Geophysical Prospecting最新文献

Seismic data interpolation using convolutional neural networks (CNNs) suffers from accuracy limitations due to the inter-band interference across different frequency bands, which negatively affects subsequent inversion and interpretation. To address this limitation, we propose a multi-band strategy that first decomposes the seismic data into multiple sub-bands through frequency filtering. Independent CNN models are then used to process each specific frequency band to isolate spectral interference. We focus on regularly missing shots interpolation, assuming that dense receiver arrays are available during seismic acquisition with sparse shots. As for the training data preparation, the spatial reciprocity of Green's function is considered, which guarantees the similarity between common shot gathers (CSGs) and common receiver gathers (CRGs). The available dense CSGs are used to train networks using the multi-band-assisted training strategy. The resulting optimized independent models are then employed to reconstruct missing shots in sparse CRGs for each frequency band separately. Interpolated multi-band data are finally fused by summation to obtain the full-band result. Numerical experiments on synthetic and field data demonstrate that the proposed multi-band-assisted training strategy provides superior interpolation accuracy compared to traditional full-band training, particularly in mitigating cross-band interference.

This study describes an integrated landslide monitoring program in the landslide-prone Tizzano Val Parma region (Italy) using traditional and innovative geoelectrical techniques, namely, electrical resistivity tomography (ERT), induced polarization (IP) and self-potential (SP) methods. Both the conventional fixed-base and the unconventional sparse gradient array configurations were adopted. The use of analytic signal amplitude (ASA) technique enabled for a better recognition of primary SP anomaly sources, for the sparse gradient arrays, providing useful insights in delineating areas of interest. The region faces recurrent landslides due to geological and geomorphological factors, leading to high hydrogeological instabilities and environmental risks. Borehole stratigraphy reveals a complex lithology of sandstones, clayey marl, and coarse materials. The ERT–IP survey provides insights into various landslide types, identifying distinct domains including complex quiescent, active and undetermined landslides. Active fault evolution is observed, indicating potential risk zones. Sparse gradient SP monitoring captures short-term electrokinetic anomalies and stable long-term variations between 2022 and 2023. Both fixed-base and sparse gradient SP monitoring highlight displacement anomalies towards the valley, suggesting potential landslide movements. Interpretation of SP maps allowed to identify preferential water flow directions which denote probable accentuating risks during intense rainfall events. This study emphasizes the significance of integrated geoelectrical monitoring for early landslide detection. Non-conventional and conventional SP arrays provide insights into anomaly repeatability and stability. Comparison of ERT–IP results with borehole information enables the extrapolation of geological characteristics, yielding a holistic understanding of subsurface structures and potential risk zones. Integration of these methodologies contributes to effective landslide management, underscoring the dynamic nature of landslide-prone regions and the necessity for ongoing risk assessment and monitoring.

Missing seismic traces from data acquisition limits often significantly degrade data quality. This study presents an unsupervised method using implicit neural representation (INR), specifically sinusoidal representation network (SIREN), to enhance seismic data quality from a single shot gather. Notably, the unsupervised framework trains the SIREN by optimizing it on the observed traces in the single-gather data. The network learns a continuous function, enabling the reconstruction of missing data at any spatio-temporal coordinate. This algorithm directly addresses both missing trace interpolation and the enhancement of sparsely sampled data resolution. Key network design choices, such as exponential frequency scaling and dense skip connections, are shown to enhance reconstruction accuracy by mitigating spectral bias and incorporating multi-scale features. Furthermore, our analysis of different coordinate handling strategies identifies a key trade-off on the geometry setting. Reframing interpolation as a super-resolution task enables the successful reconstruction of up to 75% regularly missing traces and can maintain continuity across large gaps of up to 10 traces. However, this method proves geometrically inaccurate for irregular missing data, as it discards true physical coordinates, leading to incorrect solutions. In contrast, strategies that maintain physical coordinates show significantly degraded performance when faced with such large-scale data gap. The proposed framework successfully interpolated multichannel seismic data and enhanced sparse ocean bottom cable (OBC) data resolution. Although challenges remain for large irregular gaps and computational efficiency, this work establishes SIREN as a promising unsupervised tool for single-gather seismic interpolation and sparse data resolution enhancement without requiring external training data.

Stochastic reservoir characterisation relies on the careful integration of geological modelling and geophysical data, enabling prediction and uncertainty quantification for reservoir decision-making. In this paper, we address some of the computational challenges associated with Bayesian reservoir characterisation, focusing on key obstacles: demanding geophysical forward modelling, high dimensionality of spatial variables and effective posterior sampling of reservoir variables given geophysical data and well information. Leveraging a pseudo-Bayesian approach, we replace the intricate forward model for seismic amplitude-versus-offset data with a computationally efficient multivariate adaptive regression splines method, resulting in a 34-times acceleration in computations. For handling high-dimensional variables modelled by Gaussian random fields, we employ a fast Fourier transform technique. We use a preconditioned Crank–Nicolson method for efficient Markov chain Monte Carlo sampling from the posterior of the reservoir variables. The approach is motivated by challenging reservoir conditions at the Alvheim field in the North Sea, where we demonstrate our approach for Bayesian posterior sampling of oil and gas saturation and clay content conditional on seismic amplitude data and well information. We compare our results with those obtained from an approximate ensemble-based Kalman method for posterior sampling.

Methane is a primary component of natural gas that is the cleanest fossil fuel to support the sustainable development of our society. Electrical survey methods are frequently employed for the exploration of methane that is majorly existing in hydrocarbon systems in the subsurface earth. However, although the quantitative interpretation of electrical survey data relies on the knowledge about the electrical properties of methane bearing rocks, it remains poorly understood about how dissolved methane affects the electrical resistivity of brine and brine-saturated sandstones. We bridge this knowledge gap by measuring the electrical resistivity of brine and brine-saturated artificial sandstone samples with varying brine salinity, pore pressure and temperature, as a function of dissolved methane. We find that the dissolution of methane improves the electrical resistivity of brine, and the improvement can be best fitted by a regression equation comprising both a constant and an exponential part. We also find that the improvement in the brine resistivity increases with reducing brine salinity, pore pressure and temperature, where reducing brine salinity, pore pressure and temperature also improve the brine resistivity with no dissolve methane. Experiment on the rock samples shows that the resistivity of the artificial sandstones with dissolved methane behaves in a similar way to the brine resistivity, in terms of its dependence on the brine salinity, pore pressure and temperature. Further analyses demonstrate that the dissolution of methane in brine does not affect the cementation exponent of the rocks, and therefore the rock resistivity with dissolved methane can be predicted on basis of its constant cementation exponent and the brine resistivity with dissolved methane. The results not only reveal the effects of dissolved methane on the electrical resistivity of brine and sandstones at varying conditions but also pave the way to the interpretation of electrical survey data for the better quantification of dissolved methane in the hydrocarbon systems.