Imaging of Subsurface Velocity Structure on Volcanic Area of South East Java: An Application of Passive Seismic Ambient Noise Tomography for Sub-Volcanic Hydrocarbon Exploration

Petroleum exploration in sub-volcanic area always poses an inevitable challenge. Active seismic exploration method fails to obtain reliable imaging of the sediment beneath volcanic formation due to massive attenuation. This issue has been a long-standing problem in onshore seismic activity in Indonesia, especially in areas where volcanic formations present above the sedimentary formation of interest. To address this issue, we propose an alternative method utilizing a passive seismic approach to obtain reliable subsurface information. This paper discusses our experience in employing ambient noise tomography to evaluate the sedimentary structure beneath the volcanic area in Southern Malang, East Java. The passive seismic network deploying 70 seismometers were installed in a relatively regular grid. With the maximum offset between two furthest stations was 44.5km, we can capture the maximum wavelength of 15 km which is associated with the minimum frequency as low as 0.08 Hz to be used in the inversion. In principle, the seismometers record the coherent seismic noise coming from the atmospheric activity, sea wave, or industrial activity in the surface. Cross correlation between signal received in each station and their continuous stacking yields useful signals to reveal the dispersion curve which can produce the subsurface velocity profile through an inversion technique. From the inversion result we obtain the subsurface s-wave velocity structure down to a depth of 6 km. Higher s-wave velocity structure on the shallow depth in the northern area of the survey confirms the presence of the thick volcanic sediment situated near volcanic mountain. Towards the southern area we observe a slower s-wave velocity profile that indicates the thinning of volcanic formation. Although the method has successfully delivered a reliable s-wave structure over an entire survey area, its resolution is limited due to large spacing between stations. We suggest deploying denser stations to improve the velocity resolution.

FACIES CLASSIFICATION OF A COMPLEX RESERVOIR USING MACHINE LEARNING: CASE STUDY FROM VOLCANIC FORMATION, THE YURIHARA OIL FIELD, JAPAN

Lithological facies classification using well logs is essential in the reservoir characterization. The facies are manually classified from characteristic log responses derived, which is challenging and time consuming for geologically complex reservoirs due to high variation of log responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the Yurihara oil field is petrologically classified into five lithofacies: mudstone, hyaloclastite, pillow lava, sheet lava, and dolerite, with pillow lava being predominant reservoir. The former four lithofacies are the members of the volcanic system in Miocene, and dolerite randomly intruded later into those. Understanding the distribution of omnidirectional tight dykes at the well location is important for the estimation of potential near-lateral seal distribution compartmentalizing the reservoir. The facies are best classified by core data, which are unfortunately available in a limited number of wells. The conventional logs, with the help of the borehole image log, have been used for the facies classification in most of the wells. However, distinguishing dolerite from sheet lava by manual classification is very ambiguous, as they appear similar in these logs. Therefore, automated clustering of well logs with ML was attempted for the facies classification. All the available log data was audited in the target well prior to applying ML. A total of 10 well logs are available in the reservoir depth interval. To prioritize the logs for the clustering, the information of each log was first analyzed by Principal Component Analysis (PCA). The dimension of variable space was reduced from 10 to 5 using PCA. Final set of 5 variables, gamma-ray, density, formation photoelectric factor, neutron porosity, and laterolog resistivity, were used for the next clustering process. ML was applied to the selected 5 logs for automated clustering. Cross-Entropy Clustering (CEC) was first initialized using k-means++ algorithm. Multiple initialization processes were randomly conducted to find the global minimum of cost function, which automatically derived the optimized number of classes. The resulting classes were further refined by the Gaussian Mixture Model (GMM) and subsequently by the Hidden Markov Model (HMM), which takes the serial dependency of the classes between successive depths into account. Resulting 14 classes were manually merged into 5 classes referring to the lithofacies defined by the borehole image log analysis. The difference of the log responses between basaltic sheet lava and dolerite was too subtle to be captured with confidence by the conventional manual workflow, while the ML technique could successfully capture it. The result was verified by the petrological analyses on sidewall cores (SWCs) and cuttings. In this study, the automated clustering with the combination of several ML algorithms was demonstrated more efficient and reasonable facies classification. The unsupervised learning approach would provide supportive information to reveal the regional facies distribution when it is applied in the other wells, and to comprehend the dynamic behavior of the fluids in the reservoir.

