Seismic Waveform Research Articles

Intelligent Prestack Multi-trace Seismic Inversion Constrained by Probabilistic Geological Information

Seismic inversion uses observed data including well-logging and seismic data to infer rock parameters. However, it still suffers from non-unique solutions. The non-uniqueness increase when extrapolating away from well location. To mitigate this effect, we incorporate geological information into a deep learning (DL) framework in the form of generated probabilistic labels. We transform geological information into reservoir heterogeneity (RH) weights and geological pattern constraint (GPC) weights to generate probabilistic labels. The RH weights are used to capture the variability in accuracy during the spatial extrapolation of well data. The RH weights are used to balance the constraint weights between well and seismic data in the DL model. The GPC weights are used to characterize the changes of geological patterns from well locations to non-well locations. Based on variations in local prestack seismic waveforms, the DL model learns extrapolation patterns of well data with similar geological patterns. By combining the rock parameters derived from well logs with these two types of weights, we can generate a series of probability labels. Two types of geological information and well-logging data are deeply integrated into a supervised 2D-ResUNet framework. In this way, we develop a novel DL-based prestack multi-trace seismic inversion method. We validate our method on a 3D synthetic model and a 3D field dataset. The results show that our method exhibits great potential for characterizing the spatial variation of subsurface rocks and outperforms traditional deep learning methods.

Discriminating Landslide Waveforms in Continuous Seismic Data Using Power Spectral Density Analysis

AbstractDiscriminating landslides from other events in seismic records is challenging due to unclear phases and overlapped frequency content. We analyze the seismic waveform power spectral density (PSD) and its skewness to discriminate landslides from earthquakes and background noise. By comparing PSDs of landslides with small‐magnitude earthquakes and noise in the Alaskan region, we find distinct power decay trends in the 0.01–5 Hz frequency range. The method was successfully tested on the seismic waveforms of seven global landslides. Further, the statistical significance of the approach was tested on 835 landslide waveforms using probability density, skewness and crosscorrelation of waveform PSD. This novel integration of seismic waveform PSDs and their skewness analysis is found to be robust and statistically significant for automatic landslide detection in continuous seismic data, with vast potential for early warning through real‐time seismic networks.

Prediction of grain shoal reservoirs via seismic forward modeling and waveform classification: Application to the northern slope of the central Sichuan Uplift, Sichuan Basin, SW China

Grain shoals are one of the principal reservoir facies of carbonate rocks in oil and gas exploration domains. With the discovery of oil and gas in the grain shoal reservoirs of the Longwangmiao Formation in the central Sichuan paleo-uplift, the characteristics and distribution of grain shoal reservoirs are still unclear in the northern slope of the central Sichuan paleo-uplift. Through integrated analysis of wireline logs and high-quality 3D seismic data, combined with seismic forward modeling and waveform classification, reservoir seismic facies, reservoir classification, and the reservoir distribution of different grades are investigated. The results show that (1) under the thin thickness (i.e., the thickness is around 80 m) of the Longwangmiao Formation (i) when reservoirs are not developed, the seismic waveform is manifested as a single peak, and the top interface of the Longwangmiao Formation corresponds to 1/8 [Formula: see text] above the single peak. (ii) When a single set of reservoirs is merely developed in the second member of the Longwangmiao Formation (SMLF), the waveform displays a single peak, and the top peak moves downward. In addition, when multiple sets of reservoirs are developed in the SMLF, the seismic reflections of multiple reservoirs are consistent with those of a single reservoir with the same cumulative thickness. (iii) When the reservoirs are developed in the first member of the Longwangmiao Formation (FMLF) and SMLF, the single peak moves downward, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (2) Under the thick thickness (i.e., the thickness is approximately 110 m) of the Longwangmiao Formation, (i) when reservoirs are not developed, the waveform of the Longwangmiao Formation exhibits a double peak, and the top interface of the Longwangmiao Formation corresponds to the extreme value of the upper peak. (ii) When only a single set of reservoirs or multiple sets of reservoirs are developed in the SMLF, the Longwangmiao Formation exhibits a single peak, and the top interface of the Longwangmiao Formation corresponds to the trough. (iii) With the reservoir development in the first and second members of the Longwangmiao Formation, the waveform of the Longwangmiao Formation shows an asymmetric low-frequency peak, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (3) Using the waveform classification method, seismic waveforms corresponding to the Longwangmiao Formation could be summarized into four types (i.e., two types of waveforms associated with reservoir development and two types of waveforms associated with reservoir un-development). Two reservoir waveforms are distributed as two strips oriented approximately east–west and spread in the central study area, according to the planar study results that combine seismic attributes and waveform classification. The reservoir stripes near well PS1 represent the better-developed reservoir zones in the study area.

