William K. Mohanty

Ground Motion in Delhi from Future Large/Great Earthquakes in the Central Seismic Gap of the Himalayan Arc

We estimate ground motions in Delhi from possible future large/great earthquakes in the central seismic gap in the Himalayan arc. The closest distance from the rupture areas of such postulated earthquakes to Delhi may be about 200 km. We have used two methods to synthesize the expected ground motions. In the first, recordings in Delhi (three on soft sites and one on a hard site) of the 1999 Chamoli earthquake (Mw 6.5; epicentral distance, 300 km), which was located in the gap, are used as empirical Greens functions (EGFs). The ground motion during the target event is synthesized by random summation of the EGFs. In the second, the stochastic method, the motions have been estimated from the expected Fourier spec- trum of the ground motion in Delhi through the application of Parsevals theorem and results from random vibration theory. We apply two versions of the stochastic method: the first assumes a point source while the second considers the source to be finite. The predictions from the two methods are in reasonable agreement for Mw 7.5. For Mw 7.5 events, the finiteness of the source becomes important. Several rupture scenarios are considered in the application of the finite-source stochastic method. The largest ground motions are predicted in Delhi for rupture occurring between the main boundary thrust and main central thrust and the hypocenter located at the northeast edge of the fault. For this rupture scenario and a postulated Mw 8.0 earthquake, the maximum expected horizontal acceleration (A max ), and velocity (V max ) at soft sites in Delhi range between 96 and 140 gal and 8 to 19 cm/sec, respectively. For M w 8.5 event, the corresponding values range between 174 and 218 gal and 17 to 36 cm/sec. A max at the hard sites are 3 to 4 times less than at the soft sites. The differences are somewhat smaller for Vmax, which are roughly 2 to 3 times at soft sites as compared to the hard site. The horizontal Amax and Vmax estimated by Khattri (1999) for Mw 8.5, using a composite source model, are remarkably similar to those estimated here. The seismic hazard in Delhi may be especially high to the east of Yamuna river because the area is underlain by recent fluvial deposits. More extensive earthquake recordings, microzonation studies, research on liquefaction po- tential of the fluvial deposits, and further work on the estimation of expected ground motions in Delhi area are urgently needed.

Delineation of subsurface structures using self-potential, gravity, and resistivity surveys from South Purulia Shear Zone, India: Implication to uranium mineralization

The unexplored South Purulia Shear Zone (SPSZ) at the north of Singhbhum Shear Zone (SSZ) in Eastern India is a prospective zone for structural-guided hydrothermal mineralization. We carried out an integrated geophysical study using self-potential (SP), gradient-resistivity profiling (GRP), and gravity study across the SPSZ to identify the near-surface structural features and probable correlation with the uranium mineralization of the region. We studied a broad low SP, anomaly zone correlated with corresponding low-gravity and lowresistive zone across the same part of the study area. This conductive and low-density zone was identified as the width of the brittle-to-ductile and highly altered SPSZ. The 2D modeling of SP and residual gravity data along a northeast–southwest profile across the shear zone between Raghunathpur and Barabazar localities revealed the northerly dipping shear zone with an average width of ∼4.5 km. However, the 2D modeling of the SP data suggested numerous thick, sheet-type vertical and/or inclined structures intervening the shear zone, which were well correlated with the vertical structures delineated by the 2D gravity inverse model. The vertical alteration zones (density and conductivity) at ∼40-, 200-, and 400-m depths have been identified over this region. These alteration zones are likely to be mineralized zone because a hydrouranium anomaly has also been reported from those locations earlier. We studied the efficacy of an integrated approach using GRP, SP, and gravity surveys for the investigation of near-surface vertical to dipping conducting structures associated with uranium mineralization in such shear zone regions.

Geophysical anomalies associated with uranium mineralization from Beldih mine, South Purulia Shear Zone, India

Beldih mine at the central part of the South Purulia Shear Zone (SPSZ) has been reported with low grade uranium-bearing formation within quartz-magnetite-apatite host in kaolinized formation. Therefore, the present integrated geophysical study with gravity, magnetic, radiometric, very low frequency electromagnetic (VLF) and gradient resistivity profiling methods around the known mineralized zones aimed at identifying the exact geophysical signatures and lateral extent of these uranium mineralization bands. The closely spaced gravity-magnetic contours over the low to high anomaly transition zones of Bouguer, reduced-to-pole magnetic, and trend surface separated residual gravity-magnetic anomaly maps indicate the possibility of high altered zone(s) along NW-SE direction at the central part of the study area. High current density plots of VLF method and the low resistive zones in gradient resistivity study depict the coincidence with low gravity, moderately high magnetic and low resistivity anomalies at the same locations. Moderate high radioactive zones have also been observed over these locations. This also suggests the existence of radioactive mineralization over this region. Along profile P2, drilled borehole data revealed the presence of uranium mineralization at a depth of ∼100 m. The vertical projection of this mineralization band also identified as low gravity, low resistivity and high magnetic anomaly zone. Thus, the application of integrated geophysical techniques supported by geological information successfully recognized the nature of geophysical signatures associated with the uranium mineralization of this region. This enhances the scope of further integrated geophysical investigations in the unexplored regions of SPSZ.

