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Collaborating Authors
Building a geothermal formation model using microtremor array measurement
Tian, Baoqing (China Earthquake Administration, China Earthquake Administration) | You, Zhiwei (Real Estate Assessment Center, Chinese Academy of Sciences) | Wang, Guangjie (Chinese Academy of Sciences) | Zhang, Jiangjie (Chinese Academy of Sciences)
ABSTRACT A comprehensive understanding of the internal structure and construction of a geomechanical formation model play an important role in developing and using geothermal resources. Formation models help in identifying the channel and cycling modes of the heat flow. Due to urban sprawl and development, constructing a formation model of geothermal resources based on data from traditional geophysical methods is challenging. The microtremor survey method (MSM) is adopted to obtain critical information in Jimo, which is famous for rare seawater geothermal resources in China. Three microtremor survey lines are deployed to identify subsurface structures up to 2 km into the ground. Dispersion curves of Rayleigh waves with frequencies from 0.4 to 10 Hz are extracted using the spatial autocorrelation method. An empirical equation is adopted to obtain the apparent shear wave (S-wave) velocity of each survey point and to plot the apparent S-wave velocity sections. The obtained sections reveal the development of two interacting faults, which form a channel for the heat-flow cycle. Two conceptual models are established to depict the formation and cycling modes of seawater geothermal resources in Jimo, based on the results and analysis. Our model will help verify the geothermal system and scientifically guide the development of unique geothermal resources. Moreover, the developed model verifies that MSM is effective and dependable for identifying fracture zones and strata.
- Geology > Rock Type (0.94)
- Geology > Structural Geology > Tectonics > Plate Tectonics (0.68)
- Energy > Renewable > Geothermal > Geothermal Resource (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Europe > Ireland > County Meath > Dublin Basin (0.99)
- Asia > Taiwan > Taipei Basin (0.99)
- Africa > Middle East > Libya > Murzuq District > Murzuq Basin > Block NC 115 > Field A Field > Silurian Tanezzuft Formation > A6 Well (0.99)
ABSTRACT We have conducted microtremor array measurements to estimate shallow S-wave velocity () structures at two sites (the 921 Earthquake Museum of Taiwan and the Taiwan Provincial Consultative Council) located near surface ruptures of the Chelungpu Fault. Ten stations, consisting of three different-aperture triangles and a central station, are adopted for each array deployment. Using the array data, we calculate dispersion curves of Rayleigh waves using the frequency-wavenumber spectrum method and then estimate structures by the surface-wave inversion technique. The obtained 2D profiles could clearly show compressive and flexural deformation structures with the surface ruptures located at relatively weak (low ) zones. This indicates compressive buckling as the most likely mechanism for surface rupturing along these low zones. Importantly, this study successfully depicts strata disturbances in a fault fracture zone using microtremor array measurements and forward numerical modeling of trishear fault-propagation folds.
- Asia > Taiwan (0.90)
- Asia > Middle East > Turkey (0.28)
- Asia > Taiwan > Taipei Basin (0.99)
- Asia > Taiwan > Taichung Basin (0.99)
The microtremor survey method (MSM) has been established as a method to estimate shallow S-wave velocity profiles from passive seismic data (i.e., microtremors). Although array analyses usually used in MSM are robust, a two-station microtremor analysis is attractive because it has a large potential to improve lateral resolution and applicability of MSM. However, applications of two-station microtremor analysis are limited probably because heterogeneous source distributions of microtremors expected in exploration studies (>1 Hz) are not considered in conventional processing. In this study, we propose a workflow to estimate surface-wave dispersion curves between two-stations considering heterogeneous distribution of microtremor energy for exploration data. In our approach, we estimate both phase and group velocity dispersion curves between each station pair, modeling azimuthal-dependent microtremor energy. Our field data application demonstrates that compared to conventional two-station analyses without consideration of noise heterogeneity, we can extract reliable phase velocity dispersion curves in wider frequency ranges. A number of dispersion curves between two-stations by our approach can be therefore useful to construct high-resolution 3D S-wave velocity structure by applying surface-wave tomography. Presentation Date: Monday, October 12, 2020 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: 351D Presentation Type: Oral
Common-midpoint spatial autocorrelation analysis of seismic ambient noise obtained from a spatially unaliased sensor distribution
