نشریه پژوهش های ژئوفیزیک کاربردی
سال هفتم شماره 3 (پیاپی 15، پاییز 1400)

 The history of UAV or unmanned aerial system (UAS) or drone is backed to 1924 in which the first airplane has been flown successfully while it has been fully controlled by the ground station. At that time, the motivation toward applying an UAV was to reduce the peril of fatalities in risky military missions. By date, the UAVs are of interest to a wide variety of applications. The geology, ecology, agriculture, forestry are some examples of Unmanned Aerial Vehicle applications. Magnetometry is one of geophysical method that investigates the variation of the Earth’s magnetic field. Magnetometry is a robust tool in oil and mineral exploration. One of the privileges of magnetometry, compared to the other geophysical methods. is that it is possible to perform it in form of airborne. Hence, aeromagnetic method has high coverage and is very cost-effective. In this study, the advantages and the importance of UAV in the field of geophysics are investigated. In this study, the methods of installation of magnetometers on UAVs to mitigate the noise effect of the UAV on the sensor are examined. Then, different UAV platforms are investigated. Finally, the challenges and the prospects of the UAV application on the aeromagnetic field are discussed.

 The aerial magnetometry is performed by helicopters or aircrafts, which are traditional tools for aeromagnetic surveys. As the advanced technology for UAVs has evolved, miniature sensors have also been developed. By equipping the UAVs with such sensors, they are ready to be applied in various scientific researches. Considering the advantages of UAVs, for instance low cost, zero risk of fatality, low repeating operation costs, high precision positioning system and high versatility, they have become powerful tools for aeromagnetic surveys. The UAVs can fill the gap between high resolution and low coverage land data and low resolution and high coverage airborne data obtained by aircrafts or helicopters. At the time of writing this paper, no scientific report of aeromagnetic surveys using UAVs has been found in Iran. Hence, the author wishes that this investigation serves as a reference for future studies and applications in the field of aeromagnetic surveys using UAVs in Iran.

In this study, 43 papers in the context of aeromagnetic surveys using UAVs have been considered in which two, nine and six papers have been published in 2008, 2016 and 2019 respectively. These results show that although aeromagnetic surveys using UAVs are in their infancy period, they are developing rapidly. Moreover, the geography of the application of the research on the field of aeromagnetic surveys using UAVs has been investigated. The results show that this technology has been performed in 10 countries. Canada is the pioneer of applying this state of the art technology as 14 publications out of the total 43 papers are related to this country. China, Russia, and Japan have lower number of publications with 7, 6, and 5 publications, respectively.

The amplitude of seismic waves is generally reduced while passing through the earth under the influence of the two main factors of geometrical spreading and apparent attenuation of seismic waves. Scattering attenuation, as an elastic phenomenon, redistributes seismic wave energy due to the collision of seismic waves (P, S and surface waves) with randomly distributed heterogeneities. The attenuation of coda wave, backscattered waves form heterogeneities, is one of the most important parameters in the estimation of seismic wave attenuation. In this study, the attenuation of seismic coda waves as scattered body waves has been estimated using single back-scattering model.

The most common method to estimate attenuation is single back-scattering model. Here, we use short-time Fourier transform (STFT) instead of bandpass filter. For each individual frequency, the envelopes of STFT coefficients of extracted waveforms for lapse times of 30, 40, 50, 60, 70, 80 and 90 seconds are measured as the BPF method. The obtained envelopes have been used in order to estimate attenuation at each individual frequency. Since the length of the window is constant in the STFT method, it is possible to determine fairly accurate frequency in a short window. However, to this purpose, each waveform is divided into 1 s windows (50 samples) overlapped by 90% (45 samples), and the STFT has been calculated for each window.

In this study, attenuation parameter has been estimated in northwestern Iranian plateau using the STFT method. The results show good correlation between seismicity and tectonic of the study area for lapse times greater than twice the S- wave travel time, especially for lapse time of 40 s. At lapse time of 40 s, the frequency-dependent relationship has been estimated as Q = (103±1)f(0.88±0.04) using the STFT method. It is concluded that the STFT model can be used as an appropriate time-frequency tool to study the energy attenuation of high-frequency coda waves due to the high correlation coefficients and low standard deviations of the relationship. Furthermore, the relatively low values of quality factor in frequency attenuation relation show that there is high heterogeneity among shallow layers of Iran northwestern region that is possibly due to the relatively high seismicity of the region.

