Complex Subsurface Structures Research Articles

Resolution enhancement for a seismic velocity model using machine learning

Summary To address complex subsurface structures, a high-resolution velocity model must be constructed. Conventionally, algorithms such as full waveform inversion (FWI) have been used to derive accurate high-resolution velocity structures, but obstacles such as high computational costs remain. Therefore, we propose a high-resolution U-NET (HR U-NET) machine learning model to derive a high-resolution velocity model from a low-resolution velocity model. The low-resolution velocity model and migration data obtained through the corresponding velocity information were used as input data for training. In addition, we tried to improve the accuracy of the high-resolution velocity model by using prior information containing accurate velocity values. A prior model generated through geophysical logging data and a weight model including the reliability information of the prior model were also utilized. Therefore, the HR U-NET model was trained using the low-resolution velocity model, the migration data, the prior model, and the weight model. Numerical experiments conducted using synthetic and field data demonstrated that the proposed model could accurately construct a high-resolution velocity model and verified that the prior model and weight model play important roles in the training process. Additionally, we confirmed that the proposed method derived almost similar results using only 8.2 per cent of the computational cost of the conventional inversion method. In other words, there is an advantage that it is possible to predict high-resolution velocity information more efficiently in terms of computational cost.

Tomographic imaging of South Rewa basin, Central India: Implications of Gondwana rifting and Late Cretaceous volcanism

We have imaged thick (3.0–5.0 km) sub-trappean Gondwana sediments deposited in a typical rift-environment of the south Rewa basin in Central India masked by the Deccan basalts. The basalts intrude through the basin forming several dykes, sills, and other intrusions along with large-scale undulations of the basement showing complex geology and tectonic settings due to Late Cretaceous volcanism (∼65 Ma). To delineate complex subsurface geological structures and understand the tectonic settings, we use long-offset seismic reflection data along the N-S trending 127 km Kuvari-Shahdol profile of the basin. The conventional stack and pre-stack time migration techniques fail to image the complex subsurface structures dominated by steeply dipping faults. The steeply dipping faults with small-scale structures are influenced by strong lateral and vertical velocity variations that cause problems in imaging the basin. Hence, a robust tomographic inversion with pre-stack depth migration technique is adopted to image fine-scale subtle subsurface geological structures with smooth velocity variations. The tomographic velocity model and pre-stack depth migration seismic image could decipher basaltic trap thickness, numerous dyke intrusions, alternate horsts and grabens with deposition of thick hydrocarbon-bearing sub-trappean Gondwana rocks along with the basement configuration. The deep basinal faults with highly distorted basement structures represent the intense tectonic activity of this Gondwana rift-basin. The deep-seated hydrocarbon reservoir is discovered with an excellent trapping mechanism forming both pre-rift and syn-rift settings of the rift-basin. The seismic image is further constrained and corroborated by the inversion of residual Bouguer gravity anomaly data to obtain corresponding density model derived from tomographic velocity model. The large basement faults, horsts and grabens, several intrusives like dyke, sill, laccolith, and thick sub-trappean Gondwana formations with Deccan trap cover indicate complex geology and tectonic settings of south Rewa basin forming the Late Cretaceous Volcanic Province of India.

Automatic Extraction of Martian Subsurface Layer from Radargrams Based on PDE Denoising and KL Filter

The polar regions of Mars, including the South and North Poles, are crucial for studying Martian climate and geological history, as they contain the largest reservoir of subsurface water ice. This study introduces a new approach for reflector detection, which includes radargram denoising to effectively enhance the signal of underground reflectors, peak detection to extract the positions of subsurface stratification from the radar echoes, and peak points connection to form continuous layers. The mapped enhancement denoising process involves a linear brightness adjustment and a fourth-order diffusion equation to enhance the signal of the subsurface layers for effective detection. The subsurface detection extracts the surface and subsurface peak points based on a peak detection algorithm, while using locally window-enhanced peak filtering and Kullback–Leibler (KL) divergence mapping to filter out non-stratified peak points. Finally, the layered connection process uses the proximity parameter to connect peak points in the same layer. Applied to multiple SHARAD (Shallow Radar) images at the Martian poles, this algorithm demonstrated a false detection rate below 5%. Compared to other methods, this method has a missed detection rate of less than 5% and, additionally, exhibits fewer discontinuities in layer connectivity. Therefore, this algorithm shows exceptional proficiency and applicability in analyzing the complex subsurface structures of the Martian polar regions.

