Geophysical Processing Research Articles

CORE-VTRCC Cruise Report: Geophysical Acquisition and Seafloor Sampling along the Vitória-Trindade Ridge

This work reports the data obtained by the multidisciplinary cruise CORE-VTRCC surveying on the region around the Vitória-Trindade Ridge (VTR) aboard the vessel NHo Cruzeiro do Sul (October 18th to November 6th, 2021). The main objectives of the VTRCC Cruise were (1) to assess the role of the VTR as an oceanographic barrier for bottom currents along the Brazilian margin; and (2) to characterize the morphology of the volcanic seamounts and its relationships with the carbonate sediments that cover it. For that, we performed multibeam bathymetry, magnetometry, high-resolution multichannel seismic survey, subbottom profiling survey, and seafloor sampling along the VTR and along the Columbia Deepwater Channel. The raw dataset was processed for quality control and organized for public access. When required, additional geophysical processing occurred to improve data quality. Seafloor samples are characterized based on the concentration of its biofacies, the sediment grain-sizes and the morphology of rhodoliths sampled. The dataset reveals comprehensively the geological and oceanographic heterogeneity of the region around the VTR.

The correlation between the active/buried faults and aeromagnetic data in inner and inner-east Anatolia, Turkey

The paper focuses on investigating the active/buried faults in inner and inner-east Anatolia, Turkey based on aeromagnetic data and comparison with the active fault map. This region includes the major tectonic units of Turkey and the largest basins of Turkey (Tuzg.lü Basin (TB) and Sivas Basin (SB)). Thus, the region deserves a detail investigation to delineate the lineaments of the active/buried fault system or the causative sources located in the upper crust. To this end, the region has been divided into two parts as region-1 and 2 including TB and SB, respectively. In this context, the geophysical data processing techniques including power spectrum analysis, high‑pass filter, and second vertical derivative method (SVD) have been applied to the aeromagnetic data of the both regions. Firstly, by utilizing power spectrum analysis, the average depths of the deep and shallow causative sources in the region-1 have been calculated as 11.37 km and 5.01 km whereas those depths are 8.5 km and 3.24 km for region-2. Afterwards, to enhance the responses of the residual anomalies, the high‑pass filter and the SVD method have been applied to the data. Hereby, these potential lineaments produced from the SVD map have been compared with the active fault map to delineate the uncovering buried faults and their lineaments in the region. Contrary to the discussion in the literature, this study revealed for the first time that the Tuzgölü fault zone located in the eastern part of lake does not merge with the Ecemis fault (EF) zone. Furthermore, the study has shown that this fault zone extended from Kayseri city to Sivas city does not junction with North Anatolian Fault Zone in the north, as well.

The Application of Mathematics in Geophysical Image Processing and Analysis

With the widespread application of big data technology across various industries, cultivating big data analysis capabilities in accounting-related majors has become a crucial direction in higher education. This study aims to explore the construction of a practical training base for big data analysis in accounting majors in the "intelligent" era. By analyzing the current application status of big data technology in the accounting field and combining it with educational and teaching needs, a systematic and scientific training base construction plan is proposed. The research results show that building a big data analysis training base can not only enhance students' data analysis capabilities and practical skills but also provide strong support for the teaching reform of accounting majors in colleges and universities.

Machine Learning‐Based Hydrofacies Classification: Effects of Noise and Regularization

AbstractThis study utilizes an unsupervised ML approach, the expectation‐maximization (EM) algorithm using Gaussian Mixture Models (GMM), to integrate near‐surface geophysics measurements for hydrofacies classification. We examined the impact of noise and noise estimation on classification across two synthetic models with varying lateral heterogeneity, simulating and inverting resistivity and seismic data with noise levels ranging from minimal to very high. The algorithm proved robust in accurately reconstructing hydrofacies when noise was correctly estimated or not significantly underestimated, showing minimal misclassification in shallow hydrofacies. However, severe underestimation of noise during inversion led to increased misclassifications and artifact‐laden hydrofacies models, especially in shallow regions. Higher lateral heterogeneity lessened the negative impact of noise, slightly improving algorithm performance when noise was correctly estimated. We also explored the influence of geophysical measurement uncertainties on classification uncertainty through hydrofacies probability maps, noting the greatest impact when noise was underestimated. Additionally, we investigated the effect of the regularization trade‐off parameter on the hydrofacies classification and show how the performance of the algorithm can be evaluated in the absence of ground truth data using the average silhouette score of the classification with data obtained from a basement complex field site. We found that moderate regularization (λ = 200) yielded the best hydrofacies model, as indicated by the highest average silhouette score. Our findings underscore the effectiveness of unsupervised ML for facies classification and emphasizes the critical role of accurate noise characterization in geophysical data processing for enhancing the integration of subsurface heterogeneity information into hydrological models.