Hayashi, Koichi (Geometrics/OYO Corporation) | Craig, Mitchell (California State University) | Tan, Shunjia (School of Geophysics and Measurement Control Technology) | Konishi, Chisato (OYO Corporation) | Suzuki, Haruhiko (OYO Corporation) | Tahara, Michitaka (OYO Corporation) | Falkenstein, Kent (Geometrics) | He, Bin (Laurel Geophysical Instruments LTD) | Cheng, Daxiang (Laurel Geophysical Instruments LTD)
Abstract We have introduced a passive surface-wave method using seismic ambient noise obtained from dozens of receivers forming spatially unaliased 2D arrays. The method delineates 2D or 3D S-wave velocity () models to depths of several hundreds of meters, without using any sources. Typical data acquisition uses 50–100 vertical-component 2 Hz geophones on the surface with 5–30 m receiver spacing. Cableless seismographs with GPS record 20–60 min of ambient noise. We establish a 2D grid covering the investigation area and use a common-midpoint spatial autocorrelation (CMP-SPAC) method to calculate phase velocities, resulting in a dispersion curve for each grid point. The method provides dozens of ispersion curves in the investigation area. We use a 1D nonlinear inversion to estimate a 1D profile for each grid point, and then we construct pseudo-2D or pseudo-3D models from the 1D profiles. The precision and accuracy of the CMP-SPAC method were tested with a numerical simulation using a 3D finite-difference method. The results of the simulation demonstrated the applicability of the method to complex velocity structures. We applied the method to an active fault investigation in China. Sixty-four cableless seismographs were deployed in an investigation area of 330 × 660 m (217,800 m) with 5 and 30 m receiver spacings for dense and sparse grids, respectively. A 3D model was obtained to a depth of 150 m from CMP-SPAC analysis. The resultant 3D model indicates approximately 50 m of vertical displacement on a known fault.
- Asia (1.00)
- North America > United States > California (0.94)
- Geology > Structural Geology > Fault (0.88)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.48)
Constraining depth to basement for mineral exploration using microtremor: A demonstration study from remote inland Australia
Smith, Nicholas R. A (University of Tasmania, OZ Minerals Ltd.) | Reading, Anya M. (University of Tasmania) | Asten, Michael W. (Monash University and Flagstaff GeoConsultants Pty. Ltd.) | Funk, Charles W. (OZ Minerals Ltd.)
ABSTRACT We constrain the depth and seismic structure of stiff sediment cover overlying a prospective basement terrane using a passive seismic technique which uses surface wave energy from microtremor (also known as ambient seismic energy or seismic noise). This may be applied to mineral exploration under cover to decrease the inherent ambiguity in modeling potential field data for exploration targeting. We use data from arrays of portable broadband seismometers, processed using both the multimode spatially averaged coherency (MMSPAC) method and the horizontal to vertical spectral ratio (HVSR) method, to produce profiles of seismic velocity structure along a 12-km transect. We have developed field protocols to ensure consistent acquisition of high-quality data in near-mine and remote locations and a variety of ground conditions. A wavefield approaching the theoretical ideal for MMSPAC processing is created by combining the energy content of an off-road vehicle, driven around the seismometer array, and ambient sources. We found that this combination results in significantly higher-quality MMSPAC waveforms in comparison with that obtained using ambient energy alone. Under ideal conditions, a theoretical maximum depth of investigation of 600 m can be achieved with a hexagonal sensor array with 50-m radius and MMSPAC and HVSR. The modeling procedure we employ is sensitive to layer thicknesses of . A high-velocity layer in the sediment package reduces the sensitivity to deeper structure. This can limit the modeling of underlying layers but may be addressed by detailed analysis of the HVSR peaks. Microtremor recordings including off-road vehicle noise, combined with the MMSPAC and HVSR processing techniques, may therefore be used to constrain sediment structure and depth to basement in a cost-effective and efficient method that could contribute greatly to future mineral exploration under cover.
- North America (1.00)
- Oceania > Australia > South Australia (0.68)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- Geology > Mineral (1.00)
- Geology > Structural Geology > Tectonics > Plate Tectonics (0.94)
- Geology > Geological Subdiscipline > Stratigraphy (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.47)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (1.00)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Oceania > Australia > South Australia > Eromanga Basin (0.99)
- Oceania > Australia > South Australia > Arckaringa Basin (0.99)
- Oceania > Australia > Queensland > Eromanga Basin (0.99)
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