Can towed-streamer seismic data be used for elastic wave imaging? Synthetic and field data examples

In marine seismic exploration, towed-streamer seismic data acquisition stands out as a prevalent and economical approach. These data record scalar pressure and capture P waves, including those that are converted from subseabed S waves. Theoretically, towed-streamer seismic data can be used to reconstruct S wavefields for elastic wave imaging via backward extrapolation. Our research applies an acoustic-elastic coupled equation that includes a pressure term to perform elastic reverse time migration (ERTM), using the pressure seismic data as the boundary condition for backward extrapolation. The feasibility of using towed-streamer seismic data for elastic wave imaging of complex subsurface structures is confirmed through synthetic and field data examples. The investigation demonstrates how the smoothness of velocity and density models impacts the quality of ERTM using towed-streamer seismic data. We find that using smooth velocity and density models for elastic wave imaging can enhance imaging quality by using transmitted S waves. Furthermore, certain nonphysical S waves generated during backward extrapolation can contribute to PS images, as evidenced by a synthetic data example. Overall, the results indicate that the towed-streamer seismic data for elastic wave imaging have potential in marine seismic exploration. Moving forward, it is crucial to focus on refining the quality of PS images and reducing artifacts caused by the backward-extrapolated nonphysical waves.

Image-guided structure-constrained inversion of electrical resistivity data for improving contaminations characterization

Electrical resistivity tomography often has high uncertainty in contamination characterization due to the complex subsurface structure. Utilizing available prior information is crucial for enhancing geological plausibility. We propose an improved structure-constrained method that updates the smooth weights of all eight elements surrounding a boundary element using three different magnitudes. The methodology is tested on seven synthetic models and a field case covering four different types of structural boundaries. Structural constraints are vital to increasing boundary contrast, and the improvement is more significant with increasing depth. With the increase of data points from an optimized array, the influence of structural constraint is reduced. The implementation of accurate structural constraints can enhance the characterization of contamination anomaly zones. However, misplaced structural constraints in the proximity of the contamination can lead to worse depiction. Without knowing the boundary exactly, we propose a method to obtain local constraints from borehole logs, which is verified by both numerical and field data. This method reduces the interference from uncertain structural boundaries and enhances the robustness of the image-guided structure-constrained inversion. In the end, we conclude that correct boundary information even with scattered constraints from borehole logs can increase the identification of the abnormal contamination resistivity.

Delineation of the proper locations for wastewater disposal in arid regions using TEM and ERT techniques: A case study at Rubiki industrial zone, Badr City, Egypt

Proper wastewater disposal is crucial for the sustainable development of rapidly growing megacities worldwide. The emergence of waterlogging near evaporation ponds (EVP) and in desert areas surrounding these cities poses a significant environmental threat. This study employs an integrated approach, combining transient electromagnetic (TEM) and 2D-electrical resistivity tomography (2D-ERT) methods with remote sensing techniques, to understand wastewater flow patterns around EVPs and identify suitable disposal sites. The research focuses on a complex subsurface structure in the Cairo-Suez district, characterized by extensive surface waterlogging. Initial monitoring relies on satellite imagery and land use-land cover maps. TEM provides an overview of subsurface layer distribution, while 2D-ERT offers detailed insights into near-surface flow paths. To address the limitations of geoelectrical methods in locating conductive layers, advanced inversion techniques (AIS) are applied. The findings reveal a four-layer subsurface composition, with waterlogging primarily attributed to the argillaceous limestone layer interbedded with shale. This layer is shallower in the eastern and northwest areas, correlating with higher instances of waterlogging. Moving northward, the argillaceous limestone layer deepens, and the upper clastic layer thickens, resulting in reduced waterlogging. The study underscores the influence of subsurface structure on near-surface layer distribution. Based on these findings, optimal wastewater disposal sites are recommended, with a focus on the northern region where the argillaceous limestone layer is deeper and the upper clastic layer is thicker. To mitigate waterlogging, the installation of disposal wells is highly advised, leveraging the insights derived from this research. This study offers a versatile and reproducible approach suitable for both pre-project planning and post-project phases in desert regions.