Magnetotellurics is widely used for exploration of geothermal resources because of its potential in the conductivity contrast of deep geological structures in geothermal regions. Hence, 2D inversion of the MT data is a major step in determination of physical properties of exploration targets and their geometric quantitative dimensions. In recent years, many attempts have been made on the development of 2D MT inversion algorithms and interpretation of the results. The solutions for inverse problems of MT data are nonunique and unstable since the measured data are accompanied by noise. The Tikhonov regularization theory is a common method for resolving unstable and ill posed inverse problems. In solving this regularized inverse problem, selection of an optimal regulation parameter value is important for achieving an ideal inverse modeling result. This parameter controls the balancing between the minimization process of the stabilizing and the misfit function. Therefore, it must be chosen carefully. In this paper, a novel method has been
presented in which the optimal value of the regularization parameter for 2D inversion of the MT data is selected based on the LB method to increase the speed and accuracy of the results of the inversion.

Numerical modeling of geophysical response for a given geophysical model is known as forward problem. The directly inverting of the forward sensitivity matrix is very difficult especially in large scale problems, instead, one needs to use iterative methods to calculate it quickly. To achieve this, a fast iterative solver like LB algorithm is used for smooth inversion of MT field data. In this method, the large forward sensitivity matrix is substituted by a smaller dimension bidiagonal matrix, which avoids large matrix multiplication and basis vectors storing. Therefore, the inversion process speed in solving the large inversion problems and obtaining accurate results will increase, and the required memory and inversion calculation time will also be decreased. In order to find an appropriate value for the regularization parameter, a novel method has been presented in which adaptive regularization, has been used and compared with two conventional methods, namely MGCV and ACB. All these methods have been used in MATLAB codes and combined with the LB method, and then has been has been added to software package MT2DInvMatlab developed by Lee (2009). To demonstrate the efficiency of the proposed technique comprising of the above-mentioned methods, it has been applied on synthetic MT data having 3 percent Gaussian noise, and also, real MT data of the Bushli (Nir) geothermal field in Ardabil Province, Iran.

The models produced from the inversion of the synthetic MT data and the Bushli MT field data set using the adaptive regularization, MGCV, and ACB methods are almost similar. The constructed model from using adaptive regularization is slightly better than the model obtained from MGCV and ACB methods. therefore, with respect to the results obtained from 2D inversion of the synthetic MT data and the Bushli (Nir) MT field data, the adaptive regularization

provides a more accurate solution especially in estimating the conductive layer and reservoir boundaries. In addition, this method, compared to the MGCV and ACB methods, is faster and requires less memory in the inversion process. Hence, this method can be considered as the most reliable method for selection of the optimal regularization parameter in the inversion of large 2D and 3D magnetotelluric data sets.

Seismic methods are common techniques that are used in the fields of engineering and exploration. In seismic refraction method the aim is to measure travel times of P-waves from the shot point to the geophones, which are located at known points on the surface. In this method, the first wave arrivals are used to produce time-distance plots, which are then used in the calculation of the depths of refracting interfaces and compressional wave velocity with respect to depth. After gathering and preprocessing of seismic data, the inversion stage is performed to estimate model parameters. Similar to other kind of geophysical data, the inversion of seismic data is also faced to non-uniqueness challenge. In this study to tackle the non-uniqueness of seismic data inversion, a method based on artificial intelligence is proposed. Creating a neural network with the highest possible performance and decreasing the risk and level of error is a major goal in this regard. A large amount of effort is usually spent on fine tuning the network to make sure it provides the best results.However, a single classification or regression model can hardly produce the best results for every sub-section of a complex problem space. Creating multiple neural networks (or any other machine learning model) and combining the results of those networks could potentially improve the performance and decrease the risk and level of error.