Investigation of the bedrock (metamorphic basement) geometry of Santorini island using single-station ambient noise data

<p>We investigate the geometry of the metamorphic basement of the Santorini volcanic island using ambient noise data to determine the pre-Alpine/pre-volcanic bedrock structure. The geometry of pre-volcanic Santorini is important in order to constrain the recent volcanic history of the island and also to study the site-effect of the volcanic formations on seismic motions. Santorini is the most active volcano of the Southern Aegean Volcanic Arc and is the southernmost island of the Cyclades islands metamorphic complex. As a result, the volcanic material that has accumulated during the last 600+ Kyrs has been superimposed on the pre-volcanic Santorini (Cycladic) island. To map the thickness of volcanic material, we have performed a large number (>200) of single-station noise measurements in the Santorini area.  Measurements were mainly performed using conventional acquisition systems (Guralp-40T 30sec seismometer and Reftek-130A digitizer). We also employed additional single-station noise data from several previous studies (Dimitriadis et al. 2006, PROTEUS Project 2015), as well as permanent stations from the Hellenic Seismological Network in the same region. HVSR curves were calculated using single-station noise data and were used to estimate the fundamental frequency, f<sub>0</sub>, as well as the corresponding maximum HVSR amplitude, A<sub>0</sub><sup>HVSR</sup>. The majority of HVSR curves showed prominent peaks (A<sub>0</sub><sup>HVSR</sup> locally larger than 7-8), indicating a clear impedance contrast between volcanics and metamorphic formations. To map the bedrock depth, we estimated the thickness of the upper volcanic formations using the quarter-wavelength approximation for each site. For this assessment, the average shear-wave velocity (Vs) of the volcanic formations was estimated from the inversion of several passive ambient noise array data, as well as additional constraints from selected MASW measurements. Where possible, the reliability of the spatial variation of volcanic formation thickness was checked with independent geological information. Using the digital elevation model and the volcanic formation thickness for each site of the single-station noise data, we estimated the spatial distribution of the pre-Alpine, metamorphic bedrock depth. The resulting geometry of the pre-volcanic Santorini island shows very deep basins (now filled with volcanic formations) around the pre-Alpine bedrock outcrop in the southern part of Santorini (Profitis Ilias), increasing to 100+ meters in the Kamari-Perissa basin area (southeastern Santorini) and to more than 400+ in the central (Fira-Imerovigli) and the north Santorini areas (Oia), in agreement with recent larger-scale tomographic results (Heath et al., 2019). The results are also in very good agreement with the pre-Alpine bedrock geometry independently inferred from gravity data inversion (Tzanis et al., 2019.)</p><p><strong>This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ), the Hellenic Foundation for Research and Innovation (HFRI) under the “First Call for HFRI Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 2924) and the Institute for the Study and Monitoring Of the SAntorini Volcano (ISMOSAV). </strong></p>

Surface and subsurface engineering geological mapping for empirical designs of excavation method and support system of Tunnel 6 of Jakarta-Bandung high speed railway, Indonesia

Empirical design of support system at the Tunnel 6 of the Jakarta – Bandung high-speed railway was based on the Basic Quality (BQ) system, which had not been adopted in Indonesia. This research was carried out to better understand the rock mass quality at the tunnel construction site by comparing rock mass quality determined by the BQ system to that determined by two more popular rock mass classifications, namely the Geological Strength Index (GSI) and Rock Mass Rating (RMR). Surface and subsurface engineering geological mapping were carried out and tunnel excavation method and support system were proposed. The engineering geological model of the BQ, GSI, and RMR systems showed that the sedimentary rock masses of the Miocene Jatiluhur Formation generally had poor to very poor quality, while those of the Quaternary Volcanic Formation had very poor to good quality. Based on the RMR, the stand-up time values of the sedimentary rock masses were predicted to be relatively low as compared with those of the Quaternary Volcanic Formation, implying requirement of a relatively quick support system installation after excavation. In general, a combination of systematic bolt, shotcrete and steel ribs is the recommended support system for this tunnel.