Geothermal energy appraisal and subsurface structural mapping of the Rafin Rewa warm spring region, Precambrian basement complex of Nigeria

This research work aims at evaluating the geothermal energy potentials of the Rafin Rewa warm spring (RRWS) of the Precambrian Basement Complex in Nigeria as an alternative energy source using integrated aeromagnetic geophysical techniques. Four aeromagnetic dataset were acquired, assemblage, analyzed, and interpreted using integrated geophysical processing techniques of spectral analysis and Euler deconvolution. Qualitative interpretation of the residual anomalous map reveals a distribution of positive anomalies (> 53 nT) majorly in the central and southeastern regions, which are traced to the granitic rocks, while the low anomalies (< − 1.5 nT) have been traced to the RRWS location emanating from the coastal plain sands of the Pliocene, Pleistocene, Oligocene, and Miocene ages. Quantitatively, the depth to the top (DTT) of the anomalous bodies reveals a depression that is almost intersecting with the Curie point depth (CPD) plot at the RRWS location, which indicates high heat flow in the RRWS region. The Spectral Analysis results reveal that the DTT and the CPD in this area ranges from 0.512 to 0.761 km and 6.504 to 10.582 km, respectively while the average CPD is 8.543 ± 0.325 km. It is observed that the DTT and CPD decrease as one move away from the RRWS region. The computed heat flow average was 160.76 ± 19.09 mW/m2 within the RRWS region. The Euler deconvolution result reveals the presence of geological structures, which were interpreted as faults and fractures. The major fractures trend in the east–west (E-W) directions, while the minor fractures trend northeast-southwest (NE-SW) directions. The geochemical result presented shows that iconic compositions impact the convective heat transfer processes associated with geothermal systems. It was conclusively believed that regions with comparable shallow CPDs could be viable for further geothermal energy investigations.

Chebyshev polynomial-based Fourier transformation and its use in low pass filter of gravity data

In this paper, we introduce the novel Chebyshev Polynomials Least-Squares Fourier Transformation (C-LSQ-FT) and its robust variant with the Iteratively Reweighted Least-Squares technique (C-IRLS-FT). These innovative techniques for Fourier transformation are predicated on the concept of inversion, and the C-LSQ-FT method establishes an overdetermined inverse problem within the realm of Fourier transformation. However, given the LSQ approach’s vulnerability to data outliers, we note the potential for considerable errors and potentially unrepresentative model estimations. To circumvent these shortcomings, we incorporate Steiner’s Most Frequent Value method into our framework, thereby providing a more reliable alternative. The fusion of the Iteratively Reweighted Least-Squares (IRLS) algorithm with Cauchy-Steiner weights enhances the robustness of our Fourier transformation process, culminating in the C-IRLS-FT method. We use Chebyshev polynomials as the basis functions in both methods, leading to the approximation of continuous Fourier spectra through a finite series of Chebyshev polynomials and their corresponding coefficients. The coefficients were obtained by solving an overdetermined non-linear inverse problem. We validated the performance of both the traditional Discrete Fourier Transform (DFT) and the newly developed C-IRLS-FT through numerical tests on synthetic datasets. The results distinctly exhibited the reduced sensitivity of the C-IRLS-FT method to outliers and dispersed noise, in comparison with the traditional DFT. We leveraged the newly proposed (C-IRLS-FT) technique in the application of low-pass filtering in the context of gravity data. The results corroborate the technique’s robustness and adaptability, making it a promising method for future applications in geophysical data processing.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components, an improved soft thresholding technique is introduced. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

Introduction to this special section: Imaging faults and fractures

The exploration and management of subsurface reservoirs relies heavily on unraveling the intricate complexities of faults and fractures. In this special section, we present a collection of papers that, in the absence of world-class outcrop exposures such as that shown on this issue's cover, utilize various data sets and geophysical processing techniques to illuminate fault and fracture networks. The collected papers address a spectrum of fault- and fracture-related topics pertinent to scientists across disciplines, spanning hydrocarbon plays to geothermal systems and carbon storage sites.