In order to perform the inversion of travel times of seismic refracted waves using artificial neural networks, different synthetic models having different thicknesses and P-wave velocities were produced for the purpose of training the neural networks. Moreover, 20% of generated data (synthetic models) were considered as test (unseen) data for evaluation of the proposed inversion method. Many networks with different structures were tested using cross- correlation method (i.e. K-fold method). Then, networks with better performance or lower prediction error were selected. This is equal to assigning larger weights to better performing networks while assigning very small weights to those with large errors. Then, the output results of the individual networks were combined using linear averaging method. Finally, the proposed inversion algorithm was run on an actual dataset. In order to further investigate the proposed neural network based inversion algorithm, the actual dataset was additionally inverted using tomography method. The results of the two methods were then compared.

In this study, a new inversion algorithm based on artificial intelligence is introduced. Then, the capability of the proposed inversion algorithm was evaluated using a test data and an actual dataset. The results show that the applied method is a fast and powerful technique in automatic inversion of seismic refraction data. Furthermore, the performance of the applied inversion algorithm was compared with tomography inversion method. The results show that the introduced inversion algorithm, which is based on neural networks, is capable of automatically estimating model parameters, including the number of layers, thicknesses and P-wave velocities of the layers without having auxiliary data. Furthermore, the inversion of the experimental data using the proposed inversion algorithm resulted in a two-layer model, which had a good correlation with the geological and seismic evidence of the study area. The models produced from the inversion of the synthetic MT data and the Bushli MT field data set using the adaptive regularization, MGCV, and ACB methods are almost similar. The constructed model from using adaptive regularization is slightly better than the model obtained from MGCV and ACB methods. therefore, with respect to the results obtained from 2D inversion of the synthetic MT data and the Bushli (Nir) MT field data, the adaptive regularization metho provides a more accurate solution especially in estimating the conductive layer and reservoir boundaries. In addition, this method, compared to the MGCV and ACB methods, is faster and requires less memory in the inversion process. Hence, this method can be considered as the most reliable method for selection of the optimal regularization parameter in the inversion of large 2D and 3D magnetotelluric data sets.

Reservoir modeling is the process of creating a three-dimensional numerical model to show the spatial distribution of geological or petrophysical properties of the reservoir. The process of obtaining elastic properties from seismic data is called seismic inversion. There are different methods for seismic inversion, which are classified into two main groups:deterministic methods and stochastic methods. Understanding the differences between these two methods and their restrictions is important for their correct application and interpretation. Due to the band-limited nature of the seismic data, the results of deterministic methods are smooth maps of acoustic impedance and may be far from the reservoir facts. In contrast, stochastic inversion produces high-resolution maps of the acoustic impedance because the spatial continuity models (variograms) control the frequency content of stochastic inversion results. A well-known challenge of stochastic inversion is that it is often extremely expensive from computational point of view. In this study, a stochastic method has been used to obtain the facies and other properties of the reservoir.

In this study, to show the ability of the introduced method in modeling reservoir facies, a two-dimensional artificial model has been used. The formation in the reference model of this study consists of sandstone facies with high porosity (reservoir interval) and dense shale facies (non-reservoir interval). The formation is located at a depth of 2000 to 2200 m. At the top and bottom of the formation, a 50-m layer of shale with constant properties is considered. In order to model the facies of the reservoir, variogram parameters for different facies have been calculated from the well logs of the reference model. In the next step, the conditional probability of occurrence of the facies in each cell has been calculated using the sequential indicator simulation method with different random seeds. Then, the probability perturbation optimization algorithm has been applied to update each facies model until the model become consistent with the seismic data. At each step, a geophysical forward model is constructed and compared with seismic data.

After implementing the stochastic inversion method to the seismic data with a signal-to-noise ratio of 9 in the reference model, it was found that the optimized facies had an 81.83% correlation with the reference facies and was improved the initial facies model by 19.97%. The correlation values decreased to 77.67% and 72.38% when the seismic data with the signal-to-noise ratios of 4 and 2 were respectively used. When the seismic data with the signal-to-noise ratio of 4 was used, the initial model was improved by 15.81%, and when the seismic data with the signal-to-noise ratio of 2 was used, the correlation decreased to 10.52%.

