فصلنامه فیزیک زمین و فضا
سال چهل و نهم شماره 4 (Winter 2024)

The gravity and the magnetic data sets are utilized to model the Hematite ore body. The cross-gradient joint inversion is used to invert the data sets simultaneously. To discretize the model space, the advanced meshing algorithm (Octree mesh) has been applied. The sparse norm and cross-gradient inversion modules in Python, accessible through Simulation and Parameter Estimation in Geophysics (SimPEG, version 0.17.0) website, have been applied to the inversion process. The sparse norm inversions do not provide reasonable results, particularly for the gravity data set. The estimated density contrasts through the inversion process are very low and unrealistic and on the other hand, the north-south cross sections do not represent a real image from the subsurface sources. The magnetic modeling results obtained through sparse norm inversion also show unrealistic characters, particularly for the 3-dimensional figure of the subsurface anomaly.The cross-gradient inversion acts quite successfully for both gravity and magnetic models in spite of high noise level in gravity data and the weak signal of magnetic data. The results are in good agreement with geological evidences and also former geophysical survey in the survey area. The priority of cross-gradient inversion of gravity and magnetic data sets to separate inversion is quite clear, despite the weak magnetic signal.

Porosity is one of the most important petrophysical parameters, studied in the subject of reservoir characterization. Determining porosity and how it changes in hydrocarbon reservoirs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive Inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon reservoirs and increase the accuracy of porosity determination in reservoirs to a great extent.

The determination of the geoid using the Stokes integral involves transforming gravity data from their measurement altitude to the geoid/ellipsoid surface. This study focuses on improving the accuracy of analytical downward continuation (ADC) for reducing terrestrial gravity anomalies to the geoid. The ADC method uses the Taylor series and successive vertical gradients of the gravity anomalies. The Moritz integral formula, which is based on Poisson's integral, is used to derive the vertical gravity gradient. To enhance its accuracy, a mean vertical gradient is proposed by introducing an analytical formula based on planar approximation. This formula improves accuracy by 50%. Numerical analysis, using simulated free air anomalies up to harmonic degree/order 5540/5540, reveals that the difference between mean and point ADC results in geoidal height can be several decimeters. The study also finds that the ADC of 2'×2' anomalies remains stable even with different levels of noise, while the Taylor series of 1'×1' gravity anomalies diverges.

Ground gravity and magnetic geophysical surveys are proven veritable tool in the imagery of the subsurface through the analyses of their responses (potential field data). This research attempts to delineate subsurface linear structures that are possible conduits for mineral accumulation at a 2,500 m2 active exploration site of FUNAAB, Abeokuta region. Abeokuta is embayed in the Dahomey Basin comprising of magmatic older granites of Precambrian age to early Paleozoic age. The qualitative interpretation technique employed by visualizing the grids reveals variations in density contrasts and susceptibilities, which connote the lithological distribution of the basement. Areas of high gravity and magnetic values directly correspond to areas of high density bodies and magnetically susceptible mineral contents, respectively. While the lows and steep discontinuities of both gravity and magnetic maps predict possible entrapments of mineral accumulation in the study area. The depth evaluation techniques employed are Peter’s Half Slope Method (PHSM) and 3D Euler deconvolution, showing the magnetic depths to basement range as 3.18 m to 5.88 m for PHSM and 1.00 m to 4.43 m for 3D Euler deconvolution. The gravity depth to basement reveals 7.03 m to 14.72 m for PHSM and 0.96 m to 4.13 m for 3D Euler deconvolution. The average depth results obtained clearly show that the study location is composed of shallow depth intrusive sources.

