This research aims at improving the methods of prediction of shear wave velocity in underground layers. We propose and showcase our methodology using a case study on the Mashhad plain in north eastern part of Iran. Geotechnical investigations had previously reported nine measurements of the SASW (Spectral Analysis of Surface Waves) method over this field and above wells which have DHT (Down Hole Test) result. Since SASW utilizes an analytical formula (which suffers from some simplicities and noise) for evaluating shear wave velocity, we use the results of SASW in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between SASW results and those obtained by the method of DHT (treated here as real values). Our results show that an appropriately trained neural network can reliably predict the shear wave velocity between wells accurately.
A B S T R A C TThe proton magnetometers, also known as the proton precession magnetometers, are among the most widely used instruments for magnetometry surveys, owing to their high resolution which is about 0.01 nT. As the economically attractive ferromagnetic deposits generate a magnetic field anomaly which exceeds 100 nT, this resolution is not needed for detecting such large signals. The recently introduced magneto-inductive technology has led to the development of a low-cost magnetometer that can measure the ground magnetic field up to a resolution of around 10 nT. These magnetometers are inexpensive, come in a very small size, and are lightweight compared to the more common magnetometers such as the proton precession type. In this research, a low-cost magneto-inductive sensor and a highly precise proton magnetometer are simultaneously utilized at the same profiles on the Galali iron ore deposit in the northwest of Iran. The discrepancy of two measurements is less than 400 nT, often less than 100 nT, while the changes in the total magnetic field on this anomaly are around 7500 nT, which is 11 times larger than the maximum difference between the two measurements. In addition, the value of the regression coefficient between the two measurements for all profiles is more than 0.97. Also, the slope of the fitted line for the two measurements for all profiles is close to one. These results demonstrate the applicability of the new low-cost magneto-inductive sensor in magnetic prospecting.
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