Acta Geodaetica et Geophysica最新文献

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.

Amplitude Versus Offset (AVO) analysis has been successfully applied in the hydrocarbon industry for more than three decades. This effective tool of the joint seismic and well-log data processing enables to predict and analyze fluid saturated porous geological formations. AVO methodology is based on the anomalous behavior of the pre-stack reflected amplitudes observed from fluid bearing rocks. However, the potential of AVO methodology is still unexploited in geothermal exploration, although the lithology and rock physical properties are very similar. In this study, we summarize the theoretical backgrounds and calculate synthetic AVO responses of a known geothermal reservoir located in the fractured carbonates of the Mesozoic basement. We demonstrate that the AVO response of a deep geothermal reservoir can be quite different from the amplitude response observed from a hydrocarbon bearing clastic formation. AVO attributes of the investigated geothermal reservoir are presented, and the potential of its detection by seismic amplitude data are discussed.

Aseismic dip-slip normal fault displacement related to numerous fast-slipping active faults was recently observed in several localities in the western Anatolia extensional province. Still, the characteristics of displacements along with the behavior of individual fault segments are poorly known. Here we analyze an aseismically active normal fault affecting the settlement area of the Sarıgöl district, Turkey, at the surface rupture area of the 1969 magnitude 6.5 Alaşehir earthquake. A precise leveling method was implemented in this area between July 2017 and 2020, to determine the vertical movements of the hanging wall relative to the footwall of the Sarıgöl fault. The yearly vertical movement on the surface along Profile 1 was − 7.0, − 7.3, and − 7.0 cm, respectively, for the three years starting in July 2017, and on Profile 2 it was − 7.7, − 8.7, and − 7.8 cm for the same time period. This persistent deformation, especially in the summer and fall seasons, suggests that may be related to groundwater level changes. Intensive agriculture is conducted in the region and a high level of irrigation activity in the summer period causes a decrease in groundwater levels. In addition, the continued deformation together with intensive precipitation in winter and spring despite high groundwater levels leads to the idea that tectonic creep movement could be a second reason for the deformation in the area. In the current study, the most important result is that the aseismic deformation starting after the 1969 Mw6.5 Alaşehir earthquake still continues rapidly today with a velocity of 70–80 mm/year down-dip. This indicates that the damage zone of the Sarıgöl fault is not appropriate for settlements in the Sarıgöl district due to continuous high amounts of vertical displacement, and that appropriate building policy and awareness campaigns are needed.

Multipath effect is a main source of error in relative positioning, which cannot be eliminated or mitigated by differential algorithm. We discuss the topic of mitigating multipath at a static station in observation domain with GPS and BDS systems. At present, the sidereal filtering as one of the most commonly used multipath mitigating methods relies on the repetition period of multipath, which cannot be accurately estimated influenced by the maneuver of satellite orbit and the time interval of satellite ephemeris. The window matching method as a real-time method was proposed to reduce this effect. However, this method is affected by similarity measures in the process of real-time window matching. We propose a near real-time window matching method based on sidereal filtering. In the modified method, the satellite single difference residual is divided into segments and the cross-correlation method is used to obtain the multipath repeat time. At the same time, the second segment series overlaps with the previous segment to ensure a near real-time performance. Based on the obtained repeat time, the template window and matched window are formed by epochs in the segment and then an affine transformation is applied to determine the value of multipath correction between the two windows. Tests were conducted for GPS and BDS systems respectively using the baseline observations at static stations in the Ha-Jia high-speed railway. The experimental results show that the modified method can mitigate the multipath error in double difference observation, and finally provide higher positioning results than methods without model and traditional model. In practice, application of the modified method in near real-time baseline positioning can effectively mitigate the multipath error.