Seismic reverse time migration based on biaxial wavefield decomposition

Seismic images generated by reverse time migration (RTM) are often contaminated by low-wavenumber artifacts. Decomposition of up- and downgoing waves during propagation can effectively reduce these artifacts. However, decomposition along the vertical axis is not sufficient to eliminate the migration artifacts. We introduce biaxial wavefield decomposition to decompose seismic wavefields in the lateral and vertical directions. After decomposition, there are 16 crosscorrelation terms between different components, and there are only four crosscorrelation terms that can be used separately to image flat layers and tilted interfaces. Within these four terms, the remaining artifacts can be easily identified and reduced by other methods, and the resolution can be further improved by adjusting the weights of the four terms. The decomposition method based on the analytic wavefield via the Hilbert transform has been shown to agree with the viscoacoustic wave equation with fractional spatial derivatives. Therefore, this biaxial wavefield decomposition method is suitable for decomposing the wavefields for the attenuation-compensated RTM. The decomposed and recombined migration results can better image a complex subsurface structure with high resolution.

Deep carbonate fault–karst reservoir characterization by multi‐task learning

AbstractThe carbonate fault–karst reservoir is a special and significant reservoir in the Shunbei area, and the development of the cave has been controlled by strike‐slip faults. Due to the complex subsurface structures, fault–karst reservoir characterization is generally divided into fault and cave detection tasks. The potential spatial relationships between faults and caves might be neglected by using the separate detection scheme. The multi‐task learning network can perform multiple tasks simultaneously and exploit the potential features of training data by using a deep neural network. In this study, we built fault–karst models based on the geological background of the Shunbei area and synthesized fault–karst training data using three‐dimensional point spread function convolution. Then, we developed a multi‐task learning network to learn fault–karst features and detect faults and caves simultaneously. The test result demonstrates that the multi‐task learning network trained by synthetic fault–karst data can effectively identify the faults and caves in field seismic data. The comparisons of the multi‐task learning network, single‐task learning networks and conventional methods demonstrate the importance of spatial relationships between faults and caves and show the superiority of the multi‐task learning network. This technique could significantly assist in the exploration, development and well deployment for an ultra‐deep carbonate reservoir.

Image-guided Structure-constrained Inversion of Electrical Resistivity Data for Improving Contaminations Characterization

Electrical resistivity tomography often has high uncertainty in contamination characterization due to the complex subsurface structure. Utilizing available prior information is crucial for enhancing geological plausibility. We propose an improved structure-constrained method that updates the smooth weights of all eight elements surrounding a boundary element using three different magnitudes. The methodology is tested on seven synthetic models and a field case covering four different types of structural boundaries. Structural constraints are vital to increasing boundary contrast. With the increase of data points from an optimized array, the influence of structural constraint is reduced. Correct structural constraints can tighten the abnormal contamination resistivity. However, misplaced structural constraints in the proximity of the contamination can lead to worse characterization. Without knowing exactly the boundary information, we propose a method to obtain local constraints from borehole logging, which is verified by both numerical and field data. This method reduces the interference of uncertain structural boundaries and enhances the robustness of the IIGSC method in contamination surveys. In the end, we conclude that correct boundary information even with limited constraints from borehole logging can increase the identification of the abnormal contamination resistivity.

Sensitivity of Subsurface Permeability in Coastal Deltas to Their Morphodynamic and Geomorphic Characteristics

AbstractThe amount of fresh water moving through coastal deltas worldwide is controlled by the complex subsurface structures within a delta. Morphodynamic influences produced by the feeding river, waves, and tides, in addition to sea level transgressions and regressions, have resulted in deltaic aquifers that are highly heterogeneous. We use 171 unique two‐dimensional morphodynamic models to explore the range of subsurface permeability, hydraulic gradient, and groundwater flux within three end‐member delta types (fluvial, wave, and tidal). We quantify the connectiveness of the subsurface permeability and estimate the horizontal heterogeneity and anisotropy of the permeability. A distance‐based generalized sensitivity analysis is used to investigate the impact morphodynamic influences (fluvial, wave, and tidal), basin conditions (sediment concentration and bathymetric gradient), and geomorphic characteristics (number of channels, shape of the delta plain, and shoreline rugosity) have on the subsurface permeability, hydraulic gradient, and connectivity. We find that the median permeability in deltaic landforms is 4.0 × 10−12 m2 (relating to a hydraulic conductivity of 2.1 × 10−5 m/s), the average hydraulic gradient is 3.9 × 10−4, and the mean specific discharge is 1.3 × 10−8 m/s. High permeability bodies are highly connected and are associated with channelization. Subsurface permeability, hydraulic gradient, and the connectiveness of high permeability areas are sensitive to morphodynamic influences (fluvial, wave, and tidal) and the geomorphic characteristics (number of channels and shoreline rugosity) within a delta. Since morphodynamic influences and geomorphic characteristics are easily identified by looking at the surface of the delta, we suggest that the deltaic subsurface can be characterized by identifying features on the delta surface.

