Magnetotelluric (MT) data inversion seeks to recover resistivity models of the subsurface. Solving the inversion problem is a non-trivial task, as multiple plausible solutions can be recovered due to the nonlinearity of the problem. To reduce this nonlinearity, we propose a data-driven approach where a 1-D cumulative resistance model is estimated from MT data via a direct data transformation. We define the cumulative representation of layered models as the weighted sum of layer thickness divided by resistivity from surface to any depth level, which is the cumulative conductance. Its inverse, cumulative resistance, is directly related to the real part of the impedance computed from MT data. We train a neural network to transform the MT impedance into a resistance model. The corresponding 1-D resistivity model is obtained without a priori information. We validate our approach using synthetic and real data, opening the discussion for future developments of this new perspective.

The cross-correlation of high-frequency ambient noise (>1 Hz) is usually interpreted as the empirical Green’s function between two stations and used for imaging the near surface. However, high-frequency ambient noise mainly originates from human activities with nonuniform distributions, which may lead to spurious arrival in cross-correlation and bias the analysis of surface waves. Here, we develop an algorithm for improving high-frequency surface wave cross-correlation using an attention mechanism-based neural network, CCformer. The CCformer takes two-station cross-correlations of different time segments as input. Instead of directly producing an improved cross-correlation, the CCformer integrates the process of stacking individual cross-correlations to enhance its explainability. By identifying coherent information between each segment and generating stacking weights, the CCformer improves the desired coherent signals and attenuates spurious and incoherent noises, ultimately resulting in a well-stacked cross-correlation. After training with a synthetic dataset of 200 000 labeled samples, the CCformer presents a good ability to improve the quality of stacked cross-correlation for a synthetic noise-added test dataset with dispersion, source distribution, and acquisition parameters different from the training dataset. The dispersion spectrum of the improved cross-correlation is more continuous than the results of linear stack (LS) and phase-weighted stack, and the spectral maxima agree with the theoretical dispersion curve. Moreover, a real dataset acquired from a test site also indicates the generalizability of CCformer for laterally varying media according to the symmetry of improved cross-correlation, dispersion spectrum maxima consistent with that of active data, and inversion results validated by known targets. Therefore, the proposed algorithm provides a practical solution for automatically extracting effective surface wave signals from high-frequency ambient noise.

The surface-waves methods are well-established techniques for subsurface S-wave velocity (VS) reconstruction. Recently, the sensitivity of surface-wave skin depth to Poisson ratio was applied to also estimate P-wave velocity (VP) models from surface-wave records. We use this technique within the framework of three surface-wave methods, the wavelength/depth data transform, the laterally constrained inversion, and surface-wave tomography to estimate both VS and VP models. We apply these methods to a 3-D test data set from a mining site that is characterized by stiff material and by significant elevation contrast. The data were recorded using a regular grid of receivers and an irregular source layout. Pseudo 3D VS and VP models were obtained down to 140 m depth over approximately 900 × 1500 m2 area. The estimated models from the methods well-match the geological information available for the site. Less than 6 % difference is observed between the estimated VS models from the three methods, whereas this value is 7.1 % for the retrieved VP models. The different methods are critically compared in terms of resolution and efficiency.

The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (V _s and V _p) models. We used Poisson’s ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both V _s and V _p also from these methods. The pseudo-3D V _s and V _p models were obtained down to 140 m depth over an area of approximately 900 × 1500 m ². The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated V _s models from the three methods, whereas this value was 7.1 % for the retrieved V _p models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.

Seismic surface and body wave analyses are powerful tools for the geotechnical characterization of sites. The use of landstreamers facilitates the acquisition of dense data sets over large areas. However, efficient processing workflows are needed to estimate 3D velocity models from these massive data sets. For surface wave analysis, the manual picking of dispersion curves (DCs) of large data sets is very time-consuming, whereas the accuracy can be biased by operator choices. We apply a semi-automatic workflow for the analysis, processing, and interpretation of a large-scale landstreamer data set acquired for engineering purposes in the Middle East. The workflow involves the application of a validated automatic DC picking algorithm, and the transformation of the DCs into S- and P-wave velocity models through the wavelength-depth technique. The method has a high level of automation, is data driven and does not require extensive data inversion. Another remarkable benefit is that the auto-picking is more than 1,000 times more efficient than standard manual picking and the estimated velocities are in good agreement with available geotechnical and geophysical information. We conclude that the semi-automatic approach may represent a fast and straightforward method suitable for both research and industrial projects, thus enhancing further collaborations and developments.

