Statistics and Data Analysis in Geology

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of PSO algorithm (particle swarm optimization) has been presented. The PSO algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results.

PSO (Particle Swarm Optimization) for Interpretation of Magnetic Anomalies Caused by Simple Geometrical Structures

Ground vibrations caused by blasting operations in cement canisters is among the main mining issues that cause significant disruptions to nearby buildings and infrastructure. This research was performed in a limestone quarry situated southeast of Helwan City, Egypt, to investigate the impact of ground motion vibration due to cement blast action in limestone rocks. To reduce the environmental impact of quarry blasting, continuous monitoring, and accurate medium’s peak particle velocity ( PPV ) assessment are required. Recently, machine learning ( ML ) models are employed in diverse applications. The default hyperparameters of such models must be modified to fit the problem concerned. The hyperparameters optimization for ML models impacts the employed model’s performance and efficiency. In this research, different regression models are implemented for predicting the PPV values. A dataset representing 1438 blast incidents in the Helwan area was built and utilized to evaluate the considered ML models. This dataset incorporates the relationship of the ground vibration amplitude to both the explosive charge weight per delay and distance from the blast. The predictive models’ output performance has been evaluated using the root-mean-squared error ( RMSE ) and the coefficient of determination ( $R^{2}$ ). The PPV dataset has been divided into training and testing data to produce statistically significant results and to make the dataset more representative to avoid overfitting. The utilized test PPV dataset acts as a proxy for any new PPV data prediction. There was evidence of higher performance in the developed Decision Trees model with the lowest RMSE and the highest $R^{2}$ on training and testing data. The decision tree is, therefore, an acceptable algorithm for the construction of a predictive PPV model for other quarry blasting areas with conditions identical to those in Helwan. Finally, comparative experimental results have shown that optimized models can predict PPV values with lower errors and greater prediction accuracy.

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Development of an Optimized Regression Model to Predict Blast-Driven Ground Vibrations

Multilayer stress from gravity and its tectonic implications in urban active fault zone: A case study in Shenzhen, South China

We have developed a new method for the interpretation of a magnetic anomaly profile by idealized-geometrical bodies using the variance analysis. This method is based on estimating the fourth horizontal derivative of the magnetic data profile. The advantage behind the use of the fourth horizontal derivative method is to reduce the regional background effect. The model parameters estimated are the depth and shape of the buried body using all available window lengths (s-value). Then, the variance value for the calculated depth at each shape value is estimated using all available s-values. The minimum variance is considered as the best criterion for determining the best-inverted parameters (depth and shape). The developed method has been verified on some noise free examples. Following that, the accuracy of the method was assessed by studying the impact of synthetic data with 5%, 10%, and 15% random noise, the effect of the regional background, the influence of interfering structures, and the selection for the wrong origin of the body. The presented method has been successfully applied to three real case studies from Brazil and India from mineral exploration.

Magnetic Data Profiles Interpretation for Mineralized Buried Structures Identification Applying the Variance Analysis Method

Implementation of magnetic and gravity methods to delineate the subsurface structural features of the basement complex in central Sinai area, Egypt

The integration of geological, geophysical, and geotechnical interpretation at the southern part of 15th of May City, have been used to evaluate the subsurface stratigraphy, especially the clay layer which may cause serious danger to construction. Those techniques have been used to delineate the subsurface structures as normal faults, which play a critical role on the stability of buildings. Geological setting of the area has been evaluated through the construction of a geological map from different geological sections and samples obtained from more than 30 observation points. Geophysical tools such as vertical electrical soundings (12 VESs), 2-D dipole–dipole array (7 sections), P-wave shallow seismic refraction (31 profiles) and multiple channel analysis of surface waves (31 MASW profiles) have been carried out to image the subsurface situation. Geotechnical evaluation using 26 boreholes, samples, laboratory tests and geotechnical parameters has been done at the area of interest. The geological setting demonstrates that the city had been constructed on the second and third members of Qurn Formation (Upper Eocene) composed of argillaceous limestone, marl and shale. Two normal faults are passing through the area were observed. The resistivity (VES and dipole–dipole) and seismic (P-waves and MASW) results reflect the presence of the two normal faults cross the study area, affecting the obtained section of marl, clayey marl and limestone layers. The geotechnical information indicate the presence of the normal faults and the existence of clay layer with swelling ability reaching 140%, which may cause cracks in the upper layers and/or subsidence.

Sultan Awad Sultan Araffa

Papers

Implementation of magnetic and gravity methods to delineate the subsurface structural features of the basement complex in central Sinai area, Egypt

Delineation of Near-Surface Structure in the Southern Part of 15th of May City, Cairo, Egypt Using Geological, Geophysical and Geotechnical Techniques

Delineating active faults by using integrated geophysical data at northeastern part of Cairo, Egypt

Geophysical investigation in the Northwestern part of the Gulf of Suez, Egypt

Delineation of water potentiality areas at Wadi El-Arish, Sinai, Egypt, using hydrological and geophysical techniques