Well logging data shows the change of physical properties of rocks and fluids in lithology units with depth. Well logging is one of the main parts of natural resources exploration. But in some cases, due to the lack of geophysical equipment or due to high exploration costs, it is not possible to record some geophysical logs. In this paper, electromagnetic log predicted using electrical logs for the first time. In such cases, estimating the desired log using other geophysical logs is a suitable solution. For the estimation of geophysical logs, machine learning algorithms are used in most cases. In this research, a new strategy developed for processing and preparation of geophysical logs. This strategy consists of three parts: data smoothing, correlation intensifier, and MLR (Multiple Linear Regression) or ANN (Artificial Neural Network). The purpose of the data smoothing and correlation intensifier section is to remove outliers and identify the pattern of main changes in the log data, and as a result, the accuracy in estimating the target log increases. In this article, the determination of the electromagnetic log has been done using electric logs. The well logging data have been recorded in Southern California and the Central Valley. A total of six wells have been selected, four wells for MLR and ANN training and two wells for testing. By applying data smoothing and correlation intensifier to these data, the correlation between electrical and electromagnetic data increased significantly and caused the estimation accuracy of electromagnetic log to be above 95%. The use of this strategy is not limited to the estimation of electromagnetic log and can be used in all well logging data.

Both aeromagnetic and radiometric data were used to evaluate the Curie point depth and radiogenic heat production (RHP) of the young granitic regions of the Jos Plateau. An area of 55 by 110 square kilometers is bounded by latitude 9°30' to 10°00' N and longitude 8°30' to 9°30' E in central Nigeria. The magnetic data was subjected to spectral analysis to obtain the Curie depth, which was subsequently used to evaluate the geothermal gradient and heat flow for the area. Also, the concentration of radioelements (potassium, thorium and uranium) and the average density of the in-situ rock were used to estimate the radiogenic heat production at each point where the Curie point was evaluated. The heat flow in the study area ranges from 10 to 165.5 mW/m2 with an average value of 111.00 mW/m2. The regions with anomalous heat flow of 165.5 mW/m2 are located around Bowon Dodo, Dan Tsofo, Kadunu, Gimi, Kaura and Zankan in plateau state. The geothermal gradient values range from 5 to 68 °C/Km with an average of 26.16 °C/Km. The radiometric data analysis resulted in radiogenic heat values ranging from 0.4 µWm3   to 6 µW/m3 with an average radiogenic heat value of 3.36 µW/m3. Both analyses revealed that regions such as Ataka, Gimi, Jimjim and Pari could be investigated for geothermal energy potential. The high concentration of uranium, thorium and potassium associated with the study area is likely due to the weathering of the in-situ granitic basement rocks.

The applicability of the Gravity Recovery and Climate Experiment (GRACE) level 1B range-rate data to detect gravity changes caused by significant earthquakes (M6.0-6.9) has been investigated. The most common product of the GRACE mission is the level 2, science data, as the spherical harmonic Stokes’ coefficients of the geopotential. These coefficients have been generated from Level 1B data, resulting in missing some information during the smoothing process. In this study, the GRACE level 1B K-band range-rate measurements over three selected cells in Iran were analyzed, including two cells containing the epicenters of the Borujerd earthquake (6.1 Mw) and the Zarand earthquake (6.4 Mw), which occurred on March 31, 2006, and February 22, 2005, respectively, and one cell far enough from those two cells. Additionally, the range-rate time series attributed to Iran's main catchments containing the aforementioned zones have been extracted to distinguish between the impacts of earthquakes and hydrology on the range-rate time series. Besides, the impact of factors other than earthquakes, such as tides and non-gravitational accelerations acting on the GRACE satellites has been corrected. To better explore the extracted signals, their details have been derived using wavelet transforms, and the corresponding anomalies have been detected using the boxplot method. The considerable anomalies observed in areas within or near the  epicenters of earthquakes before and after the events indicate that the GRACE and GRACE Follow-On range-rate time series can be considered as potential precursors to a major earthquake.

