scholarly journals Multiple Regression and Modified Faust Equation on Well Logging Data in Application to Seismic Procedures: Polish Outer Carpathians Case Study

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6300
Author(s):  
Kamila Wawrzyniak-Guz ◽  
Jadwiga A. Jarzyna ◽  
Krzysztof Pieniądz ◽  
Krzysztof Starzec
Keyword(s):  
Multiple Regression ◽  
Velocity Model ◽  
Poor Quality ◽  
P Wave ◽  
Quality Of Data ◽  
Interval Velocity ◽  
Resistivity Logs ◽  
Compaction Factor ◽  
Outer Carpathians ◽  
Sonic Logs

An appropriate velocity model from well logs is a key issue in the processing and interpretation of seismic data. In a deep borehole located in the central part of the Polish Outer Carpathians, the sonic measurements were inadequate for seismic purposes due to the poor quality of data and gaps in the logging. Multiple regression (MR) and a modified Faust equation were proposed to model the velocity log. MR estimated the P-wave slowness as a dependent variable on the basis of sets of various logs as independent variables. The solutions were verified by the interval velocity from Check Shots (CS) and by the convergence of synthetic seismograms and the real seismic traces. MR proved to be an effective method when a set of other logs was available. The modified Faust method allowed computation of P-wave velocity based on the shallow resistivity logs, depth, and compaction factor. Faust coefficients were determined according to the lithology and stratigraphy divisions and were calibrated with the use of the velocity previously determined in the MR analysis. The modified Faust equation may be applied in nearby old wells with limited logging data, particularly with no sonic logs, where MR could not be successfully applied.

Anisotropic correction of sonic logs in wells with large relative dip

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. E1-E5 ◽  
Author(s):  
Lev Vernik
Keyword(s):  
Reservoir Characterization ◽  
Low Frequency ◽  
P Wave ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Avo Inversion ◽  
Pore Pressure Prediction ◽  
Siliciclastic Rocks ◽  
Dip Angle ◽  
Sonic Logs

Seismic reservoir characterization and pore-pressure prediction projects rely heavily on the accuracy and consistency of sonic logs. Sonic data acquisition in wells with large relative dip is known to suffer from anisotropic effects related to microanisotropy of shales and thin-bed laminations of sand, silt, and shale. Nonetheless, if anisotropy parameters can be related to shale content [Formula: see text] in siliciclastic rocks, then I show that it is straightforward to compute the anisotropy correction to both compressional and shear logs using [Formula: see text] and the formation relative dip angle. The resulting rotated P-wave sonic logs can be used to enhance time-depth ties, velocity to effective stress transforms, and low-frequency models necessary for prestack seismic amplitude variation with offset (AVO) inversion.

Lithospheric architecture of the Ligurian Basin from seismic travel time tomography

2021 ◽  
Author(s):  
Anke Dannowski ◽  
Heidrun Kopp ◽  
Ingo Grevemeyer ◽  
Grazia Caielli ◽  
Roberto de Franco ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
Central Basin ◽  
Uppermost Mantle ◽  
Mantle Boundary ◽  
Oceanic Spreading ◽  
Refraction Seismic ◽  
Back Arc ◽  
Ligurian Basin

<p>The Ligurian Basin is located north-west of Corsica at the transition from the western Alpine orogen to the Apennine system. The Back-arc basin was generated by the southeast retreat of the Apennines-Calabrian subduction zone. The opening took place from late Oligocene to Miocene. While the extension led to extreme continental thinning little is known about the style of back-arc rifting. Today, seismicity indicates the closure of this back-arc basin. In the basin, earthquake clusters occur in the lower crust and uppermost mantle and are related to re-activated, inverted, normal faults created during rifting.</p><p>To shed light on the present day crustal and lithospheric architecture of the Ligurian Basin, active seismic data have been recorded on short period ocean bottom seismometers in the framework of SPP2017 4D-MB, the German component of AlpArray. An amphibious refraction seismic profile was shot across the Ligurian Basin in an E-W direction from the Gulf of Lion to Corsica. The profile comprises 35 OBS and three land stations at Corsica to give a complete image of the continental thinning including the necking zone.</p><p>The majority of the refraction seismic data show mantle phases with offsets up to 70 km. The arrivals of seismic phases were picked and used to generate a 2-D P-wave velocity model. The results show a crust-mantle boundary in the central basin at ~12 km depth below sea surface. The P-wave velocities in the crust reach 6.6 km/s at the base. The uppermost mantle shows velocities >7.8 km/s. The crust-mantle boundary becomes shallower from ~18 km to ~12 km depth within 30 km from Corsica towards the basin centre. The velocity model does not reveal an axial valley as expected for oceanic spreading. Further, it is difficult to interpret the seismic data whether the continental lithosphere was thinned until the mantle was exposed to the seafloor. However, an extremely thinned continental crust indicates a long lasting rifting process that possibly did not initiate oceanic spreading before the opening of the Ligurian Basin stopped. The distribution of earthquakes and their fault plane solutions, projected along our seismic velocity model, is in-line with the counter-clockwise opening of the Ligurian Basin.</p>