A Novel Preprocessing Scheme to Improve the Prediction of Sand Fraction From Seismic Attributes Using Neural Networks

This paper presents a novel preprocessing scheme to improve the prediction of sand fraction from multiple seismic attributes such as seismic impedance, amplitude, and frequency using machine learning and information filtering. The available well logs along with the three-dimensional (3-D) seismic data have been used to benchmark the proposed preprocessing stage using a methodology that primarily consists of three steps: 1) preprocessing; 2) training; and 3) postprocessing. An artificial neural network (ANN) with conjugate-gradient learning algorithm has been used to model the sand fraction. The available sand fraction data from the high-resolution well logs have far more information content than the low-resolution seismic attributes. Therefore, regularization schemes based on Fourier transform (FT), wavelet decomposition (WD), and empirical mode decomposition (EMD) have been proposed to shape the high-resolution sand fraction data for effective machine learning. The input data sets have been segregated into training, testing, and validation sets. The test results are primarily used to check different network structures and activation function performances. Once the network passes the testing phase with an acceptable performance in terms of the selected evaluators, the validation phase follows. In the validation stage, the prediction model is tested against unseen data. The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume. Finally, a postprocessing scheme using 3-D spatial filtering is implemented for smoothing the sand fraction in the volume. Prediction of lithological properties using this framework is helpful for reservoir characterization (RC).

Integrating Apparent Conductance in Resistivity Sounding to Constrain 2D Gravity Modeling for Subsurface Structure Associated with Uranium Mineralization across South Purulia Shear Zone, West Bengal, India

South Purulia Shear Zone (SPSZ) is an important area for the prospect of uranium mineralization and no detailed geophysical investigations have been carried out in this region. To delineate the subsurface structure in the present area, vertical electrical soundings using Schlumberger array and gravity survey were carried out along a profile perpendicular to the SPSZ. Apparent conductance in the subsurface revealed a possible connection from SPSZ to Raghunathpur. The gravity model reveals the presence of a northerly dipping low density zone (most likely the shear zone) extending up to Raghunathpur under a thin cover of granitic schist of Chotanagpur Granite Gneissic Complex (CGGC). The gravity model also depicts the depth of the zone of density low within this shear zone at ~400 m near Raghunathpur village and this zone truncates with a steep slope. Integration of resistivity and gravity study revealed two possible contact zones within this low density zone in the subsurface at depth of 40 m and 200 m. Our study reveals a good correlation with previous studies in Raghunathpur area characterized by medium to high hydro-uranium anomaly. Thus the conducting zone coinciding with the low gravity anomaly is inferred to be a possible uranium mineralized zone.

A Novel Framework Based on SVDD to Classify Water Saturation from Seismic Attributes

Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context, this paper proposes a generalized Support Vector Data Description (SVDD) based novel classification framework to classify water saturation into two classes (Class high and Class low) from three seismic attributes - seismic impedance, amplitude envelop, and seismic sweetness. G-metric means and program execution time are used to quantify the performance of the proposed framework along with established supervised classifiers. The documented results imply that the proposed framework is superior to existing classifiers. The present study is envisioned to contribute in further reservoir modeling.

Laterite Covered Mafic-Ultramafic Rocks: Potential Target for Chromite Exploration - A Case Study from Southern Part of Tangarparha, Odisha

Exposed chromite deposits in the Sukinda belt, Odisha, India, have already been identified and exploited; but a large part of the area is covered by laterite and remains unexplored. As a case study to establish the feasibility of chromite exploration under laterite rocks, an integrated ground-based gravity, magnetic and very low frequency (VLF) - electromagnetic study was performed over a laterite-covered area at Tangarparha within the belt. North of the present laterite-covered area, a quartzofeldspathic gneiss contains proved chromite pods within ultramafic complexes. The gneiss-laterite contact is depicted by a transition from low to high in both gravity and magnetic anomaly maps at the northern part of the present study area. High Bouguer and residual anomalies (> 10 mGal and > 1 mGal, respectively) within the laterite-covered area indicates the existence of a high density rock in the sub-surface. The 2D models of the residual gravity anomaly depict the presence of high density (> 3570 kg/m3) layer under the laterite cover. The 2D magnetic models mostly reveal shallow surface effects of laterite covers. However, along profiles P2 and P3 high magnetic susceptibilities are detected at depths ≥ 20 m, and are likely to be caused by sub-surface chromite mineralization, as the locations are coincident with gravity highs. High current densities in VLF profiles are also recorded at the same locations confirming the presence of conducting sub-surface layer. Thus, the zone with high density, magnetic susceptibility and conductivity is most likely to be a chromitite-bearing sub-surface layer. The targeted chromite ore is distributed in east-west direction in the form of discontinuous pods of variable vertical thickness and strike lengths at the centraleastern part of profiles P2 and P3. The present study demonstrates that integrated use of ground gravity, magnetic and VLF techniques can effectively identify the target chromite deposits even under lateritic cover.

Assessment of Similarity Between Well Logs Using Synchronization Measures

In oil exploration, studying the similarity between patterns of the same geophysical properties in different wells is essential for making early decisions on future planning as well as for assessing the lithology of the area under survey. Geoscientists either rely on visual tools or resort to correlation studies between the different wells to match portions of the well logs. This is a tedious process involving several trial and error runs, which includes shifting, stretching, and sometimes preprocessing of the well logs by experienced geoscientists. However, this can be simplified by automating the process of matching. The well logs, a measure of the lithology, fall under the class of nonlinear signals. Therefore, linear methods are inadequate for matching these sequences. In this letter, we introduce similarity measures based on the concept of synchronization as used in matching nonlinear signals such as chaotic time series data. Two recently proposed methods, i.e., synchronization likelihood (SL) and visibility graph similarity (VGS), have been applied on the gamma-ray and porosity logs along different wells. These are considered as depth sequences, which can also be converted to suitable time series with the availability of the velocity profile. The data for this study originate from 12 existing wells in the western coast of India. The values of SL and VGS as well as the correlation are computed between these wells. Higher values indicate the existence of similarities. This has also been verified from the overlapped plots of well-log data.

Gravity-magnetic studies for uranium exploration over Manbazar-Kutni area of South Purulia Shear Zone (SPSZ), West Bengal, India using hydro- uranium anomalies as guidance Correlation between gravity-magnetic and hydro-uranium anomalies for prospecting of uranium mineralization

Both tectonic belt, the South Purulia Shear Zone (SPSZ) and the Singhbhum Shear Zone (SSZ) within the Singhbhum craton of East Indian Shield, has been identified with similar geometrical shape and mineralization. The mineralization of these regions is mainly structural guided and hydrothermally generated. An integrated gravity-magnetic study has been conducted around Manbazar-Kutni area across SPSZ to decipher the subsurface configurations, presence of faults/fractures. These structural features may form favourable condition for mineralization. The first degree trend surface separated residual gravity as well as the Bouguer gravity and magnetic anomaly maps depicted the ESE-WNW trending SPSZ on the SW part of the area. The observed negative gravity and moderately high magnetic anomalies around Dighi, Chepua villages are also characterized by medium to high hydrouranium anomaly from earlier hydro-uranium anomaly studies. Therefore, the negative gravity and moderate positive magnetic anomaly zones are concluded to be hydrothermally altered brecciated zone and the possible uranium mineralized zone. The interpreted faults /lineaments from the gravity-magnetic anomaly maps show good correlation with the exposed one and with the hydro-uranium anomalous zones. Further, the 2D gravity model across the shear zone depicts three low density altered zones (most likely sheared granite and mineralization zone) over the granitic basement along SW-NE profile from Kutni to Chepua village under a thin cover of granitic schist of CGGC. Since surface signature of nuclear radiation has not been observed, uranium mineralized zone could be at a large depth within these altered zones. Thus, the study demonstrates the effectiveness of gravity-magnetic methods in delineating subsurface configuration and to identify the altered zones/faults/lineaments which will act as favourable factors for structural guided radioactive mineralization in conjunction with other know mineralization indication.

Well-Log and Seismic Data Integration for Reservoir Characterization: A Signal Processing and Machine-Learning Perspective

Reservoir characterization (RC) is a process of finding petrophysical properties of the subsurface mainly from the seismic and well-log data. The nonlinear and heterogeneous nature of the subsurface is the major bottleneck in estimating the reservoir properties. In the past two decades, the RC has eventually turned out to be an interdisciplinary field of research involving computational science, signal processing, geostatistics, and geophysics. This article provides the interdisciplinary perspective in RC focusing on the applications of signal processing and machine learning (ML). We provide an account of various state-of-the-art algorithms while categorizing them into three stages in an RC framework: preprocessing, prediction, and postprocessing. RC has been known to be a highly data-driven problem. Huge volumes of seismic and well-log data are cleverly integrated by experts to decipher the subsurface properties. Some of the anomalies may lead to the existence of a potential reservoir. The signal processing tools are primarily required for information matching, preprocessing for noise and artifacts, and postprocessing for removing irregularities in the prediction, whereas the ML tools are required to map the seismic data to well logs. This article provides a comprehensive study on the recent advances in RC involving seismic volumes and well logs.