Application of Non-negative Matrix Factorization in Processing Self-potential Signals Caused by Geo-microbe

Microorganisms play a significant role in the evolutionary process of the Earth and are closely related to the modification and evolution of the Earth’s surface circles at different spatial and temporal scales. Given geological microbiology in the self-potential signal data can be represented with non-negative matrix, which makes use of non-negative matrix factorization method to deal with the data become possible. This paper introduces the basic principle of non-negative matrix factorization method, based on non-negative matrix factorization method is discussed to isolate the self-potential signal data in geological microbes may and effect. By separating two natural potential signals containing noise with blind sources, the separation effect is good and the non-negative matrix factorization method is found to be an effective method for extracting their self-potential signal data. Although this numerical simulation is the self-potential signal is introduced in the work of geological microbiology, but this method also has reference significance for other signal data processing, can also be used to other geophysical data processing, the implementation of the research on environmental protection, to improve the cognitive complex biological geological process of shallow earth environment has important significance.

A Multi‐Source GRACE Fusion Solution via Uncertainty Quantification of GRACE‐Derived Terrestrial Water Storage (TWS) Change

AbstractIn analyzing terrestrial water storage (TWS) data observed by Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On satellites, quantifying uncertainties proves challenging due to the scarcity of sufficient independent observations of mass changes at scales commensurate with these missions. Moreover, owing to the diverse geophysical background models and processing techniques utilized by data processing centers, reaching consistent mass change estimations within specific regions of published solutions often proves arduous. We, therefore, quantified the uncertainty of the GRACE‐derived TWS changes by using the generalized three‐cornered hat method without relying on any prior knowledge and fused it to generate a higher‐quality solution. The findings reveal that of the six solutions, the Center for Space Research spherical harmonics (SH) solution exhibits the lowest uncertainty and highest signal‐to‐noise ratio (SNR) at both global and basin scales, and the Goddard Space Flight Center Mascon solution outperforms other Mascon counterparts. The fusion solution has an average 36.56% reduction in uncertainty and a 1.92‐fold improvement in SNR at the basin scale, and the improvement in SNR is particularly significant in regions with drastic mass changes. The global distribution patterns of the uncertainties associated with Mascon and SH solutions exhibit distinct differences. Mascon solutions result in significant signal leakage around regions characterized by the most substantial global mass variability. Additionally, transient mass changes triggered by super earthquake events in the ocean also produce similar “scars” in the global spatial distribution of uncertainties. The analysis of 142 basins worldwide shows that basins with more significant TWS annual oscillations have larger uncertainties but also better SNR.

Towards Operational Fiducial Reference Measurement (FRM) Data for the Calibration and Validation of the Sentinel-3 Surface Topography Mission over Inland Waters, Sea Ice, and Land Ice

The Copernicus Sentinel-3 Surface Topography Mission (STM) Land Altimetry provides valuable surface elevation information over inland waters, sea ice, and land ice, thanks to its synthetic aperture radar (SAR) altimeter and its orbit that covers high-latitude polar regions. To ensure that these measurements are reliable and to maximise the return on investment, adequate validation of the geophysical retrieval methods, processing algorithms, and corrections must be performed using independent observations. The EU-ESA project St3TART (started July 2021) aims to generalise the concept of Fiducial Reference Measurements (FRMs) for the Copernicus Sentinel-3 STM. This work has gathered existing data, made new observations during field campaigns, and ensured that these observations meet the criteria of FRM standards so that they can be used to validate Sentinel-3 STM Land Altimetry products operationally. A roadmap for the operational provision of the FRM, including the definition, consolidation, and identification of the most relevant and cost-effective methods and protocols to be maintained, supported, or implemented, has been developed. The roadmap includes guidelines for SI traceability, definitions of FRM measurement procedures, processing methods, and uncertainty budget estimations.

Evaluation of the Possibilities Validation of Interval Velocity Models Using Non-Seismic Data and Its Impact on Geological Interpretation of PreSDM Results

The paper presents the problem of generation and validation of Velocity Interval Depth (VID) models with the application of non-seismic geophysical and geological data. The study area is a part of the Carpathian Foredeep located close to its contact with the Carpathian Overthrust. In this area of complicated geological structure, hydrocarbon deposits have been successfully explored for decades with seismic methods and drilling. The research applied the Simultaneous Joint Inversion (SJI) of independent geophysical data, which is a modern methodology of geophysical data processing, that is still under development. Such an attempt was necessary due to the lack of a sufficiently dense grid of wells in the study area, in which seismic velocities would be correctly recorded. Such data would be then applied for the generation of relevant VID models, which in turn, could be used to perform the Prestack Depth Migration (PreSDM) procedures. The application of procedures taking advantage of independent geophysical and geological data enabled researchers to control the generation process of the spatial VID model in the areas without wells. The analyses aimed to verify the correctness of VID model evaluation and its influence on the quality of seismic imaging in the area of the Carpathian Overthrust. Precisely, the influence was tested of such non-standard generation procedure of seismic velocity fields, not only on the PreSDM results but also on the geological interpretation of both the Rączyna and the Jodłówka gas deposits. The latter aspect of the presented results seems to be crucial to the effectiveness of petroleum exploration in the transition zone between the Carpathian Orogen and the Carpathian Foredeep.