Three-dimensional magnetotelluric imaging of the Edough massif and its overlying formations (Northeast Algeria)

The Edough massif (NE Algeria) is the easternmost coastal crystalline massif of the Maghrebide belt, whose current structural position remains poorly known. Magnetotelluric (MT) data from 15 stations were collected along three profiles across the Edough massif and its neighboring formations in the period range of 0.001–1000 s to assess the deep electrical resistivity structure. A phase tensor dimensionality analysis indicates that the measured data are dominated by 3-D effect of underlying geological structure, thus revealing a complex subsurface structure. A 3-D inversion model was obtained using the full impedance tensor and the vertical magnetic field transfer function including bathymetry and topography. Our preferred 3-D geo-electrical model shows heterogeneous electrical structures and are related to the known geological features of the region. We mainly observe a clear resistivity contrast characterizing the upper/middle crust. The Edough massif appears generally as a homogenous resistive structure. The shallow conductive structures are associated with the Mio-Plio-Quaternary deposits and Flysch units. Most of conductive bodies are likely related to the iron/magnetic anomaly. These may likely correspond to the suture zone. Under the Neogene basin, the high resistivities with a north dipping strong angle may correspond to the African basement.

Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling

AbstractPhysics‐based simulations of earthquake ground motion are useful to complement recorded ground motions. However, the computational expense of performing numerical simulations hinders their applicability to tasks that require real‐time solutions or ensembles of solutions for different earthquake sources. To enable rapid physics‐based solutions, we present a reduced‐order modeling approach based on interpolated proper orthogonal decomposition (POD) to predict peak ground velocities (PGVs). As a demonstrator, we consider PGVs from regional 3D wave propagation simulations at the location of the 2008 MW 5.4 Chino Hills earthquake using double‐couple sources with varying depth and focal mechanisms. These simulations resolve frequencies ≤1.0 Hz and include topography, viscoelastic attenuation, and S‐wave speeds ≥500 m/s. We evaluate the accuracy of the interpolated POD reduced‐order model (ROM) as a function of the approximation method. Comparing the radial basis function (RBF), multilayer perceptron neural network, random forest, and k‐nearest neighbor, we find that the RBF interpolation gives the lowest error (≈0.1 cm/s) when tested against an independent data set. We also find that evaluating the ROM is 107–108 times faster than the wave propagation simulations. We use the ROM to generate PGV maps for 1 million different focal mechanisms, in which we identify potentially damaging ground motions and quantify correlations between focal mechanism, depth, and accuracy of the predicted PGV. Our results demonstrate that the ROM can rapidly and accurately approximate the PGV from wave propagation simulations with variable source properties, topography, and complex subsurface structure.

Automatic first break picking with structured random forests

AbstractIn the seismic wave exploration field, first break picking is an important underlying task. With the emergence of massive seismic data, it is urgent to develop automatic and accurate picking algorithms to relieve the huge workload of manual picking. Although many traditional and machine learning based techniques have been developed, it is still a challenging task to obtain satisfactory picking results in practice because of complex subsurface structures and low signal‐to‐noise ratio. Ensemble learning has been demonstrated to be a powerful tool to produce good prediction results in many fields. Its core idea is to create multiple models with some special techniques and then to combine the prediction results of each model with a fusion strategy. Structured random forests , a type of ensemble learning method, have been proven to be quite effective in implementing structured learning tasks. Based on the observation that adjacent traces can provide useful information to pick the first arrival time for a specific trace, we propose in this paper a novel structured random forest based first break picking framework. In particular, a multi‐scale normalization technique is presented to make full use of the amplitude information at different scales. To capture local features of first breaks, an enhanced feature map based on the short‐term/long‐term average ratio method is also computed. By extracting patches from two channel feature maps, we construct a structured random forest to predict the locations of the first breaks. On the basis of the probability score map produced by the structured random forest, an effective post‐processing strategy is proposed to further deal with the detected results. By conducting experiments with synthetic and field data, the proposed method is shown to be effective in identifying first breaks. It is significantly superior to the short‐term/long‐term average ratio method and support vector machine in terms of picking accuracy. With the advantage of parallel computing, the computational cost of structured random forest is acceptable, that is, it is much lower than the support vector machine while being higher than the short‐term/long‐term average ratio method. In addition, the experiments also confirm that the multi‐scale normalization plays an important role in improving the picking performance of the structured random forest.

