Machine Learning for Seismic Exploration: where are we and how far are we from the Holy Grail?

Machine Learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented to almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency and in some cases for improving the results. We carried out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derived a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extracted various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata shows that the main targets of ML applications for seismic processing are denoising, velocity model building and first break picking, whereas for seismic interpretation, they are fault detection, lithofacies classification and geo-body identification. Through the metadata available in publications, we obtained indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc. and we used them to approximate the level of efficiency, effectivity and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks show that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based QC is more effective and applicable compared to other processing tasks. Among the interpretation tasks, ML-based impedance inversion shows high efficiency, whereas high effectivity is depicted for fault detection. ML-based Lithofacies classification, stratigraphic sequence identification and petro/rock properties inversion exhibit high applicability among other interpretation tasks

Estimation of horizontal-to-vertical spectral ratios (ellipticity) of Rayleigh waves from multistation active-seismic records

The horizontal-to-vertical spectral-ratio (HVSR) analysis of ambient noise recordings is a popular reconnaissance tool used worldwide for seismic microzonation and earthquake site characterization. We have expanded this single-station passive HVSR technique to active multicomponent data. We focus on the calculation of the HVSR of Rayleigh waves from active-seismic records. We separate different modes of Rayleigh waves in seismic dispersion spectra and then estimate the HVSR for the fundamental mode. The mode separation is implemented in the frequency-phase velocity (f-v) domain through the high-resolution linear Radon transformation. The estimated Rayleigh-wave HVSR curve after mode separation is consistent with the theoretical HVSR curve, which is computed by solving the Rayleigh-wave eigenproblem in the laterally homogeneous layered medium. We find that the HVSR peak and trough frequencies are very sensitive to velocity contrast and interface depth and that HVSR curves contain information on lateral velocity variations. Using synthetic and field data, we determine the validity of estimating active Rayleigh-wave HVSR after mode separation. Our approach can be a viable and more accurate alternative to the empirical HVSR analysis method and brings a novel approach for the analysis of active multicomponent seismic data

Multimodal surface-wave tomography to obtain S- and P-wave velocities applied to the recordings of unmanned aerial vehicle deployed sensors

Exploration seismic surveys in hard-to-access areas such as foothills and forests are extremely challenging. The Multiphysics Exploration Technologies Integrated System (METIS) research project was initiated to design an exploration system, facilitating the acquisition in these areas by delivering the receivers from the sky using unmanned aerial vehicles. Air dropping of the sensors in vegetated areas results in an irregular geometry for the acquisition. This irregularity can limit the application of conventional surface wave methods. We have developed a surface wave workflow for estimating the S-wave velocity (VS) and P-wave velocity (VP) models and that supports the irregular geometry of the deployed sources and receivers. The method consists of a multimodal surface-wave tomography (SWT) technique to compute the VS model and a data transform method (the wavelength/depth [W/D] method) to determine the Poisson's ratio and VP model. We applied the method to the METIS's first pilot records, which were acquired in the forest of Papua New Guinea. Application of SWT to the data resulted in the first 90 m of the VS model. The W/D method provided the Poisson's ratio averaged over the area and the VP model between 10 and 70 m from the surface. The impact of the acquisition scale and layout on the resolution of the estimated model and the advantages of including the higher modes of surface waves in the tomographic inversion are assessed in detail. The presence of shots from diverse site locations significantly improves the resolution of the obtained model. Including the higher modes enhances the data coverage and increases the investigation depth

Scale properties of the seismic wavefield - perspectives for full waveform matching

Starting from the nondimensionalization of equations of motion we partition the set of the velocity models in equivalence classes, such that the full waveform of an element in a given class can be calculated from the full waveform of any other element in the same class by scaling model parameters. We give a formal derivation of the seismic wavefield scale properties and we prove their capability through the use of numerical examples. Besides this, we introduce how the scale properties can be used to save computational time in full waveform modeling and inversion. In forward modeling we can use them for the calculation of the full waveform of any model in the same equivalence class of a model whose full waveform has been previously calculated. In full waveform inversion, scale properties can be used for full waveform matching: Given an experimental seismogram and a synthetic one, we can choose, in the same class of the synthetic model, another element whose waveform is closer to the experimental on

Application of surface-wave tomography to mineral exploration : a case study from Siilinjarvi, Finland

In order to assess the feasibility and validity of surface-wave tomography as a tool for mineral exploration, we present an active seismic three-dimensional case study from the Siilinjarvi mine in Eastern Finland. The aim of the survey is to identify the formation carrying the mineralization in an area south of the main pit, which will be mined in the future. Before acquiring the data, we performed an accurate survey design to maximize data coverage and minimize the time for deployment and recollection of the equipment. We extract path-averaged Rayleigh-wave phase-velocity dispersion curves by means of a two-station method. We invert them using a computationally efficient tomographic code which does not require the computation of phase-velocity maps and inverts directly for one-dimensional S-wave velocity models. The retrieved velocities are in good agreement with the data from a borehole in the vicinity, and the pseudo three-dimensional S-wave velocity volume allows us to identify the geological contact between the formation hosting most of the mineralization and the surrounding rock. We conclude that the proposed method is a valid tool, given the small amount of equipment used and the acceptable amount of time required to process the data.Peer reviewe

Surface-wave tomography for mineral exploration : a successful combination of passive and active data (Siilinjärvi phosphorus mine, Finland)

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 Siilinjarvi 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 shearwave 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.Peer reviewe

Laterally constrained inversion of ground roll from seismic reflection records

Seismic reflection data contain surface waves that can be processed and interpreted to supply shear-wave velocity models along seismic reflection lines. The coverage of seismic reflection data allows the use of automated multifold processing to extract high-quality dispersion curves and experimental uncertainties in amoving spatial window. The dispersion curves are then inverted using a deterministic, laterally constrained inversion to obtain a pseudo-2D model of the shear-wave velocity. A Monte Carlo global search inversion algorithm optimizes the parameterization. When the strategy is used with synthetic and field data, consistent final models ith smooth lateral variations are successfully retrieved. This method constitutes an improvement over the individual inversion of single dispersion curve

A new misfit function for multimodal inversion of surface waves

Higher-mode contribution is important in surface-wave inversion because it allows more information to be exploited, increases investigation depth, and improves model resolution. A new misfit function for multimodal inversion of surface waves, based on the Haskell-Thomson matrix method, allows higher modes to be taken into account without the need to associate experimental data points to a specific mode, thus avoiding mode-misidentification errors in the retrieved velocity profiles. Computing cost is reduced by avoiding the need for calculating synthetic apparent or modal dispersion curves. Based on several synthetic and real examples with inversion results from the classical and the proposed methods, we find that correct velocity models can be retrieved through the multimodal inversion when higher modes are superimposed in the apparent dispersion-curve or when it is not trivial to determine a priori to which mode each data point of the experimental dispersion curve belongs. The main drawback of the method is related to the presence of several local minima in the misfit function. This feature makes the choice of a consistent initial model very importan