Geophysical Prospecting最新文献

The overburden structures often can distort the responses of the target region in seismic data, especially in land datasets. Ideally, all effects of the overburden and underburden structures should be removed, leaving only the responses of the target region. This can be achieved using the Marchenko method. The Marchenko method is capable of estimating Green's functions between the surface of the Earth and arbitrary locations in the subsurface. These Green's functions can then be used to redatum wavefields to a level in the subsurface. As a result, the Marchenko method enables the isolation of the response of a specific layer or package of layers, free from the influence of the overburden and underburden. In this study, we apply the Marchenko-based isolation technique to land S-wave seismic data acquired in the Groningen province, the Netherlands. We apply the technique for combined removal of the overburden and underburden, which leaves the isolated response of the target region, which is selected between 30 and 270 m depth. Our results indicate that this approach enhances the resolution of reflection data. These enhanced reflections can be utilised for imaging and monitoring applications.

Distributed fibre-optic sensing (DFOS) technologies have emerged as cost-effective high-resolution monitoring alternatives over conventional geophysical techniques. However, due to the large volume and noisy nature of the measurements, significant processing is required and expert, fit-for-purpose tools must be designed to interpret and utilize DFOS measurements, including temperature and acoustics. Deep learning techniques provide the flexibility and efficiency to process and utilize DFOS measurements to estimate subsurface energy resource properties. We propose a deep learning-based dual latent space method to process distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) measurements and estimate the injection point location and relative multiphase flow rates along a flow-loop equipped with a DFOS unit. The dual latent space method is composed of two identical convolutional U-Net AutoEncoders to compress and reconstruct the DAS and DTS data, respectively. The AutoEncoders are capable of determining an optimal latent representation of the DAS and DTS measurements, which are then combined and trained using one experimental trial and used to estimate the physical flow properties along five different test experimental trials. The predictions are obtained within 7 ms and with over 99.98% similarity and less than $��3.68�\times �{10}^{�-�9�}�$ absolute error. The method is also shown to be robust to Gaussian noise and can be applied to different multiphase scenarios with a single pre-training procedure. The proposed method is therefore capable of fast and accurate estimation of physical flow properties at the laboratory scale and can potentially be used for rapid and accurate estimation in different laboratory or field subsurface energy resource applications.

Borehole seismic investigations play a major role for high-resolution imaging of geological structures at depth. The resulting borehole seismic data enable a direct characterisation of the target units as well as their physical properties along the well and in its direct vicinity. Analysing seismic data acquired at different scales within the borehole provides additional notable insights and allows an improved geological and petrophysical interpretation. In our work, we processed zero offset vertical seismic profiling data and full waveform sonic log data as part of a multiscale borehole seismic imaging workflow to better characterise a mineral exploration target at Ludvika Mines (Blötberget mining area, Central Sweden). Data processing mainly comprised wavefield separation and corridor stacking, followed by migration of the full waveform sonic log data using a diffraction stack approach. Additional borehole data, that is, impedance logs and a lithological borehole profile, were used for the integrated interpretation to provide the basis for an assignment of the reflectors to a specific lithological unit. Besides the existing structural models derived from surface seismic investigations, the new images from borehole seismic data reveal the internal structure of the mineralisation at a significantly higher resolution, complement the geophysical characterisation and can be used for a subsequent reliable mineral resource estimate.

For this investigation, we exploit local-distance P- and S-wave observations generated by mining-related and small-magnitude events in the Klerksdorp, Orkney, Stilfontein and Harteesfontein (KOSH) mining region of South Africa to explore the robustness and variability of low-yield P-to-S-wave amplitude ratios. P/S amplitude ratios are traditionally used in discrimination studies between earthquakes and explosions recorded at regional and teleseismic distances ($�>$ 200 km) and for relatively large magnitude events. Few studies have explored the variability of P/S amplitude ratios using data recorded at local distances, distances 200 km, where more scrutiny of wave propagation, near-surface geology, and source and strain release patterns is required. We took advantage of the dense surface accelerometer cluster network, KOSH, for our variability analysis. Final results show that most of the locally recorded low-magnitude events in the Klerksdorp region have comparable shear wave energy to low-magnitude earthquakes. Consequently, our time-domain rms-based P and S amplitude measurements result in stable event average P/S ratios likely to separate from explosive sources. We demonstrate the expected variability of the ratios with smaller network simulations (three-, five-, seven-station) to show that ratios remain relatively stable between 1 and 30 Hz.

Radiomagnetotellurics (RMTs) is an efficient frequency-domain electromagnetic technique for mapping subsurface electrical resistivity, particularly suited for near-surface investigations. This method utilizes commonly available civil and military radio transmitters, broadcasting between 10 kHz and 1 MHz, as sources to measure electric and magnetic field responses at the surface. Modern RMT receiver systems comprise five components (two electrical antennas and three magnetic coils), allowing for the estimation of the full impedance tensor and the tipper transfer function for the vertical magnetic field. In this study, RMT data were acquired to investigate the shallow structure of the Himalayan Frontal Thrust (HFT) fault in the Sub-Himalayan region around Uttarakhand, India. Data were collected at 312 stations along eight profiles over an area of roughly 500 m × 70 m. The dense station distribution enables a 3D inversion of the dataset in the extended frequency range of up to 1 MHz. The observed data were processed using scalar as well as tensor estimations to obtain full impedances and tipper transfer function. We integrated scalar-estimated data from zones with an approximately 2D conductivity distribution in the full-tensor dataset. This approach ensured robust 3D modelling during the initial RMT inversion performed with the ModEM algorithm. To date, a joint 3D interpretation of RMT full impedance tensor and tipper transfer function has not yet been reported. Furthermore, the near-surface manifestations of the HFT have not previously been explored by RMT. The derived 3D model from combined scalar, tensor and tipper data reveals a conductivity contrast zone that aligns well with the HFT fault outcrop and complementary geological information. The derived geo-electrical structure recovers the local sediment thickness and shallow fault inclination.