The main goal of the research is to assess the effectiveness of aerial ground-penetrating radar (GPR) in delineating boundaries between disturbed clayey ground and rock formations along the old Wadi. The study also tackles technical challenges associated with drone-based GPR applications in urban open areas, considering geological heterogeneity, topographic variations, and environmental conditions. The potential of using drone-based GPR equipped with an unshielded dipole antenna as a transformative tool in geospatial analysis was demonstrated. The research encompasses academic insights and field borehole drilling experiments to validate the accuracy and reliability of drone-based GPR data in real-world scenarios. Anticipated contributions include advancements in understanding drone-based GPR technology for mapping disturbed soil boundaries in foundation engineering applications and other relevant areas and recommendations for optimizing its performance in challenging terrains.

Chert rock is a by-product of sand quarrying (about 40% in the geological section). All attempts to use ordinary technologies for aggregate production from chert have been economically unsuccessful, resulting in significant chert "waste" accumulation covering vast quarry areas. This paper presents the study results of the effect of microwave irradiation on the mechanical properties of chert gravel, which is mineralogically relatively homogenous and consists of fine quartz grains. The results show that an increase in irradiation time decreases the strength of chert gravel (by a factor of 4-6 for the 2.5 min of irradiation), while the quenching changes the fractional content of the samples after the crushing test, decreasing the Gravel-to-Sand ratio.

The paper deals with applying Artificial Intelligence techniques to examine CHIRP-recorded data in sand and sandstone sea-bottom sites. The provided analysis of the state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom were rarely used as the features for machine learning models. The results of the examination indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms, and the advantages of its combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.

This paper deals with the quantitative analysis of measured fracture-induced electromagnetic radiation (FEMR) in the vicinity of the Dead Sea Transform using the Angel-M1 instrument, which enables the recording of FEMR signals in a 3D manner. Results show both the possibility of estimating the sizes of micro-fractures that are the sources of the radiation, and of assessing the direction of their location to the measuring device and the range of magnitude Mw of the impending "events" (EQ's) associated with the present FEMR measurements. Moreover, the relation between the measured FEMR activity (the number of FEMR hits per unit time) and the FEMR event magnitudes shows consistency with the Gutenberg-Richter relationship for the region. Such measurements could therefore constitute a preliminary 'field reinforcement' towards an EMR valid method for a real earthquake forecast, which would provide much earlier warnings than the seismic ones. Obviously, the present observed FEMR measurements can only be used as an assessment of the stress concentration and micro-fracturing in the region since they relate to the very initial nucleation phase of a "virtual" (extremely low Richter scale value) earthquake, but they do provide the necessary feasibility test for a prediction method since all lab-measured FEMR features were confirmed also in the field.