Reconnaissance seismic refraction-reflection surveys in northwestern New Mexico

1984 ◽  
Vol 74 (4) ◽  
pp. 1263-1274
Author(s):  
Lawrence H. Jaksha ◽  
David H. Evans
Keyword(s):  
New Mexico ◽  
Wave Velocity ◽  
Lower Crust ◽  
Seismic Waves ◽  
Seismic Refraction ◽  
Velocity Model ◽  
P Wave ◽  
Uppermost Mantle ◽  
P Wave Velocity ◽  
Crustal Thinning

Abstract A velocity model of the crust in northwestern New Mexico has been constructed from an interpretation of direct, refracted, and reflected seismic waves. The model suggests a sedimentary section about 3 km thick with an average P-wave velocity of 3.6 km/sec. The crystalline upper crust is 28 km thick and has a P-wave velocity of 6.1 km/sec. The lower crust below the Conrad discontinuity has an average P-wave velocity of about 7.0 km/sec and a thickness near 17 km. Some evidence suggests that velocity in both the upper and lower crust increases with depth. The P-wave velocity in the uppermost mantle is 7.95 ± 0.15 km/sec. The total crustal thickness near Farmington, New Mexico, is about 48 km (datum = 1.6 km above sea level), and there is evidence for crustal thinning to the southeast.

Estimates of velocity structure and source depth using multiple P waves from aftershocks of the 1987 Elmore Ranch and Superstition Hills, California, earthquakes

1991 ◽  
Vol 81 (2) ◽  
pp. 508-523
Author(s):  
Jim Mori
Keyword(s):  
Velocity Structure ◽  
Velocity Model ◽  
P Wave ◽  
Sediment Layer ◽  
Source Depth ◽  
Imperial Valley ◽  
P Waves ◽  
Main Shocks ◽  
Aftershock Sequences ◽  
Event Record

Abstract Event record sections, which are constructed by plotting seismograms from many closely spaced earthquakes recorded on a few stations, show multiple free-surface reflections (PP, PPP, PPPP) of the P wave in the Imperial Valley, California. The relative timing of these arrivals is used to estimate the strength of the P-wave velocity gradient within the upper 5 km of the sediment layer. Consistent with previous studies, a velocity model with a value of 1.8 km/sec at the surface increasing linearly to 5.8 km/sec at a depth of 5.5 km fits the data well. The relative amplitudes of the P and PP arrivals are used to estimate the source depth for the aftershock distributions of the Elmore Ranch and Superstition Hills main shocks. Although the depth determination has large uncertainties, both the Elmore Ranch and Superstition Hills aftershock sequences appear to have similar depth distribution in the range of 4 to 10 km.

Poor Quality of Data in Africa: What Are the Issues?

2018 ◽  
Vol 46 (6) ◽  
pp. 851-877 ◽  
Author(s):  
Abel Kinyondo ◽  
Riccardo Pelizzo
Keyword(s):  
Poor Quality ◽  
Quality Of Data

Passive seismic tomography using recorded microseismicity: Application to mining-induced seismicity

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. B41-B57 ◽  
Author(s):  
Himanshu Barthwal ◽  
Mirko van der Baan
Keyword(s):  
Seismic Tomography ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
Lateral Velocity ◽  
Study Region ◽  
Arrival Times ◽  
3D Velocity Model ◽  
Passive Seismic ◽  
Dip Angle