An adaptive parameter-free seismic data denoising approach by combining general cross-validation thresholding and pixel connectivity in synchrosqueezed domain

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising.Graphical

Explosive eruption processes inferred from high-frequency seismic waveforms of eruption tremor and explosion events

Summary We investigated the relation between high-frequency seismic signals and eruption size and duration using seismic data of eruption tremor and explosion events generated during sub-Plinian and Vulcanian eruptions, respectively, at various volcanoes. We estimated source amplitude functions from seismic envelope seismograms in the 5−10 Hz band, in which S-waves are assumed to radiate isotropically. Because seismic data associated with explosive eruptions can be contaminated by infrasound signals, we confirmed that contamination did not significantly affect the source amplitude functions quantified from our analysed waveforms. We approximated the source amplitude functions of eruption tremor and explosion events by quadrilateral and triangular shapes. For eruption tremor, the durations of the source amplitude functions increased with decreasing slope of the initial phase, i.e. between onset and maximum amplitude. For explosion events, both the maximum and cumulative amplitudes of the source amplitude functions increased with increasing slope of the initial phase, but the overall durations clustered around a typical value. Moreover, the initial phase durations of eruption tremor were longer than those of explosion events. Based on eruption models proposed by previous studies, Vulcanian and sub-Plinian eruptions have been thought to be triggered by accumulation of magma at a shallow part in a conduit and mixing of cool mushy magma with hot fresh magma in a reservoir, respectively. The above differences between the source amplitude functions of eruption tremor and explosion events can be explained by the distinct eruption triggering processes of sub-Plinian and Vulcanian eruptions. Our results suggest that source amplitude functions are useful for investigating eruption processes and estimating eruption sizes and durations for seismic eruption monitoring.

Multi‐Method Structural Investigation of the Schneiderberg–Baalberge Burial Mound (Saxony‐Anhalt, Germany) Including Seismic Full‐Waveform Inversion (FWI)

ABSTRACTThe construction history and subsequent usage of burial mounds are an important testimony for socio‐economic transformation in prehistoric societies. The Baalberge–Schneiderberg burial mound, subject of the presented study, falls in this category as it is considered as an important monument that indicates the emergence of early social stratification during the Chalcolithic period in central Europe. This hypothesis relies on the chronological development of the burial mound, which is not fully understood until now. Therefore, a reconstruction of the complex stratigraphy of the burial mound including construction phases and later alterations is highly relevant for archaeological research, but the required excavations would be onerous and inconsistent with preservation efforts. In this paper, we demonstrate that non‐invasive geophysical prospection, especially seismic sounding with shear and Love waves, is suitable to obtain the required stratigraphic information, if seismic full waveform inversion (FWI) and reflection imaging are applied. Complementary information on the preservation state of the mound is obtained through Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI) measurements. To support the seismic and geoelectric results, we utilize Dynamic Testing (DynP), geoarchaeological corings, 14C‐Dating and archaeological records. Our investigations reveal two construction phases of the Baalberge–Schneiderberg mound. The 14C‐Dating yields dates for the older burial mound that are contemporary to the Chalcolithic Baalberge group (4000–3400 bc). During the Early Bronze Age (EBA), the mound was enlarged to its final size by people of the Aunjetitz/Únětice society (2300–1600 bc). However, both seismic and geoelectric depth sections show an extensive disturbance of the original stratigraphy due to former excavations. For this reason, the exact shape of the older burial mound cannot be determined exactly. Based on our data, we estimate that its height was below 2 m. In consequence, the original Baalberge burial mound was less monumental as until now assumed, which potentially prompting a revision of its significance as indicator for social differentiation.

