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Abstract Observations of fracture-induced electromagnetic radiation (FEMR) were conducted along the Dead Sea Transform (DST) from Sodom to Jericho, coinciding with a magnitude (Mw) 6.3 aftershock earthquake (EQ) in the Turkey-Syrian region on February 20, 2023. The FEMR parameters (“hits,” Benioff strain release, frequency, rise-time, energy) and associated crack dimensions were analyzed, focusing on trends leading up to the EQ. This study investigated the Benioff Strain plot and other parameters in three consecutive earthquake nucleation stages leading to the catastrophe. The first stage showed increased FEMR hits and frequency, decreased rise time (T′), and crack dimensions. In the second stage, FEMR hits and crack width decreased while other parameters continued to rise, accumulating the second-highest energy, likely due to high-stress drop. The third stage exhibited steadily increasing FEMR hits and energy and a notable increase in crack dimensions, suggesting an imminent macro failure event. The cyclic trend in FEMR hits indicates alternating periods of high activity and silence, potentially linked to stress changes during crack propagation. Taken shortly before the earthquake, these measurements offer valuable insights into how FEMR parameters vary before seismic events, bridging the gap between lab-scale studies of rock collapses under stress and large-scale failure phenomena.

The present analysis of state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom are rarely used as features for machine learning models. The paper deals with the artificial intelligence techniques used to examine CHIRP-recorded data. The data were collected using a CHIRP sub-bottom profiler to study two sand bottom sites and two sandstone bottom sites in the offshore zone of Ashqelon City (Southern Israel). The first reflection time series and spectra of all the traces from the four sites generated two training and two test sets. Two logistic regression models were trained using the training sets and evaluated for accuracy using the test sets. The examination results 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 their 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) near the Dead Sea Transform using the Angel-M1 instrument, which enables the recording of FEMR signals in a 3D manner. The results showed both the possibility of estimating the sizes of micro-fractures that are the sources of radiation and assessing the direction of the fractures’ locations to the measuring device, as well as the range of magnitude (Mw) of the impending “events” (EQs) associated with the FEMR measurements. Moreover, the relation between the measured FEMR activity (the number of FEMR hits per unit of time) and the FEMR event magnitudes showed consistency with the Gutenberg–Richter relationship for the region. Such measurements could therefore constitute a preliminary ‘field reinforcement’ towards a valid EMR method for a real earthquake forecast, which would provide much earlier warnings than seismic methods. The observed FEMR measurements could only be used to assess the stress concentrations and micro-fracturing in the region since they related to the very initial nucleation phase of a “virtual” earthquake. Nonetheless, they provide the necessary feasibility test for a forecasting method since all of the lab-measured FEMR features were confirmed in the field.

Modern studies which dealt with the frequency domain analysis showed that a frequency-domain approach has an essential advantage and mentioned an inner qualitative relationship between the subsurface structure and its frequency spectra. This paper deals with the acoustic spectral response of sand and sandstone sediments at the sea bottom. An acoustic data collection campaign was conducted over two sand sites and two sandstone sites. The analysis of the results shows that reflections of acoustic signals from sand and sandstone sea bottom are characterized by various spectral features in the 2.75–6.75 kHz range. The differences in acoustic response of sand and sandstone can be quantified by examining the maximal normalized reflected power, the mean frequency, and the number of crossings at different power levels. The statistical value distribution of these potential classifiers was calculated and analyzed. These classifiers, and especially the roughness of the spectrum quantified by the number of crossings parameter can give information to assess the probability for sand or sandstone based on the reflected spectra and be used for actual distinction between sand and sandstone in sub bottom profiler data collection campaigns.