on multisource full waveform inversion of synthetic marine data and Gulf of Mexico data show speedups of 4× and 8×, respectively, compared to conventional full waveform inversion.
•Direction numerical simulation of transient flame-wall interaction.•Transient vs steady wall heat fluxes in side-wall a quenching configuration.•Markstein numbers under instationary conditions for ...flame-wall interaction.•Fluctuations cause different Markstein lengths depending on phase angle.
This work focuses on sidewall quenching (SWQ) for premixed methane/air flames that are forced by a periodic oscillatory inflow with excitation frequency f = 100 Hz. The effects of steady-state and transient flame stretch on the near-wall flame dynamics are evaluated using two-dimensional direct numerical simulation (2D-DNS) and the GRI 3.0 reaction mechanism. The velocity fluctuations lead to significant changes in flame speed and flame stretch, as well as the associated Markstein numbers. The phenomenon of SWQ is analyzed using flame quenching distance, wall heat flux and heat release rate. For steady-state conditions, there is a strong correlation between the maximum wall heat flux WHFmax and the flame quenching Peclet Number (Peq), as well as between the flame speed and the flame stretch; for transient conditions, the flame quenching distance (dq) increases continuously from phase angles of 1/4f−1 to 3/4f−1 in one cycle as time progresses, and the fluctuation of the quenching distance (Δdq) decreases gradually with increasing equivalence ratio (∅). The flame stretch changes from negative to positive in the process from 1/4f−1 to 3/4f−1, while the heat release rate and fuel reaction rate near the wall gradually decrease. Furthermore, the FWI region is dominated by negative flame stretch while positive flame stretch is present at the base of the flame. Moreover, the methane/air flame has a nearly twofold increase in the consumption speed during the oscillation from phase angle 3/4f−1 to the next cycle at 1/4f−1 at ∅ = 0.5 and ∅ = 1.0. These results show that flow field perturbations are not negligible in elucidating the effects of flame-wall interactions.
Imaging of the subsurface is central in seismic exploration and a topic of great economic interest. A promising technique for seismic imaging is a wave-equation-based method called full-waveform ...inversion (FWI). FWI is a data-fitting technique that minimises the difference between the observed data in the seismic records and the simulated data, which is extracted from the solution of the wave equation. Usually, FWI is formulated as an optimisation problem that minimises the least-squares distance. In the perspective of likelihood theory, the minimisation of the least-squares distance assumes a Gaussian distribution of the residual data. In this work, we deal with the q-Gaussian distribution associated with the Tsallis statistics to construct a robust optimisation problem, which we call q-FWI. We tested our method in a typical geophysics velocity model with noisy data. Our results show that q-FWI, based on the q-statistics, is a powerful methodology in noisy environments, especially in the presence of outliers. The long tail of the q-distribution exploits the outliers’ information, which helps in the image reconstruction. Furthermore, q-FWI provides better image reconstruction without additional computational cost compared to the traditional approach of using the Gaussian distribution for the residuals.
•Full-waveform inversion (FWI) is an intricate problem in seismic imaging.•FWI is an optimisation problem that depends on the difference between observed and simulated data.•We construct a new objective function based on q-Gaussian distribution.•The seismic inversion based on the q-statistics performs better than the usual inversion.•The best results of the q-statistics FWI are related to data strongly contaminated by outliers.
In this paper, a guided wave tomography method based on full waveform inversion (FWI) is developed for accurate and high-resolution reconstruction of the remaining wall thickness in isotropic plates. ...The forward model is computed in the frequency domain by solving a full-wave equation in a two-dimensional (2-D) acoustic model, accounting for higher order effects such as diffractions and multiple scattering. Both numerical simulations and experiments were carried out to obtain the signals of a dispersive guided mode propagating through defects. The inversion was based on local optimization of a waveform misfit function between modeled and measured data, and was applied iteratively to discrete frequency components from low to high frequencies. The resulting wave velocity maps were then converted to thickness maps by the dispersion characteristics of selected guided modes. The results suggest that the FWI method is capable to reconstruct the thickness map of a irregularly shaped defect accurately on a 10-mm-thick plate with the thickness error within 0.5 mm.
Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing ...physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder-decoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent.
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented by solving for the ...frequency-domain representation of the wavefield using the finite-difference method. The approach requires matrix inversions and affords limited flexibility to accommodate irregular model geometries. On the other hand, the physics-informed neural network (PINN) uses the underlying physical laws as loss functions to train the neural network (NN) to provide flexible continuous functional approximations of the solutions without matrix inversions. By including a data-constrained term in the loss function, the trained NN can reconstruct a wavefield that simultaneously fits the recorded data and satisfies the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we rely on a small-size NN to predict the velocity using the reconstructed wavefield. In this velocity prediction NN, spatial coordinates are used as input data to the network, and the scattered Helmholtz equation is used to define the loss function. After we train this network, we are able to predict the velocity in the domain of interest. We develop this PINN-based WRI method and demonstrate its potential using a part of the Sigsbee2A model and a modified Marmousi model. The results show that the PINN-based WRI is able to invert for a reasonable velocity with very limited iterations and frequencies, which can be used in a subsequent FWI application.
•GPR FWI and seismic FWI are effectively integrated into a joint procedure.•Medium elastic and electromagnetic properties are simultaneously constructed.•Joint FWI procedure improves the structures ...of inverted electromagnetic properties.
Full waveform inversion (FWI) of ground penetrating radar (GPR) and seismic data is a promising imaging tool for the detailed characterization of latent cavities and tunnels. In this study, surface-based GPR FWI and seismic FWI are combined to quantitatively image the geometry and geophysical attributes of the subsurface. These two FWI methods are integrated into a joint procedure and linked by cross-gradient functions which can provide structural constraints between the parameters. The elastic and electromagnetic properties of the medium (P-wave velocity, permittivity, and conductivity) are linked and simultaneously inverted in the joint FWI approach. This joint FWI approach is tested using three numerical examples. In the first two examples, target objects with various shapes are contained in both homogeneous and inhomogeneous media. In the third example, two sediment-filled cavities and a fractured area are analyzed in the calcarenite layers. The results show that the joint FWI approach can integrate the common structure of models and correct the model structures that are inaccurately reconstructed in the individual FWI methods, providing a comprehensive structural interpretation of the study areas.
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