Acta Geodaetica et Geophysica最新文献

The Persistent Scatterer Interferometry (PSI) and Global Navigation Satellite System (GNSS) techniques were used to identify the deformation rates in the Saurashtra region, western India. A sizable number of mild to severe earthquakes (with up to M5.1) have been observed in this part of the Indian plate. In order to calculate the crustal deformation, 241 Sentinel 1A images of path 107 with frame numbers 518 and 523, acquired between 2017 and 2020, were used. Similarly, processing of the GNSS dataset was done for four sites between 2009 and 2020. The foremost geodetic results from Saurashtra indicate the existence of a significant amount of deformation. PSI results show movements towards the satellite line of sight (LOS) of up to 5.0 ± 2.0 mm/year at several places and GNSS results indicate horizontal movements of less than 1.0 ± 0.4 mm/year and vertical movements of up to 2.3 ± 0.5 mm/year in the Saurashtra region. The projected LOS displacement of the GNSS is closely comparable with the PSI derived displacement. The results highlight isolated deformation pockets in various parts of the study area. Further, two loci with significant linear displacement were observed in south and east Saurashtra. Considering the seismic activity of the region, the inferred deformation rates may pose increased seismic risk for the region.

China’s BeiDou navigation system has been completed and is currently in operation. As the newest constellation, BD-3 is composed of 30 satellite configurations. The application of BD-3 has improved rapidly, however, using the BD-3 signal as a signal resource in the global navigation satellite system (GNSS) passive radar domain to monitor deformation has not been proven to be feasible. The authors of his paper designed a BD-3 passive radar model and carried out a proof-of-concept experimental demonstration for deformation monitoring. As a result, a method based on the B3I signal as a signal resource for GNSS radar (GNSS-R) deformation monitoring is proven to be feasible. The experimental scenarios include laboratory simulation and proof of concept field displacement deformation. The test error reaches a value of 0.023 m for a simulation scenario, 0.0435 m for a field scene of deformation based on a target translation measurement, and 0.1011 m for a field scene of deformation based on a target rotation measurement.

A geophysical test site (GTS) is an all-important engine room in geophysics for pedagogical field-based exercises because it is a fundamental bridge builder that links the hypothetical information given in the classroom and the real problem-solving research. This paper examines the ability of 163 undergraduate and 84 postgraduate students in the identification and use of some selected geophysical equipment and geophysical data processing software to address the general decline in literacy, technical efficiency and field skills. The students' unfamiliarity with simple geophysical data processing software and geophysical equipment, which has left a question mark on the quality of national geophysical education. In addressing this, the authors applied empirical data using a diagnostic questionnaire and field-based exercise. The analysis shows that more than 70% of undergraduate and 50% of postgraduate students lack the requisite university skills to face real problem-solving in the industrial sectors. To standardize geophysical education, this study identified the establishment of GTS as one of the major factors that influence teaching, learning outcomes and research development. Student perception feedback, unemployable graduates and educational implications of GTSs were extensively examined to link up teaching and research in geophysics. Consequently, conclusions and recommendations were drawn to bridge the missing links and the shortcomings of the theoretical knowledge-based emphasized in the classroom with the real problem-solving research that will provide geophysics trainees and graduates with the kind of lifetime requisite and professional skills required to be successful in their future tasks.

The determination of Precipitable Water Vapor (PWV) using GNSS data with Precise Point Processing (PPP) is a useful alternative for precisely estimating atmospheric water vapor. The use of GNSS data allows for all-weather PWV tracking 24 h a day, 7 days a week. The weighted mean temperature (T_m) is an important variable in deriving GNSS-PWV values with high accuracy, especially in tropical zones. Unfortunately, the usual method of obtaining T_m is through expensive meteorological instruments such as radiosondes which are not available in every region of Thailand. This current research derives empirical models of T_m based on two data sets: one provided by the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua platform, and one provided in the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ERA5 Reanalysis. GNSS-PWV was calculated for 76 GNSS CORS around Thailand using T_m values calculated by the developed models in conjunction with GipsyX software. Results were validated against AIRS-PWV values, and compared against other existing T_m models. Results based on the developed AIRS-Tm and ERA5-Tm models, as well as the existing Suwantong, Bevis, Mendes, Schueler, and GPT3 models, showed mean biases in PWV difference against AIRS-PWV values of 0.3, 0.2, − 0.3, 1.0, 0.8, 1.8, and 1.1 mm, respectively. These results conclude that the mean bias of GPS-PWV estimations can be reduced when a more localized countrywide T_m model is used versus a global model.

In geodetic studies, Global Positioning System (GPS) is widely preferred since it can be operated day and night and in all weather conditions. Also, GPS is used especially in the studies which require high accuracy such as monitoring deformations and determining tectonic movements. However, GPS error sources must be eliminated to achieve precise positioning. The troposphere, one of the major error sources, causes signal delays due to its dry air and water vapor content. Due to the fact that composition of the troposphere changes heavily both temporally and spatially, tropospheric delay is determined in zenith direction although it occurs along the signal path. This relation between the zenith direction and signal path is provided by the mapping functions (MFs). For the tropospheric delays the zenith signal delays are mapped to satellites at a given ground-based stations using MFs. In this study, the effects of most preferred MFs in the literature such as the Niell Mapping Function, the Global Mapping Function and the Vienna Mapping Function 1 have been investigated to show their effects on GPS network solution. Three GPS networks that have different baseline lengths have been analyzed. According to the results, it can be stated that the differences between the MFs are negligible, especially in the horizontal component. Moreover, since the vertical coordinate differences are greater in the network that has largest baselines, the choice of MF can significantly affect the results of the studies which require larger baselines.

