Geoimaging (Cyprus)

The present study evaluates an automated Spread–F (SFP) detection algorithm by integrating SF-related (QF, FF) and ionospheric parameters (hmF2, h’F), acting as an indicator for SF events, from SAO Explorer auto-scaled (ARTIST) data, compared to manually identified SF events (SFM) across nine European midlatitude ionospheric stations. The stations were categorized into four latitude sectors to evaluate latitudinal influence in an analysis within the period 2009–2021 from low to high solar activity levels. The results revealed an inverse correlation between solar activity and agreement between SFP and SFM, with stronger agreement during the solar minimum. In the 55°–60° N sector, the SFP–SFM match varied from 71% during the solar minimum to 47% during the solar maximum, with overestimation associated with LSTID activity. In the 50°–55° N sector, agreement ranged from 66% to 56%, with overestimation associated with MSTIDs and oblique traces. The 40°–45° N sector exhibited the highest variability (89% to 42%), where Satellite Traces (STs), Multiple Reflected Echoes (MREs), and spread Es led to both over– and underestimations. In the 35°–40° N sector, agreement dropped to 30% during the solar maximum, with wintertime overestimation and summer underestimation significantly characterized by STs, MREs, and Es–layer interference.

The activation as well as the consequences of landslides are difficult to predict, as they depend on factors characterized by large variability and uncertainties. The aim of this study is to establish a correlation between geological, geotechnical and geomorgpohlogical characteristics and the spatial distribution of recorded landslides. The study area is located in the southwestern (SW) part of the island of Cyprus, covering an area of 552km2. During the past years, more than 1800 landslides, active and inactive (dormant and relict), have been recorded within this area through detailed mapping based on field observations, rendering the area an ideal test bed. At the beginning of this research study, all recorded landslides were digitized in raster format. Consequently, the study area was partitioned into 15 x 15m size cells having three classes: no landslides, inactive landslides and active landslides. Additionally, regarding the geological aspect, polygons encompassing 100% rock mass formations within recorded landslides were categorized as rock mass landslides, while the rest were characterized as landslides in argillaceous (soft rock and soil) materials. A series of correlation analyses were conducted using the Random Forest and SHAP (Shapley Additive explanation) methods. Considering the outcomes of the Random Forest method in argillaceous materials, it turns out that the most important factors for both active and inactive landslides are the Plasticity Index (PI) and the clay fraction (CF), followed by the factors associated with the geomorphology and the bedding structure (e.g. slope angle and bedding dip). The ranking results for inactive and active landslides in rock mass show that the most important factor is the Uniaxial Compressive Strength (UCS), followed by the Geological Strength Index (GSI). Furthermore, the orientation (azimuth) difference between slope and bedding dip (dip direction difference) appears to be more important than the slope angle. Similar ranking results were obtained using the SHAP method for argillaceous materials. Regarding the contribution of each factor in the inactive landslides, it appears that the PI and the slope angle increase proportionally to the possibility of landslide occurrence, while the CF does not exhibit a specific trend. Regarding the dip direction difference, small values contribute more to the occurrence of landslides. The active landslides show a similar picture, but with the CF exhibiting a stronger correlation than in the case of inactive landslides. According to the SHAP analysis for rock mass, the parameters of importance in both inactive and active landslides are UCS and GSI, followed by the slope angle and the dip direction difference. This research was funded by the European Commission (Marie Sklodowska-Curie Actions, Hybland-Society and Enterprise panel, Project No.: 101027880).

Summary Full-waveform inversion is often advertised as a method that can deliver high resolution velocity models. However, the details (or short wavelengths) often come from the short offsets (small scattering angles) which amplitudes are sensitive to both density and velocity contrasts. As the number of FWI results on field data increase, a timely reminder on the relationship between the model and data spaces seems appropriate. In particular, one should remember that the long to intermediate wavelengths of the model relate mostly to velocity variations (tomographic mode), whereas the short wavelengths of the model relate mostly to impedance contrasts (migration mode). Ignoring this fact can mislead the interpretation of our FWI models and yield erroneous conclusions regarding the validity of the method.

With ill‐posed inverse problems such as Full‐Waveform Inversion (FWI), regularization schemes are needed to constrain the solution. Whereas many regularization schemes end up smoothing the model, an undesirable effect with FWI where high‐resolution maps are sought, blocky regularization doesn't: it identifies and preserves strong velocity contrasts leading to step‐like functions. In addition, blocky models might be required in some geological settings. These models might be needed for imaging with wave‐equation based techniques such as Reverse Time Migration (RTM). Enforcing blockiness in the model space amounts to enforcing sparseness in the derivative of the model. Sparseness can be obtained using the ℓ1 norm or Cauchy function which are related to long‐tailed density functions. By using different directions for the derivatives, the model can be made blocky in more than one direction: horizontally, vertically, or both. Whereas the Cauchy function produces better blocky models, the ℓ1 norm has the advantage of being convex without any need for hyperparameter estimation.