Microseismicity is recorded during an underground mine development by a network of seven boreholes. After an initial preprocessing, 488 events are identified with a minimum of 12 P-wave arrival-time picks per event. We have developed a three-step approach for P-wave passive seismic tomography: (1) a probabilistic grid search algorithm for locating the events, (2) joint inversion for a 1D velocity model and event locations using absolute arrival times, and (3) double-difference tomography using reliable differential arrival times obtained from waveform crosscorrelation. The originally diffusive microseismic-event cloud tightens after tomography between depths of 0.45 and 0.5 km toward the center of the tunnel network. The geometry of the event clusters suggests occurrence on a planar geologic fault. The best-fitting plane has a strike of 164.7° north and dip angle of 55.0° toward the west. The study region has known faults striking in the north-northwest–south-southeast direction with a dip angle of 60°, but the relocated event clusters do not fall along any mapped fault. Based on the cluster geometry and the waveform similarity, we hypothesize that the microseismic events occur due to slips along an unmapped fault facilitated by the mining activity. The 3D velocity model we obtained from double-difference tomography indicates lateral velocity contrasts between depths of 0.4 and 0.5 km. We interpret the lateral velocity contrasts in terms of the altered rock types due to ore deposition. The known geotechnical zones in the mine indicate a good correlation with the inverted velocities. Thus, we conclude that passive seismic tomography using microseismic data could provide information beyond the excavation damaged zones and can act as an effective tool to complement geotechnical evaluations.

Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Velocity Analysis ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Full Waveform ◽  
P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

A hybrid method to calculate teleseismic body waves in a regional 3D model using GEMINI and SPECFEM.

2021 ◽  
Author(s):  
Thomas Möller ◽  
Wolfgang Friederich
Keyword(s):  
Wave Propagation ◽  
Hybrid Method ◽  
Velocity Model ◽  
Absorbing Boundary Conditions ◽  
Body Waves ◽  
P Wave ◽  
Spherically Symmetric ◽  
Target Region ◽  
3D Simulations ◽  
Earth Models

<p>Modeling waveforms of teleseismic body waves requires the solution of the seismic wave equation in the entire Earth. Since fully-numerical 3D simulations on a global scale with periods of a few seconds are far too computationally expensive, we resort to a hybrid approach in which fully-numerical 3D simulations are performed only within the target region and wave propagation through the rest of the Earth is modeled using methods that are much faster but apply only to spherically symmetric Earth models.</p><p>We present a hybrid method that uses GEMINI to compute wave fields for a spherically symmetric Earth model up to the boundaries of a regional box. The wavefield is injected at the boundaries, where wave propagation is continued using SPECFEM-Cartesian. Inside the box, local heterogeneities in the velocity distribution are allowed, which can cause scattered and reflected waves. To prevent these waves from reflecting off the edges of the box absorbing boundary conditions are specifically applied to these parts of the wavefields. They are identified as the difference between the wavefield calculated with SPECFEM at the edges and the incident wavefield.</p><p>The hybrid method is applied to a target region in and around the Alps as a test case. The region covers an area of 1800 by 1350 km centered at 46.2°N and 10.87°E and includes crust and mantle to a depth of 600 km. We compare seismograms with a period of up to ten seconds calculated with the hybrid method to those calculated using GEMINI only for identical 1D earth models. The comparison of the seismograms shows only very small differences and thus validates the hybrid method. In addition, we demonstrate the potential of the method by calculating seismograms where the 1D velocity model inside the box is replaced by a velocity model generated using P-wave traveltime tomography.</p>

Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

2013 ◽  
pp. 376-436 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

3D Fault Structure Inferred from a Refined Aftershock Catalog for the 2015 Gorkha Earthquake in Nepal

2019 ◽  
Vol 110 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Masumi Yamada ◽  
Thakur Kandel ◽  
Koji Tamaribuchi ◽  
Abhijit Ghosh
Keyword(s):  
Velocity Structure ◽  
Complex Geometry ◽  
Slip Surface ◽  
Hanging Wall ◽  
Velocity Model ◽  
P Wave ◽  
Gorkha Earthquake ◽  
Hypocenter Determination ◽  
The North ◽  
2015 Gorkha Earthquake

ABSTRACT In this article, we created a well-resolved aftershock catalog for the 2015 Gorkha earthquake in Nepal by processing 11 months of continuous data using an automatic onset and hypocenter determination procedure. Aftershocks were detected by the NAMASTE temporary seismic network that is densely distributed covering the rupture area and became fully operational about 50 days after the mainshock. The catalog was refined using a joint hypocenter determination technique and an optimal 1D velocity model with station correction factors determined simultaneously. We found around 15,000 aftershocks with the magnitude of completeness of ML 2. Our catalog shows that there are two large aftershock clusters along the north side of the Gorkha–Pokhara anticlinorium and smaller shallow aftershock clusters in the south. The patterns of aftershock distribution in the northern and southern clusters reflect the complex geometry of the Main Himalayan thrust. The aftershocks are located both on the slip surface and through the entire hanging wall. The 1D velocity structure obtained from this study is almost constant at a P-wave velocity (VP) of 6.0  km/s for a depth of 0–20 km, similar to VP of the shallow continental crust.

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