Applying Map Filters and Inversion Model for Gravity Data in Matlab-based Application

In general, there are two types of processes using the gravity method, that is the basic processes and the advanced processes. The basic process includes Gravity Meter reading conversion until the gravity corrections process. While the advanced process is a process to put an edge on the geological appearance of the research area that is already in the form of a map. The advanced process includes map filtering and inversion modeling. This advanced processing of gravity data is very useful, one of which is to separate regional anomalies and local anomalies, while inversion modeling is useful for knowing the approximate location of the anomaly depth. What will be applied to the software is advanced processing, because in its application this processing is required in every geophysical research and requires data processing support tools. However, for now, gravity data processing software, especially for the filtering and data modeling process is available as a licensed software. Where not everyone can access it because of limited costs and those who are still in the learning stage in lectures need practicing to process the geophysical data. The purpose of implementing this filtering process and inversion modeling is to provide access to gravity data processing for free and open source so that it can make it easier for students to process the data while practicing in the field, and in the future this software can be the basis for learning and development in the field of geophysical computing. This research was made using a descriptive method with a complete step and explanation of the creating and applying of the geophysical data processing into a software, so the detail processes inside it can be known. A literature study was also conducted to determine the basic formula that will be used as a reference in calculating gravity data in order to obtain the expected processing and modeling results. After that, the final step is to apply all of the components in a program to create a software based on MATLAB for the final result of this research. This software will containing map filtering and inverse modeling for gravity data which can be used for data processing.

Sensing prior constraints in deep neural networks for solving exploration geophysical problems

One of the key objectives in geophysics is to characterize the subsurface through the process of analyzing and interpreting geophysical field data that are typically acquired at the surface. Data-driven deep learning methods have enormous potential for accelerating and simplifying the process but also face many challenges, including poor generalizability, weak interpretability, and physical inconsistency. We present three strategies for imposing domain knowledge constraints on deep neural networks (DNNs) to help address these challenges. The first strategy is to integrate constraints into data by generating synthetic training datasets through geological and geophysical forward modeling and properly encoding prior knowledge as part of the input fed into the DNNs. The second strategy is to design nontrainable custom layers of physical operators and preconditioners in the DNN architecture to modify or shape feature maps calculated within the network to make them consistent with the prior knowledge. The final strategy is to implement prior geological information and geophysical laws as regularization terms in loss functions for training the DNNs. We discuss the implementation of these strategies in detail and demonstrate their effectiveness by applying them to geophysical data processing, imaging, interpretation, and subsurface model building.

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

As an important geophysical data processing technique, seismic inversion estimates subsurface rock properties with seismic observations. However, anisotropic inversion, intended for a vertical transverse isotropy (VTI) media that primarily describes shale gas/oil resources, suffers from high nonlinearity. Simulated annealing is a widely used global optimization algorithm for solving nonlinear seismic inverse problems, but it involves multiple optimization parameters (e.g., initial temperature, search limit, and perturbation range). The importance of such parameters has been proven whilst the relevant analysis is limited in seismic inversion studies. This work hereby proposes a sequential anisotropic inversion method for VTI media, wherein we combine Bayesian linear and simulated annealing nonlinear inversion schemes. The simulated annealing is featured by adaptive optimization parameters aided by the linear result. Rather than the conventional method, the adaptive setting can be implemented trace by trace for complex reservoirs, which endows the method with enhanced stability and extended applicability. Synthetic tests and practical application demonstrate the validity of the method, wherein the obtained stiffness parameters facilitate the characterization of potential shale reservoirs with an improved accuracy.