Past Earthquake Simulations Using Ambient Seismic Noise in Vrancea, Romania

Abstract The Vrancea seismic zone (VSZ), located in Romania, at the sharp bend of the southeast Carpathians, is an anomalous intraplate seismic nest releasing the largest strain in continental Europe. The last powerful earthquake in Vrancea occurred in 1977 causing significant damage to the densely populated cities in southeast Romania. The seismic infrastructure was underdeveloped at that time and the earthquake was not well recorded locally. Using ambient seismic noise recorded at modern broadband seismic stations around Vrancea in the year 2020, and the approximate moment tensor solutions of significant earthquakes Mw>6 since 1977, we reconstruct ground-motion waveforms recorded by modern seismometers decades after their occurrence. The virtual earthquake approach uses empirical Green’s functions from ambient noise cross correlations between pairs of seismic stations and adds the signatures of a model earthquake: double couple mechanism, buried source, and a realistic earth model in the epicentral area. Ambient noise data capture the signatures of complex subsurface structures and the 3D path effects, providing a more physically accurate representation of the seismic wavefield than purely synthetic data. Our new results demonstrate the viability of this innovative method and provide a unique opportunity for more accurate seismic hazard analysis in intraplate seismic zones with insufficient instrumental data.

Exploring the Geological Formation and Sub-Surface Lithology Modelling of Hadejia, Jigawa State Nigeria

This research aims to explore the geological formation and sub-surface lithology modelling of Hadejia, Jigawa State using the VES (vertical electrical sounding) method. The VES method is a geophysical technique that measures the electrical resistivity of the subsurface, which is related to the lithology of the rock units. The study area is located in the north-western part of Nigeria, on latitudes 12025’23.73‘‘N and longitudes 10°04’06.74‘‘E, and is characterized by a chad formation geological setting with a variety of rock units, including sandstones, shales, and limestone. The research was conducted using a total of 50 VES stations, which were distributed over the study area in a grid pattern. The data collected from the VES stations were analyzed using the IPI2WIN software. The results were used to create a subsurface electrical resistivity model, which was then used to infer the lithology units present in the subsurface. The results showed that the study area is characterized by a complex subsurface structure, with several rock units present, including sandstones, silt, and clay. The subsurface electrical resistivity model revealed that the sandstone units are located at shallow depths, while the silt units are found at deeper depths. The clay is present at intermediate depths and is interbedded with sandstones and clay. The results provide valuable information for hydro-geological and mineral exploration in the area.

Study of the sub-surface geological condition of the Sibalaya flow liquefaction using seismic refraction method

Understanding the occurrence of flow liquefaction requires knowledge of the sub-surface condition controlling its occurrence. This paper presents the results of seismic refraction surveys conducted to evaluate the sub-surface geological condition in the Sibalaya flow liquefaction area. From the interpretation of the seismic refraction model, an inclined boundary presents between the near-surface sediment layers. Further examinations of the SRT images demonstrate the existence of localized basinal structures in the rock layer with the highest P-wave velocity. The basinal structure may be associated with a hidden normal fault system of horst-graben structures. Thus, this study indicates that such a complex sub-surface geological structure also contributed to the Sibalaya flow liquefaction occurrence.