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Spatially-varied crustal deformation indicating seismicity at faults intersection in the SE margin of the Tibetan Plateau: Evidence of S-wave splitting from microseismic identification

The Sanjiang Lateral Collision Zone (SLCZ) in the SE margin of the Tibetan Plateau is a special area where several strike-slip faults intersect, resulting in strong deformation and frequent earthquakes. We employ seismic waveforms recorded by a dense temporary broadband array (SJ array) and regional permanent stations to construct more complete microseismic catalogs by the microseismic identification in the SLCZ. New microseismic catalogs effectively increase the number of small earthquakes, revealing the details of the fault structures and providing many more records for S-wave splitting (SWS) analysis. It provides with an uncommon opportunity to detect the detailed upper crustal anisotropy in the fault intersection zone of SLCZ and to dissect the influence of faults, such as the Lijiang-Xiaojinhe fault and Red river fault, on crustal deformation. The spatial distribution of SWS parameters suggests multiple disturbance mechanisms to the upper crustal anisotropy in the study zone. Spatial distribution of dual dominant polarization directions of fast S waves near the block boundary faults uncovers the stress-focus range. Strong deformation from SWS data indicates frequent local seismicity. It reveals the spatial upper crustal deformation indicated by SWS parameters is closely related to not only stress, fault and local structure, but also local seismicity.

A Composite Seismic Source Model for the First Major Event During the 2022 Hunga (Tonga) Volcanic Eruption

AbstractThe violent eruption of the Hunga (Tonga) submarine volcano on 15 January 2022 caused a 58 km‐heigh ash plume, catastrophic tsunami, and significant global seismic and infrasound waves. However, the physical mechanism underpinning its multiple‐explosive events remains unclear, and its resolvability relies on the seismic waveform source inversion. The studies of two different point‐source models, the seismic moment tensor (MT) and the single force (SF), have been performed separately for this eruption, which, interestingly, can explain the seismic data adequately. Here, we use a joint inversion of MT and SF to unravel a composite source of an explosion‐like MT and a significant upward force for the first major explosive event. Regarding the direction and magnitude, we propose that the upward force is likely a rebound force in response to the pressure drop on the seafloor because the water body above the volcano was abruptly uplifted by the shallow underwater explosion.

Upper mantle structure beneath the Mongolian region from multimode surface waves: Implications for the western margin of Amurian plate

Multimode phase speeds of surface waves are used to build a new radially anisotropic S wave model in the eastern Eurasian and Mongolian regions. Our dataset includes seismic waveforms of over 1655 teleseismic events (Mw≥5.8) from 2009 to 2021, recorded at permanent and temporary stations in and around Mongolia. The multimode dispersion curves of Love and Rayleigh waves were extracted using the nonlinear waveform fitting method for individual seismograms. Then, we retrieved phase speed maps for each mode and frequency, incorporating finite-frequency effects. Finally, localized multimode dispersion curves extracted from the phase speed maps were inverted for local 1-D SV and SH wave profiles, which are combined into a radially anisotropic 3-D shear wave model. Our new model exhibits significant lateral variations of S wave speeds at 70–100 km depth beneath Mongolia, i.e., slow anomalies in the tectonically active western Mongolia in contrast to fast anomalies in stable eastern Mongolia. In the radial anisotropy model, SH waves are faster than SV waves in most areas of the Mongolian lithosphere above 100 km depth, except for the northeast of the Altay Mountains. The Hangay Dome region is characterized by significantly slower velocities that may relate to its uplifting. A large-scale low velocity beneath the northeast of the Hangay Dome with a slower SV wave speed than SH may indicate the existence of partially molten layers. This study also reveals distinct lateral variations of S wave speeds across the boundary between the Amurian and Eurasian plates, characterized by the fast anomaly in eastern Mongolia, corresponding to the lithosphere in the western Amurian plate.

Seismic Facies-Controlled Porosity Prediction in a Tight Sandstone Reservoir Based on the XGBoost Algorithm

Porosity is one of the most important properties for evaluating hydrocarbon reservoirs. There is a strong nonlinearity between seismic data and rock physical properties. Machine learning methods possess the powerful ability to capture complex nonlinear relationships between these two parameters, holding significant potential for porosity prediction in tight sandstone reservoirs. In this study, we propose a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through seismic meme inversion method,we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were utilized instead of the variogram function. We create labels for model training using porosity curves and borehole side P-impedance data, applying them to the extreme gradient boosting regressor. To demonstrate its feasibility, we apply the model to a real 3D case from an oil field in the Songliao Basin. Our application results demonstrate that this method can provide porosity predictions for tight sandstone reservoirs, maintaining consistency with seismic waveforms and adhering to seismic facies control patterns. The method uses only acoustic travel time, density, gamma rays, and spontaneous potential well-log data and post-stack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared to the seismic facies-controlled inversion combined with support vector machine and multi-Layer perceptron methods, the method proposed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Application of machine learning methods for earthquake detection from high-density temporary observation seismic records on a volcanic island