Determination of buried active faults and earthquake potential for Izmir and its surroundings (western Turkey) using aeromagnetic anomalies and seismological data

The paper aims to delineate buried faults in Izmir city and its surroundings, western Turkey using aeromagnetic and seismological data. In this context, the geophysical data processing techniques including reduced‑to‑pole transform (RTP), power spectrum analysis, high-pass filter and second vertical derivative method (SVD) have been applied to the total field aeromagnetic data of the study area. First, to remove the undesirable effects caused by the dipolar nature of the Earth field, RTP transform has been applied to those data. Second, the average depths of the regional and residual sources in the region have been calculated as 16.84 km and 3.75 km, respectively using power spectrum analysis. After, to emphasize the effects of the residual anomalies, the high-pass filter has been applied to the RTP data. Finally, the second vertical derivative method (SVD) has applied to the filtered data for delineating the uncovering buried faults and their lineaments in the eastern part of the Aegean extension. The results from those methods show five major fault zones that could cause devastating earthquakes in the area. Especially, the study reveals for the first time that one of these faults which lies from Doganbey to the city center of Izmir in the literature actually reaches out to Manisa city in the NE direction. As a result, these lineaments can be evaluated as traces of buried faults could be an important clue in predicting earthquake potential. A comparison of seismicity map and the heat flow map shows that the region (between Cesme and Seferihisar) represented with a low b‑value has a high potential earthquake. The spatial distribution of the earthquakes, b‑values and heat flow values in the depths may be related to the existence thin lithospheric mantle. Furthermore, the region represented by strong aeromagnetic anomalies may be considered to be magmatic material arising from the magma filling inside the strike-slip faults. The fault structure observed on the SVD map are also important for the geothermal energy potential of the region as well.

3-D Forward Modeling of Transient EM Field in Rough Media Using Implicit Time-Domain Finite-Element Method

In a heterogeneous medium (usually called a rough medium) with fractured formations, the propagation of an electromagnetic (EM) field is a type of subdiffusion. Current mainstream geophysical EM data processing methods cannot be applied to data acquired on heterogeneous Earth, as they are not governed by the classic diffusion theory. To evaluate the influence of roughness on the transient EM (TEM) signal for a complex model and contribute to data inversion, we proposed a novel three-dimensional (3-D) forward modeling scheme for TEM in rough media. First, we derived the governing equation with a fractional-order time derivative for the subdiffusion of EM waves in rough media. Then, we proposed a novel time discretization using an unequal step length for the Caputo operator, which significantly reduces the total number of time steps. Finally, an implicit time-domain finite-element method using unstructured tetrahedron discretization was adopted to solve the 3-D forward problem. Furthermore, an efficient time segmentation strategy combined with parallel RHS construction was proposed to accelerate modeling. The numerical results prove that the proposed method is accurate and efficient, and will be a powerful numerical method for analyzing TEM wave propagation and processing TEM data in areas with multiscale fractures or porosity.

Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure.

Geophysical surveying for the detection of ferrous-based objects: possibilities for depth estimative combining analytic signal and vertical integral of the anomalous magnetic field

The detection of buried clandestine objects challenges forensic and archeologic search group teams on varying terrains, and variable scales of research. Therefore, the study of controlled buried objects is useful for trainings in geophysical acquisition and processing. In this study, we applied ground survey data for testing the magnetic method at controlled geophysical sites for the location of ordinary objects and firearms. We used data filtering techniques in order to facilitate the location of magnetic targets. Also, we experienced the 3D inversion of analytic signal of the vertically integrated magnetic field (ASVI), for the location of targets in depth. As a result, the study determined the location of four magnetic targets, and a three-dimensional view was constructed from the estimated magnetic susceptibility. We concluded that modeling transformed magnetic data is an affordable technique for application in near-surface investigations. Also, this experiment exemplifies the relevance of magnetic methods for location of excavation sites on the basis of geophysical methods.

High-resolution imaging of subsurface infrastructure using deep learning artificial intelligence on drone magnetometry

The use of drones fo r geophysical data acquisition and artificial intelligence (AI) for geophysical data processing, imaging, and interpretation are active focus areas in current industry and academic applications. Unlocking their cumulative potential in single-focus applications can have a transformative impact, possibly leading to dramatic cost reductions in key use cases and new application areas for enhanced actionable business intelligence. We present field study results from Texas and California that show the potential for imaging pipelines and other subsurface infrastructure by using AI-based methods on high-resolution aboveground magnetic data. The superior resolution and interpretability over conventional geophysical inversion is demonstrated. The method has the potential to provide actionable intelligence in several business-use cases for detecting and characterizing pipelines, crossing zones for multiple pipes, etc. at dramatically reduced costs. The advanced algorithms and workflows used resulted in a 100-fold increase in efficiency and delivered results in two days compared to what could take several months using generally available open-source deep learning AI workflows and software. Future direction of development is to validate against excavation-/drill-bit-/inline-tool-based ground truth and further extend and develop this process to deliver near real-time results. The techniques used are general and can be applied to other geophysical data including seismic, electromagnetic, and gravity at various scales and resolution.