dilshad-dataxpert / pyseismic

seismic segmentation and geobody detection in 3D seismic reflection data

pyseismic: seismic segmentation and geobody detection in 3D seismic

Workflow to automatically segment 3D seismic reflection data into objects and detect and extract geobodies.

Usage

For seismic segmentation

import open3d as o3d
from pyntcloud import PyntCloud

from PointCloudSeismicInterpretation import PointCloudSeismicInterpretation

#first load your 3D seismic
#seismic_data is your 3D seismic data loaded as a numpy array

#initiate seismic data object
pointCloudInterpretor = PointCloudSeismicInterpretation(seismic_array=seismic_data)

#extract a point cloud of trace extrema from the seismic
pointCloudInterpretor.extrema_extraction_dask()

#compute semblance attribute and filter points based on a semblance cut-off value
pointCloudInterpretor.filter_point_cloud_with_semblance(kernel=(3,3,9), thr=0.85, in_place=True)

#filter points based on an amplitude cut-off value
pointCloudInterpretor.filter_point_cloud_with_amplitude(thr=0.20, in_place=True)

#create an open3D point cloud object and segment the point cloud with DBSCAN
pcd_pyntcloud = PyntCloud(pd.DataFrame(data={'x':pointCloudInterpretor.point_cloud.T[0], 
                                        'y':pointCloudInterpretor.point_cloud.T[1], 
                                        'z':pointCloudInterpretor.point_cloud.T[2], 
                                        'amplitude':pointCloudInterpretor.amplitude_point_cloud})[:])
pcd_o3d = pcd_pyntcloud.to_instance("open3d", mesh=False)
with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
    labels = np.array(
        pcd_o3d.cluster_dbscan(eps=2, min_points=8, print_progress=True))

For geobody detection in a segmented seismic

from lshashpy3 import LSHash
from PointCloudObjectRetrieval import PointCloudObjectRetrieval

#merge seismic point cloud and amplitude point cloud in an numpy array ([n_points, 4])
pcd = np.zeros((len(pointCloudInterpretor.point_cloud),4))
pcd[:,:3] = pointCloudInterpretor.point_cloud[:,:]
pcd[:,3]= pointCloudInterpretor.amplitude_point_cloud
#initiate feature extraction object
FeatureExtractor = PointCloudObjectRetrieval(
    pcd, 
    pointCloudInterpretor.semblance_point_cloud,
    labels
)

#extract features for each point cloud object
standardized_featureDF = FeatureExtractor.get_features(selected_clusters)

#contruct lsh tables
feature_dict = dict(zip(standardized_featureDF.index, standardized_featureDF[selected_features].values))
k = 7 # hash size
L = 20  # number of tables
d = len(selected_features) #2048 # Dimension of Feature vector
lsh = LSHash(hash_size=k, input_dim=d, num_hashtables=L)
for ID, vec in notebook.tqdm(feature_dict.items()):
    lsh.index(vec.flatten(), extra_data=ID)

#query similar geobodies to a specific geobody example previously identified
n_items = 10 #number of similar geobodies
feature_example = feature_dict[0].flatten() #features of the geobody example 
                                            #replace 0 by the id of the identified example
response = lsh_variable.query(feature_example, num_results=n_items, distance_func='hamming')

Notebook demo

For a more complete demo run the the file notebooks/F3dataset-segmentation.ipynb

It gives a complete example of seismic segmentation of the open-source F3-dataset (https://terranubis.com/datainfo/F3-Demo-2020) and geobody detection.

Point cloud seismic segmentation of F3 seismic data

Geobody detection - elongated geobodies - in the F3 seismic data (150-closest objects)