Silicic volcanism in the Scottish Lower Carboniferous; lavas, intrusions and ignimbrites of the Garleton Hills Volcanic Formation, SE Scotland

Extensional tectonics and incipient rifting on the north side of the Iapetus suture were associated with eruption of (mainly) mildly alkaline olivine basalts. Initially in the Tournaisian (Southern Uplands Terrane), magmatic activity migrated northwards producing the Garleton Hills Volcanic Formation (GHVF) across an anomalous sector of the Southern Uplands. The latter was followed by resumption of volcanism in the Midland Valley Terrane, yielding the Arthur's Seat Volcanic Formation. Later larger-scale activity generated the Clyde Plateau Volcanic Formation (CPVF) and the Kintyre lavas on the Grampian Highlands Terrane. Comparable volcanic successions occur in Limerick, Ireland. This short-lived (c. 30 myr) phase was unique in the magmatic history of the Phanerozoic of the British Isles in which mildly alkaline basaltic magmatism locally led to trachytic differentiates. The Bangly Member of the GHVF represents the largest area occupied by such silicic rocks. The most widespread lavas and intrusions are silica-saturated/oversaturated trachytes for which new whole-rock and isotopic data are presented. Previously unrecognized ignimbrites are described. Sparse data from the fiamme suggest that the magma responsible for the repetitive ignimbrite eruptions was a highly fluid rhyolite. The Bangly Member probably represents the remains of a central-type volcano, the details of which are enigmatic.

The Geology of the Perches - Terrier Rouge Area, A Potential Copper and Gold Mineralized Belt in Northern Haïti.

A geological reconnaissance at an approximated scale of 1:100000 was done over 112 km2, between the townships of Roche Platte to the West, Jasquezyl to the North, Grand Basin to the East, and Perches to the South. A total of 56 points were described for an average of one geological observation per two square kilometers.Also, 18 grab samples were taken from mineralized showings, as well as three heavy mineral concentrates and two samples for whole rock analysis.All the collected information was kept in a database created on MS Access by the author of this report. The database, including the results of the analysis, can be consulted on Mendeley at doi:10.17632/b35fmjntnr.1.A model for the regional geological evolution of the area is presented, as well as a proposal for the denomination of the three basic geological structures found in the area, The Douvray Volcanic Group, the Grand Basin Magmatic Complex, and the Bercera Volcanic Formation, which the author interpreted as the last volcanic event of the Douvray Volcanic Group.This study revealed several new prospective zones for copper to the North, and gold-copper-lead-zinc mineralization to the South, along the volcanic belt and its orogenic intrusives, chiefly related to the occurrences of iron hats.The author recommends more detailed prospecting work, including ground geophysics and trenching, to better evaluate the ore potential of these new areas.

Reinterpreting the age of the uppermost ‘Old Red Sandstone' and Early Carboniferous in Scotland

ABSTRACTIn Scotland, the base of the Ballagan Formation has traditionally been placed at the first grey mudstone within a contiguous Late Devonian to Carboniferous succession. This convention places the Devonian–Carboniferous boundary within the Old Red Sandstone (ORS) Kinnesswood Formation. The consequences of this placement are that tetrapods from the Ballagan Formation were dated as late Tournaisian in age and that the ranges of typically Devonian fish found in the Kinnesswood Formation continued into the Carboniferous. The Pease Bay specimen of the fish Remigolepis is from the Kinnesswood Formation. Comparisons with its range in Greenland, calibrated against spores, show it was Famennian in age. Detailed palynological sampling at Burnmouth from the base of the Ballagan Formation proves that the early Tournaisian spore zones (VI and HD plus Cl 1) are present. The Schopfites species that occurs through most of the succession is Schopfites delicatus rather than Schopfites claviger. The latter species defines the late Tournaisian CM spore zone. The first spore assemblage that has been found in Upper ‘ORS' strata underlying the Ballagan Formation (Preston, Whiteadder Water), contains Retispora lepidophyta and is from the early latest Famennian LL spore zone. The spore samples are interbedded with volcaniclastic debris, which shows that the Kelso Volcanic Formation is, in part, early latest Famennian in age. These findings demonstrate that the Ballagan Formation includes most of the Tournaisian with the Devonian–Carboniferous boundary positioned close to the top of the Kinnesswood Formation. The Stage 6 calcrete at Pease Bay can be correlated to the equivalent section at Carham, showing that it represents a time gap equivalent to the latest Famennian glaciation(s). Importantly, some of the recently described Ballagan Formation tetrapods are older than previously dated and now fill the key early part of Romer's Gap.