Subsurface channels are stratigraphic features in seismic data that can act as reservoirs or conduits for hydrocarbons. However, detecting and characterizing these channels is challenging due to the limitations of seismic resolution and the complexity of the subsurface geology. Seismic inversion is a technique that can enhance the seismic data by transforming the seismic traces into quantitative estimates such as acoustic impedance (AI), which is a key reservoir rock property. AI inversion can help to identify and delineate the subsurface channels by providing more contrast and detail of the channel geometry, fill, and surrounding sediments. Seismic inversion is often challenged by the non-uniqueness, ambiguity and uncertainty of the inversion results due to noise and band-limited data. This paper uses a fuzzy model-based seismic inversion method that integrates prior information and fuzzy clustering constraints to produce more realistic and reliable AI models. This method assigns data points to multiple clusters with varying degrees of membership, which can capture the overlapping of AI values of different geological formations. The method is applied to the 3D Poseidon seismic data from the Browse Basin, offshore Western Australia, and the results are compared with those of conventional model-based inversion. Since there is no well-data in an interest channel zone, a qualitative evaluation with seismic attributes is performed. The subsurface structures are further interpreted by various seismic attributes. The comparison shows that the fuzzy model-based inversion method can improve the resolution, contrast and stability of the AI models and reveal more detail of the subsurface geology.

Understanding the stress field is crucial for assessing seismic risks in Northwestern Iran, a region known for its high seismic activity and geological volatility. The intricate tectonic arrangements involving the Arabian, Anatolian and Eurasian plates contribute to the unstable nature of the area. This study focuses on deducing stress regimes through stress inversion analysis of earthquake focal mechanisms in the North Tabriz Fault system. Analyzing the stress field is essential for understanding the elastic characteristics and geodynamics of the region. This study considers the stress field surrounding the Tabriz Fault, aiming to determine stress parameters and principal stress orientations using focal mechanisms. By analyzing 35 earthquake focal mechanism datasets from the Global Centered Moment Tensor and the Iranian Seismological Center, stress field inversions were conducted using Michael's linear inversion method and the iterative joint inversion method. The two techniques yielded distinct outcomes, with the iterative joint inversion method proving more accurate in determining stress fields and principal stress orientations. The Plunge values of  and  were observed to be relatively insignificant, measuring 3.24 and 2.06, respectively. A value close to 90 degrees, specifically 86.14, was determined for . The trend values for  and  were found to be 146.08 and 55.97, respectively, while  exhibited a trend value of 293.51. To estimate the orientation of the maximum horizontal stress (SH), the iterative joint inversion method was employed, yielding an estimation of . The trend and plunge calculated from this method for ,  and  were also utilized in this estimation. The findings indicate the existence of strike-slip faults in proximity to the North Tabriz Fault. The stress direction observed and the trajectory of the fault system suggest the influence of a transpressional mechanism. The predominant right-lateral strike-slip motion observed aligns with the prevailing tectonic regime in the region, providing evidence of strike-slip and thrust faulting stress regimes. The results contribute to a better understanding of the stress field and geodynamic situation in Northwestern Iran. They provide valuable insights for spatial analysis of future earthquakes and assessing seismic hazards in the region.

We developed an artificial neural network as an air quality model and estimated the scope of the climate change impact on future (until 2064) summertime trends of hourly ozone concentrations at an urban air quality station in Tehran, Iran. Our developed scenarios assume that present-time emissions conditions of ozone precursors will remain constant in the future. Therefore, only the climate change impact on future ozone concentrations is investigated in this study. General Circulation Model (GCM) projections indicate more favorable climate conditions for ozone formation over the study area in the future: the surface temperature increases over all months of the year, solar radiation increases, and precipitation decreases in future summers, and summertime daily maximum temperature increases about 1.2C to 3C until 2064. In the scenario based on present-time ozone conditions in the 2012 summer without any exceedances, the summertime exceedance days of the 8-hr ozone standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, in the scenario based on present-time ozone conditions in the 2010 summer with 58 days of exceedance from the 8-hr ozone standard, exceedances are projected to increase by about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in the 8-hr Air Quality Index (AQI) is also projected to increase based on pollution scenarios of both summers.