We applied two machine learning models to detect earthquakes from records observed with seismometers temporarily installed on a volcanic island. The two models are based on different principles: one regards seismic waveforms as images, using a convolutional neural network (CNN) to determine the first arrival times of P-waves, S-waves. The other model regards seismic waveforms as series data. The model processes seismic waveforms as data in a specific order of noise, P-wave, and S-wave, similar to natural language.The purpose of this study is to present the results of using machine learning first arrival times identification models with two principles for noisy seismic waveforms, caused by sea waves and strong winds in volcanic islands, and to evaluate the effectiveness of machine learning models for noisy observation records.We created a Confusion Matrix using first arrival times determined by an expert and evaluated the detection performance of these two models using some metrics of the matrix. Additionally, we assessed accuracy of the model-identified first arrival times by generating a frequency distribution of the difference from the expert's detecting time.The study discovered that the model treating data as series had superior detection ability for noisy data compared to the one treating data as images and the accuracy of the first arrival time detection was also better for the series data model too.We compared the results obtained on this island with those obtained at the permanent station, which is considered to have less noise interference, described in Mousavi et al., 2020. It was found that the difference in detection ability between the two models is slight for data obtained at permanent stations with low noise interference, but that the difference in detection ability between the algorithms of the two models is significant in noisy environments.

Variational prior replacement in Bayesian inference and inversion

SUMMARY Many scientific investigations require that the values of a set of model parameters are estimated using recorded data. In Bayesian inference, information from both observed data and prior knowledge is combined to update model parameters probabilistically by calculating the posterior probability distribution function. Prior information is often described by a prior probability distribution. Situations arise in which we wish to change prior information during the course of a scientific project. However, estimating the solution to any single Bayesian inference problem is often computationally costly, as it typically requires many model samples to be drawn, and the data set that would have been recorded if each sample was true must be simulated. Recalculating the Bayesian inference solution every time prior information changes can therefore be extremely expensive. We develop a mathematical formulation that allows the prior information that is embedded within a solution, to be changed using variational methods, without recalculating the original Bayesian inference. In this method, existing prior information is removed from a previously obtained posterior distribution and is replaced by new prior information. We therefore call the methodology variational prior replacement (VPR). We demonstrate VPR using a 2-D seismic full waveform inversion example, in which VPR provides similar posterior solutions to those obtained by solving independent inference problems using different prior distributions. The former can be completed within minutes on a laptop computer, whereas the latter requires days of computations using high-performance computing resources. We demonstrate the value of the method by comparing the posterior solutions obtained using three different types of prior information: uniform, smoothing and geological prior distributions.

Full Waveform Inversion Based on Dynamic Time Warping and Application to Reveal the Crustal Structure of Western Yunnan, Southwest China

AbstractWe develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle‐skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft‐shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe‐Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft‐shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle‐skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well‐aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft‐shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.

An Investigation into the Impact of Time-Varying Non-Conservative Loads on the Seismic Stability of Concrete-Filled Steel-Tube Arch Bridges

When the arch rib of the mid-bearing through and lower-bearing through arch bridges undergoes out-of-plane deformation, it is usually subject to the resilience force provided by the flexible hanger, which is known as the “non-conservative force effect” of the suspender. In contrast to the static condition, in the dynamic scenario, the time-varying non-conservative force exerted by the flexible suspender becomes more complex due to dynamic changes in external load. Moreover, the difference in fundamental frequency and vibration period between the bridge system and arch rib may influence the stress distribution within the arch rib during ground motion. This paper investigates the impact of time-varying non-conservative forces on the dynamic stability of arch ribs in concrete-filled steel tube (CFST) bridges under seismic loads. Specifically, it examines the influence of different seismic waveforms, frequency disparities between bridge slabs and arch ribs, and suspender stiffness on the non-conservative effect. The results reveal significant disparities in the impact of non-conservative forces exerted by the suspender during seismic events with identical intensity but varying frequency characteristics. The influence of non-conservative forces on the dynamic stability of bridges escalates as deck stiffness increases, while it remains relatively unaffected by changes in suspender stiffness.