Geophysical methods are applied approximately in all exploration steps as cheap and reliable ways, which reduce the risk of large investments in many cases (Telford et al., 1990). Similarly, using geophysical methods alongside geological studies increases the efficiency and interpretation of the results more accurately, and hence, can improve the possibility of exploration and feasibility of achieving to promising areas . Magnetometric and gravimetric methods are some of the oldest geophysical methods used for exploration of iron andchromite deposits. Correct interpretation of field magnetometric and gravimetric data integrated with other exploratory data can reduce the exploration cost and provide valuable information about the location, depth, and dimensions of the concealed iron ore deposits (Carlson and Ripley, 1997; Ganiya et al, 2012; Amobi Adebisi, 2018; Sampaio, 2021) . The gravimetric and magnetometric methods consider primitive exploratory tools for detecting minerals (Sott and Geo, 2014). The majority of chromite ore deposits have been discovered in the recent decade (Yaghoobpur, 2005). Gravimetric and electrical methods are the most important geophysical approaches used to explore chromite lenses in chromite ore deposits that are complex targets to explore due to the influence of tectonic conditions and their irregular shapes (Kogel et al.,2006). Although ophiolite rocks have a pretty high specific gravity, the density of chromite ores makes them recognizable from the host rock (Hornicka et al., 2020). Therefore, gravimetry is a common method to explore chromite resources (Aghajani, 2012; Moazam, et al., 2019; John, 1997). Kamkar Rouhani (2008) and Azad, et al. (1392) could detect the anomalies of chromite deposits in the Faryab mine area by processing the gravimetric data collected from the area.

To perform exploration operations in the Shovin mineralized area, first, the area was visited, and then, all data, reports, and maps of the area were collected, and by incorporating these two sources (field studies and previous documentation), a relatively adequate knowledge of the geological characteristics in the area was attained. These data were used to plan future exploration operation. In the second step, several operative groups with different aims were equipped to commence the exploratory operation in collaboration with the mapping group. Many activities such as field geological studies, geophysical surveys, and sampling of the favorable areas were carried out. In the third step, the data collected from various geological and geophysical studies were possessed, and the results were obtained. To conduct magnetometric surveys in the Shovin area, G856 model proton magnetometer with the sensitivity of 0.01 nT was applied to acquire magnetometric data at the points considered along 11 survey lines with 50 meters intervals passing all the suspicious ultramafic outcrops and faults in the study area. Using a theodolite instrument, the designed points have been marked on the ground. As a result, a survey network with the density of 25*50 or 50*50, depending on the proximity of ultramafic outcrops and the main faults was considered. Overall, total magnetic field measurements in 264 points were made and magnetic map of the area was obtained after performing all of the corrections on the magnetic data. Similarly, gravimetric surveys were conducted in the area, and totally, gravimetric surveys were made in 209 points with a grid of 20 meters by 20 meters along 11 survey lines in four days. For the gravimetric surveys, the Lacoste- Romberg device with the resolution of 0.01 mGal was used. After performing all corrections and calculations on the raw gravimetric data, the residual anomaly map was obtained. Finally, after processing and interpretation of the magnetometric and gravimetric data, and integration of the geophysical results with geological information, possible iron and chromite mineralized zones were recognized.

Shovin iron and chromite deposit is observed along with ultramafic and mafic rocks (harzburgite to gabbro) and the iron ore found in this deposit in the form of hematite, although magnetite can be scattered in the ultramafic rocks and or as a filling within the joints and cracks that is economically valuable . Magnetometric investigations in the area reveal that no accumulation of rich iron masses exists in the subsurface, and high magnetic intensities are the results of the presence of a dyke and ultramafic rocks . The residual gravity anomaly and modeling applied on the gravity data indicate the presence of a buried mass with a density of about 4.4 gr/cm3 that can indicate high possibility of the existence of a chromatic mass in the study area.