Coupled-Learning GAN for Inversion of GPR Pipe Images

Underground pipelines are often detected and identified using ground-penetrating radar (GPR), which requires an inversion process to obtain specific pipeline properties. Unfortunately, this process is an ill-posed problem that involves inferring complex subsurface structures from a small number of observations, which leads to diverse and complex descriptions of the target. To address this challenge, this study proposes an inversion method based on a coupled-learning generative adversarial network. First, we extract hyperbolic waves from GPR buried-object B-scan images and filter out non-pipe and non-homogeneous media information from the resulting dielectric constant predictions. The training set comprises two data pairs: simulated clutter-free data with simulated clutter data pairing and simulated clutter-free data with dielectric constant data pairing. An experiment with real measured B-scan sequences showed that our proposed method provides clear and visually analyzed results about the location and direction of underground pipes. This approach enhances the accuracy and reliability of the inversion process and improves the clarity of subsurface pipeline property descriptions.

Application of Reverse Time Migration to Faults Imaging in Rakhine Basin, Myanmar

The Rakhine Basin in the northeastern Bay of Bengal is an active field in hydrocarbon exploration and development. It contains fault structures and steeply sloping stratigraphic reservoirs, both primary features of interest for hydrocarbon exploration that needs to be accurately imaged to improve the interpretation of seismic data and facilitate the accurate identification of features of interest. Although faults are an indicator of possible hydrocarbon traps, they are difficult to identify in seismic images using traditional stack or prestack time migration due to the rather complex behaviors of wave propagation. On the other hand, prestack depth migration (PSDM) can significantly improve the accuracy of seismic images, especially of complex subsurface structures such as faults, folds, overthrusts, and salt domes. Among the various PSDM approaches, reverse time migration (RTM) has been shown to be the most powerful. Here, we show how PSDM-RTM can significantly improve the representation of fault structures and steeply dipping structures in seismic images from field data collected in Rakhine Basin, which is characterized by complex geology including stratigraphic and strati-structural traps as well as complex channel systems. Typically, these structures appear heavily blurred and are difficult to identify using normal stack and prestack time migration. We demonstrate that they become clearer and easier to detect with the PSDM–RTM approach, making this approach particularly suitable for seismic interpretations of geologically complex areas within the context of hydrocarbon prospecting.

Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation

• A hybrid method MC-GAN is proposed for hydrological modeling. • An improved DCGAN model is embedded into the Monte-Carlo simulation process. • Joint loss functions are used to improve the performance of MC-GAN. • Various hydrological patterns can be reproduced efficiently. • Multiple-scale realizations can be obtained by using the same trained model. Characterization of complex subsurface structures is challenging due to the demand to preserve geological realism of the training images in earth and environmental sciences. In this work, we propose a novel method to reconstruct complex hydrological structures by using deep convolutional generative adversarial networks (DCGAN) in the Monte-Carlo simulation process, named MC-GAN. Network architectures for reconstructing both two-dimensional (2D) and three-dimensional (3D) complex spatial structures are provided in this method. We first exploit the robust DCGAN to reproduce abundant and various spatial pattern blocks. Then, we combine the various heterogeneous patterns to reconstruct a complex hydrological structure by using the Monte-Carlo stochastic simulation process. The method is able to represent multiple-scale spatial structures under the premise of using the same generative adversarial network architecture. It not only ensures the simulation efficiency, but also makes the heterogeneous patterns in the realizations more diverse. Three sets of training images were used to test the capability of the proposed method. The experiment results demonstrate that our method can accurately characterize complex heterogeneous spatial structures. At the same time, the trained deep learning model can be reused effectively to generate multiple-scale spatial structures.

In-situ physical properties of reclaimed lands in Singapore

Reclaimed land construction is crucial for sustainable development of the land-scarce city-state of Singapore. To characterize the physical properties of two different types of reclaimed lands — one with well-sorted sands and the other with municipal solid waste — we conduct both active and passive seismic surveys at the sites and compare them with natural geologic formations. From the multichannel surface wave analysis, we observe more complex features related to mode kissing, mode jump, and mode loss on the dispersion spectra of the reclaimed lands, indicating complex subsurface structures. To utilize multiple orders of Rayleigh waves for inversion, we design a quantitative procedure to optimize the mode-order assignment. Seismic data and inverted results show that an older municipal solid waste landfill is more heterogeneous and stronger in its shear strength compared to the younger landfill. The reclaimed land filled with sand is more homogeneous and weaker in shear strength than the natural sedimentary rock formation. The contrast of physical properties at the four sites suggest a significant acceleration in soil hardening when landfill materials are chemically reactive with each other and with the environment. These in-situ measurements are crucial for civil engineering constructions and redevelopment of reclaimed lands in the future.