A Coda-Duration Magnitude Scale for the Mesopotamian Seismological Network in Iraq

One of the routine tasks of local, regional, and global seismic networks is to calculate the earthquake magnitude using one or more scales. This study aims to derive a duration magnitude equation using the earthquake data recorded by the local Mesopotamian Seismological Network (MPSN) in Iraq. The seismic waveform data were collected from 13 broadband seismic stations of the local MPSN for the period June 2014 to October 2023 years. The source parameters of these earthquakes were extracted from the International Seismological Centre (ISC). To establish a coda duration magnitude scale, 476 events were used and 100 events were employed to test the derived equation. Coda-duration magnitude equations were derived using linear regression analysis. To ensure the applicability of the magnitude equation for the network, the station correction coefficient (Sc) was estimated. The established coda duration magnitude scales are as follows: (4 ≤ ML ≤ 6) (4 ≤ ML ≤ 5) There is a good agreement between the MD values calculated using the above equation with those estimated for the same events using coda-duration magnitude equation derived in previous study from data of the Iraqi Meteorological Organization and Seismology (IMOS). The significant strong scaling relationships derived between MD–ML, MD–mb, MD–Ms, and MD–MW can be used quickly and reliably in MD calculation for earthquakes recorded by the MPNS stations and vice versa.

A Novel Approach to Automatically Digitize Analog Seismograms

Abstract Before the widespread adoption of the digital seismographs, seismic records were stored in analog form on paper and manually read by analysts. These analog seismograms contained various useful information and were crucial for seismic research. To meet the demands of the modern computational analysis, researchers must digitize historical analog seismograms and extract information. In this article, we present a novel approach to automatically digitize analog seismograms. Initially, Otsu threshold segmentation was applied to the analog seismograms to remove underlying noise and improve their clarity. Subsequently, a novel dynamic distributed seismic waveform onset-point-search algorithm was implemented, which automatically locates the onset point of each seismic waveform baseline in analog seismograms and accurately determines the total number of seismic waveform curves. To address the complexity and diversity of seismic waveforms, we implemented an innovative seismic waveform classification algorithm that can distinguish between complex waveforms and smooth waveforms, and further implemented a new smooth waveform removal method to eliminate interference from smooth waveforms during complex waveform extraction. Then, we used a YOLOv9s-based model to identify time markers within the seismic waveforms for removal. In addition, in the seismic waveform digitization extraction and reconstruction phase, we implemented a novel method for extracting significant seismic waveform features and geometric restoration for peak and trough feature extraction and geometric restoration, as well as vertical feature extraction of seismic waveforms. Finally, we implemented a new waveform sequence integration and time mapping model, which can effectively reconstruct seismic waveform data based on the extracted features and map arrival times to each waveform point. Experiments have verified the significant superiority and stability of the methods implemented in this article for digitizing analog seismograms.

Seismic sedimentology analysis of meter-scale thin sand bodies in the Jurassic Qigu Formation, Yongjin area, Junggar Basin

Seismic sedimentology is an interdisciplinary field that integrates sedimentary geology and geophysics to accurately characterize the spatial and temporal distributions of sedimentary systems. This paper focuses on the Qigu Formation in the Yongjin area of the Junggar Basin, China. The objectives are to establish a high-resolution sequence stratigraphic framework and to clarify the types of sedimentary systems present. Various technical methods, including 90° phase adjustment, attribute clustering, red-green-blue attribute fusion, stratal slicing, and seismic phase-controlled waveform indication inversion, are employed to restore the vertical evolution history of the sedimentary system and quantitatively characterize the spatial-temporal distribution of the thin sand bodies in the Qigu Formation. The results revealed that the Qigu Formation is divided into a third-order sequence, consisting of a lowstand systems tract and a lacustrine transgressive systems tract. During the lowstand systems tract, a thick channel sand body developed in the delta front, exhibiting a non-muddy, intermittent normal grading structure. The lacustrine transgressive systems tract features thin, discontinuous channel sand bodies with mud layers, also forming an intermittent normal grading structure. Two distinct types of delta fronts are identified: the first, formed during the lowstand systems tract, is characterized by branching subaqueous distributary channels in a shallow-water environment. The second, associated with the lacustrine transgressive systems tract, displays a network-like distribution of subaqueous distributary channels in a relatively deeper-water environment.

Bridging science and art: Auralization and visualization of the ocean soundscape

As a group of researchers and artists, we have collaborated on a science-art project that converts seismic and hydroacoustic data into immersive digital artworks, revealing the hidden realm of ocean soundscape. We integrated artistic methodologies to produce soundtracks and videos from seismic waveforms recorded in the ocean, which enables the general public to gain a multisensory experience of the scientific data studied by specialists. Through exhibitions in multiple venues, our interdisciplinary approach was well received by diverse audiences, showcasing the potential for creative representations of scientific information.

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.