We develop a new inversion approach to construct a 3-D structural and shear-wave velocity model of the crust based on teleseismic P-to-S converted waves. The proposed approach does not require local earthquakes such as body wave tomography, nor a large aperture seismic network such as ambient noise tomography, but a three-component station network with spacing similar to the expected crustal thickness. The main features of the new method are: (1) a novel model parametrization with 3-D mesh nodes that are fixed in the horizontal directions but can flexibly vary vertically; (2) the implementation of both sharp velocity changes across discontinuities and smooth gradients; (3) an accurate ray propagator that respects Snell’s law in 3-D at any interface geometry. Model parameters are inverted using a stochastic method composed of simulated annealing followed by a pattern search algorithm. The first application is carried out over the Central Alps, where long-standing permanent and the temporary AlpArray Seismic Network stations provide an ideal coverage. For this study we invert 4 independent parameters, which are the Moho discontinuity depth, the Conrad discontinuity depth, the P-velocity change at the Conrad and the average Vp/Vs of the crust. The 3-D inversion results clearly image the roots of the Alpine orogen, including the Ivrea Geophysical Body. The lower crust's thickness appears fairly constant. Average crustal Vp/Vs ratios are relatively higher beneath the orogen, and a low-Vp/Vs area in the northern foreland seems to correlate with lower crustal earthquakes, which can be related to mechanical differences in rock properties, probably inherited. Our results are in agreement with those found by 3-D ambient noise tomography, though our method inherently performs better at localizing discontinuities. Future developments of this technique can incorporate joint inversions, as well as more efficient parameter space exploration.

The Sirente main crater is a ≈ 130 m wide, in plan view droplet-shaped depression with an elevated rim, surrounded by 30 smaller depressions. It was proposed to be of meteorite impact origin. Given the age of formation in the 3rd to 5th centuries A.D., the inferred catastrophic origin was related to the celestial sign (“Chi Rho”) said to have been seen by Emperor Constantine in 312 A.D. and suggested to have changed the course of both Roman and Christian history. However, the meteoritic origin is not yet confirmed. This paper presents new results from synthetic modelling of Electric Resistivity Tomography field data collected at the Sirente main crater which provide further clues around the controversy of its origin. This study arises from the need to validate the observed structural features which include possible upturned strata (i.e., overturning of strata below impact crater rims) and compaction-fissure-like features below and just outside the crater rim, well-developed “breccia lens”, as well as an ejecta layer, and provide key indicators for objective and quantitative interpretation of the measured resistivity pattern. The results from this study are consistent with the hypothesis of a small impact crater in a low-strength target, with a relatively shallow apparent crater and do not support other proposed mechanisms of formation such as karst, mud volcano or merely anthropogenic origin.

The power of Machine Learning is demonstrated for automatic interpretation of well logs and determining reservoir properties for volume of shale, porosity, and water saturation respectively for tight clastic sequences. Random Forest algorithms are reputed for their efficiency as they belong to a class of algorithms called ensemble methods, which are traditionally seen as weak learners, but can be transformed into strong performers and they promise to deliver highly accurate results. The study area is located offshore Australia in the Poseidon and Crown fields situated in the Browse Basin, which are gas fields in tight complex clastic reservoirs. There are 5 wells used in this study with one well manually interpreted which is subsequently used in developing a machine learning model which predicts the output for the other 4 wells. The basic open hole logs namely Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, P-wave and S-wave sonic travel-time, are used in interpretation. One of the wells has a missing S-wave travel-time log which was also predicted by developing a Random Forest Machine Learning model. The results indicate a very robust improvement in performance when Random Forest algorithm was combined with Adaptive Boosting when interpreting the well logs. The training accuracy using Random Forest alone was 98.21%, but testing was 77.62% which suggested over-fitting by the Random Forest model. The Adaptive Boosting of the Random Forest algorithm resulted in the overall training accuracy of 99.40% and an overall testing accuracy of 97.03%, indicating a drastic improvement in performance. S-wave travel-time log was predicted by preparing a training set consisting of Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, and P-wave travel-time logs for the 4 wells using Random Forest which gave a training accuracy of 99.79% and a testing accuracy of 98.54%. Machine learning algorithms can be successfully applied for interpreting well log data in complex sedimentary environment and their performance can be drastically improved using Adaptive Boosting.

The D-layer of the ionosphere doesn’t respond instantaneously to the incoming solar irradiation, rather, there’s a measurable amount of time delay ((Delta t)) between the incoming solar X-ray flux ((phi (t))) during a solar flare and the respective change in the electron density profile ((N_e(t))). The (Delta t) depends on the peak of the incoming X-ray flux ((phi _{max})) during the flare. We solve the ‘electron continuity equation’ for the D-layer by numerical method for a selected set of 455 solar flares to obtain (Delta t) over six suitably chosen latitudes of the mid-latitude regions of both hemispheres and analyse the (Delta t)–(phi _{max}) profile. To analyse the latitude dependence of the dispersed nature of (Delta t)–(phi _{max}) profile, we define and compute two parameters, namely, (i) the RMS value of the D-layer response time delay ((Delta t_{rms})) and (ii) the gradient of the slope (m) of the linear fitting on (Delta t)–(log_{10}(phi _{max})) profile over each of those chosen latitudes. Further, we compute the latitudinal variation of D-layer response time delay ((Delta _{lat}(Delta t))) for selected pairs of chosen latitudes. To analyse the (Delta _{lat}(Delta t))–(phi _{max}) profile, we compute a third parameter, namely, the RMS value of latitudinal variation of D-layer response time delay ((Delta _{lat}(Delta t)_{rms})). We do a comparative analysis of these parameters across the chosen set of latitudes. Finally, we conclude quantitatively with possible explanations about the systematic latitude dependence and variation of the dispersed nature of (Delta t)–(phi _{max}) profile.