Modelling downward longwave radiation (DLR) in Equatorial Africa is challenging due to dense cloud cover and data scarcity. In this twofold study, daily cloudless DLR in Ilorin (8° 32′ N, 4° 34′ E), Nigeria, was modelled using two atmospheric factors, namely water vapour pressure and air temperature. Firstly, four cloudless DLR models were reformed and tested with others. Secondly, both particle swarm optimization (PSO) and genetic algorithm (GA) were deployed to optimize the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN). The statistical measures used to evaluate the performance of the models were the coefficient of determination (r2), the mean bias error, and the mean square error (MSE). While restructuring clear skies DLR models typically reduces the estimation errors, it may not necessarily impact r2 positively. The regression models have r2 values ranging from approximately 0.82 to 0.87, while MSE lies between 56.6 W/m2 and 767.5 W/m2. There are instances where MSE drastically reduces from 692.6 to 72.3 (W/m2) and from 767.5 to 66.2 (W/m2) after restructuring two different models. A recently developed expression for the region remains the best, possibly because of its format. During the training phase of the computationally intelligent systems, r2 approximately ranges between 88% and 92% but lies between 55% and 76% during testing. Although reproducibility inclusion in the code can meaningfully improve ANN systems at training, GA optimizes better than PSO. Furthermore, hybrid intelligent systems had higher r2 values than standalone computationally intelligent modes at the testing phase. Due to the efficient generalization based on r2 during the testing phase, ANN-GA is viable for modelling cloudless DLR at this site, though ANFIS has the lowest MSE at this same stage.

Multi-wavelength observations could help discriminate the fundamental differences for the mechanism of the jet-like structures of the hot and cool material in the inner atmosphere. We use the Atmospheric Imaging Assembly (AIA) on-board the Solar Dynamics Observatory (SDO) for unprecedented temporal and spatial resolving power for a wide range of wavelengths to increase our knowledge about the origin and the evolution of jets. The dynamical behavior of the jets in polar plumes (PP’s) is considered for the first time based on the simultaneously observed in space EUV hot, and cool emission lines also observed in W-L at total eclipses.
At the USA 2017 total solar eclipse, we observed white-light polar plumes (W-L PPs) with an excellent resolution from different sites. We tried several combinations of pictures taken (a) from different sites on the ground, using different image processing (techniques), and (b) from space, taken with different coronal filters and time sequences. The resolution of the faint polar regions space images are of low signal/noise (S/N) ratio, and instead of summing pixels of images to reduce the noise of individual images, we found it more efficient to sum a burst of 5 to 10 min consecutive AIA frames from a sequence taken around the total eclipse time. This should smear out the fast dynamical events showing fast dynamical events, but the jet part is not significantly smeared, as the flow being linear. We also found promising results using the 171 Å filter with temperature sensitivity extended from 0.6 MK to 2 MK due to lines of Fe IX, Fe X and Ni XIV but also performed the analysis with the higher temperature sensitivity filters, at 193 and 211 Å. The relationships of W-L PP’s with inner parts off-limb EUV emission lines jet-like structures demonstrated a highly correlated behavior. In most cases, we found a cross-correlation coefficient in order of 85% in coronal hole regions; however, this correlation is not perfect in some cases. Therefore, in these features, the direct connection was not detectable obviously, the disparity could be related to Doppler dimming effect in high velocity jet like plums. However, most W-L plumes have counterparts with EUV ray.

The aim of the present study is to investigate the spatial changes of seasonal ETo in Iran in the future (2020–2050), based on SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios of CMIP6 models (MRI-ESM2 and GFDL-ESM4) compared to the observed data (1992–2014). The FAO56-PM method was used to estimate ETo, and the CV was utilized to investigate the changes. The results showed that ETO will decrease in all seasons across the country under all GFDL-ESM4 scenarios (except winter under SSP5-8.5). However based on the scenarios of the MRI-ESM2 model for 2020–2050, the amount of ETO will increase in the southeastern and southern regions in winter, but in the northwest, west, east, and areas corresponding to the Zagros highlands, ETo will decrease. In spring and summer, ETo will increase in the Caspian Coast, northeastern, western and interior areas of Iran, and even in the northwest (in summer). In the fall, ETO will increase in the eastern and western regions of the country, east of the Caspian Sea and the northern Iranian plateau. Fall, summer, winter and spring, respectively, represent the highest levels of spatial changes in ETo, but it will expand only according to the MRI-ESM2 model in the winter (21% – 25%) under SSP1-2.6. Other seasons show fewer changes than in the past, based on models. Accordingly, the need for detailed planning in water resource management is emphasized, especially in the southern and eastern parts of Iran toward the inner areas.

Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception.