Seismic Traces Research Articles

Evaluation of seismic responses from well-associated gas producing sands using integrated approaches for optimal field production in Zamzama gas field, Pakistan

The producing fields often observe early decline due to many misapprehended factors concerning structural interpretation, facies identification, and proper estimation of geological properties. The accuracy of the estimation of these characteristics is critical for enhanced recovery. An integrated approach based on well identified potential lithofacies distribution in relation to seismic trace responses and accurate fluid estimation is vital. In the present study, gas-bearing sands are appraised for their reservoir characteristics within a complex anticline structure. The faulted anticline's hanging wall provided suitable locations for optimum gas entrapment; however, its connection with the footwall through lateral ramps allows the aquifer's early encroachment into the reservoir. For enhanced recovery, the combination of well-derived petrophysical parameters with seismic extracted responses provided insight into the producing facies with quantitative fluids identification using fluid replacement modeling (FRM) and their amplitude variation with offset (AVO) responses. The reliable AVO responses of substituted fluids comprising 85% gas with 15% brine are observed for identified potential sands, classifying sands as class-II. Similarly, the intercept (A), gradient (B), and product (A*B) of angle dependent seismic traces were mapped within the Pab Formation and showed amplitude anomalies for porous channelized gas-bearing sands. Finally, crossplotting intercept-gradient volumes differentiated the gas-bearing sands and precisely demarcated the identified gas sands throughout the field, with confirmation at producing well locations. Hence, the integrated assessment of the outcomes resulting from structural interpretation, petrophysical evaluation, and seismic trace valuation can be readily employed for distinguishing the producing sand facies in complex structures and demarcating risk-efficient zones.

Sequence stratigraphic evaluation for the Abu Madi Formation, Abu Madi/El Qar'a/Khilala gas fields, onshore Nile Delta, Egypt

The present study aims to integrate a large set of geological and geophysical data into a comprehensive model describing the depositional features of the Abu Madi/El Qar'a/Khilala gas fields. The model is based on the sequence stratigraphic framework of the Abu Madi Formation defined using cores, well logs, and time-migrated seismic data. Seismic trace attribute sections and relative acoustic impedance sections are also used. A possible depositional pattern for the main Level III is established, based on the lithological and petrophysical information derived from the seismic data analysis. The Abu Madi Formation can be regarded as a depositional sequence recording the progressive drowning of the incised valley. The sequence is bounded at the base by an erosional unconformity, created by a drop in the level of the Late Messinian Sea, and at the top by a drowning unconformity related to the Early Pliocene transgression. The bottom of Level II divides the Abu Madi sequence into two smaller sequences. In both sequences, gas-bearing traps can be found in the Lowstand Systems Tracts, represented by the fluvial Level III and fluvial-deltaic Level II, respectively.

3D model of water saturation, effective porosity and volume of shale in upper Assam Shelf, India using multi-attribute regression and cascade-probabilistic neural network

Quantitative interpretation of geophysical data for reservoir identification to study the vertical and horizontal variation of lithofacies, involves integration of multiple disparate datasets. We use a 3D seismic data and two wells (Km and Te) to predict a petrophysical model within the Paleocene to Oligocene formations, and the Precambrian basement in the upper Assam Shelf. The conventional well data were examined in each well to identify reservoirs within the formations. This is followed by a model based seismic inversion of 3D post-stack seismic data resulting in a 3D impedance model. Next, the cascade-probabilistic neural network (CPNN) was trained using the well log derived attributes (target label data) at the collocated seismic observations (observed features). We were able to successfully train the network to predict water saturation, effective porosity, and volume of shale from the inverted seismic traces with the cross-correlation of range 0.96, 0.97, 0.97, respectively. The optimum set of multi-attributes at well-seismic tie locations are extracted from the seismic data and the selected optimum set of attributes are used at all locations in the application of CPNN. CPNN is found to train the target data better than probabilistic neural network and multi-layer feedforward neural network. Hydrocarbon reservoirs are found in shaly sandstones within the Kopili and Sylhet formations of Eocene and Paleocene ages, and Precambrian basement. The hydrocarbon reservoir shows the variation in water saturation 20% to 50%, with the volume of shale of range 40% to 60%, and the effective porosity of range 8% to 30% respectively.

Automatic characterization and quantitative analysis of seismic facies in naturally fractured reservoir: Case study of Amguid Messaoud field, Algeria

Purpose. Natural fractured reservoirs are a special category of reservoirs due to the effects of porosity and permeability. Optimizing the exploitation of hydrocarbon reserves in this type of reservoir requires a specific study compared to other conventional reservoirs. Methods. We have focused on the quantitative analysis of seismic traces for the purpose of an automatic seismic facies recognition strategy. The study area, the Amguid-Messaoud Basin, is formed by a series of horsts and grabens bounded by submeridional “North-East and South-West” faults, as well as perpendicular “North-West and South-East” faults with-out outcrops of fractures, which have a great influence on reservoir fracturing. A set of statistical data analysis methods, such as principal component analysis, discriminant factor analysis, and automatic classification, have been tested on real data from geophysical seismic data interpretation, in particular the stratigraphic interpretation. Findings. The results obtained show a better use of data, which, however, are of a different nature, leading to a reliable interpretation of the geological environment. Originality. The methodology proved to be useful for constructing a reservoir model and predicting the geological properties of the reservoir along a field. Practical implications. The results obtained clearly demonstrate the best use of data, which, however, are of a different nature, which leads to a reliable interpretation of the geological environment. These methods have proved to be very useful for constructing a reservoir model and predicting the geological properties of the latter along a field.

Multi-source wavefield reconstruction combining interferometry and compressive sensing: application to a linear receiver array

SUMMARY Seismic interferometry (SI) is a technique that allows one to estimate the wavefields accounting for the wave propagation between seismometers, any of which can act as a virtual source (VS). Interferometry, particularly noise interferometry, has been applied to several geophysical disciplines such as passive monitoring and distributed acoustic sensing. In practice, one requires long recordings of seismic noise for noise interferometry. Additionally, one can have missing seismic interferometric traces because some receivers in seismic arrays may be absent or inoperative due to issues of receiver installation and malfunction. Thus, filling the gap of seismic interferometric profile requires wavefield reconstruction and regularization techniques. Compressive sensing (CS) is one such method that can reconstruct seismic interferometric wavefields and help mitigate the limitations by exploiting the sparsity of seismic waves. In our work, we use CS to reconstruct missing seismic interferometric wavefields. One can interpolate interferometric wavefields using correlograms provided by one VS. We call this method of reconstructing an individual VS gather single-source wavefield reconstruction. We propose an alternative technique called multi-source wavefield reconstruction, which applies CS to reconstruct multiple interferometric wavefields using a volume of VS gathers provided from all available VSs. Using numerical examples, we show that one can apply CS to recover interferometric wavefields resulting from interferometry of a linear seismic array. To exploit the sparsity of interferometric wavefields, we apply the Fourier and Curvelet transforms to the two reconstruction schemes. Using the signal-to-noise ratio (SNR) to compare reconstruction of interferometric wavefields, the Fourier multi-source method improves the recovery of interferometric wavefields by approximately 50 dB compared to the Fourier and Curvelet single-source wavefield reconstructions.

Assessing energy balance via seismic and mechanical observations in high temperature induced crack damage in marbles

Assessing the rock physical and mechanical behavior under different temperatures has become of utmost importance. It is well known that thermal stresses induce significant crack damage in rocks due to thermal expansion or phase transformation and volume changes. Quantifying and forecasting the evolution of rock physical and mechanical parameters with temperature is thus crucial for evaluating rock integrity in many applications such as geothermal fields, nuclear waste storage, wildfire or volcanic processes. In marbles the degree of previous exposure to temperature and the chemical composition (i.e. calcite vs dolomite) plays a key role for controlling the mechanical evolution under temperature. Moreover separating out the energy contribution provided by anelastic processes driving crack damage and elastic reversible deformation under increasing temperature remains an open challenge. With these aims, three sample sets of marbles with different contents of calcite and dolomite from two Brazilian quarries were tested under different temperature conditions (from room temperature up to 600 °C). A marked increase of thermal cracking was observed after 400 °C, accompanying mass loss up to 1% and porosity increase. Moreover, a significant drop in seismic wave velocities, uniaxial compressive strength and electrical resistivity, in wet conditions, was also detected.Spectral behavior from seismic traces and energy dissipation from stress-strain curves were analyzed. A dominance of the dissipated energy compared to the elastic one was observed and related to the generation of new fracturing surfaces. This hypothesis was supported by the spectral behavior showing multiple scattering effects in the high frequency components, with an increase in attenuation. The results suggested that the percentage of dolomite has a high influence on the mechanical behavior even at low temperature, mirroring the prevalence of brittle processes in dolomitic marbles. This study represents a comprehensive benchmark for the study of effect of temperature on rocks because of its multidisciplinary and multimethod approach and the demonstrated sensitivity to subtle textural changes. Moreover, it provides a reliable tool for crack damage analysis at each thermal stress.

Multi-station volcano tectonic earthquake monitoring based on transfer learning

Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed.Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems—a recurrent neural network based on long short-term memory cells (RNN–LSTM) and a temporal convolutional network (TCN)—both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption).Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals.

An expanded idea of imaging in geophysics through multimodal data analysis

In situations where visual stimuli are uncertain or degraded, auditory perception is crucial and can complement visual perception. Research on multimodal perception has confirmed in many areas of study that the existence of one stimulus can impact the perception of another type of stimulus. Based on these concepts, which are well-established in cognitive sciences, we introduce the idea of expanded (or augmented) imaging in geophysics, which refers to an integrated and coherent data representation based on dual-sensory (audiovisual) perception of the same data set. First, we explain the basic principles of multimodal analysis of seismic data using the theory of augmented imaging. Then, we provide examples and applications on real data at varying spatial scales, from individual seismic traces to entire seismic sections.

Amplitude preservation and seismic inversion reliability post spectral shaping

Resolution enhancement is often applied to poststack seismic data to obtain higher frequency or wider frequency bandwidth. This enhancement could help seismic interpreters in resolving thinner layers that may not be visible on the original seismic data. The seismic inversion method is the other way to interpret seismic data. The inversion process produces an impedance volume as a layer property that can be used not only for interpretation but also for reservoir characterization. This research aims to study whether vertical resolution enhancement of seismic data preserves the amplitudes and how enhancement affects seismic inversion results. The experiments in this research are performed on synthetic and real data. The modeling is carried out by generating zero-offset synthetic traces using a convolutional model. Meanwhile, real data processing is carried out at the North Sea Volve Data Village at the Ty and Hugin formations. The resolution of the seismic trace is enhanced using a spectral division operator. The inversion techniques used in this research are model-based inversion and sparse spike inversion. The seismic trace after vertical resolution enhancement shows that the separation of events can be improved by this procedure. Inversion of the original and of the enhanced traces shows that the enhancement process still allows obtaining reliable inversion results both from model-based and sparse spike methods; however, there is no significant difference between the inverted impedance using the original trace and enhanced traces. This research concludes that the enhancement process keeps the seismic amplitude preserved for inversion, but it barely improves the inversion result.

The back-and-forth method for the quadratic Wasserstein distance-based full-waveform inversion

The conventional least-squares misfit function compares synthetic data to observed data in a point-by-point style. The Wasserstein distance function, also called the optimal transport function, matches patterns. The kinematic information of seismograms is therefore efficiently extracted. This property makes it more convex than the conventional least-squares function. Computing the 1D Wasserstein function is fast. Processing the 2D or 3D seismic data volume trace by trace, however, loses the interreceiver coherency. The main difficulty of extending to the high-dimensional Wasserstein function is the heavy computation cost. This computational challenge can be alleviated by a back-and-forth method. After explaining the computation strategy and incorporating it into full-waveform inversion, we illustrate the superior performances of the high-dimensional Wasserstein function with a Camembert model and the Marmousi model. The superiority is also demonstrated with the Chevron 2014 blind test.

Self-supervised time-frequency representation based on generative adversarial networks

Time-frequency (TF) transforms are commonly used to analyze local features of nonstationary seismic data and to help uncover structural or geologic information. Traditional TF transforms, such as short-time Fourier transform, continuous wavelet transform, and S-transform, suffer from the Heisenberg uncertainty principle, and their TF resolution is limited. The sparse TF (STF) transform has been proposed to address this disadvantage; however, expensive calculation and parameter selection present difficulties. We have developed a self-supervised TF representation based on a generative adversarial networks (STFR-GANs) model to map a 1D seismic signal into a 2D STF image. This model includes three components: a generator, a discriminator, and a reconstruction module. The generator is used to generate the STF spectrum of the input seismic trace, whereas the discriminator distinguishes if this generated STF spectrum is optimal. The reconstruction module serves as a physical constraint to ensure the accuracy of the generated STF spectrum. When implementing model training, the discriminator learns to identify the ideal STF and guides the generator to produce a TF spectrum closer to the ideal one. After model training, we applied the model to synthetic and field data to demonstrate its effectiveness and stability in characterizing the TF features of seismic data. Our results find that STFR-GAN can effectively provide TF representations with higher readability than those of traditional TF methods, further benefiting fluvial channel delineation.

Deep decomposition learning for reflectivity inversion

AbstractWe report a combination of classical regularization theory with a null space neural network approach based on deep decomposition learning, paying particular attention to the solution of one ubiquitous problem in seismic exploration: the recovery of full‐band reflectivity from band‐limited seismic traces. The method extends the popular post‐processing approach by learning how to improve an initial reconstruction with estimated missing components from the null space of the forward operator, which in our case, are the missing frequency components of the reflectivity. We integrate the null space element prediction to act in conjunction with convolutional neural network based denoising and a data‐consistent algorithm. The proposed framework honours the input measurements while enforcing generalization. Numerical experiments on synthetic and real datasets show that the proposed method naturally enforces a high‐resolution prediction consistent with the low‐resolution input seismic traces. We compare its performance with state‐of‐the‐art thin‐bed reflectivity estimation methods.

Direct Visualisation of Seismic Facies Boundaries and DHI Detection of Anomalous Gas

Complex seismic trace attribute analysis separates the amplitude and the phase information contained in the data and produces two basic attributes known as the instantaneous amplitude and instantaneous phase. Using these, two new attributes are being introduced, namely, the facies attribute called NRG derived as an extension of the instantaneous amplitude, and the second one based on instantaneous phase attribute is used to compute the reflector overlay times plotted on the display. The NRG attribute is intended as a first step in the seismic interpretation process because it delineates subsurface boundaries both laterally and vertically. It also detects DHI effects for bright spot and non-bright or dim spot anomalies as well as indications of gas/oil migration through the subsurface. In one of the real data applications the bright spot colour display is calibrated to agree with the producible gas. Besides, there are attribute signatures for both oil and gas when the geology does not support bright spots. The application of the second attribute on real seismic data demonstrates how flat spots and faults are immediately recognisable. The unambiguous determination of geologically meaningful lateral as well as vertical boundaries cannot be overstated for direct facies analysis to be useful.

Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults.

A general method for extracting the amplitude spectrum of the seismic wavelet from the seismic traces

When solving geophysical problems using reflection seismology, extracting the amplitude spectrum of the seismic wavelet (ASSW) from a seismic trace is the basis for many downstream works, such as deconvolution and sparse reflection coefficient inversion. This paper pertains to statistical methods for estimating ASSW, that is, separating the scattering effect of the formation from the amplitude spectrum of a seismic trace to obtain a band-limited ASSW. Common methods assume the reflection coefficient sequence (RCS) is white, making them unsatisfactory in many cases. Gao et al (2017 Inverse Problems 33 085005) proposed an ASSW extraction method based on contraction operator mapping (COM method) and experimentally proved it effective for non-white RCSs. However, this method is only valid for unimodal ASSW, which makes it unsuitable for most sources. We propose a general method for estimating ASSW which does not require the whiteness assumption of the RCS and does not restrict the specific form of ASSW. We show that the COM method has function representation limitations and redefines a new operator, proving that it is also a contractive mapping. Our method can be regarded as a general form of the COM method, filling the theoretical gap that existing methods cannot be applied to non-white RCS and non-unimodal ASSW. It provides a general method for the estimation of ASSW. We thoroughly verify this generality by simulating different source types while considering wavelet attenuation. Compared with the COM method, the proposed method has absolute advantages.

Geophysical Characterization and Attenuation Correction Applied for Hydrate Bearing Sediments in Japan Sea

Estimation of rock properties from seismic data is important for exploration and production activities in the petroleum industry. Considering the compressional velocity—the speed of propagating body waves in formations—and the quality factor (Q)—a measure of the frequency-selective energy losses of waves propagating through formations—both properties are usually estimated from multichannel seismic data. Velocity is estimated during multichannel processing of seismic reflection data in either the time or depth domain. In marine seismic acquisition, Q can be estimated from the following sources: Vertical Seismic Profile (VSP) surveys, where sources are located near the sea surface and geophones are distributed at depth along a borehole; and multichannel reflection data, where sources are also located near the sea surface and receivers are distributed either at the sea surface (conventional seismic survey with streamers) or on the sea floor (use of nodes or Ocean Bottom Cables (OBC)). The aforementioned acquisition devices, VSP, conventional streamers, nodes, and OBCs are much more expensive than single-channel acquisition with one receiver per shot due to the cost of operation. There are numerous old and new datasets from academia and the oil industry that have been acquired with single-channel acquisition devices. However, there is a paucity of work addressing the estimation of velocity and Q from this type of equipment. We investigate the estimation of Q and velocity from single-channel seismic data. Using the windowed discrete Fourier transform for a single seismic trace, we calculate the peak and dominant frequency that changes with time. In the geologic environment, higher frequencies are attenuated at shallow depths (time), while lower frequencies remain at deeper positions. From the rate at which higher frequencies are attenuated with time, we estimate the effective quality factor (Qeff). However, when using Kirchhoff migration to process single-channel seismic data, events far from the vertical projection of the receiver contribute to the trace at a given time. Then, an underestimation of the effective quality factor occurs. To compensate for the effects of more distant events with lower-frequency content contaminating the shorter events, we propose a linear equation to correct the effective quality factor estimated from migrated seismic data. Effective Q and its correction are estimated in five single-channel seismic lines surveyed along the Joetsu Knoll, a SW-NE anticline structure on the eastern margin of the Sea of Japan. These results are linked to geomorphological and geological features and the velocity field. Joetsu Knoll is a known site of massive gas hydrates (GH), which occur in the first hundred metres of Neogene sediments and, together with gas chimneys, play an important role in seismic wave absorption. Qeff estimated from migrated seismic data maintains the spatial relationship between high and low Q regions. The region of low Q, which is below 124 and has an average value of 57, occurs near the anticlinal hinge and tends to coincide with the region in which the Bottom Simulating Reflector (BSR) resides. The coexistence of GH and free gas coincides with the very low P velocity gradient of 0.225 s−1. BSR occurrence, Qeff and the geometry of the Joetsu anticline testify to progressive gas hydrate depletion northward along the dome.

Upscaling method based on sedimentary cycle prior and its application to well‐to‐seismic tie

AbstractA well‐to‐seismic tie is a typical upscaling problem. The traditional well‐to‐seismic tie methods often use the velocity log directly to tie well logs to seismic data without considering the upscaling problem led by the velocity dispersion due to thin beds. We propose an upscaling method based on the Backus average method to upscale the well‐log velocity to seismic velocity and apply it to a well‐to‐seismic tie problem. For Backus average method, choosing an appropriate average window is a crucial issue. We introduce a synchrosqueezing optimal basic wavelet transform to calculate an average window. The synchrosqueezing optimal basic wavelet transform is a time–frequency representation method, which can extract the sedimentary cycle information from seismic data. Considering the relationship between the sedimentary cycle and the thin‐bed thickness, we propose a workflow to estimate the instantaneous thin‐bed thickness based on synchrosqueezing optimal basic wavelet transform and determine the window length of the Backus average at each depth point. Then, the Backus average is used to estimate the effective velocity, which is further used to calculate the initial time–depth function and transfer the reflection coefficients from the depth domain to the time domain. Choosing an appropriate seismic wavelet is another fundamental problem for the well‐to‐seismic tie process. This paper introduces an alternating iterative deep neural network‐based method to estimate the seismic wavelet. Thus realistic synthetic seismograms can be computed by convolving the estimated seismic wavelet and the reflection coefficient, which can be stretched or squeezed to match the near‐well seismic trace. We show that the proposed workflow effectively reduces the errors in a well‐to‐seismic tie procedure caused by velocity dispersion of thin beds.

Research on seismic hydrocarbon prediction based on a self-attention semi-supervised model

Predictions of oil and gas reservoirs play an essential role in hydrocarbon reservoir exploration. Multi-attribute seismic data can provide abundant reservoir information. However, traditional reservoir prediction models only reflect the characteristics of local seismic channels, ignoring information on global geological spatial structures. Thus, a semi-supervised prediction model based on self-attention is proposed to extract reservoirs' oil and gas characteristics. With the high cost of labeled samples, neighborhood similarity measurement based on Grey Relation Analysis is applied to determine the homogeneity of adjacent seismic traces for the first time to obtain pseudo-labels while considering geological spatial structure. As a semi-supervised prediction model, a multilayer convolutional neural network with a multi-head self-attention mechanism is utilized, which can extract features from multiple dimensions and investigate global structural information. The experimental results demonstrate that the algorithm in this paper's reservoir oil and gas prediction is more accurate than that of conventional approaches and is consistent with real drilling data.

Nonstationary seismic–well tying with time-varying wavelets

Seismic–well tying is an important technique for correlating well-logging curves in depth with seismic traces in time. An appropriate seismic–well tying technique must account for two types of nonstationarity: the nonstationary time errors in the synthetic seismic trace caused by the inaccurate time–depth relationship established based on sonic-logging velocity and the nonstationary seismic signals due to the time-varying wavelets during wave propagation. The nonstationary problems related to the time–depth relationship and the time-varying wavelets are interrelated in seismic-well tying procedure. We implemented a nonstationary seismic–well tying method by iteratively updating the time-depth relationship and estimating the time-varying wavelets. From the estimated time-varying wavelets, we also estimated a Q-value by assuming that the subsurface medium has a constant Q at depth and used the constant Q to constrain the variation of the seismic wavelet during propagation. Then, we used the improved time–depth relationship and time-varying wavelets with the Q constraint for further iterations. In the iterative procedure, we quantified the accuracy of the seismic–well tying result using the correlation coefficient between the synthetic and the true seismic trace in each iteration and evaluated the reliability using the normalized mean-square errors among the wavelets estimated in different iterations.

Integrated Identification of Lithology Using Seismic and Magnetic Anomaly Data for Granite and Gneiss Basement: A Case Study of the LiShui Depression in the East China Sea Basin

Granite and gneiss buried hill reservoirs are controlled by their lithology and dark mineral content. Therefore, lithological identification and dark mineral content analysis are important research tools in the early stage of buried hill exploration. In this paper, the relationships between the seismic facies and lithology, magnetic susceptibility, and magnetic anomalies of granite and gneiss are analyzed based on the lithological characteristics of the LiShui depression (LS depression) in the East China Sea Basin (ECSB). The waveform classification method is used to identify granite and gneiss, and the waveform classification results reveal that areas with continuous distribution of a single seismic trace model or two seismic trace models represent good continuity, and can be interpreted as gneiss. Areas with a mixed distribution of multiple seismic trace models represent chaotic and poor continuity, which can be interpreted as granite. The mixed linear zone with multi-seismic trace models is a fault zone, and the rock is cataclasite. In addition, reduction to the pole (RTP) and downward continuation technique for magnetic data processing were used to determine the dark mineral content. Overall, the granite and gneiss can be divided into three types based on magnetic anomaly data: high, moderate, and low magnetic anomaly areas. The areas in which granite with moderate and low magnetic anomalies is distributed are the favorable exploration target areas. The above method provides a technical means of lithological identification in the early stage of buried hill exploration.