This study presents a novel methodology to transform 1D resistivity data into layered resistivity models without prior information by using the concept of cumulative reference models. The proposed methodology involves deriving an error function that transforms apparent resistance measurements into a cumulative resistance, which is then transformed into a layered resistivity model. We applied the methodology to simulated data from various 1D models with different physical parameters, and the results demonstrate that our method can be used to directly transform the data into a layered resistivity model without requiring prior information. This methodology provides a valuable alternative to inversion methods when one local model is available and multiple measurements are available over an area with similar physical parameters. Furthermore, the retrieved rescaled model can be used as a reference model for the inversion process, reducing computational and economic costs. This study highlights the potential of cumulative reference models for subsurface characterization, providing a new paradigm to study the subsurface with increased efficiency.

Surface waves are widely used to model shear-wave velocity of the subsurface. Surface wave tomography (SWT) has recently gained popularity for near-surface studies. Some researchers have used straight-ray SWT in which it is assumed that surface waves propagate along the straight line between receiver pairs. Alternatively, curved-ray SWT can be employed by computing the exact paths between the receiver pairs. SWT is a well-established method in seismology and has been employed in numerous seismological studies. However, it is important to make a comparison between these two SWT approaches for near-surface applications since the amount of information and the level of complexity in near-surface are different from seismological studies. We apply straight-ray and curved-ray SWT to four near-surface examples and compare the results in terms of the quality of the final model and the computational cost.

Surface wave (SW) methods offer promising options for an effective and sustainable development of seismic exploration, but they still remain under-exploited in hard rock sites. We present a successful application of active and passive surface wave tomography for the characterization of the southern continuation of the Siilinjärvi phosphate deposit (Finland). A semi-automatic workflow for the extraction of the path-average dispersion curves (DCs) from ambient seismic noise data is proposed, including identification of time windows with strong coherent SW signal, azimuth analysis and two-station method for DC picking. DCs retrieved from passive data are compared with active SW tomography results recently obtained at the site. Passive data are found to carry information at longer wavelengths, thus extending the investigation depth. Active and passive DCs are consequently inverted together to retrieve a deep pseudo-3D shear-wave velocity model for the site, with improved resolution. The southern continuation of the mineralization, its contacts with the host rocks and different sets of cross-cutting diabase dikes are well imaged in the final velocity model. The seismic results are compared with the latest available geological models to both validate the proposed workflow and improve the interpretation of the geometry and extent of the mineralization. Important large-scale geological boundaries and structural discontinuities are recognized from the results, demonstrating the effectiveness and advantages of the methods for mineral exploration perspectives.

Joint inversion strategies and physical constraints on model parameters may be used to mitigate equivalence problems caused by solution non-uniqueness. This strategy is quite a common practice in exploration geophysics, where dedicated rock physical studies are usually carried out, while it is not so frequent in near surface geophysics. We use porosity as a constraint among seismic wave velocities and electrical resistivity in a deterministic joint inversion algorithm for surface wave dispersion, P-wave traveltimes and apparent resistivity from vertical electrical sounding. These data are often available for near surface characterization. We show that the physical constraint among model parameters leads to internally consistent geophysical models in which solution non-uniqueness is mitigated. Moreover, an estimate of soil porosity is obtained as a relevant side product of the procedure. In particular, we consider a clean sand deposit and hence the appropriate formulations for the computation of porosity from seismic velocities and resistivity are implemented in the algorithm. We first demonstrate how the non-uniqueness of the solution is reduced in a synthetic case and then we applied the algorithm to a real-case study. The algorithm is here developed for one-dimensional condition and for granular soils to better investigate the physical constraint only, but it can be extended to the two-dimensional or three-dimensional case as well as to other materials with the adoption of proper rock physical relationships.